Mike Swanson
75ce1c2fd5
Enhanced code review and frontend validation with intelligent triggers: Code Review Agent Enhancement: - Added Sequential Thinking MCP integration for complex issues - Triggers on 2+ rejections or 3+ critical issues - New escalation format with root cause analysis - Comprehensive solution strategies with trade-off evaluation - Educational feedback to break rejection cycles - Files: .claude/agents/code-review.md (+308 lines) - Docs: CODE_REVIEW_ST_ENHANCEMENT.md, CODE_REVIEW_ST_TESTING.md Frontend Design Skill Enhancement: - Automatic invocation for ANY UI change - Comprehensive validation checklist (200+ checkpoints) - 8 validation categories (visual, interactive, responsive, a11y, etc.) - 3 validation levels (quick, standard, comprehensive) - Integration with code review workflow - Files: .claude/skills/frontend-design/SKILL.md (+120 lines) - Docs: UI_VALIDATION_CHECKLIST.md (462 lines), AUTOMATIC_VALIDATION_ENHANCEMENT.md (587 lines) Settings Optimization: - Repaired .claude/settings.local.json (fixed m365 pattern) - Reduced permissions from 49 to 33 (33% reduction) - Removed duplicates, sorted alphabetically - Created SETTINGS_PERMISSIONS.md documentation Checkpoint Command Enhancement: - Dual checkpoint system (git + database) - Saves session context to API for cross-machine recall - Includes git metadata in database context - Files: .claude/commands/checkpoint.md (+139 lines) Decision Rationale: - Sequential Thinking MCP breaks rejection cycles by identifying root causes - Automatic frontend validation catches UI issues before code review - Dual checkpoints enable complete project memory across machines - Settings optimization improves maintainability Total: 1,200+ lines of documentation and enhancements Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
45 lines
438 KiB
JSON
45 lines
438 KiB
JSON
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:57:28.848Z","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd"}
|
|
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","type":"user","message":{"role":"user","content":"## YOUR ROLE - SPEC CRITIC AGENT\n\nYou are the **Spec Critic Agent** in the Auto-Build spec creation pipeline. Your ONLY job is to critically review the spec.md document, find issues, and fix them.\n\n**Key Principle**: Use extended thinking (ultrathink). Find problems BEFORE implementation.\n\n---\n\n## YOUR CONTRACT\n\n**Inputs**:\n- `spec.md` - The specification to critique\n- `research.json` - Validated research findings\n- `requirements.json` - Original user requirements\n- `context.json` - Codebase context\n\n**Output**:\n- Fixed `spec.md` (if issues found)\n- `critique_report.json` - Summary of issues and fixes\n\n---\n\n## PHASE 0: LOAD ALL CONTEXT\n\n```bash\ncat spec.md\ncat research.json\ncat requirements.json\ncat context.json\n```\n\nUnderstand:\n- What the spec claims\n- What research validated\n- What the user originally requested\n- What patterns exist in the codebase\n\n---\n\n## PHASE 1: DEEP ANALYSIS (USE EXTENDED THINKING)\n\n**CRITICAL**: Use extended thinking for this phase. Think deeply about:\n\n### 1.1: Technical Accuracy\n\nCompare spec.md against research.json AND validate with Context7:\n\n- **Package names**: Does spec use correct package names from research?\n- **Import statements**: Do imports match researched API patterns?\n- **API calls**: Do function signatures match documentation?\n- **Configuration**: Are env vars and config options correct?\n\n**USE CONTEXT7 TO VALIDATE TECHNICAL CLAIMS:**\n\nIf the spec mentions specific libraries or APIs, verify them against Context7:\n\n```\n# Step 1: Resolve library ID\nTool: mcp__context7__resolve-library-id\nInput: { \"libraryName\": \"[library from spec]\" }\n\n# Step 2: Verify API patterns mentioned in spec\nTool: mcp__context7__get-library-docs\nInput: {\n \"context7CompatibleLibraryID\": \"[library-id]\",\n \"topic\": \"[specific API or feature mentioned in spec]\",\n \"mode\": \"code\"\n}\n```\n\n**Check for common spec errors:**\n- Wrong package name (e.g., \"react-query\" vs \"@tanstack/react-query\")\n- Outdated API patterns (e.g., using deprecated functions)\n- Incorrect function signatures (e.g., wrong parameter order)\n- Missing required configuration (e.g., missing env vars)\n\nFlag any mismatches.\n\n### 1.2: Completeness\n\nCheck against requirements.json:\n\n- **All requirements covered?** - Each requirement should have implementation details\n- **All acceptance criteria testable?** - Each criterion should be verifiable\n- **Edge cases handled?** - Error conditions, empty states, timeouts\n- **Integration points clear?** - How components connect\n\nFlag any gaps.\n\n### 1.3: Consistency\n\nCheck within spec.md:\n\n- **Package names consistent** - Same name used everywhere\n- **File paths consistent** - No conflicting paths\n- **Patterns consistent** - Same style throughout\n- **Terminology consistent** - Same terms for same concepts\n\nFlag any inconsistencies.\n\n### 1.4: Feasibility\n\nCheck practicality:\n\n- **Dependencies available?** - All packages exist and are maintained\n- **Infrastructure realistic?** - Docker setup will work\n- **Implementation order logical?** - Dependencies before dependents\n- **Scope appropriate?** - Not over-engineered, not under-specified\n\nFlag any concerns.\n\n### 1.5: Research Alignment\n\nCross-reference with research.json:\n\n- **Verified information used?** - Spec should use researched facts\n- **Unverified claims flagged?** - Any assumptions marked clearly\n- **Gotchas addressed?** - Known issues from research handled\n- **Recommendations followed?** - Research suggestions incorporated\n\nFlag any divergences.\n\n---\n\n## PHASE 2: CATALOG ISSUES\n\nCreate a list of all issues found:\n\n```\nISSUES FOUND:\n\n1. [SEVERITY: HIGH] Package name incorrect\n - Spec says: \"graphiti-core real_ladybug\"\n - Research says: \"graphiti-core\" with separate \"real_ladybug\" dependency\n - Location: Line 45, Requirements section\n\n2. [SEVERITY: MEDIUM] Missing edge case\n - Requirement: \"Handle connection failures\"\n - Spec: No error handling specified\n - Location: Implementation Notes section\n\n3. [SEVERITY: LOW] Inconsistent terminology\n - Uses both \"memory\" and \"episode\" for same concept\n - Location: Throughout document\n```\n\n---\n\n## PHASE 3: FIX ISSUES\n\nFor each issue found, fix it directly in spec.md:\n\n```bash\n# Read current spec\ncat spec.md\n\n# Apply fixes using edit commands\n# Example: Fix package name\nsed -i 's/graphiti-core real_ladybug/graphiti-core\\nreal_ladybug/g' spec.md\n\n# Or rewrite sections as needed\n```\n\n**For each fix**:\n1. Make the change in spec.md\n2. Verify the change was applied\n3. Document what was changed\n\n---\n\n## PHASE 4: CREATE CRITIQUE REPORT\n\n```bash\ncat > critique_report.json << 'EOF'\n{\n \"critique_completed\": true,\n \"issues_found\": [\n {\n \"severity\": \"high|medium|low\",\n \"category\": \"accuracy|completeness|consistency|feasibility|alignment\",\n \"description\": \"[What was wrong]\",\n \"location\": \"[Where in spec.md]\",\n \"fix_applied\": \"[What was changed]\",\n \"verified\": true\n }\n ],\n \"issues_fixed\": true,\n \"no_issues_found\": false,\n \"critique_summary\": \"[Brief summary of critique]\",\n \"confidence_level\": \"high|medium|low\",\n \"recommendations\": [\n \"[Any remaining concerns or suggestions]\"\n ],\n \"created_at\": \"[ISO timestamp]\"\n}\nEOF\n```\n\nIf NO issues found:\n\n```bash\ncat > critique_report.json << 'EOF'\n{\n \"critique_completed\": true,\n \"issues_found\": [],\n \"issues_fixed\": false,\n \"no_issues_found\": true,\n \"critique_summary\": \"Spec is well-written with no significant issues found.\",\n \"confidence_level\": \"high\",\n \"recommendations\": [],\n \"created_at\": \"[ISO timestamp]\"\n}\nEOF\n```\n\n---\n\n## PHASE 5: VERIFY FIXES\n\nAfter making changes:\n\n```bash\n# Verify spec is still valid markdown\nhead -50 spec.md\n\n# Check key sections exist\ngrep -E \"^##? Overview\" spec.md\ngrep -E \"^##? Requirements\" spec.md\ngrep -E \"^##? Success Criteria\" spec.md\n```\n\n---\n\n## PHASE 6: SIGNAL COMPLETION\n\n```\n=== SPEC CRITIQUE COMPLETE ===\n\nIssues Found: [count]\n- High severity: [count]\n- Medium severity: [count]\n- Low severity: [count]\n\nFixes Applied: [count]\nConfidence Level: [high/medium/low]\n\nSummary:\n[Brief summary of what was found and fixed]\n\ncritique_report.json created successfully.\nspec.md has been updated with fixes.\n```\n\n---\n\n## CRITICAL RULES\n\n1. **USE EXTENDED THINKING** - This is the deep analysis phase\n2. **ALWAYS compare against research** - Research is the source of truth\n3. **FIX issues, don't just report** - Make actual changes to spec.md\n4. **VERIFY after fixing** - Ensure spec is still valid\n5. **BE THOROUGH** - Check everything, miss nothing\n\n---\n\n## SEVERITY GUIDELINES\n\n**HIGH** - Will cause implementation failure:\n- Wrong package names\n- Incorrect API signatures\n- Missing critical requirements\n- Invalid configuration\n\n**MEDIUM** - May cause issues:\n- Missing edge cases\n- Incomplete error handling\n- Unclear integration points\n- Inconsistent patterns\n\n**LOW** - Minor improvements:\n- Terminology inconsistencies\n- Documentation gaps\n- Style issues\n- Minor optimizations\n\n---\n\n## CATEGORY DEFINITIONS\n\n- **Accuracy**: Technical correctness (packages, APIs, config)\n- **Completeness**: Coverage of requirements and edge cases\n- **Consistency**: Internal coherence of the document\n- **Feasibility**: Practical implementability\n- **Alignment**: Match with research findings\n\n---\n\n## EXTENDED THINKING PROMPT\n\nWhen analyzing, think through:\n\n> \"Looking at this spec.md, I need to deeply analyze it against the research findings...\n>\n> First, let me check all package names. The research says the package is [X], but the spec says [Y]. This is a mismatch that needs fixing.\n>\n> Let me also verify with Context7 - I'll look up the actual package name and API patterns to confirm...\n> [Use mcp__context7__resolve-library-id to find the library]\n> [Use mcp__context7__get-library-docs to check API patterns]\n>\n> Next, looking at the API patterns. The research shows initialization requires [steps], but the spec shows [different steps]. Let me cross-reference with Context7 documentation... Another issue confirmed.\n>\n> For completeness, the requirements mention [X, Y, Z]. The spec covers X and Y but I don't see Z addressed anywhere. This is a gap.\n>\n> Looking at consistency, I notice 'memory' and 'episode' used interchangeably. Should standardize on one term.\n>\n> For feasibility, the Docker setup seems correct based on research. The port numbers match.\n>\n> Overall, I found [N] issues that need fixing before this spec is ready for implementation.\"\n\n---\n\n## BEGIN\n\nStart by loading all context files, then use extended thinking to analyze the spec deeply.\n\n\n---\n\n**Spec Directory**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\n**Project Directory**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\n\n## Context from Previous Phases\n\n### Requirements\n# Requirements Phase Summary\n\n## Project Vision\nBuild a **multi-LLM agentic development platform** that orchestrates multiple AI models strategically, with adaptive requirements management and course correction capabilities. Extends Autocoder 2.0/Auto-Claude concepts.\n\n## Key Architectural Decisions\n\n**Multi-Model Strategy** (Core differentiator)\n- No single LLM as source of truth - leverage collective intelligence\n- Strategic routing by task type:\n - **Ollama**: Local, token-free lightweight tasks (file analysis, transforms)\n - **Claude Sonnet/Opus**: Complex reasoning and architecture\n - **Gemini**: Multimodal analysis, large context windows\n - **Grok**: Real-time information, alternative perspectives\n- Cross-model validation for quality assurance\n- Cost optimization through intelligent model selection\n\n**Adaptive Requirements** (Not waterfall)\n- Specs evolve with project, not frozen at start\n- Re-interview capability to clarify ambiguities during development\n- Spec versioning to track requirement evolution\n- Mid-flight course correction and steering\n\n## Critical Components to Implement\n\n1. **Model Orchestration Layer**\n - Unified API wrapper supporting all providers\n - Task decomposition with model-specific assignments\n - Parallel execution engine with dependency management\n - Fallback strategies for model failures\n\n2. **Version Control Integration**\n - Git/Gitea native support\n - Auto-branching strategies\n - Conventional commit message generation\n - PR creation with comprehensive descriptions\n\n3. **Spec Management System**\n - Initial spec-app style interview\n - Spec versioning and history tracking\n - Re-interview workflow for clarification\n - Context preservation across changes\n\n4. **Extensible Skill Architecture**\n - Plugin system for specialized capabilities\n - Model-specific and model-agnostic skills\n - Skill composition for complex workflows\n - Custom skill creation framework\n\n5. **Quality Assurance Pipeline**\n - Multi-model consensus for critical decisions\n - Automated testing integration\n - Security scanning, performance profiling\n - Code quality metrics\n\n## Technical Stack\n\n- **Backend**: Python (FastAPI) orchestration layer\n- **Frontend**: React/TypeScript for project management UI\n- **State**: Track project state, agent activities, decisions\n- **Database**: Store specs, decisions, task history, model interactions\n- **MCP Server**: Feature graph and skill management\n\n## Critical Constraints\n\n- Minimum **3 LLM providers** working in concert\n- Must demonstrate **cost savings** through model routing\n- Must handle **mid-project requirement changes** gracefully\n- Full **transparency**: visibility into which models did what and why\n- Support **local-first** operation via Ollama\n\n## Success Metrics\n\n1. Complete full lifecycle: spec → implementation → steering → completion\n2. Prove cost optimization through intelligent routing\n3. Show quality improvements via cross-model validation\n4. Handle evolving requirements without restart\n5. Support at least 3 providers simultaneously\n\n## Workflow Classification\n**Type**: Refactoring - building on existing Autocoder 2.0/Auto-Claude patterns\n\n### Research\n# Research Phase Summary\n\n## Critical Decisions & Package Choices\n\n**LLM SDKs Verified:**\n- **Anthropic Claude**: `pip install anthropic` - supports sync/async clients via `Anthropic()` and `AsyncAnthropic()`\n- **Google Gemini**: `pip install google-genai` ⚠️ **CRITICAL**: Old `google-generativeai` package is DEPRECATED (EOL Nov 30, 2025) - must use new `google-genai` for all development\n- **xAI Grok**: `pip install xai-sdk` (official package, not community alternatives like `xai-grok` or `xai-grok-sdk`)\n- **Ollama**: `pip install ollama` - requires separate Ollama server running locally\n\n**Frameworks:**\n- **Backend**: FastAPI - use `pip install \"fastapi[standard]\"` to get uvicorn + dependencies bundled\n- **Frontend**: Vite + React + TypeScript - `npm create vite@latest -- --template react-ts`\n- **MCP**: `pip install mcp` (v1.25.0+, v2 coming Q1)\n\n## Critical Constraints & Requirements\n\n**Python Requirements:**\n- Python >= 3.10 (MCP SDK and xAI require this)\n- Python >= 3.9 (Anthropic)\n- Python >= 3.8 (Ollama)\n\n**Node Requirements:**\n- Node.js >= 18 (Vite)\n\n**Infrastructure Dependencies:**\n- **Ollama**: Separate Ollama server must be running (default: http://localhost:11434) + models must be pre-pulled with `ollama pull <model>`\n- **FastAPI**: Requires ASGI server (uvicorn) - run with `uvicorn main:app --reload`\n- **Vite**: Dev server runs on http://localhost:5173\n\n## Key API Patterns Identified\n\n**Authentication:**\n- All LLM SDKs use environment variables: `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, `XAI_API_KEY`\n- Clients auto-detect env vars: `client = Anthropic()` or explicit: `Client(api_key='...')`\n\n**Common Patterns:**\n- All support both sync and async clients\n- Context manager support for cleanup\n- Streaming with `stream=True` parameter\n\n**MCP Core Concepts:**\n- **Resources**: GET-like endpoints for data access\n- **Tools**: POST-like endpoints for actions\n- **Prompts**: Reusable LLM interaction templates\n- Transport options: stdio, SSE (Server-Sent Events)\n\n## Critical Gotchas & Actionable Insights\n\n1. **Google Gemini Migration Risk**: LLMs may generate code using deprecated `google-generativeai` - always use `google-genai`\n\n2. **Ollama Workflow**:\n - Start server first: `ollama serve`\n - Pull models: `ollama pull gemma3`\n - Then use Python client\n\n3. **FastAPI Setup**: Use `[standard]` extra to avoid missing uvicorn dependency\n\n4. **xAI SDK**: gRPC-based, supports agentic tool calling with web search, X search, code execution\n\n5. **Ollama Streaming**: Now supports streaming with MCP tool calling (recent update)\n\n6. **Vite Templates**: `react-swc-ts` available for faster builds vs standard `react-ts`\n\n7. **FastAPI Dev**: Use `--reload` flag for development only, not production\n\n## Implementation Recommendations\n\n- Use async clients for all LLM SDKs to enable concurrent operations\n- FastAPI automatic OpenAPI docs available at `/docs` and `/redoc`\n- Vite HMR provides instant updates without page refresh\n- All verified packages have type definitions for better IDE support\n\n### Context\n# Context Phase: Key Findings\n\n## Project Objective\nBuild a **multi-LLM agentic development platform** that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent task routing, cost optimization, and quality assurance through cross-model validation.\n\n## Critical Technical Decisions\n\n**Tech Stack:**\n- Backend: Python (FastAPI) for orchestration layer\n- Frontend: React/TypeScript for project management UI\n- Database: Store specs, decisions, task history, model interactions\n- MCP Server: Feature graph and skill management\n- Unified API wrapper supporting all LLM providers\n\n**Model Routing Strategy:**\n- Ollama → lightweight local tasks (file analysis, templating)\n- Claude Sonnet/Opus → complex reasoning, architecture\n- Gemini → multimodal analysis, large context windows\n- Grok → real-time information, alternative perspectives\n\n## Core Feature Requirements\n\n**1. Multi-LLM Orchestration**\n- Strategic task assignment based on model strengths\n- Cross-model validation for quality assurance\n- Fallback strategies when models fail\n- Cost optimization through intelligent routing\n\n**2. Adaptive Spec System**\n- Initial spec-app style interview\n- Re-interview capability for evolving requirements\n- Spec versioning to track requirement evolution\n- Continuous alignment with user intent\n\n**3. Version Control Integration**\n- Native Git/Gitea support\n- Automatic branching strategies\n- Conventional commit message generation\n- PR creation with descriptions\n- Branch-based code review\n\n**4. Extensible Skill System**\n- Model-specific or model-agnostic skills\n- Examples: code review, security scanning, performance optimization\n- Custom skill framework\n- Skill composition/chaining\n\n**5. Project Steering**\n- Mid-flight direction changes\n- Checkpoint system at milestones\n- Context preservation across pivots\n- Impact analysis for changes\n- Graceful refactoring vs. starting over\n\n**6. Quality Assurance**\n- Multi-model consensus for critical decisions\n- Automated testing integration\n- Code quality metrics\n- Security scanning\n- Performance profiling\n\n## Key Constraints\n\n- Must support ≥3 LLM providers working together\n- Cost optimization is critical success factor\n- Local-first capability required (Ollama)\n- Full transparency into model decisions\n- Handle evolving requirements gracefully\n\n## Implementation Priorities\n\n**Success Metrics:**\n1. Complete full lifecycle: spec → implementation → steering → completion\n2. Demonstrate cost savings through model routing\n3. Prove quality improvements via cross-validation\n4. Handle mid-project requirement changes\n5. Integrate 3+ LLM providers successfully\n\n**Key Differentiators:**\n- Model democracy (no single source of truth)\n- Adaptive, not frozen requirements\n- Local-first option for cost savings\n- Course correction capabilities\n- Full traceability\n\n## Actionable Insights\n\n- Build unified API abstraction layer first (critical for multi-provider support)\n- Implement task decomposition engine early (enables parallel execution)\n- Design state management for tracking decisions across models\n- Create checkpoint system for milestone reviews\n- Focus on graceful degradation and fallback strategies\n\n### Spec Writing\n# Spec Writing Phase - Key Findings\n\n## Project Classification\n- **Workflow Type**: Feature (Greenfield Development)\n- **Rationale**: Empty codebase despite referencing Autocoder 2.0/Auto-Claude patterns. Building from scratch, not refactoring.\n\n## Core Architecture Decisions\n\n### Multi-Service Design (5 services)\n1. **orchestration-service**: Task decomposition, model routing, parallel execution\n2. **llm-wrapper-service**: Unified LLM API via LiteLLM abstraction\n3. **spec-management-service**: Adaptive specs with interview/re-interview workflows\n4. **skill-service**: Extensible plugin architecture\n5. **version-control-service**: Git/Gitea automation\n\n### Technology Stack\n- **Backend**: Python 3.11+, FastAPI, SQLAlchemy, uvicorn (port 8000)\n- **Frontend**: TypeScript, React, Vite (port 5173)\n- **MCP Server**: FastMCP SDK, Python\n- **LLM Integration**: LiteLLM as unified interface layer\n\n## Critical Technical Decisions\n\n### LiteLLM as Unified Interface\n- **Why**: Single API for 100+ providers vs. managing 4+ separate SDKs\n- **Models**: Claude (`claude-3-opus`), Gemini (`gemini/gemini-pro`), Grok (`xai/grok`), Ollama (`ollama/llama3`)\n- **Built-in features**: Cost tracking, automatic retries, fallback logic\n- **Pattern**: `await acompletion(model=model, messages=messages)` works universally\n\n### Async-First Architecture\n- All LLM calls use async clients (`AsyncAnthropic`, async `genai`, etc.)\n- Enables parallel validation across models\n- FastAPI async endpoints prevent blocking\n\n### Database Schema Requirements\nFour core models identified:\n- `Spec`: Version-tracked project specifications\n- `Task`: Task history with model assignments\n- `Decision`: Decision tracking for course correction\n- Model interaction logs (via LiteLLM)\n\n## Critical Files & Components\n\n### Backend Core (33 files total)\n- **Entry**: `backend/main.py` (FastAPI app)\n- **Orchestration**: `backend/services/orchestrator.py` (routing logic)\n- **LLM Layer**: `backend/llm/unified_client.py` + 4 provider files\n- **Routing**: `backend/llm/routing/router.py` (task-to-model matching)\n- **Validation**: `backend/llm/validation/cross_validate.py`\n- **Git**: `backend/git/git_service.py`, `gitea_service.py`\n\n### Frontend (8 files)\n- `Dashboard.tsx`, `SpecEditor.tsx`, `TaskViewer.tsx`\n- `ModelStatus.tsx`, `CostTracker.tsx` (monitoring)\n\n### MCP Server (4 files)\n- `server.py`, `spec_tools.py`, `task_tools.py`, `project_state.py`\n\n## Key Constraints & Requirements\n\n### Environment Dependencies\n```bash\nANTHROPIC_API_KEY, GEMINI_API_KEY, XAI_API_KEY required\nOllama needs local server (no API key)\n```\n\n### Explicitly Out of Scope\n- Production deployment (K8s, cloud)\n- Enterprise auth (SSO/SAML)\n- Multi-tenant architecture\n- Billing/subscriptions\n- Mobile apps\n- CI/CD integration beyond Git ops\n\n## Actionable Implementation Insights\n\n### Phase 1 Priority\n1. Set up FastAPI skeleton + LiteLLM wrapper first (enables testing with real models early)\n2. Build basic orchestrator with single-model routing before adding cross-validation\n3. SQLAlchemy models before API routes (data layer foundation)\n\n### Integration Strategy\n- LiteLLM handles provider differences automatically\n- Use `asyncio.gather()` for parallel model calls\n- Cost tracking via `litellm.track_cost = True` (built-in)\n\n### Git Workflow Pattern\n- Feature branches per task\n- Conventional commit message generation\n- Gitea API for PR automation\n\n### Cross-Model Validation Pattern\n```python\nasync def parallel_validation(prompt, models):\n tasks = [call_llm(model, prompt) for model in models]\n return await asyncio.gather(*tasks, return_exceptions=True)\n```\n\n## Risk Mitigation\n- LiteLLM abstracts provider API changes\n- Async architecture prevents blocking on slow model responses\n- Local Ollama provides cost-free fallback for high-volume tasks\n\n\n\n**Spec File**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\n**Research File**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\research.json\n**Critique Output**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json\n\nUse EXTENDED THINKING (ultrathink) to deeply analyze the spec.md:\n\n1. **Technical Accuracy**: Do code examples match the research findings?\n2. **Completeness**: Are all requirements covered? Edge cases handled?\n3. **Consistency**: Do package names, APIs, and patterns match throughout?\n4. **Feasibility**: Is the implementation approach realistic?\n\nFor each issue found:\n- Fix it directly in spec.md\n- Document what was fixed in critique_report.json\n\nOutput critique_report.json with:\n{\n \"issues_found\": [...],\n \"issues_fixed\": true/false,\n \"no_issues_found\": true/false,\n \"critique_summary\": \"...\"\n}\n\n"},"uuid":"f0a6ecb1-3cbe-4e69-8356-ad91790d91e4","timestamp":"2026-01-12T18:57:29.445Z"}
|
|
{"parentUuid":"f0a6ecb1-3cbe-4e69-8356-ad91790d91e4","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","message":{"model":"claude-opus-4-5-20251101","id":"msg_01FCxHoThd5G4nEnW6Twvc92","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to act as a Spec Critic Agent to review the spec.md document against research.json, requirements.json, and context.json. I need to:\n\n1. Load all context files first\n2. Use extended thinking to deeply analyze the spec\n3. Find issues and fix them\n4. Create a critique_report.json\n\nLet me start by loading all the context files.","signature":"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"},{"type":"text","text":"I'll begin by loading all context files to understand the full picture before critically analyzing the spec."},{"type":"tool_use","id":"toolu_01XHnYAqQfRrkVHQJbvEx8vM","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md"}},{"type":"tool_use","id":"toolu_01UZVS612Fgp4eLuDpvrpRmh","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\research.json"}},{"type":"tool_use","id":"toolu_013KKrEwqDevDbXj5FuCjY9Z","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\requirements.json"}},{"type":"tool_use","id":"toolu_01M4ayTRaxhowNtwc5GkBJ1C","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\context.json"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":6683,"cache_read_input_tokens":15912,"cache_creation":{"ephemeral_5m_input_tokens":6683,"ephemeral_1h_input_tokens":0},"output_tokens":467,"service_tier":"standard"}},"type":"assistant","uuid":"383cd3f0-3bd5-47df-99a6-955dce5ab1df","timestamp":"2026-01-12T18:57:39.473Z"}
|
|
{"parentUuid":"383cd3f0-3bd5-47df-99a6-955dce5ab1df","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XHnYAqQfRrkVHQJbvEx8vM","type":"tool_result","content":" 1→# Specification: Multi-LLM Agentic Development Platform\n 2→\n 3→## Overview\n 4→\n 5→Build an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n 6→\n 7→## Workflow Type\n 8→\n 9→**Type**: Feature (Greenfield Development)\n 10→\n 11→**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n 12→\n 13→## Task Scope\n 14→\n 15→### Services Involved\n 16→\n 17→- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n 18→- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n 19→- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n 20→- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n 21→- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n 22→- **frontend-ui** (integration) - React/TypeScript project management dashboard\n 23→\n 24→### This Task Will:\n 25→\n 26→- [ ] Create Python/FastAPI backend with modular service architecture\n 27→- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n 28→- [ ] Build task orchestration engine with model-specific routing\n 29→- [ ] Implement cross-model validation for quality assurance\n 30→- [ ] Create adaptive spec system with interview and re-interview workflows\n 31→- [ ] Build extensible skill/addon plugin architecture\n 32→- [ ] Integrate Git/Gitea for version control automation\n 33→- [ ] Implement checkpoint system for project steering and course correction\n 34→- [ ] Create React/TypeScript frontend for project management\n 35→- [ ] Design database schema for specs, decisions, task history, model interactions\n 36→- [ ] Build MCP server for feature graph and skill management\n 37→\n 38→### Out of Scope:\n 39→\n 40→- Production deployment infrastructure (Kubernetes, cloud deployment)\n 41→- Enterprise SSO/SAML authentication\n 42→- Multi-tenant user management\n 43→- Billing and subscription management\n 44→- Mobile application\n 45→- Integration with CI/CD pipelines (beyond basic Git operations)\n 46→\n 47→## Service Context\n 48→\n 49→### Backend Orchestration Service\n 50→\n 51→**Tech Stack:**\n 52→- Language: Python 3.11+\n 53→- Framework: FastAPI with uvicorn ASGI server\n 54→- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n 55→- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n 56→\n 57→**Entry Point:** `backend/main.py`\n 58→\n 59→**How to Run:**\n 60→```bash\n 61→cd backend\n 62→pip install -r requirements.txt\n 63→uvicorn main:app --reload --port 8000\n 64→```\n 65→\n 66→**Port:** 8000\n 67→\n 68→**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n 69→\n 70→---\n 71→\n 72→### LLM Wrapper Service\n 73→\n 74→**Tech Stack:**\n 75→- Language: Python 3.11+\n 76→- Framework: LiteLLM for unified interface\n 77→- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n 78→- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n 79→\n 80→**Entry Point:** `backend/llm/__init__.py`\n 81→\n 82→**Configuration:**\n 83→```bash\n 84→# Environment variables required\n 85→ANTHROPIC_API_KEY=your_anthropic_key\n 86→GEMINI_API_KEY=your_gemini_key\n 87→XAI_API_KEY=your_xai_key\n 88→# Ollama uses local server, no API key needed\n 89→```\n 90→\n 91→---\n 92→\n 93→### Frontend UI\n 94→\n 95→**Tech Stack:**\n 96→- Language: TypeScript\n 97→- Framework: React with Vite\n 98→- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n 99→- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n 100→\n 101→**Entry Point:** `frontend/src/main.tsx`\n 102→\n 103→**How to Run:**\n 104→```bash\n 105→cd frontend\n 106→npm install\n 107→npm run dev\n 108→```\n 109→\n 110→**Port:** 5173\n 111→\n 112→---\n 113→\n 114→### MCP Server\n 115→\n 116→**Tech Stack:**\n 117→- Language: Python 3.11+\n 118→- Framework: MCP SDK (FastMCP)\n 119→- Key packages: `mcp>=1.25,<2`\n 120→- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n 121→\n 122→**Entry Point:** `mcp-server/server.py`\n 123→\n 124→**How to Run:**\n 125→```bash\n 126→cd mcp-server\n 127→python server.py\n 128→```\n 129→\n 130→## Files to Create\n 131→\n 132→| File | Service | Purpose |\n 133→|------|---------|---------|\n 134→| `backend/main.py` | orchestration | FastAPI application entry point |\n 135→| `backend/requirements.txt` | orchestration | Python dependencies |\n 136→| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n 137→| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n 138→| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n 139→| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n 140→| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n 141→| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n 142→| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n 143→| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n 144→| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n 145→| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n 146→| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n 147→| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n 148→| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n 149→| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n 150→| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n 151→| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n 152→| `backend/models/spec.py` | orchestration | Spec data model |\n 153→| `backend/models/task.py` | orchestration | Task data model |\n 154→| `backend/models/decision.py` | orchestration | Decision tracking model |\n 155→| `backend/git/git_service.py` | version-control | GitPython wrapper |\n 156→| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n 157→| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n 158→| `mcp-server/server.py` | mcp | FastMCP server entry |\n 159→| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n 160→| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n 161→| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n 162→| `frontend/package.json` | frontend | NPM dependencies |\n 163→| `frontend/vite.config.ts` | frontend | Vite configuration |\n 164→| `frontend/src/main.tsx` | frontend | React entry point |\n 165→| `frontend/src/App.tsx` | frontend | Main application component |\n 166→| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n 167→| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n 168→| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n 169→| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n 170→| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n 171→| `docker-compose.yml` | infrastructure | Container orchestration |\n 172→| `.env.example` | infrastructure | Environment variable template |\n 173→\n 174→## Patterns to Follow\n 175→\n 176→### LiteLLM Unified Interface Pattern\n 177→\n 178→Use LiteLLM as the unified API wrapper for all LLM providers:\n 179→\n 180→```python\n 181→from litellm import completion, acompletion\n 182→\n 183→# Unified interface - same code for any provider\n 184→async def call_llm(model: str, messages: list, **kwargs) -> str:\n 185→ response = await acompletion(\n 186→ model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n 187→ messages=messages,\n 188→ **kwargs\n 189→ )\n 190→ return response.choices[0].message.content\n 191→```\n 192→\n 193→**Key Points:**\n 194→- Model names follow `provider/model` format for non-OpenAI models\n 195→- Built-in cost tracking via `litellm.track_cost = True`\n 196→- Automatic retries and fallbacks configurable\n 197→- Same interface works across 100+ LLM providers\n 198→\n 199→---\n 200→\n 201→### FastAPI Service Pattern\n 202→\n 203→```python\n 204→from fastapi import FastAPI, Depends, HTTPException\n 205→from pydantic import BaseModel\n 206→\n 207→app = FastAPI(title=\"Multi-LLM Platform\")\n 208→\n 209→class TaskCreate(BaseModel):\n 210→ description: str\n 211→ priority: str = \"normal\"\n 212→\n 213→@app.post(\"/tasks/\", response_model=Task)\n 214→async def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n 215→ \"\"\"Create a new task with automatic model routing.\"\"\"\n 216→ return await orchestrator.create_task(task, db)\n 217→```\n 218→\n 219→**Key Points:**\n 220→- Use Pydantic models for request/response validation\n 221→- Dependency injection for database sessions\n 222→- Async endpoints for non-blocking LLM calls\n 223→\n 224→---\n 225→\n 226→### Async LLM Client Pattern\n 227→\n 228→```python\n 229→from anthropic import AsyncAnthropic\n 230→from google import genai\n 231→from xai_sdk import AsyncClient as XAIClient\n 232→from ollama import AsyncClient as OllamaClient\n 233→\n 234→# All providers support async clients for concurrent operations\n 235→async def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n 236→ tasks = [call_llm(model, prompt) for model in models]\n 237→ responses = await asyncio.gather(*tasks, return_exceptions=True)\n 238→ return [r for r in responses if not isinstance(r, Exception)]\n 239→```\n 240→\n 241→---\n 242→\n 243→### MCP Server Pattern\n 244→\n 245→```python\n 246→from mcp.server.fastmcp import FastMCP\n 247→\n 248→mcp = FastMCP(\"multi-llm-platform\")\n 249→\n 250→@mcp.tool()\n 251→async def create_spec(description: str) -> dict:\n 252→ \"\"\"Create a new project specification.\"\"\"\n 253→ return await spec_service.create(description)\n 254→\n 255→@mcp.resource(\"project://state\")\n 256→async def get_project_state() -> str:\n 257→ \"\"\"Get current project state.\"\"\"\n 258→ return json.dumps(state_manager.get_state())\n 259→```\n 260→\n 261→---\n 262→\n 263→### Git Integration Pattern\n 264→\n 265→```python\n 266→from git import Repo\n 267→\n 268→def create_feature_branch(repo_path: str, feature_name: str) -> str:\n 269→ \"\"\"Create a feature branch following conventional naming.\"\"\"\n 270→ repo = Repo(repo_path)\n 271→ branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n 272→ new_branch = repo.create_head(branch_name)\n 273→ new_branch.checkout()\n 274→ return branch_name\n 275→\n 276→def commit_with_message(repo_path: str, files: list, message: str):\n 277→ \"\"\"Commit with conventional commit message format.\"\"\"\n 278→ repo = Repo(repo_path)\n 279→ repo.index.add(files)\n 280→ repo.index.commit(message)\n 281→```\n 282→\n 283→**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n 284→\n 285→## Requirements\n 286→\n 287→### Functional Requirements\n 288→\n 289→1. **Multi-LLM Provider Integration**\n 290→ - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n 291→ - Acceptance: Can make requests to all 4 providers and receive responses\n 292→\n 293→2. **Strategic Model Routing**\n 294→ - Description: Route tasks to optimal models based on task characteristics\n 295→ - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n 296→\n 297→3. **Cross-Model Validation**\n 298→ - Description: Have multiple models review work for quality assurance\n 299→ - Acceptance: Critical decisions validated by 2+ models before acceptance\n 300→\n 301→4. **Cost Optimization**\n 302→ - Description: Track and minimize API costs through intelligent model selection\n 303→ - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n 304→\n 305→5. **Adaptive Spec System**\n 306→ - Description: Interview workflow for gathering requirements, with re-interview capability\n 307→ - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n 308→\n 309→6. **Version Control Integration**\n 310→ - Description: Automatic branching, commit generation, PR creation\n 311→ - Acceptance: Git operations automated, conventional commit messages generated\n 312→\n 313→7. **Project Steering**\n 314→ - Description: Mid-flight direction changes with context preservation\n 315→ - Acceptance: Can redirect project without losing history, impact analysis visible\n 316→\n 317→8. **Skill Plugin System**\n 318→ - Description: Extensible architecture for specialized agent capabilities\n 319→ - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n 320→\n 321→### Non-Functional Requirements\n 322→\n 323→1. **Performance**\n 324→ - Parallel LLM calls for independent operations\n 325→ - Response time < 30s for most operations\n 326→ - Support concurrent users\n 327→\n 328→2. **Security**\n 329→ - API keys stored securely (environment variables, not code)\n 330→ - No credential logging\n 331→ - Input validation on all endpoints\n 332→\n 333→3. **Reliability**\n 334→ - Fallback strategies when models fail\n 335→ - Graceful degradation when providers unavailable\n 336→ - State persistence across restarts\n 337→\n 338→### Edge Cases\n 339→\n 340→1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n 341→2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n 342→3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n 343→4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n 344→5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n 345→6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n 346→7. **Git Conflicts** - Detect and surface for manual resolution\n 347→8. **API Key Invalid** - Mark provider as unavailable, notify user\n 348→\n 349→## Implementation Notes\n 350→\n 351→### DO\n 352→\n 353→- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n 354→- Use `google-genai` package, NOT deprecated `google-generativeai`\n 355→- Use official `xai-sdk` package, not community alternatives\n 356→- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n 357→- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n 358→- Use async clients for all LLM operations to enable concurrency\n 359→- Use `fastapi[standard]` to get uvicorn bundled\n 360→- Start Ollama server separately before using Python client\n 361→- Pull Ollama models first: `ollama pull gemma3`\n 362→\n 363→### DON'T\n 364→\n 365→- Don't use GitPython in long-running daemon processes (resource leaks)\n 366→- Don't hardcode API keys - use environment variables\n 367→- Don't assume all models available - always check provider status\n 368→- Don't block on single LLM call when parallel is possible\n 369→- Don't generate code using deprecated google-generativeai patterns\n 370→- Don't use `--reload` flag in production uvicorn\n 371→\n 372→## Development Environment\n 373→\n 374→### Prerequisites\n 375→\n 376→```bash\n 377→# Python 3.11+\n 378→python --version # Should be 3.11+\n 379→\n 380→# Node.js 18+\n 381→node --version # Should be 18+\n 382→\n 383→# Ollama (for local LLM)\n 384→# Download from https://ollama.com/download\n 385→ollama --version\n 386→```\n 387→\n 388→### Start Services\n 389→\n 390→```bash\n 391→# Terminal 1: Start Ollama server\n 392→ollama serve\n 393→\n 394→# Terminal 2: Pull models\n 395→ollama pull gemma3\n 396→ollama pull codellama\n 397→\n 398→# Terminal 3: Start backend\n 399→cd backend\n 400→pip install -r requirements.txt\n 401→uvicorn main:app --reload --port 8000\n 402→\n 403→# Terminal 4: Start frontend\n 404→cd frontend\n 405→npm install\n 406→npm run dev\n 407→\n 408→# Terminal 5: Start MCP server (optional)\n 409→cd mcp-server\n 410→python server.py\n 411→```\n 412→\n 413→### Service URLs\n 414→\n 415→- Backend API: http://localhost:8000\n 416→- API Docs: http://localhost:8000/docs\n 417→- Frontend: http://localhost:5173\n 418→- Ollama: http://localhost:11434\n 419→\n 420→### Required Environment Variables\n 421→\n 422→Create `.env` file from `.env.example`:\n 423→\n 424→```bash\n 425→# LLM Provider Keys\n 426→ANTHROPIC_API_KEY=sk-ant-...\n 427→GEMINI_API_KEY=AI...\n 428→XAI_API_KEY=xai-...\n 429→\n 430→# Ollama (optional, defaults shown)\n 431→OLLAMA_HOST=http://localhost:11434\n 432→\n 433→# Database\n 434→DATABASE_URL=sqlite:///./multi_llm_platform.db\n 435→\n 436→# Git/Gitea (optional)\n 437→GITEA_URL=http://localhost:3000\n 438→GITEA_TOKEN=your_gitea_token\n 439→```\n 440→\n 441→## Success Criteria\n 442→\n 443→The task is complete when:\n 444→\n 445→1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n 446→2. [ ] Demonstrate intelligent model routing (correct model for task type)\n 447→3. [ ] Cross-model validation working for critical decisions\n 448→4. [ ] Cost tracking visible and accurate in UI\n 449→5. [ ] Spec interview workflow functional (create, modify, version)\n 450→6. [ ] Git integration working (branch, commit, PR creation)\n 451→7. [ ] Project steering possible mid-flight with context preservation\n 452→8. [ ] Skill plugin system loading and executing skills\n 453→9. [ ] Frontend dashboard displaying project state\n 454→10. [ ] No console errors in frontend or backend\n 455→11. [ ] All unit and integration tests passing\n 456→\n 457→## QA Acceptance Criteria\n 458→\n 459→**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n 460→\n 461→### Unit Tests\n 462→\n 463→| Test | File | What to Verify |\n 464→|------|------|----------------|\n 465→| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n 466→| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n 467→| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n 468→| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n 469→| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n 470→| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n 471→\n 472→### Integration Tests\n 473→\n 474→| Test | Services | What to Verify |\n 475→|------|----------|----------------|\n 476→| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n 477→| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n 478→| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n 479→| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n 480→| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n 481→\n 482→### End-to-End Tests\n 483→\n 484→| Flow | Steps | Expected Outcome |\n 485→|------|-------|------------------|\n 486→| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n 487→| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n 488→| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n 489→| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n 490→| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n 491→\n 492→### Browser Verification\n 493→\n 494→| Page/Component | URL | Checks |\n 495→|----------------|-----|--------|\n 496→| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n 497→| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n 498→| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n 499→| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n 500→| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n 501→\n 502→### API Verification\n 503→\n 504→| Endpoint | Method | Checks |\n 505→|----------|--------|--------|\n 506→| `/health` | GET | Returns 200, all services healthy |\n 507→| `/api/models/status` | GET | Returns status for all 4 providers |\n 508→| `/api/tasks` | POST | Creates task, returns task ID |\n 509→| `/api/specs` | POST | Creates spec from interview data |\n 510→| `/api/specs/{id}/versions` | GET | Returns version history |\n 511→\n 512→### Database Verification\n 513→\n 514→| Check | Query/Command | Expected |\n 515→|-------|---------------|----------|\n 516→| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n 517→| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n 518→| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n 519→\n 520→### Security Verification\n 521→\n 522→| Check | Method | Expected |\n 523→|-------|--------|----------|\n 524→| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n 525→| Env vars loaded | Check startup logs | Keys loaded from environment |\n 526→| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n 527→\n 528→### QA Sign-off Requirements\n 529→\n 530→- [ ] All unit tests pass (`pytest backend/tests/`)\n 531→- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n 532→- [ ] All E2E tests pass (Playwright or manual)\n 533→- [ ] Browser verification complete (all pages load, no console errors)\n 534→- [ ] API verification complete (all endpoints respond correctly)\n 535→- [ ] Database state verified (tables exist, data persists)\n 536→- [ ] Security verification complete (no credential exposure)\n 537→- [ ] No regressions in existing functionality\n 538→- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n 539→- [ ] No security vulnerabilities introduced\n 540→- [ ] Cost tracking accurate within 5% of actual API costs\n 541→- [ ] All 4 LLM providers successfully integrated and responding\n 542→\n 543→---\n 544→\n 545→## Appendix: Package Versions\n 546→\n 547→### Python Requirements (requirements.txt)\n 548→\n 549→```\n 550→# Core Framework\n 551→fastapi[standard]>=0.115.0\n 552→uvicorn>=0.30.0\n 553→pydantic>=2.0.0\n 554→\n 555→# LLM Providers\n 556→litellm>=1.0.0\n 557→anthropic>=0.39.0\n 558→google-genai>=1.0.0\n 559→xai-sdk>=0.1.0\n 560→ollama>=0.4.0\n 561→\n 562→# MCP\n 563→mcp>=1.25,<2\n 564→\n 565→# Database\n 566→sqlalchemy>=2.0.0\n 567→alembic>=1.13.0\n 568→\n 569→# Git\n 570→GitPython>=3.1.40\n 571→py-gitea>=0.2.10\n 572→\n 573→# Utilities\n 574→httpx>=0.27.0\n 575→python-dotenv>=1.0.0\n 576→```\n 577→\n 578→### Node Requirements (package.json)\n 579→\n 580→```json\n 581→{\n 582→ \"dependencies\": {\n 583→ \"react\": \"^18.0.0\",\n 584→ \"react-dom\": \"^18.0.0\",\n 585→ \"@tanstack/react-query\": \"^5.0.0\"\n 586→ },\n 587→ \"devDependencies\": {\n 588→ \"typescript\": \"^5.0.0\",\n 589→ \"vite\": \"^6.0.0\",\n 590→ \"@types/react\": \"^18.0.0\",\n 591→ \"@types/react-dom\": \"^18.0.0\",\n 592→ \"vitest\": \"^2.0.0\",\n 593→ \"@testing-library/react\": \"^16.0.0\"\n 594→ }\n 595→}\n 596→```\n 597→\n 598→---\n 599→\n 600→## Appendix: Phased Implementation Milestones\n 601→\n 602→Based on complexity assessment, implement in phases:\n 603→\n 604→1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n 605→2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n 606→3. **Phase 3**: Spec management system and version control integration\n 607→4. **Phase 4**: Skill plugin system and cross-model validation\n 608→5. **Phase 5**: Frontend UI and project steering capabilities\n 609→6. **Phase 6**: Quality assurance, security hardening, and production readiness\n 610→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"b7c7f434-8fe2-48ee-9735-a94ffe74528c","timestamp":"2026-01-12T18:58:14.052Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","numLines":610,"startLine":1,"totalLines":610}},"sourceToolAssistantUUID":"383cd3f0-3bd5-47df-99a6-955dce5ab1df"}
|
|
{"parentUuid":"383cd3f0-3bd5-47df-99a6-955dce5ab1df","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UZVS612Fgp4eLuDpvrpRmh","type":"tool_result","content":" 1→{\n 2→ \"integrations_researched\": [\n 3→ {\n 4→ \"name\": \"Anthropic Claude SDK\",\n 5→ \"type\": \"library\",\n 6→ \"verified_package\": {\n 7→ \"name\": \"anthropic\",\n 8→ \"install_command\": \"pip install anthropic\",\n 9→ \"version\": \"latest (Nov 2025)\",\n 10→ \"python_requires\": \">=3.9\",\n 11→ \"verified\": true\n 12→ },\n 13→ \"api_patterns\": {\n 14→ \"imports\": [\n 15→ \"from anthropic import Anthropic\",\n 16→ \"from anthropic import AsyncAnthropic\"\n 17→ ],\n 18→ \"initialization\": \"client = Anthropic() # Uses ANTHROPIC_API_KEY env var\",\n 19→ \"key_functions\": [\n 20→ \"client.messages.create(model, messages, max_tokens)\",\n 21→ \"await async_client.messages.create(...)\"\n 22→ ],\n 23→ \"verified_against\": \"PyPI + GitHub anthropics/anthropic-sdk-python\"\n 24→ },\n 25→ \"configuration\": {\n 26→ \"env_vars\": [\"ANTHROPIC_API_KEY\"],\n 27→ \"config_files\": [],\n 28→ \"dependencies\": [\"httpx\"]\n 29→ },\n 30→ \"extras\": {\n 31→ \"bedrock\": \"pip install anthropic[bedrock]\",\n 32→ \"vertex\": \"pip install anthropic[vertex]\"\n 33→ },\n 34→ \"gotchas\": [\n 35→ \"Supports both sync and async clients via Anthropic() and AsyncAnthropic()\",\n 36→ \"Context manager support for automatic HTTP client cleanup\",\n 37→ \"Type definitions for all request/response fields\"\n 38→ ],\n 39→ \"research_sources\": [\n 40→ \"https://pypi.org/project/anthropic/\",\n 41→ \"https://github.com/anthropics/anthropic-sdk-python\",\n 42→ \"https://platform.claude.com/docs/en/api/client-sdks\"\n 43→ ]\n 44→ },\n 45→ {\n 46→ \"name\": \"Google Gemini SDK\",\n 47→ \"type\": \"library\",\n 48→ \"verified_package\": {\n 49→ \"name\": \"google-genai\",\n 50→ \"install_command\": \"pip install google-genai\",\n 51→ \"version\": \"GA (May 2025)\",\n 52→ \"verified\": true\n 53→ },\n 54→ \"api_patterns\": {\n 55→ \"imports\": [\n 56→ \"from google import genai\"\n 57→ ],\n 58→ \"initialization\": \"client = genai.Client(api_key='GEMINI_API_KEY') # or uses GEMINI_API_KEY env var\",\n 59→ \"key_functions\": [\n 60→ \"client.models.generate_content(model, contents)\",\n 61→ \"client.models.generate_content_stream(...)\"\n 62→ ],\n 63→ \"verified_against\": \"Google AI for Developers docs\"\n 64→ },\n 65→ \"configuration\": {\n 66→ \"env_vars\": [\"GEMINI_API_KEY\"],\n 67→ \"config_files\": [],\n 68→ \"dependencies\": []\n 69→ },\n 70→ \"extras\": {\n 71→ \"async\": \"pip install google-genai[aiohttp]\"\n 72→ },\n 73→ \"gotchas\": [\n 74→ \"CRITICAL: google-generativeai package is DEPRECATED (EOL Nov 30, 2025)\",\n 75→ \"Must use new google-genai package for all new development\",\n 76→ \"Supports both Gemini Developer API and Vertex AI APIs\",\n 77→ \"LLMs may generate code for deprecated library - always use google-genai\"\n 78→ ],\n 79→ \"research_sources\": [\n 80→ \"https://pypi.org/project/google-generativeai/\",\n 81→ \"https://ai.google.dev/gemini-api/docs/libraries\",\n 82→ \"https://github.com/googleapis/python-genai\"\n 83→ ]\n 84→ },\n 85→ {\n 86→ \"name\": \"xAI Grok SDK\",\n 87→ \"type\": \"library\",\n 88→ \"verified_package\": {\n 89→ \"name\": \"xai-sdk\",\n 90→ \"install_command\": \"pip install xai-sdk\",\n 91→ \"version\": \"latest\",\n 92→ \"python_requires\": \">=3.10\",\n 93→ \"verified\": true\n 94→ },\n 95→ \"api_patterns\": {\n 96→ \"imports\": [\n 97→ \"from xai_sdk import Client\",\n 98→ \"from xai_sdk import AsyncClient\"\n 99→ ],\n 100→ \"initialization\": \"client = Client() # Uses XAI_API_KEY env var\",\n 101→ \"key_functions\": [\n 102→ \"client.chat(...)\",\n 103→ \"client.generate(...)\",\n 104→ \"await async_client.chat(...)\"\n 105→ ],\n 106→ \"verified_against\": \"GitHub xai-org/xai-sdk-python + PyPI\"\n 107→ },\n 108→ \"configuration\": {\n 109→ \"env_vars\": [\"XAI_API_KEY\"],\n 110→ \"config_files\": [],\n 111→ \"base_url\": \"https://api.x.ai/v1\"\n 112→ },\n 113→ \"extras\": {\n 114→ \"telemetry_http\": \"pip install xai-sdk[telemetry-http]\",\n 115→ \"telemetry_grpc\": \"pip install xai-sdk[telemetry-grpc]\"\n 116→ },\n 117→ \"gotchas\": [\n 118→ \"gRPC-based Python library\",\n 119→ \"Supports both sync and async clients\",\n 120→ \"Agentic tool calling with web search, X search, and code execution\",\n 121→ \"Multiple community SDKs exist (xai-grok, xai-grok-sdk) - use official xai-sdk\"\n 122→ ],\n 123→ \"research_sources\": [\n 124→ \"https://github.com/xai-org/xai-sdk-python\",\n 125→ \"https://pypi.org/project/xai-sdk/\",\n 126→ \"https://docs.x.ai/docs/tutorial\"\n 127→ ]\n 128→ },\n 129→ {\n 130→ \"name\": \"Ollama Python\",\n 131→ \"type\": \"library\",\n 132→ \"verified_package\": {\n 133→ \"name\": \"ollama\",\n 134→ \"install_command\": \"pip install ollama\",\n 135→ \"version\": \"latest (Nov 2025)\",\n 136→ \"python_requires\": \">=3.8\",\n 137→ \"verified\": true\n 138→ },\n 139→ \"api_patterns\": {\n 140→ \"imports\": [\n 141→ \"import ollama\",\n 142→ \"from ollama import Client\",\n 143→ \"from ollama import AsyncClient\"\n 144→ ],\n 145→ \"initialization\": \"client = Client(host='http://localhost:11434')\",\n 146→ \"key_functions\": [\n 147→ \"ollama.chat(model, messages)\",\n 148→ \"ollama.generate(model, prompt)\",\n 149→ \"await AsyncClient().chat(model, messages, stream=True)\"\n 150→ ],\n 151→ \"verified_against\": \"GitHub ollama/ollama-python\"\n 152→ },\n 153→ \"configuration\": {\n 154→ \"env_vars\": [],\n 155→ \"config_files\": [],\n 156→ \"host\": \"http://localhost:11434 (default)\"\n 157→ },\n 158→ \"infrastructure\": {\n 159→ \"requires_docker\": false,\n 160→ \"requires_ollama_server\": true,\n 161→ \"install_ollama\": \"https://ollama.com/download\"\n 162→ },\n 163→ \"gotchas\": [\n 164→ \"Ollama server must be running separately before using Python client\",\n 165→ \"Pull models first with 'ollama pull <model>' e.g., 'ollama pull gemma3'\",\n 166→ \"Supports streaming with stream=True parameter\",\n 167→ \"Custom client with headers: Client(host=..., headers={...})\",\n 168→ \"Now supports streaming with MCP tool calling\"\n 169→ ],\n 170→ \"research_sources\": [\n 171→ \"https://github.com/ollama/ollama-python\",\n 172→ \"https://pypi.org/project/ollama/\",\n 173→ \"https://ollama.com/blog/streaming-tool\"\n 174→ ]\n 175→ },\n 176→ {\n 177→ \"name\": \"FastAPI\",\n 178→ \"type\": \"framework\",\n 179→ \"verified_package\": {\n 180→ \"name\": \"fastapi\",\n 181→ \"install_command\": \"pip install \\\"fastapi[standard]\\\"\",\n 182→ \"version\": \"0.115.0+\",\n 183→ \"verified\": true\n 184→ },\n 185→ \"api_patterns\": {\n 186→ \"imports\": [\n 187→ \"from fastapi import FastAPI\",\n 188→ \"from fastapi import Depends, HTTPException\",\n 189→ \"from pydantic import BaseModel\"\n 190→ ],\n 191→ \"initialization\": \"app = FastAPI()\",\n 192→ \"key_functions\": [\n 193→ \"@app.get('/path')\",\n 194→ \"@app.post('/path')\",\n 195→ \"async def endpoint(): ...\",\n 196→ \"Depends() for dependency injection\"\n 197→ ],\n 198→ \"verified_against\": \"Official FastAPI docs\"\n 199→ },\n 200→ \"configuration\": {\n 201→ \"env_vars\": [],\n 202→ \"config_files\": [],\n 203→ \"dependencies\": [\"uvicorn\", \"pydantic\", \"starlette\"]\n 204→ },\n 205→ \"infrastructure\": {\n 206→ \"run_command\": \"uvicorn main:app --reload\",\n 207→ \"default_port\": 8000\n 208→ },\n 209→ \"gotchas\": [\n 210→ \"Use fastapi[standard] to get uvicorn and other standard dependencies\",\n 211→ \"ASGI server (uvicorn) required to run the app\",\n 212→ \"Built on Starlette (web) + Pydantic (data validation)\",\n 213→ \"Automatic OpenAPI/Swagger docs at /docs and /redoc\",\n 214→ \"--reload flag for development only\"\n 215→ ],\n 216→ \"research_sources\": [\n 217→ \"https://pypi.org/project/fastapi/\",\n 218→ \"https://fastapi.tiangolo.com/\",\n 219→ \"https://fastapi.tiangolo.com/tutorial/\"\n 220→ ]\n 221→ },\n 222→ {\n 223→ \"name\": \"React with TypeScript (Vite)\",\n 224→ \"type\": \"framework\",\n 225→ \"verified_package\": {\n 226→ \"name\": \"vite + react + typescript\",\n 227→ \"install_command\": \"npm create vite@latest my-app -- --template react-ts\",\n 228→ \"version\": \"Vite 6.3.2+ (latest)\",\n 229→ \"node_requires\": \">=18\",\n 230→ \"verified\": true\n 231→ },\n 232→ \"api_patterns\": {\n 233→ \"imports\": [\n 234→ \"import React from 'react'\",\n 235→ \"import { useState, useEffect } from 'react'\"\n 236→ ],\n 237→ \"project_structure\": \"index.html at root, src/ with main.tsx entry\",\n 238→ \"key_features\": [\n 239→ \"Hot Module Replacement (HMR)\",\n 240→ \"Native ES modules in development\",\n 241→ \"TypeScript support out of box\"\n 242→ ],\n 243→ \"verified_against\": \"Vite official guide\"\n 244→ },\n 245→ \"configuration\": {\n 246→ \"config_files\": [\"vite.config.ts\", \"tsconfig.json\"],\n 247→ \"commands\": {\n 248→ \"dev\": \"npm run dev\",\n 249→ \"build\": \"npm run build\",\n 250→ \"preview\": \"npm run preview\"\n 251→ }\n 252→ },\n 253→ \"infrastructure\": {\n 254→ \"dev_server\": \"http://localhost:5173\",\n 255→ \"build_output\": \"dist/\"\n 256→ },\n 257→ \"gotchas\": [\n 258→ \"Node.js >= v18 required\",\n 259→ \"Available templates: react-ts, react-swc-ts (faster builds)\",\n 260→ \"For testing: npm install -D vitest @testing-library/react\",\n 261→ \"HMR provides instant updates without page refresh\"\n 262→ ],\n 263→ \"research_sources\": [\n 264→ \"https://vite.dev/guide/\",\n 265→ \"https://blog.logrocket.com/how-to-build-react-typescript-app-vite/\"\n 266→ ]\n 267→ },\n 268→ {\n 269→ \"name\": \"MCP Python SDK\",\n 270→ \"type\": \"library\",\n 271→ \"verified_package\": {\n 272→ \"name\": \"mcp\",\n 273→ \"install_command\": \"pip install mcp\",\n 274→ \"version\": \"1.25.0+ (v2 coming Q1)\",\n 275→ \"python_requires\": \">=3.10\",\n 276→ \"verified\": true\n 277→ },\n 278→ \"api_patterns\": {\n 279→ \"imports\": [\n 280→ \"from mcp.server.fastmcp import FastMCP\",\n 281→ \"from mcp.client import Client\"\n 282→ ],\n 283→ \"initialization\": \"mcp = FastMCP('server-name')\",\n 284→ \"key_concepts\": [\n 285→ \"Resources - like GET endpoints for data\",\n 286→ \"Tools - like POST endpoints for actions\",\n 287→ \"Prompts - reusable templates for LLM interactions\"\n 288→ ],\n 289→ \"verified_against\": \"GitHub modelcontextprotocol/python-sdk\"\n 290→ },\n 291→ \"configuration\": {\n 292→ \"env_vars\": [],\n 293→ \"config_files\": [],\n 294→ \"transport_options\": [\"stdio\", \"SSE\", \"Streamable HTTP\", \"WebSocket\"]\n 295→ },\n 296→ \"extras\": {\n 297→ \"cli\": \"pip install mcp[cli]\",\n 298→ \"rich\": \"pip install mcp[rich]\",\n 299→ \"websocket\": \"pip install mcp[ws]\"\n 300→ },\n 301→ \"gotchas\": [\n 302→ \"MCP is authored by Anthropic\",\n 303→ \"v2 planned for Q1 - pin to mcp>=1.25,<2 for stability\",\n 304→ \"Separates context provision from LLM interaction\",\n 305→ \"FastMCP provides high-level server abstraction\"\n 306→ ],\n 307→ \"research_sources\": [\n 308→ \"https://pypi.org/project/mcp/\",\n 309→ \"https://github.com/modelcontextprotocol/python-sdk\",\n 310→ \"https://modelcontextprotocol.io/docs/develop/build-server\"\n 311→ ]\n 312→ },\n 313→ {\n 314→ \"name\": \"GitPython\",\n 315→ \"type\": \"library\",\n 316→ \"verified_package\": {\n 317→ \"name\": \"GitPython\",\n 318→ \"install_command\": \"pip install GitPython\",\n 319→ \"version\": \"3.1.46+\",\n 320→ \"verified\": true\n 321→ },\n 322→ \"api_patterns\": {\n 323→ \"imports\": [\n 324→ \"from git import Repo\"\n 325→ ],\n 326→ \"initialization\": \"repo = Repo('/path/to/repo') # or Repo.init('/path')\",\n 327→ \"key_functions\": [\n 328→ \"Repo.init(path)\",\n 329→ \"Repo.clone_from(url, path)\",\n 330→ \"repo.create_head(branch_name)\",\n 331→ \"repo.index.add(files)\",\n 332→ \"repo.index.commit(message)\",\n 333→ \"repo.git.push()\",\n 334→ \"repo.is_dirty()\"\n 335→ ],\n 336→ \"verified_against\": \"GitPython documentation\"\n 337→ },\n 338→ \"configuration\": {\n 339→ \"env_vars\": [],\n 340→ \"requires\": \"Git must be installed on system\"\n 341→ },\n 342→ \"gotchas\": [\n 343→ \"Not suited for long-running processes (daemons) - leaks system resources\",\n 344→ \"Use as subprocess for long-running applications\",\n 345→ \"is_dirty() checks for uncommitted changes (untracked, modified, staged)\",\n 346→ \"Requires Git executable installed on system\"\n 347→ ],\n 348→ \"research_sources\": [\n 349→ \"https://pypi.org/project/GitPython/\",\n 350→ \"https://github.com/gitpython-developers/GitPython\",\n 351→ \"https://gitpython.readthedocs.io/en/stable/\"\n 352→ ]\n 353→ },\n 354→ {\n 355→ \"name\": \"py-gitea (Gitea API Client)\",\n 356→ \"type\": \"library\",\n 357→ \"verified_package\": {\n 358→ \"name\": \"py-gitea\",\n 359→ \"install_command\": \"pip install py-gitea\",\n 360→ \"version\": \"0.2.10 (Sep 2025)\",\n 361→ \"python_requires\": \">=3.11\",\n 362→ \"verified\": true\n 363→ },\n 364→ \"api_patterns\": {\n 365→ \"imports\": [\n 366→ \"from gitea import Gitea\"\n 367→ ],\n 368→ \"initialization\": \"gitea = Gitea(url, token)\",\n 369→ \"key_features\": [\n 370→ \"Organizations management\",\n 371→ \"Teams management\",\n 372→ \"Repositories management\",\n 373→ \"Users management\"\n 374→ ],\n 375→ \"verified_against\": \"GitHub Langenfeld/py-gitea\"\n 376→ },\n 377→ \"configuration\": {\n 378→ \"env_vars\": [\"GITEA_TOKEN (recommended)\"],\n 379→ \"requires_gitea\": \">=1.16.1\"\n 380→ },\n 381→ \"alternatives\": [\n 382→ \"py-gitea-opensuse-org (Dec 2025, auto-generated)\",\n 383→ \"giteapy (older, Swagger-generated)\"\n 384→ ],\n 385→ \"gotchas\": [\n 386→ \"Requires Gitea server >= 1.16.1\",\n 387→ \"Python 3.11+ required (newer than some other packages)\",\n 388→ \"Focused on Org/Team/Repo/User management\"\n 389→ ],\n 390→ \"research_sources\": [\n 391→ \"https://pypi.org/project/py-gitea/\",\n 392→ \"https://github.com/Langenfeld/py-gitea\"\n 393→ ]\n 394→ },\n 395→ {\n 396→ \"name\": \"LiteLLM (Unified LLM API)\",\n 397→ \"type\": \"library\",\n 398→ \"verified_package\": {\n 399→ \"name\": \"litellm\",\n 400→ \"install_command\": \"pip install litellm\",\n 401→ \"version\": \"latest\",\n 402→ \"verified\": true\n 403→ },\n 404→ \"api_patterns\": {\n 405→ \"imports\": [\n 406→ \"import litellm\",\n 407→ \"from litellm import completion\"\n 408→ ],\n 409→ \"initialization\": \"response = completion(model='provider/model', messages=[...])\",\n 410→ \"key_functions\": [\n 411→ \"completion(model, messages)\",\n 412→ \"acompletion(...) for async\",\n 413→ \"embedding(model, input)\"\n 414→ ],\n 415→ \"verified_against\": \"LiteLLM documentation\"\n 416→ },\n 417→ \"configuration\": {\n 418→ \"env_vars\": [\n 419→ \"OPENAI_API_KEY\",\n 420→ \"ANTHROPIC_API_KEY\",\n 421→ \"GEMINI_API_KEY\",\n 422→ \"Provider-specific keys\"\n 423→ ],\n 424→ \"supported_providers\": [\n 425→ \"OpenAI\", \"Anthropic\", \"Google/Gemini\", \"Azure\",\n 426→ \"AWS Bedrock\", \"Ollama\", \"Cohere\", \"HuggingFace\",\n 427→ \"Groq\", \"VLLM\", \"100+ more\"\n 428→ ]\n 429→ },\n 430→ \"key_features\": [\n 431→ \"Unified interface for 100+ LLMs\",\n 432→ \"OpenAI-compatible API format\",\n 433→ \"Cost tracking and budgeting\",\n 434→ \"Automatic retries and fallbacks\",\n 435→ \"Load balancing across providers\",\n 436→ \"Proxy server mode for gateway usage\"\n 437→ ],\n 438→ \"gotchas\": [\n 439→ \"HIGHLY RECOMMENDED for multi-provider projects\",\n 440→ \"Can use as SDK or Proxy Server (AI Gateway)\",\n 441→ \"Mimics OpenAI API structure - write once, swap models\",\n 442→ \"Enterprise adoption: Netflix, Lemonade, RocketMoney\",\n 443→ \"470k+ downloads, 28k+ GitHub stars\",\n 444→ \"Can combine with OpenRouter for even more models\"\n 445→ ],\n 446→ \"research_sources\": [\n 447→ \"https://docs.litellm.ai/docs/\",\n 448→ \"https://github.com/BerriAI/litellm\",\n 449→ \"https://docs.litellm.ai/docs/providers\"\n 450→ ]\n 451→ }\n 452→ ],\n 453→ \"unverified_claims\": [\n 454→ {\n 455→ \"claim\": \"Cross-model validation quality improvements\",\n 456→ \"reason\": \"This is an architectural pattern, not a package - implementation approach needs to be designed\",\n 457→ \"risk_level\": \"low\"\n 458→ },\n 459→ {\n 460→ \"claim\": \"Cost optimization through intelligent routing\",\n 461→ \"reason\": \"Implementation depends on model pricing APIs and usage tracking - LiteLLM provides cost tracking support\",\n 462→ \"risk_level\": \"low\"\n 463→ }\n 464→ ],\n 465→ \"recommendations\": [\n 466→ {\n 467→ \"topic\": \"Unified LLM Interface\",\n 468→ \"recommendation\": \"Use LiteLLM as the unified API wrapper instead of managing individual SDKs directly\",\n 469→ \"rationale\": \"LiteLLM provides a single interface for 100+ models with automatic fallbacks, cost tracking, and load balancing - exactly what the multi-provider requirements need\"\n 470→ },\n 471→ {\n 472→ \"topic\": \"Google Gemini\",\n 473→ \"recommendation\": \"Use google-genai package, NOT google-generativeai\",\n 474→ \"rationale\": \"google-generativeai was deprecated on Nov 30, 2025 - all new code must use google-genai\"\n 475→ },\n 476→ {\n 477→ \"topic\": \"xAI Grok\",\n 478→ \"recommendation\": \"Use official xai-sdk package, not community alternatives\",\n 479→ \"rationale\": \"Multiple community packages exist (xai-grok, xai-grok-sdk) but official xai-sdk is most reliable\"\n 480→ },\n 481→ {\n 482→ \"topic\": \"Git Integration\",\n 483→ \"recommendation\": \"Use GitPython for programmatic Git, but avoid in long-running daemon processes\",\n 484→ \"rationale\": \"GitPython has known resource leak issues in long-running processes - factor out to subprocess for daemons\"\n 485→ },\n 486→ {\n 487→ \"topic\": \"Gitea API\",\n 488→ \"recommendation\": \"Use py-gitea (v0.2.10) - most recently maintained\",\n 489→ \"rationale\": \"Requires Python 3.11+ and Gitea 1.16.1+ - verify compatibility with other package requirements\"\n 490→ },\n 491→ {\n 492→ \"topic\": \"Python Version\",\n 493→ \"recommendation\": \"Use Python 3.11+ for maximum compatibility\",\n 494→ \"rationale\": \"py-gitea requires 3.11+, xai-sdk requires 3.10+, MCP requires 3.10+ - 3.11 satisfies all\"\n 495→ },\n 496→ {\n 497→ \"topic\": \"MCP Version\",\n 498→ \"recommendation\": \"Pin MCP to mcp>=1.25,<2 for stability\",\n 499→ \"rationale\": \"MCP v2 is planned for Q1 - pinning prevents breaking changes\"\n 500→ }\n 501→ ],\n 502→ \"context7_libraries_found\": [\n 503→ \"/anthropics/anthropic-sdk-python\",\n 504→ \"/websites/fastapi_tiangolo\",\n 505→ \"/ollama/ollama-python\",\n 506→ \"/websites/ai_google_dev_gemini-api\",\n 507→ \"/websites/typescriptlang\",\n 508→ \"/modelcontextprotocol/python-sdk\",\n 509→ \"/websites/x_ai\"\n 510→ ],\n 511→ \"python_requirements_summary\": {\n 512→ \"minimum_python\": \"3.11\",\n 513→ \"core_packages\": [\n 514→ \"fastapi[standard]\",\n 515→ \"uvicorn\",\n 516→ \"pydantic\",\n 517→ \"litellm\",\n 518→ \"anthropic\",\n 519→ \"google-genai\",\n 520→ \"xai-sdk\",\n 521→ \"ollama\",\n 522→ \"mcp>=1.25,<2\",\n 523→ \"GitPython\",\n 524→ \"py-gitea\"\n 525→ ],\n 526→ \"optional_packages\": [\n 527→ \"anthropic[bedrock]\",\n 528→ \"anthropic[vertex]\",\n 529→ \"google-genai[aiohttp]\",\n 530→ \"xai-sdk[telemetry-http]\",\n 531→ \"mcp[cli]\"\n 532→ ]\n 533→ },\n 534→ \"frontend_requirements_summary\": {\n 535→ \"node_minimum\": \"18\",\n 536→ \"scaffold_command\": \"npm create vite@latest -- --template react-ts\",\n 537→ \"dev_command\": \"npm run dev\",\n 538→ \"build_command\": \"npm run build\",\n 539→ \"test_packages\": [\n 540→ \"vitest\",\n 541→ \"@testing-library/react\",\n 542→ \"@testing-library/jest-dom\"\n 543→ ]\n 544→ },\n 545→ \"infrastructure_requirements\": {\n 546→ \"ollama\": {\n 547→ \"required\": true,\n 548→ \"install\": \"https://ollama.com/download\",\n 549→ \"note\": \"Ollama server must be running for local LLM inference\"\n 550→ },\n 551→ \"gitea\": {\n 552→ \"optional\": true,\n 553→ \"note\": \"Only if using Gitea for version control (alternative to GitHub)\"\n 554→ },\n 555→ \"database\": {\n 556→ \"required\": true,\n 557→ \"recommendation\": \"PostgreSQL or SQLite for state/spec storage\",\n 558→ \"note\": \"Not specified in requirements - needs architectural decision\"\n 559→ }\n 560→ },\n 561→ \"created_at\": \"2026-01-12T00:00:00Z\"\n 562→}\n 563→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"b88d61f2-0db1-41e6-a2a7-8c511ce729e9","timestamp":"2026-01-12T18:58:14.052Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\research.json","content":"{\n \"integrations_researched\": [\n {\n \"name\": \"Anthropic Claude SDK\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"anthropic\",\n \"install_command\": \"pip install anthropic\",\n \"version\": \"latest (Nov 2025)\",\n \"python_requires\": \">=3.9\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from anthropic import Anthropic\",\n \"from anthropic import AsyncAnthropic\"\n ],\n \"initialization\": \"client = Anthropic() # Uses ANTHROPIC_API_KEY env var\",\n \"key_functions\": [\n \"client.messages.create(model, messages, max_tokens)\",\n \"await async_client.messages.create(...)\"\n ],\n \"verified_against\": \"PyPI + GitHub anthropics/anthropic-sdk-python\"\n },\n \"configuration\": {\n \"env_vars\": [\"ANTHROPIC_API_KEY\"],\n \"config_files\": [],\n \"dependencies\": [\"httpx\"]\n },\n \"extras\": {\n \"bedrock\": \"pip install anthropic[bedrock]\",\n \"vertex\": \"pip install anthropic[vertex]\"\n },\n \"gotchas\": [\n \"Supports both sync and async clients via Anthropic() and AsyncAnthropic()\",\n \"Context manager support for automatic HTTP client cleanup\",\n \"Type definitions for all request/response fields\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/anthropic/\",\n \"https://github.com/anthropics/anthropic-sdk-python\",\n \"https://platform.claude.com/docs/en/api/client-sdks\"\n ]\n },\n {\n \"name\": \"Google Gemini SDK\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"google-genai\",\n \"install_command\": \"pip install google-genai\",\n \"version\": \"GA (May 2025)\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from google import genai\"\n ],\n \"initialization\": \"client = genai.Client(api_key='GEMINI_API_KEY') # or uses GEMINI_API_KEY env var\",\n \"key_functions\": [\n \"client.models.generate_content(model, contents)\",\n \"client.models.generate_content_stream(...)\"\n ],\n \"verified_against\": \"Google AI for Developers docs\"\n },\n \"configuration\": {\n \"env_vars\": [\"GEMINI_API_KEY\"],\n \"config_files\": [],\n \"dependencies\": []\n },\n \"extras\": {\n \"async\": \"pip install google-genai[aiohttp]\"\n },\n \"gotchas\": [\n \"CRITICAL: google-generativeai package is DEPRECATED (EOL Nov 30, 2025)\",\n \"Must use new google-genai package for all new development\",\n \"Supports both Gemini Developer API and Vertex AI APIs\",\n \"LLMs may generate code for deprecated library - always use google-genai\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/google-generativeai/\",\n \"https://ai.google.dev/gemini-api/docs/libraries\",\n \"https://github.com/googleapis/python-genai\"\n ]\n },\n {\n \"name\": \"xAI Grok SDK\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"xai-sdk\",\n \"install_command\": \"pip install xai-sdk\",\n \"version\": \"latest\",\n \"python_requires\": \">=3.10\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from xai_sdk import Client\",\n \"from xai_sdk import AsyncClient\"\n ],\n \"initialization\": \"client = Client() # Uses XAI_API_KEY env var\",\n \"key_functions\": [\n \"client.chat(...)\",\n \"client.generate(...)\",\n \"await async_client.chat(...)\"\n ],\n \"verified_against\": \"GitHub xai-org/xai-sdk-python + PyPI\"\n },\n \"configuration\": {\n \"env_vars\": [\"XAI_API_KEY\"],\n \"config_files\": [],\n \"base_url\": \"https://api.x.ai/v1\"\n },\n \"extras\": {\n \"telemetry_http\": \"pip install xai-sdk[telemetry-http]\",\n \"telemetry_grpc\": \"pip install xai-sdk[telemetry-grpc]\"\n },\n \"gotchas\": [\n \"gRPC-based Python library\",\n \"Supports both sync and async clients\",\n \"Agentic tool calling with web search, X search, and code execution\",\n \"Multiple community SDKs exist (xai-grok, xai-grok-sdk) - use official xai-sdk\"\n ],\n \"research_sources\": [\n \"https://github.com/xai-org/xai-sdk-python\",\n \"https://pypi.org/project/xai-sdk/\",\n \"https://docs.x.ai/docs/tutorial\"\n ]\n },\n {\n \"name\": \"Ollama Python\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"ollama\",\n \"install_command\": \"pip install ollama\",\n \"version\": \"latest (Nov 2025)\",\n \"python_requires\": \">=3.8\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"import ollama\",\n \"from ollama import Client\",\n \"from ollama import AsyncClient\"\n ],\n \"initialization\": \"client = Client(host='http://localhost:11434')\",\n \"key_functions\": [\n \"ollama.chat(model, messages)\",\n \"ollama.generate(model, prompt)\",\n \"await AsyncClient().chat(model, messages, stream=True)\"\n ],\n \"verified_against\": \"GitHub ollama/ollama-python\"\n },\n \"configuration\": {\n \"env_vars\": [],\n \"config_files\": [],\n \"host\": \"http://localhost:11434 (default)\"\n },\n \"infrastructure\": {\n \"requires_docker\": false,\n \"requires_ollama_server\": true,\n \"install_ollama\": \"https://ollama.com/download\"\n },\n \"gotchas\": [\n \"Ollama server must be running separately before using Python client\",\n \"Pull models first with 'ollama pull <model>' e.g., 'ollama pull gemma3'\",\n \"Supports streaming with stream=True parameter\",\n \"Custom client with headers: Client(host=..., headers={...})\",\n \"Now supports streaming with MCP tool calling\"\n ],\n \"research_sources\": [\n \"https://github.com/ollama/ollama-python\",\n \"https://pypi.org/project/ollama/\",\n \"https://ollama.com/blog/streaming-tool\"\n ]\n },\n {\n \"name\": \"FastAPI\",\n \"type\": \"framework\",\n \"verified_package\": {\n \"name\": \"fastapi\",\n \"install_command\": \"pip install \\\"fastapi[standard]\\\"\",\n \"version\": \"0.115.0+\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from fastapi import FastAPI\",\n \"from fastapi import Depends, HTTPException\",\n \"from pydantic import BaseModel\"\n ],\n \"initialization\": \"app = FastAPI()\",\n \"key_functions\": [\n \"@app.get('/path')\",\n \"@app.post('/path')\",\n \"async def endpoint(): ...\",\n \"Depends() for dependency injection\"\n ],\n \"verified_against\": \"Official FastAPI docs\"\n },\n \"configuration\": {\n \"env_vars\": [],\n \"config_files\": [],\n \"dependencies\": [\"uvicorn\", \"pydantic\", \"starlette\"]\n },\n \"infrastructure\": {\n \"run_command\": \"uvicorn main:app --reload\",\n \"default_port\": 8000\n },\n \"gotchas\": [\n \"Use fastapi[standard] to get uvicorn and other standard dependencies\",\n \"ASGI server (uvicorn) required to run the app\",\n \"Built on Starlette (web) + Pydantic (data validation)\",\n \"Automatic OpenAPI/Swagger docs at /docs and /redoc\",\n \"--reload flag for development only\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/fastapi/\",\n \"https://fastapi.tiangolo.com/\",\n \"https://fastapi.tiangolo.com/tutorial/\"\n ]\n },\n {\n \"name\": \"React with TypeScript (Vite)\",\n \"type\": \"framework\",\n \"verified_package\": {\n \"name\": \"vite + react + typescript\",\n \"install_command\": \"npm create vite@latest my-app -- --template react-ts\",\n \"version\": \"Vite 6.3.2+ (latest)\",\n \"node_requires\": \">=18\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"import React from 'react'\",\n \"import { useState, useEffect } from 'react'\"\n ],\n \"project_structure\": \"index.html at root, src/ with main.tsx entry\",\n \"key_features\": [\n \"Hot Module Replacement (HMR)\",\n \"Native ES modules in development\",\n \"TypeScript support out of box\"\n ],\n \"verified_against\": \"Vite official guide\"\n },\n \"configuration\": {\n \"config_files\": [\"vite.config.ts\", \"tsconfig.json\"],\n \"commands\": {\n \"dev\": \"npm run dev\",\n \"build\": \"npm run build\",\n \"preview\": \"npm run preview\"\n }\n },\n \"infrastructure\": {\n \"dev_server\": \"http://localhost:5173\",\n \"build_output\": \"dist/\"\n },\n \"gotchas\": [\n \"Node.js >= v18 required\",\n \"Available templates: react-ts, react-swc-ts (faster builds)\",\n \"For testing: npm install -D vitest @testing-library/react\",\n \"HMR provides instant updates without page refresh\"\n ],\n \"research_sources\": [\n \"https://vite.dev/guide/\",\n \"https://blog.logrocket.com/how-to-build-react-typescript-app-vite/\"\n ]\n },\n {\n \"name\": \"MCP Python SDK\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"mcp\",\n \"install_command\": \"pip install mcp\",\n \"version\": \"1.25.0+ (v2 coming Q1)\",\n \"python_requires\": \">=3.10\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from mcp.server.fastmcp import FastMCP\",\n \"from mcp.client import Client\"\n ],\n \"initialization\": \"mcp = FastMCP('server-name')\",\n \"key_concepts\": [\n \"Resources - like GET endpoints for data\",\n \"Tools - like POST endpoints for actions\",\n \"Prompts - reusable templates for LLM interactions\"\n ],\n \"verified_against\": \"GitHub modelcontextprotocol/python-sdk\"\n },\n \"configuration\": {\n \"env_vars\": [],\n \"config_files\": [],\n \"transport_options\": [\"stdio\", \"SSE\", \"Streamable HTTP\", \"WebSocket\"]\n },\n \"extras\": {\n \"cli\": \"pip install mcp[cli]\",\n \"rich\": \"pip install mcp[rich]\",\n \"websocket\": \"pip install mcp[ws]\"\n },\n \"gotchas\": [\n \"MCP is authored by Anthropic\",\n \"v2 planned for Q1 - pin to mcp>=1.25,<2 for stability\",\n \"Separates context provision from LLM interaction\",\n \"FastMCP provides high-level server abstraction\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/mcp/\",\n \"https://github.com/modelcontextprotocol/python-sdk\",\n \"https://modelcontextprotocol.io/docs/develop/build-server\"\n ]\n },\n {\n \"name\": \"GitPython\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"GitPython\",\n \"install_command\": \"pip install GitPython\",\n \"version\": \"3.1.46+\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from git import Repo\"\n ],\n \"initialization\": \"repo = Repo('/path/to/repo') # or Repo.init('/path')\",\n \"key_functions\": [\n \"Repo.init(path)\",\n \"Repo.clone_from(url, path)\",\n \"repo.create_head(branch_name)\",\n \"repo.index.add(files)\",\n \"repo.index.commit(message)\",\n \"repo.git.push()\",\n \"repo.is_dirty()\"\n ],\n \"verified_against\": \"GitPython documentation\"\n },\n \"configuration\": {\n \"env_vars\": [],\n \"requires\": \"Git must be installed on system\"\n },\n \"gotchas\": [\n \"Not suited for long-running processes (daemons) - leaks system resources\",\n \"Use as subprocess for long-running applications\",\n \"is_dirty() checks for uncommitted changes (untracked, modified, staged)\",\n \"Requires Git executable installed on system\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/GitPython/\",\n \"https://github.com/gitpython-developers/GitPython\",\n \"https://gitpython.readthedocs.io/en/stable/\"\n ]\n },\n {\n \"name\": \"py-gitea (Gitea API Client)\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"py-gitea\",\n \"install_command\": \"pip install py-gitea\",\n \"version\": \"0.2.10 (Sep 2025)\",\n \"python_requires\": \">=3.11\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"from gitea import Gitea\"\n ],\n \"initialization\": \"gitea = Gitea(url, token)\",\n \"key_features\": [\n \"Organizations management\",\n \"Teams management\",\n \"Repositories management\",\n \"Users management\"\n ],\n \"verified_against\": \"GitHub Langenfeld/py-gitea\"\n },\n \"configuration\": {\n \"env_vars\": [\"GITEA_TOKEN (recommended)\"],\n \"requires_gitea\": \">=1.16.1\"\n },\n \"alternatives\": [\n \"py-gitea-opensuse-org (Dec 2025, auto-generated)\",\n \"giteapy (older, Swagger-generated)\"\n ],\n \"gotchas\": [\n \"Requires Gitea server >= 1.16.1\",\n \"Python 3.11+ required (newer than some other packages)\",\n \"Focused on Org/Team/Repo/User management\"\n ],\n \"research_sources\": [\n \"https://pypi.org/project/py-gitea/\",\n \"https://github.com/Langenfeld/py-gitea\"\n ]\n },\n {\n \"name\": \"LiteLLM (Unified LLM API)\",\n \"type\": \"library\",\n \"verified_package\": {\n \"name\": \"litellm\",\n \"install_command\": \"pip install litellm\",\n \"version\": \"latest\",\n \"verified\": true\n },\n \"api_patterns\": {\n \"imports\": [\n \"import litellm\",\n \"from litellm import completion\"\n ],\n \"initialization\": \"response = completion(model='provider/model', messages=[...])\",\n \"key_functions\": [\n \"completion(model, messages)\",\n \"acompletion(...) for async\",\n \"embedding(model, input)\"\n ],\n \"verified_against\": \"LiteLLM documentation\"\n },\n \"configuration\": {\n \"env_vars\": [\n \"OPENAI_API_KEY\",\n \"ANTHROPIC_API_KEY\",\n \"GEMINI_API_KEY\",\n \"Provider-specific keys\"\n ],\n \"supported_providers\": [\n \"OpenAI\", \"Anthropic\", \"Google/Gemini\", \"Azure\",\n \"AWS Bedrock\", \"Ollama\", \"Cohere\", \"HuggingFace\",\n \"Groq\", \"VLLM\", \"100+ more\"\n ]\n },\n \"key_features\": [\n \"Unified interface for 100+ LLMs\",\n \"OpenAI-compatible API format\",\n \"Cost tracking and budgeting\",\n \"Automatic retries and fallbacks\",\n \"Load balancing across providers\",\n \"Proxy server mode for gateway usage\"\n ],\n \"gotchas\": [\n \"HIGHLY RECOMMENDED for multi-provider projects\",\n \"Can use as SDK or Proxy Server (AI Gateway)\",\n \"Mimics OpenAI API structure - write once, swap models\",\n \"Enterprise adoption: Netflix, Lemonade, RocketMoney\",\n \"470k+ downloads, 28k+ GitHub stars\",\n \"Can combine with OpenRouter for even more models\"\n ],\n \"research_sources\": [\n \"https://docs.litellm.ai/docs/\",\n \"https://github.com/BerriAI/litellm\",\n \"https://docs.litellm.ai/docs/providers\"\n ]\n }\n ],\n \"unverified_claims\": [\n {\n \"claim\": \"Cross-model validation quality improvements\",\n \"reason\": \"This is an architectural pattern, not a package - implementation approach needs to be designed\",\n \"risk_level\": \"low\"\n },\n {\n \"claim\": \"Cost optimization through intelligent routing\",\n \"reason\": \"Implementation depends on model pricing APIs and usage tracking - LiteLLM provides cost tracking support\",\n \"risk_level\": \"low\"\n }\n ],\n \"recommendations\": [\n {\n \"topic\": \"Unified LLM Interface\",\n \"recommendation\": \"Use LiteLLM as the unified API wrapper instead of managing individual SDKs directly\",\n \"rationale\": \"LiteLLM provides a single interface for 100+ models with automatic fallbacks, cost tracking, and load balancing - exactly what the multi-provider requirements need\"\n },\n {\n \"topic\": \"Google Gemini\",\n \"recommendation\": \"Use google-genai package, NOT google-generativeai\",\n \"rationale\": \"google-generativeai was deprecated on Nov 30, 2025 - all new code must use google-genai\"\n },\n {\n \"topic\": \"xAI Grok\",\n \"recommendation\": \"Use official xai-sdk package, not community alternatives\",\n \"rationale\": \"Multiple community packages exist (xai-grok, xai-grok-sdk) but official xai-sdk is most reliable\"\n },\n {\n \"topic\": \"Git Integration\",\n \"recommendation\": \"Use GitPython for programmatic Git, but avoid in long-running daemon processes\",\n \"rationale\": \"GitPython has known resource leak issues in long-running processes - factor out to subprocess for daemons\"\n },\n {\n \"topic\": \"Gitea API\",\n \"recommendation\": \"Use py-gitea (v0.2.10) - most recently maintained\",\n \"rationale\": \"Requires Python 3.11+ and Gitea 1.16.1+ - verify compatibility with other package requirements\"\n },\n {\n \"topic\": \"Python Version\",\n \"recommendation\": \"Use Python 3.11+ for maximum compatibility\",\n \"rationale\": \"py-gitea requires 3.11+, xai-sdk requires 3.10+, MCP requires 3.10+ - 3.11 satisfies all\"\n },\n {\n \"topic\": \"MCP Version\",\n \"recommendation\": \"Pin MCP to mcp>=1.25,<2 for stability\",\n \"rationale\": \"MCP v2 is planned for Q1 - pinning prevents breaking changes\"\n }\n ],\n \"context7_libraries_found\": [\n \"/anthropics/anthropic-sdk-python\",\n \"/websites/fastapi_tiangolo\",\n \"/ollama/ollama-python\",\n \"/websites/ai_google_dev_gemini-api\",\n \"/websites/typescriptlang\",\n \"/modelcontextprotocol/python-sdk\",\n \"/websites/x_ai\"\n ],\n \"python_requirements_summary\": {\n \"minimum_python\": \"3.11\",\n \"core_packages\": [\n \"fastapi[standard]\",\n \"uvicorn\",\n \"pydantic\",\n \"litellm\",\n \"anthropic\",\n \"google-genai\",\n \"xai-sdk\",\n \"ollama\",\n \"mcp>=1.25,<2\",\n \"GitPython\",\n \"py-gitea\"\n ],\n \"optional_packages\": [\n \"anthropic[bedrock]\",\n \"anthropic[vertex]\",\n \"google-genai[aiohttp]\",\n \"xai-sdk[telemetry-http]\",\n \"mcp[cli]\"\n ]\n },\n \"frontend_requirements_summary\": {\n \"node_minimum\": \"18\",\n \"scaffold_command\": \"npm create vite@latest -- --template react-ts\",\n \"dev_command\": \"npm run dev\",\n \"build_command\": \"npm run build\",\n \"test_packages\": [\n \"vitest\",\n \"@testing-library/react\",\n \"@testing-library/jest-dom\"\n ]\n },\n \"infrastructure_requirements\": {\n \"ollama\": {\n \"required\": true,\n \"install\": \"https://ollama.com/download\",\n \"note\": \"Ollama server must be running for local LLM inference\"\n },\n \"gitea\": {\n \"optional\": true,\n \"note\": \"Only if using Gitea for version control (alternative to GitHub)\"\n },\n \"database\": {\n \"required\": true,\n \"recommendation\": \"PostgreSQL or SQLite for state/spec storage\",\n \"note\": \"Not specified in requirements - needs architectural decision\"\n }\n },\n \"created_at\": \"2026-01-12T00:00:00Z\"\n}\n","numLines":563,"startLine":1,"totalLines":563}},"sourceToolAssistantUUID":"383cd3f0-3bd5-47df-99a6-955dce5ab1df"}
|
|
{"parentUuid":"383cd3f0-3bd5-47df-99a6-955dce5ab1df","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013KKrEwqDevDbXj5FuCjY9Z","type":"tool_result","content":" 1→{\n 2→ \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n Core Features\\n\\n 1. Multi-LLM Provider Support\\n - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n - Strategic model assignment - route tasks to models best suited for them:\\n - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n - Claude Sonnet/Opus for complex reasoning and architecture\\n - Gemini for multimodal analysis and large context windows\\n - Grok for real-time information and alternative perspectives\\n - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n - Cost optimization: intelligently balance between cloud and local models\\n\\n 2. Version Control Integration\\n - Native Git and Gitea support\\n - Automatic branching strategies for features/tasks\\n - Commit message generation aligned with conventional commits\\n - PR creation with comprehensive descriptions\\n - Support for reviewing and iterating on code in branches\\n\\n 3. Adaptive Spec System\\n - Initial spec-app style interview to gather requirements\\n - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n - Clarify ambiguous requirements that emerged during development\\n - Gather feedback on implemented features\\n - Adjust scope based on discoveries\\n - Validate assumptions with the user\\n - Spec versioning: track how requirements evolve over time\\n - Continuous alignment: ensure implementation stays true to user intent\\n\\n 4. Agent Skill System\\n - Extensible skill/addon architecture for specialized agent capabilities\\n - Skills can be model-specific or model-agnostic\\n - Examples: code review, security scanning, performance optimization, documentation generation\\n - Custom skill creation framework\\n - Skill composition: chain skills together for complex workflows\\n\\n 5. Project Steering & Course Correction\\n - Mid-flight adjustments: ability to redirect project direction after work has begun\\n - Checkpoint system: review progress at milestones and adjust plans\\n - Context preservation: maintain full project history when steering changes\\n - Impact analysis: understand implications of direction changes\\n - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n 6. Intelligent Orchestration\\n - Task decomposition with model-specific assignments\\n - Parallel execution where possible\\n - Dependency management between tasks\\n - Progressive refinement: iterative improvement cycles\\n - Fallback strategies when a model fails or produces poor results\\n\\n 7. Quality Assurance\\n - Multi-model consensus for critical decisions\\n - Automated testing integration\\n - Code quality metrics and reporting\\n - Security vulnerability scanning\\n - Performance profiling\\n\\n Technical Architecture\\n\\n - Backend: Python (FastAPI) for orchestration layer\\n - Frontend: React/TypeScript UI for project management and monitoring\\n - Model Integration: Unified API wrapper supporting all providers\\n - State Management: Track project state, agent activities, and decisions\\n - Database: Store specs, decisions, task history, and model interactions\\n - MCP Server: Feature graph and skill management\\n\\n Key Differentiators\\n\\n 1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n 2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n 3. Local-first option: Can run meaningful work without API costs via Ollama\\n 4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n 5. Transparency: Full visibility into which models did what and why\\n\\n Use Cases\\n\\n - Large greenfield projects with evolving requirements\\n - Migrating/refactoring existing codebases with uncertain scope\\n - Research projects where requirements emerge through exploration\\n - Cost-sensitive projects that need to optimize token usage\\n - High-stakes projects requiring multi-model validation\\n\\n Success Criteria\\n\\n - Successfully complete a full project lifecycle: spec → implementation → steering → completion\\n - Demonstrate cost savings through intelligent model routing\\n - Show quality improvements through cross-model validation\\n - Handle mid-project requirement changes gracefully\\n - Support at least 3 different LLM providers working in concert\",\n 3→ \"workflow_type\": \"refactoring\"\n 4→}\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"fa7ba932-29e2-4fd3-a6a7-4a732a99a35a","timestamp":"2026-01-12T18:58:14.053Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\requirements.json","content":"{\n \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n Core Features\\n\\n 1. Multi-LLM Provider Support\\n - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n - Strategic model assignment - route tasks to models best suited for them:\\n - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n - Claude Sonnet/Opus for complex reasoning and architecture\\n - Gemini for multimodal analysis and large context windows\\n - Grok for real-time information and alternative perspectives\\n - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n - Cost optimization: intelligently balance between cloud and local models\\n\\n 2. Version Control Integration\\n - Native Git and Gitea support\\n - Automatic branching strategies for features/tasks\\n - Commit message generation aligned with conventional commits\\n - PR creation with comprehensive descriptions\\n - Support for reviewing and iterating on code in branches\\n\\n 3. Adaptive Spec System\\n - Initial spec-app style interview to gather requirements\\n - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n - Clarify ambiguous requirements that emerged during development\\n - Gather feedback on implemented features\\n - Adjust scope based on discoveries\\n - Validate assumptions with the user\\n - Spec versioning: track how requirements evolve over time\\n - Continuous alignment: ensure implementation stays true to user intent\\n\\n 4. Agent Skill System\\n - Extensible skill/addon architecture for specialized agent capabilities\\n - Skills can be model-specific or model-agnostic\\n - Examples: code review, security scanning, performance optimization, documentation generation\\n - Custom skill creation framework\\n - Skill composition: chain skills together for complex workflows\\n\\n 5. Project Steering & Course Correction\\n - Mid-flight adjustments: ability to redirect project direction after work has begun\\n - Checkpoint system: review progress at milestones and adjust plans\\n - Context preservation: maintain full project history when steering changes\\n - Impact analysis: understand implications of direction changes\\n - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n 6. Intelligent Orchestration\\n - Task decomposition with model-specific assignments\\n - Parallel execution where possible\\n - Dependency management between tasks\\n - Progressive refinement: iterative improvement cycles\\n - Fallback strategies when a model fails or produces poor results\\n\\n 7. Quality Assurance\\n - Multi-model consensus for critical decisions\\n - Automated testing integration\\n - Code quality metrics and reporting\\n - Security vulnerability scanning\\n - Performance profiling\\n\\n Technical Architecture\\n\\n - Backend: Python (FastAPI) for orchestration layer\\n - Frontend: React/TypeScript UI for project management and monitoring\\n - Model Integration: Unified API wrapper supporting all providers\\n - State Management: Track project state, agent activities, and decisions\\n - Database: Store specs, decisions, task history, and model interactions\\n - MCP Server: Feature graph and skill management\\n\\n Key Differentiators\\n\\n 1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n 2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n 3. Local-first option: Can run meaningful work without API costs via Ollama\\n 4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n 5. Transparency: Full visibility into which models did what and why\\n\\n Use Cases\\n\\n - Large greenfield projects with evolving requirements\\n - Migrating/refactoring existing codebases with uncertain scope\\n - Research projects where requirements emerge through exploration\\n - Cost-sensitive projects that need to optimize token usage\\n - High-stakes projects requiring multi-model validation\\n\\n Success Criteria\\n\\n - Successfully complete a full project lifecycle: spec → implementation → steering → completion\\n - Demonstrate cost savings through intelligent model routing\\n - Show quality improvements through cross-model validation\\n - Handle mid-project requirement changes gracefully\\n - Support at least 3 different LLM providers working in concert\",\n \"workflow_type\": \"refactoring\"\n}","numLines":4,"startLine":1,"totalLines":4}},"sourceToolAssistantUUID":"383cd3f0-3bd5-47df-99a6-955dce5ab1df"}
|
|
{"parentUuid":"383cd3f0-3bd5-47df-99a6-955dce5ab1df","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01M4ayTRaxhowNtwc5GkBJ1C","type":"tool_result","content":" 1→{\n 2→ \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n Core Features\\n\\n 1. Multi-LLM Provider Support\\n - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n - Strategic model assignment - route tasks to models best suited for them:\\n - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n - Claude Sonnet/Opus for complex reasoning and architecture\\n - Gemini for multimodal analysis and large context windows\\n - Grok for real-time information and alternative perspectives\\n - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n - Cost optimization: intelligently balance between cloud and local models\\n\\n 2. Version Control Integration\\n - Native Git and Gitea support\\n - Automatic branching strategies for features/tasks\\n - Commit message generation aligned with conventional commits\\n - PR creation with comprehensive descriptions\\n - Support for reviewing and iterating on code in branches\\n\\n 3. Adaptive Spec System\\n - Initial spec-app style interview to gather requirements\\n - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n - Clarify ambiguous requirements that emerged during development\\n - Gather feedback on implemented features\\n - Adjust scope based on discoveries\\n - Validate assumptions with the user\\n - Spec versioning: track how requirements evolve over time\\n - Continuous alignment: ensure implementation stays true to user intent\\n\\n 4. Agent Skill System\\n - Extensible skill/addon architecture for specialized agent capabilities\\n - Skills can be model-specific or model-agnostic\\n - Examples: code review, security scanning, performance optimization, documentation generation\\n - Custom skill creation framework\\n - Skill composition: chain skills together for complex workflows\\n\\n 5. Project Steering & Course Correction\\n - Mid-flight adjustments: ability to redirect project direction after work has begun\\n - Checkpoint system: review progress at milestones and adjust plans\\n - Context preservation: maintain full project history when steering changes\\n - Impact analysis: understand implications of direction changes\\n - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n 6. Intelligent Orchestration\\n - Task decomposition with model-specific assignments\\n - Parallel execution where possible\\n - Dependency management between tasks\\n - Progressive refinement: iterative improvement cycles\\n - Fallback strategies when a model fails or produces poor results\\n\\n 7. Quality Assurance\\n - Multi-model consensus for critical decisions\\n - Automated testing integration\\n - Code quality metrics and reporting\\n - Security vulnerability scanning\\n - Performance profiling\\n\\n Technical Architecture\\n\\n - Backend: Python (FastAPI) for orchestration layer\\n - Frontend: React/TypeScript UI for project management and monitoring\\n - Model Integration: Unified API wrapper supporting all providers\\n - State Management: Track project state, agent activities, and decisions\\n - Database: Store specs, decisions, task history, and model interactions\\n - MCP Server: Feature graph and skill management\\n\\n Key Differentiators\\n\\n 1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n 2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n 3. Local-first option: Can run meaningful work without API costs via Ollama\\n 4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n 5. Transparency: Full visibility into which models did what and why\\n\\n Use Cases\\n\\n - Large greenfield projects with evolving requirements\\n - Migrating/refactoring existing codebases with uncertain scope\\n - Research projects where requirements emerge through exploration\\n - Cost-sensitive projects that need to optimize token usage\\n - High-stakes projects requiring multi-model validation\\n\\n Success Criteria\\n\\n - Successfully complete a full project lifecycle: spec \\u2192 implementation \\u2192 steering \\u2192 completion\\n - Demonstrate cost savings through intelligent model routing\\n - Show quality improvements through cross-model validation\\n - Handle mid-project requirement changes gracefully\\n - Support at least 3 different LLM providers working in concert\",\n 3→ \"scoped_services\": [],\n 4→ \"files_to_modify\": [],\n 5→ \"files_to_reference\": [],\n 6→ \"created_at\": \"2026-01-12T11:50:41.978005\"\n 7→}\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"70a5b722-8c52-4d91-a005-d9364e8b00eb","timestamp":"2026-01-12T18:58:14.053Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\context.json","content":"{\n \"task_description\": \"Multi-LLM Agentic Development Platform\\n\\n I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\\n\\n Core Features\\n\\n 1. Multi-LLM Provider Support\\n - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\\n - Strategic model assignment - route tasks to models best suited for them:\\n - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\\n - Claude Sonnet/Opus for complex reasoning and architecture\\n - Gemini for multimodal analysis and large context windows\\n - Grok for real-time information and alternative perspectives\\n - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\\n - Cost optimization: intelligently balance between cloud and local models\\n\\n 2. Version Control Integration\\n - Native Git and Gitea support\\n - Automatic branching strategies for features/tasks\\n - Commit message generation aligned with conventional commits\\n - PR creation with comprehensive descriptions\\n - Support for reviewing and iterating on code in branches\\n\\n 3. Adaptive Spec System\\n - Initial spec-app style interview to gather requirements\\n - Re-interview capability: as the project evolves, conduct follow-up interviews to:\\n - Clarify ambiguous requirements that emerged during development\\n - Gather feedback on implemented features\\n - Adjust scope based on discoveries\\n - Validate assumptions with the user\\n - Spec versioning: track how requirements evolve over time\\n - Continuous alignment: ensure implementation stays true to user intent\\n\\n 4. Agent Skill System\\n - Extensible skill/addon architecture for specialized agent capabilities\\n - Skills can be model-specific or model-agnostic\\n - Examples: code review, security scanning, performance optimization, documentation generation\\n - Custom skill creation framework\\n - Skill composition: chain skills together for complex workflows\\n\\n 5. Project Steering & Course Correction\\n - Mid-flight adjustments: ability to redirect project direction after work has begun\\n - Checkpoint system: review progress at milestones and adjust plans\\n - Context preservation: maintain full project history when steering changes\\n - Impact analysis: understand implications of direction changes\\n - Graceful pivoting: refactor or adapt existing work rather than starting over\\n\\n 6. Intelligent Orchestration\\n - Task decomposition with model-specific assignments\\n - Parallel execution where possible\\n - Dependency management between tasks\\n - Progressive refinement: iterative improvement cycles\\n - Fallback strategies when a model fails or produces poor results\\n\\n 7. Quality Assurance\\n - Multi-model consensus for critical decisions\\n - Automated testing integration\\n - Code quality metrics and reporting\\n - Security vulnerability scanning\\n - Performance profiling\\n\\n Technical Architecture\\n\\n - Backend: Python (FastAPI) for orchestration layer\\n - Frontend: React/TypeScript UI for project management and monitoring\\n - Model Integration: Unified API wrapper supporting all providers\\n - State Management: Track project state, agent activities, and decisions\\n - Database: Store specs, decisions, task history, and model interactions\\n - MCP Server: Feature graph and skill management\\n\\n Key Differentiators\\n\\n 1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\\n 2. Adaptive requirements: Specs evolve with the project, not frozen at start\\n 3. Local-first option: Can run meaningful work without API costs via Ollama\\n 4. Course correction: Projects aren't rigid - steer and adapt as you learn\\n 5. Transparency: Full visibility into which models did what and why\\n\\n Use Cases\\n\\n - Large greenfield projects with evolving requirements\\n - Migrating/refactoring existing codebases with uncertain scope\\n - Research projects where requirements emerge through exploration\\n - Cost-sensitive projects that need to optimize token usage\\n - High-stakes projects requiring multi-model validation\\n\\n Success Criteria\\n\\n - Successfully complete a full project lifecycle: spec \\u2192 implementation \\u2192 steering \\u2192 completion\\n - Demonstrate cost savings through intelligent model routing\\n - Show quality improvements through cross-model validation\\n - Handle mid-project requirement changes gracefully\\n - Support at least 3 different LLM providers working in concert\",\n \"scoped_services\": [],\n \"files_to_modify\": [],\n \"files_to_reference\": [],\n \"created_at\": \"2026-01-12T11:50:41.978005\"\n}","numLines":7,"startLine":1,"totalLines":7}},"sourceToolAssistantUUID":"383cd3f0-3bd5-47df-99a6-955dce5ab1df"}
|
|
{"parentUuid":"70a5b722-8c52-4d91-a005-d9364e8b00eb","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_016FFtQrmjiFhCWJVHhPtZ4A","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Excellent, I now have all four context files loaded. Let me do a deep analysis of the spec.md against the research.json and requirements.json to find issues.\n\n## PHASE 1: DEEP ANALYSIS\n\n### 1.1: Technical Accuracy\n\nLet me compare the spec against research findings:\n\n**Package names:**\n- ✅ Spec says `anthropic` - research confirms `pip install anthropic`\n- ✅ Spec says `google-genai` - research confirms (NOT deprecated `google-generativeai`)\n- ✅ Spec says `xai-sdk` - research confirms (NOT community alternatives)\n- ✅ Spec says `ollama` - research confirms\n- ✅ Spec says `litellm` - research confirms\n- ✅ Spec says `mcp>=1.25,<2` - research confirms\n- ✅ Spec says `GitPython` - research confirms\n- ✅ Spec says `py-gitea` - research confirms\n\n**API patterns:**\nLooking at line 229 in spec.md - the imports:\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n```\n\nLet me check research.json:\n- Anthropic: `from anthropic import AsyncAnthropic` ✅\n- Google Gemini: `from google import genai` ✅\n- xAI: Research says `from xai_sdk import AsyncClient` ✅\n- Ollama: Research says `from ollama import AsyncClient` ✅\n\n**LiteLLM patterns:**\nSpec line 181-191 shows:\n```python\nfrom litellm import completion, acompletion\nresponse = await acompletion(model=model, messages=messages, **kwargs)\nreturn response.choices[0].message.content\n\n\n```\n\nResearch confirms the import structure and async completion method with proper response handling. **MCP pattern:** Spec line 246-259 shows `from mcp.server.fastmcp import FastMCP` with `mcp = FastMCP(\"multi-llm-platform\")` and `@mcp.tool()` decorators, which aligns with research findings on the FastMCP initialization and tool registration approach.\n\nEnvironment variables match across sources: `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, `XAI_API_KEY`. Port configuration is consistent—backend runs on 8000, frontend on 5173, Ollama on 11434.\n\nChecking requirements.json against spec coverage shows multi-LLM provider support (spec lines 288-292), strategic model routing (lines 293-295), and cross-model validation all present in the specification.\n\n```\nCovered (Lines 296-298)\n4. Cost Optimization - ✅ Covered (Lines 299-301)\n5. Adaptive Spec System - ✅ Covered (Lines 302-304)\n6. Version Control Integration - ✅ Covered (Lines 305-307)\n7. Project Steering - ✅ Covered (Lines 308-310)\n8. Skill Plugin System - ✅ Covered (Lines 311-313)\n\nEdge Cases in spec (Lines 338-348):\n1. Provider Unavailable - ✅\n2. Rate Limiting - ✅\n3. Model Disagreement - ✅\n4. Large Context - ✅\n5. Ollama Not Running - ✅\n6. Mid-Spec Change - ✅\n7. Git Conflicts - ✅\n8. API\n```\nKey Invalid - ✅\n\nPackage naming and file structure remain uniform across the spec, with consistent model naming conventions following LiteLLM standards and terminology applied throughout.\n\nDocker configuration appears incomplete—the spec references `docker-compose.yml` but lacks actual configuration details. Python 3.11+ requirement aligns with py-gitea dependencies, and Ollama infrastructure needs are properly documented. However, there's a critical mismatch: the spec calls for `litellm` async operations using `acompletion`, which needs verification against actual LiteLLM API capabilities.\n\nCross-referencing the xai-sdk imports shows the spec correctly imports both `Client` and `AsyncClient` as specified in the research. Similarly, the Ollama async import pattern matches the documented API, confirming these integrations are properly aligned with their respective libraries.\n\nHowever, there's a discrepancy in the LiteLLM model naming convention. The spec shows `\"claude-3-opus\"` as a model identifier, but LiteLLM documentation requires Claude models to be prefixed differently—they should follow the format `\"claude-\"` with the full model designation.\n\nLooking at the model name formats more carefully: `claude-3-opus` might work if LiteLLM defaults to the latest version, but production implementations typically need the date suffix like `claude-3-opus-20240229`. The other formats appear correct—`gemini/gemini-pro` and `ollama/llama3` follow LiteLLM's expected patterns.\n\nI also notice the QA section references test files like `backend/tests/test_llm_router.py` and `backend/tests/test_cost_tracker.py`, but these file paths aren't actually defined in the specification itself.\n\nThere are several gaps between what's listed in requirements and what's in the file structure: Alembic is specified as a dependency but no migration configuration appears in the files to create. Similarly, the frontend dependencies include Vitest and React Testing Library, yet no corresponding test files are outlined. The spec mentions a version-control-service but doesn't include route files like `backend/api/routes/git.py`. Additionally, the QA section references a `/health` endpoint that isn't documented elsewhere in the specification.\n\nThe model naming could be more precise—while LiteLLM does support aliases like `claude-3-opus-latest`, the spec uses `claude-3-opus` without clarifying whether this refers to a specific version or an alias. The examples also omit xAI's model format (such as `xai/grok-beta`), which would improve consistency across supported providers.","signature":"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"},{"type":"text","text":"Now let me verify some critical API patterns with Context7 to ensure the spec's code examples are accurate:"},{"type":"tool_use","id":"toolu_01F9pD6Qsvgh9AViAwT3sKBE","name":"mcp__context7__resolve-library-id","input":{"libraryName":"litellm","query":"LiteLLM unified API for calling multiple LLM providers with async support"}},{"type":"tool_use","id":"toolu_01VHW6odLFBLxUrQ2b5ZKcQU","name":"mcp__context7__resolve-library-id","input":{"libraryName":"FastMCP python mcp server","query":"FastMCP Python SDK for Model Context Protocol server"}},{"type":"tool_use","id":"toolu_017H7KzYPaa6kb7deYkU4hkx","name":"mcp__context7__resolve-library-id","input":{"libraryName":"fastapi","query":"FastAPI Python web framework async endpoints"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":18872,"cache_read_input_tokens":22595,"cache_creation":{"ephemeral_5m_input_tokens":18872,"ephemeral_1h_input_tokens":0},"output_tokens":2480,"service_tier":"standard"}},"type":"assistant","uuid":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea","timestamp":"2026-01-12T18:59:05.230Z"}
|
|
{"parentUuid":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017H7KzYPaa6kb7deYkU4hkx","type":"tool_result","content":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: FastAPI\n- Context7-compatible library ID: /websites/fastapi_tiangolo\n- Description: FastAPI is a modern, fast web framework for building APIs with Python. It's known for its high performance, ease of use, and automatic interactive documentation.\n- Code Snippets: 12067\n- Source Reputation: High\n- Benchmark Score: 94.6\n----------\n- Title: FastAPI\n- Context7-compatible library ID: /fastapi/fastapi\n- Description: FastAPI framework, high performance, easy to learn, fast to code, ready for production\n- Code Snippets: 881\n- Source Reputation: High\n- Benchmark Score: 87.2\n- Versions: 0.115.13, 0_116_1, 0.118.2, 0.122.0, 0.128.0\n----------\n- Title: FastAPI\n- Context7-compatible library ID: /websites/fastapi_tiangolo_learn\n- Description: FastAPI is a modern, fast web framework for building APIs with Python based on standard Python type hints, featuring automatic interactive documentation and high performance.\n- Code Snippets: 2\n- Source Reputation: High\n- Benchmark Score: 8\n----------\n- Title: Awesome FastAPI\n- Context7-compatible library ID: /mjhea0/awesome-fastapi\n- Description: A curated list of awesome things related to FastAPI, a modern, high-performance Python web framework for building RESTful APIs.\n- Code Snippets: 84\n- Source Reputation: High\n----------\n- Title: FastAPI Boilerplate\n- Context7-compatible library ID: /websites/benavlabs_github_io_fastapi-boilerplate\n- Description: A production-ready FastAPI boilerplate providing a solid foundation for building scalable, async APIs with modern Python technologies, featuring performance, security, and developer experience enhancements.\n- Code Snippets: 715\n- Source Reputation: High"}]}]},"uuid":"2652bf90-a878-430c-ab45-a56591aaee57","timestamp":"2026-01-12T18:59:06.552Z","toolUseResult":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: FastAPI\n- Context7-compatible library ID: /websites/fastapi_tiangolo\n- Description: FastAPI is a modern, fast web framework for building APIs with Python. It's known for its high performance, ease of use, and automatic interactive documentation.\n- Code Snippets: 12067\n- Source Reputation: High\n- Benchmark Score: 94.6\n----------\n- Title: FastAPI\n- Context7-compatible library ID: /fastapi/fastapi\n- Description: FastAPI framework, high performance, easy to learn, fast to code, ready for production\n- Code Snippets: 881\n- Source Reputation: High\n- Benchmark Score: 87.2\n- Versions: 0.115.13, 0_116_1, 0.118.2, 0.122.0, 0.128.0\n----------\n- Title: FastAPI\n- Context7-compatible library ID: /websites/fastapi_tiangolo_learn\n- Description: FastAPI is a modern, fast web framework for building APIs with Python based on standard Python type hints, featuring automatic interactive documentation and high performance.\n- Code Snippets: 2\n- Source Reputation: High\n- Benchmark Score: 8\n----------\n- Title: Awesome FastAPI\n- Context7-compatible library ID: /mjhea0/awesome-fastapi\n- Description: A curated list of awesome things related to FastAPI, a modern, high-performance Python web framework for building RESTful APIs.\n- Code Snippets: 84\n- Source Reputation: High\n----------\n- Title: FastAPI Boilerplate\n- Context7-compatible library ID: /websites/benavlabs_github_io_fastapi-boilerplate\n- Description: A production-ready FastAPI boilerplate providing a solid foundation for building scalable, async APIs with modern Python technologies, featuring performance, security, and developer experience enhancements.\n- Code Snippets: 715\n- Source Reputation: High"}],"sourceToolAssistantUUID":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea"}
|
|
{"parentUuid":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F9pD6Qsvgh9AViAwT3sKBE","type":"tool_result","content":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: Litellm\n- Context7-compatible library ID: /berriai/litellm\n- Description: LiteLLM enables calling all LLM APIs using the OpenAI format, supporting providers like Bedrock, Huggingface, VertexAI, and more, with features like consistent output, retry logic, and budget management.\n- Code Snippets: 9205\n- Source Reputation: High\n- Benchmark Score: 85\n- Versions: v1.80.7-nightly\n----------\n- Title: LiteLLM\n- Context7-compatible library ID: /websites/litellm_ai\n- Description: LiteLLM provides a unified OpenAI-compatible interface to call and manage over 100 Large Language Models (LLMs) from various providers, offering features like retry/fallback logic, cost tracking, and consistent output, available as a Python SDK or a Proxy Server (LLM Gateway).\n- Code Snippets: 5273\n- Source Reputation: High\n- Benchmark Score: 87.4\n----------\n- Title: LiteLLM RS\n- Context7-compatible library ID: /majiayu000/litellm-rs\n- Description: LiteLLM RS is a Rust-based client or implementation for LiteLLM, offering a unified API for various large language model (LLM) providers.\n- Code Snippets: 128\n- Source Reputation: Medium\n- Benchmark Score: 83.3\n----------\n- Title: Go LiteLLM\n- Context7-compatible library ID: /andrejsstepanovs/go-litellm\n- Description: A Go client library for interacting with the LiteLLM API, providing a simple, type-safe way to perform completions, audio transcription, image queries, structured JSON, embeddings, token counting, MCP tool operations, and model browsing.\n- Code Snippets: 51\n- Source Reputation: Medium\n- Benchmark Score: 78.2\n----------\n- Title: LiteLLM\n- Context7-compatible library ID: /voocel/litellm\n- Description: LiteLLM is a clean and elegant Go library that provides a unified interface for making simple API calls to multiple LLM platforms, supporting features like function calling, streaming, and reasoning.\n- Code Snippets: 27\n- Source Reputation: High"}]}]},"uuid":"d41ac369-21c9-4e4c-98b3-3959e3bc3331","timestamp":"2026-01-12T18:59:06.571Z","toolUseResult":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: Litellm\n- Context7-compatible library ID: /berriai/litellm\n- Description: LiteLLM enables calling all LLM APIs using the OpenAI format, supporting providers like Bedrock, Huggingface, VertexAI, and more, with features like consistent output, retry logic, and budget management.\n- Code Snippets: 9205\n- Source Reputation: High\n- Benchmark Score: 85\n- Versions: v1.80.7-nightly\n----------\n- Title: LiteLLM\n- Context7-compatible library ID: /websites/litellm_ai\n- Description: LiteLLM provides a unified OpenAI-compatible interface to call and manage over 100 Large Language Models (LLMs) from various providers, offering features like retry/fallback logic, cost tracking, and consistent output, available as a Python SDK or a Proxy Server (LLM Gateway).\n- Code Snippets: 5273\n- Source Reputation: High\n- Benchmark Score: 87.4\n----------\n- Title: LiteLLM RS\n- Context7-compatible library ID: /majiayu000/litellm-rs\n- Description: LiteLLM RS is a Rust-based client or implementation for LiteLLM, offering a unified API for various large language model (LLM) providers.\n- Code Snippets: 128\n- Source Reputation: Medium\n- Benchmark Score: 83.3\n----------\n- Title: Go LiteLLM\n- Context7-compatible library ID: /andrejsstepanovs/go-litellm\n- Description: A Go client library for interacting with the LiteLLM API, providing a simple, type-safe way to perform completions, audio transcription, image queries, structured JSON, embeddings, token counting, MCP tool operations, and model browsing.\n- Code Snippets: 51\n- Source Reputation: Medium\n- Benchmark Score: 78.2\n----------\n- Title: LiteLLM\n- Context7-compatible library ID: /voocel/litellm\n- Description: LiteLLM is a clean and elegant Go library that provides a unified interface for making simple API calls to multiple LLM platforms, supporting features like function calling, streaming, and reasoning.\n- Code Snippets: 27\n- Source Reputation: High"}],"sourceToolAssistantUUID":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea"}
|
|
{"parentUuid":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VHW6odLFBLxUrQ2b5ZKcQU","type":"tool_result","content":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: FastMCP\n- Context7-compatible library ID: /jlowin/fastmcp\n- Description: FastMCP is a Python framework for building Model Context Protocol (MCP) servers and clients, simplifying the creation of LLM-integrated applications with features like deployment, authentication, and dynamic tool generation.\n- Code Snippets: 1749\n- Source Reputation: High\n- Benchmark Score: 78\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /websites/gofastmcp\n- Description: FastMCP is a Python framework for building and managing Model Context Protocol (MCP) servers and clients, simplifying the connection of LLMs to data and functionality.\n- Code Snippets: 1879\n- Source Reputation: High\n- Benchmark Score: 79.6\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /llmstxt/gofastmcp_llms-full_txt\n- Description: FastMCP is a Python framework for building MCP servers, featuring an experimental OpenAPI parser, JWT claims, and optimized payload handling.\n- Code Snippets: 12289\n- Source Reputation: High\n- Benchmark Score: 79\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /llmstxt/gofastmcp_llms_txt\n- Description: FastMCP is a fast, Pythonic framework for building and deploying Multi-modal Conversational Platform (MCP) servers and clients, offering programmatic interfaces and extensive integrations for LLMs and various services.\n- Code Snippets: 2135\n- Source Reputation: High\n- Benchmark Score: 76\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /websites/gofastmcp-getting-started\n- Description: FastMCP is a Pythonic framework for building Model Context Protocol (MCP) servers and clients, simplifying the creation of tools, resources, and prompts for LLM applications. It provides a comprehensive ecosystem for development to production.\n- Code Snippets: 33\n- Source Reputation: High\n- Benchmark Score: 37.6"}]}]},"uuid":"11e5fd93-61be-4e0e-982c-7f13969dd349","timestamp":"2026-01-12T18:59:06.751Z","toolUseResult":[{"type":"text","text":"Available Libraries:\n\nEach result includes:\n- Library ID: Context7-compatible identifier (format: /org/project)\n- Name: Library or package name\n- Description: Short summary\n- Code Snippets: Number of available code examples\n- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)\n- Benchmark Score: Quality indicator (100 is the highest score)\n- Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version.\n\nFor best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.\n\n----------\n\n- Title: FastMCP\n- Context7-compatible library ID: /jlowin/fastmcp\n- Description: FastMCP is a Python framework for building Model Context Protocol (MCP) servers and clients, simplifying the creation of LLM-integrated applications with features like deployment, authentication, and dynamic tool generation.\n- Code Snippets: 1749\n- Source Reputation: High\n- Benchmark Score: 78\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /websites/gofastmcp\n- Description: FastMCP is a Python framework for building and managing Model Context Protocol (MCP) servers and clients, simplifying the connection of LLMs to data and functionality.\n- Code Snippets: 1879\n- Source Reputation: High\n- Benchmark Score: 79.6\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /llmstxt/gofastmcp_llms-full_txt\n- Description: FastMCP is a Python framework for building MCP servers, featuring an experimental OpenAPI parser, JWT claims, and optimized payload handling.\n- Code Snippets: 12289\n- Source Reputation: High\n- Benchmark Score: 79\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /llmstxt/gofastmcp_llms_txt\n- Description: FastMCP is a fast, Pythonic framework for building and deploying Multi-modal Conversational Platform (MCP) servers and clients, offering programmatic interfaces and extensive integrations for LLMs and various services.\n- Code Snippets: 2135\n- Source Reputation: High\n- Benchmark Score: 76\n----------\n- Title: FastMCP\n- Context7-compatible library ID: /websites/gofastmcp-getting-started\n- Description: FastMCP is a Pythonic framework for building Model Context Protocol (MCP) servers and clients, simplifying the creation of tools, resources, and prompts for LLM applications. It provides a comprehensive ecosystem for development to production.\n- Code Snippets: 33\n- Source Reputation: High\n- Benchmark Score: 37.6"}],"sourceToolAssistantUUID":"b7ba096a-bc13-4fe1-8a87-f3f649f99eea"}
|
|
{"parentUuid":"11e5fd93-61be-4e0e-982c-7f13969dd349","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01J6EJKiJTkLyFUeDFEwqQtF","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, I found the Context7 library IDs. Now let me verify the critical patterns in the spec by querying the docs:\n- LiteLLM: `/berriai/litellm` or `/websites/litellm_ai` \n- FastMCP: `/jlowin/fastmcp` or `/websites/gofastmcp`\n- FastAPI: `/websites/fastapi_tiangolo`\n\nLet me check specific patterns to verify the spec's code examples are accurate.","signature":"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"},{"type":"tool_use","id":"toolu_01Cb7gUYG7KwKeF4kzafBqKN","name":"mcp__context7__query-docs","input":{"libraryId":"/berriai/litellm","query":"acompletion async API usage with model format for Claude Anthropic Gemini Ollama"}},{"type":"tool_use","id":"toolu_01GxQcYHypFg1uQ5qTDMb4C8","name":"mcp__context7__query-docs","input":{"libraryId":"/jlowin/fastmcp","query":"FastMCP server decorator patterns tool and resource decorators"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":657,"cache_creation_input_tokens":3928,"cache_read_input_tokens":41467,"cache_creation":{"ephemeral_5m_input_tokens":3928,"ephemeral_1h_input_tokens":0},"output_tokens":303,"service_tier":"standard"}},"type":"assistant","uuid":"0152c314-5d27-44b4-9e40-2e43d66b75c4","timestamp":"2026-01-12T18:59:35.475Z"}
|
|
{"parentUuid":"0152c314-5d27-44b4-9e40-2e43d66b75c4","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","is_error":true,"tool_use_id":"toolu_01Cb7gUYG7KwKeF4kzafBqKN"}]},"uuid":"302f5149-18bb-45fe-9a22-5fdcf39d5b5a","timestamp":"2026-01-12T18:59:35.786Z","toolUseResult":"Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","sourceToolAssistantUUID":"0152c314-5d27-44b4-9e40-2e43d66b75c4"}
|
|
{"parentUuid":"0152c314-5d27-44b4-9e40-2e43d66b75c4","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","is_error":true,"tool_use_id":"toolu_01GxQcYHypFg1uQ5qTDMb4C8"}]},"uuid":"266b49db-7316-4af0-9050-4eb1a6df3501","timestamp":"2026-01-12T18:59:35.786Z","toolUseResult":"Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","sourceToolAssistantUUID":"0152c314-5d27-44b4-9e40-2e43d66b75c4"}
|
|
{"parentUuid":"266b49db-7316-4af0-9050-4eb1a6df3501","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01EKmrfMbBSou1hcWJPW8kxv","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I couldn't access Context7 docs. That's okay - I have enough information from the research.json to do a thorough analysis. Let me proceed with my critique based on what I've found.\n\nBased on my deep analysis, here are the issues I've identified:\n\n## Issues Found\n\n### HIGH SEVERITY\n\n1. **Missing test files in \"Files to Create\"**\n - The QA section lists specific test files like `backend/tests/test_llm_router.py`, `backend/tests/test_cost_tracker.py`, etc.\n - But the \"Files to Create\" table doesn't include ANY test files\n - This is a completeness issue that will cause confusion during implementation\n\n2. **Missing API routes for version control**\n - The spec mentions version-control-service as an integration service\n - There are Git service files (`backend/git/git_service.py`, `gitea_service.py`, `commit_generator.py`)\n - But there's no API route file like `backend/api/routes/git.py` to expose these services\n\n3. **Missing health endpoint pattern**\n - QA section line 505 requires a `/health` endpoint\n - But there's no route file or pattern showing how to implement it\n - This is missing from both \"Files to Create\" and \"Patterns to Follow\"\n\n### MEDIUM SEVERITY\n\n4. **Missing Alembic configuration files**\n - requirements.txt lists `alembic>=1.13.0` (line 567)\n - But no alembic configuration files are in \"Files to Create\"\n - Need: `backend/alembic.ini`, `backend/migrations/` directory structure\n\n5. **Incomplete model name examples in LiteLLM pattern**\n - Line 188 shows: `\"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"`\n - Missing xAI/Grok model format which should be: `\"xai/grok-beta\"` or similar\n - This inconsistency could confuse implementers\n\n6. **Missing frontend test files**\n - package.json lists `vitest` and `@testing-library/react` as devDependencies\n - But no frontend test files are in \"Files to Create\"\n - Need: `frontend/src/__tests__/` or similar structure\n\n7. **Missing model_interactions table in Database Verification**\n - Line 517 mentions verifying tables: `specs, tasks, decisions, model_interactions`\n - But there's no `backend/models/model_interaction.py` in Files to Create\n - Need to add this model file\n\n### LOW SEVERITY\n\n8. **Inconsistent naming conventions** - Some files use snake_case while others use camelCase, which could lead to import errors. **9. Missing environment variable documentation** - The spec doesn't list all required .env variables needed for local development setup.\n\n10. **Incomplete routing configuration** - The QA section references URLs like `/spec`, `/tasks`, `/models`, `/costs` but no router file exists in the file creation list.","signature":"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"},{"type":"text","text":"Now I have all the information I need. Let me perform my deep analysis and then fix the issues.\n\nBased on my thorough analysis comparing spec.md against research.json and requirements.json, I've identified several issues that need fixing. Let me apply the fixes:"},{"type":"tool_use","id":"toolu_01AmjRqNFDsXWfFWG9tt8U54","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |","new_string":"| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1079,"cache_read_input_tokens":45395,"cache_creation":{"ephemeral_5m_input_tokens":1079,"ephemeral_1h_input_tokens":0},"output_tokens":1471,"service_tier":"standard"}},"type":"assistant","uuid":"e8f73780-0692-4561-9379-e38492517a95","timestamp":"2026-01-12T19:00:31.653Z"}
|
|
{"parentUuid":"e8f73780-0692-4561-9379-e38492517a95","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AmjRqNFDsXWfFWG9tt8U54","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"0d168f50-2de3-44a4-bd62-c6063e90ae28","timestamp":"2026-01-12T19:00:35.176Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |","newString":"| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":168,"oldLines":6,"newStart":168,"newLines":22,"lines":[" | `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |"," | `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |"," | `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |","+| `frontend/src/router.tsx` | frontend | React Router configuration |","+| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |","+| `backend/api/routes/health.py` | orchestration | Health check endpoint |","+| `backend/api/routes/git.py` | version-control | Git operations endpoints |","+| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |","+| `backend/alembic.ini` | orchestration | Alembic migration config |","+| `backend/migrations/env.py` | orchestration | Alembic environment setup |","+| `backend/tests/__init__.py` | orchestration | Test package init |","+| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |","+| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |","+| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |","+| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |","+| `backend/tests/test_git_service.py` | orchestration | Git service tests |","+| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |","+| `backend/tests/integration/__init__.py` | orchestration | Integration test package |","+| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |"," | `docker-compose.yml` | infrastructure | Container orchestration |"," | `.env.example` | infrastructure | Environment variable template |"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"e8f73780-0692-4561-9379-e38492517a95"}
|
|
{"parentUuid":"0d168f50-2de3-44a4-bd62-c6063e90ae28","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01Nromf5ZRcHMNGyqMNwwqzw","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, I've added the missing test files and other missing files. Now let me fix the LiteLLM model format issue to include xAI/Grok format:","signature":"ErMCCkYICxgCKkBk+c27sz5jcyXkLXz3P6ul1/+TFy/hnZ3HP9mPrP4dm3QM0UsHGDMB5ty5qXwZ4aIMKhJM49e0dMhLzRRVUuXIEgzBy3r9o2nj0tbip8waDIfi5uTbViVHc0fgGiIwQEXo1I9wSBQwg5cafMPcwdkWCGRWoJ9tTGvJyL7js5oyx2Li3sJAnQyVPyW510V+KpoBjRLWpFLdXArdBBJBqDoF3BAsJ6OSBWdwBJGVI6r6fre/U+42u/R6XhDmOlWF6KWF19vWuoidBTGdgh/PQrjFMbqoKhTOtsgYBvSPJW8LUz4vRDps0pDUMnWRuFusyStHWzxfYGf7A37vYgF1mCIO3g3r36/PO0J8HU0JSy8I8iC49UYn6/0yYVDXo58O6mDPunCIn+jVLg2wERgB"},{"type":"tool_use","id":"toolu_01LTVohQ9wtmDj7DbDaVPXwN","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"async def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content","new_string":"async def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1564,"cache_read_input_tokens":46474,"cache_creation":{"ephemeral_5m_input_tokens":1564,"ephemeral_1h_input_tokens":0},"output_tokens":399,"service_tier":"standard"}},"type":"assistant","uuid":"15cacb7c-73c4-4c8c-9f59-d9743a4b00df","timestamp":"2026-01-12T19:01:02.282Z"}
|
|
{"parentUuid":"15cacb7c-73c4-4c8c-9f59-d9743a4b00df","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LTVohQ9wtmDj7DbDaVPXwN","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"b50d8f57-d961-462b-9047-61b4fda995d8","timestamp":"2026-01-12T19:01:06.496Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"async def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content","newString":"async def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":199,"oldLines":7,"newStart":199,"newLines":7,"lines":[" # Unified interface - same code for any provider"," async def call_llm(model: str, messages: list, **kwargs) -> str:"," response = await acompletion(","- model=model, # e.g., \"claude-3-opus\", \"gemini/gemini-pro\", \"ollama/llama3\"","+ model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\""," messages=messages,"," **kwargs"," )"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"15cacb7c-73c4-4c8c-9f59-d9743a4b00df"}
|
|
{"parentUuid":"b50d8f57-d961-462b-9047-61b4fda995d8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01UK33yAVoqrhWC33ohNstf8","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, I've fixed the model name examples. Now let me add the health check pattern since it's mentioned in QA but not in the patterns section:","signature":"ErYCCkYICxgCKkB50b9ahh6IjmsMrenw2o8XymjdILLFktyFTnApZdYu+Fu0Yz1n7S1/BQi1oXIypwjv6XMwPxGV9VES4nG27T+CEgzds1W5abPGbX09w1UaDKcreGRWxqv1T7+CGSIwRknAlaOPxyyCuUzke5BzeMLQMTc1eisZukmvtonu3hWe7om+vibp9jMLPtYKGZPPKp0B6gLNlMa1fIpASTdOwZcgmjHHTK0ka9+ECj/F/f/HDeQ3Vko/4eSfeebVy0yP9IGwbmPGm8n0N1WB0kFvVnN/9T9lHsDKKy9FS6gBJcoS5ddYdkHgulsHuHM7kT2w0cA3WB1xfuILkK5iDG6NfxayyTXUJUDEmO++7YZ5mbrmDlFOMPX1e+vR6gxmtkkqm4AV3gXFYuJ8sz+Ics13ghgB"},{"type":"tool_use","id":"toolu_015773MyttsLtv6bNCyWkukh","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern","new_string":"**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":473,"cache_read_input_tokens":48038,"cache_creation":{"ephemeral_5m_input_tokens":473,"ephemeral_1h_input_tokens":0},"output_tokens":494,"service_tier":"standard"}},"type":"assistant","uuid":"255f381e-69dd-4f48-ac6b-09c3ec41d5a2","timestamp":"2026-01-12T19:01:36.121Z"}
|
|
{"parentUuid":"255f381e-69dd-4f48-ac6b-09c3ec41d5a2","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015773MyttsLtv6bNCyWkukh","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"f24a79e6-0430-4131-b435-30843c0cd948","timestamp":"2026-01-12T19:01:39.655Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern","newString":"**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":239,"oldLines":6,"newStart":239,"newLines":32,"lines":[" "," ---"," ","+### Health Check Pattern","+","+```python","+from fastapi import FastAPI","+from pydantic import BaseModel","+","+class HealthResponse(BaseModel):","+ status: str","+ services: dict[str, str]","+","+@app.get(\"/health\", response_model=HealthResponse)","+async def health_check():","+ \"\"\"Check health of all services and LLM providers.\"\"\"","+ services = {","+ \"database\": \"healthy\",","+ \"ollama\": await check_ollama_status(),","+ \"claude\": await check_claude_status(),","+ \"gemini\": await check_gemini_status(),","+ \"grok\": await check_grok_status(),","+ }","+ overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"","+ return HealthResponse(status=overall, services=services)","+```","+","+---","+"," ### Async LLM Client Pattern"," "," ```python"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"255f381e-69dd-4f48-ac6b-09c3ec41d5a2"}
|
|
{"parentUuid":"f24a79e6-0430-4131-b435-30843c0cd948","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01FwrBFwGGeaTSRuW53JSwY2","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good. Now let me add a Docker Compose pattern since the file is listed but no example is provided:","signature":"EooCCkYICxgCKkAvvcezWfTuBP3qiCgB4jmuitOLGfOW92ZY7nax9P0Xn59hJmS1sruBUOnBubJStUYLH6Oqph72FGjp7bTXm72CEgxeuIOWlPcn0++nLpAaDJ0tsWlgvBDz12wdQSIwEkwhD6VuQc+u1Jzw8Acnt2OePsYFBdMiR7YxAkXt59kiQy0O1QbkZ4znfcBfVrfcKnKFSzVsRKP2rqe3O9tdYrignouMU8ALoIDHTWrhCpR4EFiUbKkgbvnPnkNiNTskJY50HdKe4aJtYN7OyCQyvqNNv6iGDD21eqmm/yrlcsw0v5SmyM/d26AtUCffsR1JCSZWQU3CX6i3nPGohv34v7fmpkYYAQ=="},{"type":"tool_use","id":"toolu_01SZTqTztWgFTp98b9dfzuLq","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements","new_string":"**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n---\n\n### Docker Compose Pattern\n\n```yaml\n# docker-compose.yml\nversion: '3.8'\n\nservices:\n backend:\n build: ./backend\n ports:\n - \"8000:8000\"\n environment:\n - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - XAI_API_KEY=${XAI_API_KEY}\n - OLLAMA_HOST=http://ollama:11434\n - DATABASE_URL=sqlite:///./multi_llm_platform.db\n depends_on:\n - ollama\n\n frontend:\n build: ./frontend\n ports:\n - \"5173:5173\"\n depends_on:\n - backend\n\n ollama:\n image: ollama/ollama:latest\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n ollama_data:\n```\n\n**Key Points:**\n- Backend depends on Ollama service for local LLM access\n- Environment variables passed from host `.env` file\n- Ollama data persisted via named volume\n\n## Requirements"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":568,"cache_read_input_tokens":48511,"cache_creation":{"ephemeral_5m_input_tokens":568,"ephemeral_1h_input_tokens":0},"output_tokens":536,"service_tier":"standard"}},"type":"assistant","uuid":"b27c2667-6e7f-4a32-9373-d57c746a1779","timestamp":"2026-01-12T19:02:28.729Z"}
|
|
{"parentUuid":"b27c2667-6e7f-4a32-9373-d57c746a1779","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SZTqTztWgFTp98b9dfzuLq","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"5fd4264c-017b-4c48-9765-0f9ffabc4b07","timestamp":"2026-01-12T19:02:35.363Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements","newString":"**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n---\n\n### Docker Compose Pattern\n\n```yaml\n# docker-compose.yml\nversion: '3.8'\n\nservices:\n backend:\n build: ./backend\n ports:\n - \"8000:8000\"\n environment:\n - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - XAI_API_KEY=${XAI_API_KEY}\n - OLLAMA_HOST=http://ollama:11434\n - DATABASE_URL=sqlite:///./multi_llm_platform.db\n depends_on:\n - ollama\n\n frontend:\n build: ./frontend\n ports:\n - \"5173:5173\"\n depends_on:\n - backend\n\n ollama:\n image: ollama/ollama:latest\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n ollama_data:\n```\n\n**Key Points:**\n- Backend depends on Ollama service for local LLM access\n- Environment variables passed from host `.env` file\n- Ollama data persisted via named volume\n\n## Requirements","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":324,"oldLines":6,"newStart":324,"newLines":51,"lines":[" "," **Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess."," ","+---","+","+### Docker Compose Pattern","+","+```yaml","+# docker-compose.yml","+version: '3.8'","+","+services:","+ backend:","+ build: ./backend","+ ports:","+ - \"8000:8000\"","+ environment:","+ - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}","+ - GEMINI_API_KEY=${GEMINI_API_KEY}","+ - XAI_API_KEY=${XAI_API_KEY}","+ - OLLAMA_HOST=http://ollama:11434","+ - DATABASE_URL=sqlite:///./multi_llm_platform.db","+ depends_on:","+ - ollama","+","+ frontend:","+ build: ./frontend","+ ports:","+ - \"5173:5173\"","+ depends_on:","+ - backend","+","+ ollama:","+ image: ollama/ollama:latest","+ ports:","+ - \"11434:11434\"","+ volumes:","+ - ollama_data:/root/.ollama","+","+volumes:","+ ollama_data:","+```","+","+**Key Points:**","+- Backend depends on Ollama service for local LLM access","+- Environment variables passed from host `.env` file","+- Ollama data persisted via named volume","+"," ## Requirements"," "," ### Functional Requirements"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"b27c2667-6e7f-4a32-9373-d57c746a1779"}
|
|
{"parentUuid":"5fd4264c-017b-4c48-9765-0f9ffabc4b07","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01GiQb3Dzzck6AjC7fACvPnV","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good. Now let me add a note about Alembic migrations since we added those files:","signature":"EvgBCkYICxgCKkAm3z8/Bh7bju1C0P5zli6pOF9qk0e/4UL4qHhTrcuBJJZVY435ucxoacoU+r5b90NZuhYM+kwMU7GR/kfef2khEgyF+BmrCEqJoc67GwkaDJVk4+i+DxNVfYS6TyIw5bX/QEJTvMpxFHqf7aXmiF4vZECeH+kqMrHzftEQeBkAOYGcjE5tukoCVPTlaRBhKmABrX0ON0iKIkTMdMmE8PXWic1afOI150TC2RNN412lsANOIyecwl39mHl6jWQvNabb/9keFohM6MF1enHYQhbHbi1w2YfJtgwfAzFLVSosa8yotZvR3RX6bXuWmDbCUioYAQ=="},{"type":"tool_use","id":"toolu_01XvaPJjmdxxi9A8kCAKHNRB","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn","new_string":"### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n### Database Migrations (Alembic)\n\n```bash\n# Initialize Alembic (one-time setup)\ncd backend\nalembic init migrations\n\n# Generate migration from model changes\nalembic revision --autogenerate -m \"Add model_interactions table\"\n\n# Apply migrations\nalembic upgrade head\n```\n\n**Key Points:**\n- Use autogenerate to create migrations from SQLAlchemy model changes\n- Always review generated migrations before applying\n- Run migrations before starting the application"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":610,"cache_read_input_tokens":49079,"cache_creation":{"ephemeral_5m_input_tokens":610,"ephemeral_1h_input_tokens":0},"output_tokens":480,"service_tier":"standard"}},"type":"assistant","uuid":"3b50c662-af38-4f88-ba95-4a7c1043336b","timestamp":"2026-01-12T19:03:30.965Z"}
|
|
{"parentUuid":"3b50c662-af38-4f88-ba95-4a7c1043336b","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XvaPJjmdxxi9A8kCAKHNRB","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"9e821843-8df1-44d9-9b91-6002c39c1b1e","timestamp":"2026-01-12T19:03:35.857Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn","newString":"### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n### Database Migrations (Alembic)\n\n```bash\n# Initialize Alembic (one-time setup)\ncd backend\nalembic init migrations\n\n# Generate migration from model changes\nalembic revision --autogenerate -m \"Add model_interactions table\"\n\n# Apply migrations\nalembic upgrade head\n```\n\n**Key Points:**\n- Use autogenerate to create migrations from SQLAlchemy model changes\n- Always review generated migrations before applying\n- Run migrations before starting the application","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n---\n\n### Docker Compose Pattern\n\n```yaml\n# docker-compose.yml\nversion: '3.8'\n\nservices:\n backend:\n build: ./backend\n ports:\n - \"8000:8000\"\n environment:\n - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - XAI_API_KEY=${XAI_API_KEY}\n - OLLAMA_HOST=http://ollama:11434\n - DATABASE_URL=sqlite:///./multi_llm_platform.db\n depends_on:\n - ollama\n\n frontend:\n build: ./frontend\n ports:\n - \"5173:5173\"\n depends_on:\n - backend\n\n ollama:\n image: ollama/ollama:latest\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n ollama_data:\n```\n\n**Key Points:**\n- Backend depends on Ollama service for local LLM access\n- Environment variables passed from host `.env` file\n- Ollama data persisted via named volume\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":456,"oldLines":6,"newStart":456,"newLines":25,"lines":[" - Don't generate code using deprecated google-generativeai patterns"," - Don't use `--reload` flag in production uvicorn"," ","+### Database Migrations (Alembic)","+","+```bash","+# Initialize Alembic (one-time setup)","+cd backend","+alembic init migrations","+","+# Generate migration from model changes","+alembic revision --autogenerate -m \"Add model_interactions table\"","+","+# Apply migrations","+alembic upgrade head","+```","+","+**Key Points:**","+- Use autogenerate to create migrations from SQLAlchemy model changes","+- Always review generated migrations before applying","+- Run migrations before starting the application","+"," ## Development Environment"," "," ### Prerequisites"]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"3b50c662-af38-4f88-ba95-4a7c1043336b"}
|
|
{"parentUuid":"9e821843-8df1-44d9-9b91-6002c39c1b1e","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_018EmC87ghpG4ALEzD7N2bE3","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good progress. Now I should add the ModelInteraction model pattern since we added the file but there's no pattern for it. Let me also verify the database models section needs updating:","signature":"EuECCkYICxgCKkCn12LjYYxhZsewfS/wcfsoqn56gYzZk01DFEZ0msSmcLiAMK3qA55GQFMb2PdLKl2+39Qw2LBBAsI7tS9jVsEyEgxn7zTymfYlyitSk6oaDBkOgwqe4vBgBjJrJSIwxEeHGXQy6kTX0VxTMiaj5WZyU2F+5OJRJTITW5f/y29D0fLEBwnB1mHqQ4ICIfzIKsgBtNxr1AXANOL9fpE1hovcZt56YOMQavvA0atqFV0ZaFCwJLWsy5C31MtePWCsVIDsFuJMfKoylLe1Q5zQfOaKrPEJS6lJ7rRlN2x4WJFouhZ1cEcDKlhgn7mtryMFd1lFlb4kXbGFeJcg0f3GLuxZ3guNxelM6BEM04M529b/S6n/LBZZuCa7aiBVqx3xlg0fdgSRwB32Mf+IK7zCRCyuTlClVeDiD897IKng70FnzUZ/JmxN8T5in4FEnnm/vF3X0nyW0EIVsfgYAQ=="},{"type":"tool_use","id":"toolu_017kx32chLGgsyoZz8Vyc9TW","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","old_string":"| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |","new_string":"| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, spec_versions, tasks, decisions, model_interactions |"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":554,"cache_read_input_tokens":49689,"cache_creation":{"ephemeral_5m_input_tokens":554,"ephemeral_1h_input_tokens":0},"output_tokens":261,"service_tier":"standard"}},"type":"assistant","uuid":"e7c3e181-7b3f-4d44-a1bc-6416da352905","timestamp":"2026-01-12T19:04:01.733Z"}
|
|
{"parentUuid":"e7c3e181-7b3f-4d44-a1bc-6416da352905","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017kx32chLGgsyoZz8Vyc9TW","type":"tool_result","content":"The file C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md has been updated successfully."}]},"uuid":"5b0a2c05-e039-4f66-ba8f-26246ca5aa9b","timestamp":"2026-01-12T19:04:05.275Z","toolUseResult":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","oldString":"| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |","newString":"| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, spec_versions, tasks, decisions, model_interactions |","originalFile":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: FastAPI with uvicorn ASGI server\n- Key packages: `fastapi[standard]`, `pydantic`, `httpx`, `sqlalchemy`\n- Key directories: `backend/`, `backend/services/`, `backend/api/`, `backend/models/`\n\n**Entry Point:** `backend/main.py`\n\n**How to Run:**\n```bash\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n```\n\n**Port:** 8000\n\n**API Documentation:** http://localhost:8000/docs (Swagger) and http://localhost:8000/redoc\n\n---\n\n### LLM Wrapper Service\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: LiteLLM for unified interface\n- Key packages: `litellm`, `anthropic`, `google-genai`, `xai-sdk`, `ollama`\n- Key directories: `backend/llm/`, `backend/llm/providers/`, `backend/llm/routing/`\n\n**Entry Point:** `backend/llm/__init__.py`\n\n**Configuration:**\n```bash\n# Environment variables required\nANTHROPIC_API_KEY=your_anthropic_key\nGEMINI_API_KEY=your_gemini_key\nXAI_API_KEY=your_xai_key\n# Ollama uses local server, no API key needed\n```\n\n---\n\n### Frontend UI\n\n**Tech Stack:**\n- Language: TypeScript\n- Framework: React with Vite\n- Key packages: `react`, `react-dom`, `typescript`, `@tanstack/react-query`\n- Key directories: `frontend/src/`, `frontend/src/components/`, `frontend/src/pages/`\n\n**Entry Point:** `frontend/src/main.tsx`\n\n**How to Run:**\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\n**Port:** 5173\n\n---\n\n### MCP Server\n\n**Tech Stack:**\n- Language: Python 3.11+\n- Framework: MCP SDK (FastMCP)\n- Key packages: `mcp>=1.25,<2`\n- Key directories: `mcp-server/`, `mcp-server/tools/`, `mcp-server/resources/`\n\n**Entry Point:** `mcp-server/server.py`\n\n**How to Run:**\n```bash\ncd mcp-server\npython server.py\n```\n\n## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:\n\n```python\nfrom litellm import completion, acompletion\n\n# Unified interface - same code for any provider\nasync def call_llm(model: str, messages: list, **kwargs) -> str:\n response = await acompletion(\n model=model, # e.g., \"claude-3-opus-20240229\", \"gemini/gemini-pro\", \"xai/grok-beta\", \"ollama/llama3\"\n messages=messages,\n **kwargs\n )\n return response.choices[0].message.content\n```\n\n**Key Points:**\n- Model names follow `provider/model` format for non-OpenAI models\n- Built-in cost tracking via `litellm.track_cost = True`\n- Automatic retries and fallbacks configurable\n- Same interface works across 100+ LLM providers\n\n---\n\n### FastAPI Service Pattern\n\n```python\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom pydantic import BaseModel\n\napp = FastAPI(title=\"Multi-LLM Platform\")\n\nclass TaskCreate(BaseModel):\n description: str\n priority: str = \"normal\"\n\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python\nfrom git import Repo\n\ndef create_feature_branch(repo_path: str, feature_name: str) -> str:\n \"\"\"Create a feature branch following conventional naming.\"\"\"\n repo = Repo(repo_path)\n branch_name = f\"feature/{feature_name.lower().replace(' ', '-')}\"\n new_branch = repo.create_head(branch_name)\n new_branch.checkout()\n return branch_name\n\ndef commit_with_message(repo_path: str, files: list, message: str):\n \"\"\"Commit with conventional commit message format.\"\"\"\n repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n---\n\n### Docker Compose Pattern\n\n```yaml\n# docker-compose.yml\nversion: '3.8'\n\nservices:\n backend:\n build: ./backend\n ports:\n - \"8000:8000\"\n environment:\n - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - XAI_API_KEY=${XAI_API_KEY}\n - OLLAMA_HOST=http://ollama:11434\n - DATABASE_URL=sqlite:///./multi_llm_platform.db\n depends_on:\n - ollama\n\n frontend:\n build: ./frontend\n ports:\n - \"5173:5173\"\n depends_on:\n - backend\n\n ollama:\n image: ollama/ollama:latest\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n ollama_data:\n```\n\n**Key Points:**\n- Backend depends on Ollama service for local LLM access\n- Environment variables passed from host `.env` file\n- Ollama data persisted via named volume\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n\n2. **Strategic Model Routing**\n - Description: Route tasks to optimal models based on task characteristics\n - Acceptance: Routing logic correctly assigns file analysis to Ollama, complex reasoning to Claude, multimodal to Gemini, real-time info to Grok\n\n3. **Cross-Model Validation**\n - Description: Have multiple models review work for quality assurance\n - Acceptance: Critical decisions validated by 2+ models before acceptance\n\n4. **Cost Optimization**\n - Description: Track and minimize API costs through intelligent model selection\n - Acceptance: Cost tracking visible in UI, local models used for appropriate tasks\n\n5. **Adaptive Spec System**\n - Description: Interview workflow for gathering requirements, with re-interview capability\n - Acceptance: Can create spec via interview, modify spec mid-project, track spec versions\n\n6. **Version Control Integration**\n - Description: Automatic branching, commit generation, PR creation\n - Acceptance: Git operations automated, conventional commit messages generated\n\n7. **Project Steering**\n - Description: Mid-flight direction changes with context preservation\n - Acceptance: Can redirect project without losing history, impact analysis visible\n\n8. **Skill Plugin System**\n - Description: Extensible architecture for specialized agent capabilities\n - Acceptance: Can load/unload skills, skills can be model-specific or agnostic\n\n### Non-Functional Requirements\n\n1. **Performance**\n - Parallel LLM calls for independent operations\n - Response time < 30s for most operations\n - Support concurrent users\n\n2. **Security**\n - API keys stored securely (environment variables, not code)\n - No credential logging\n - Input validation on all endpoints\n\n3. **Reliability**\n - Fallback strategies when models fail\n - Graceful degradation when providers unavailable\n - State persistence across restarts\n\n### Edge Cases\n\n1. **Provider Unavailable** - Fall back to alternate provider or queue for retry\n2. **Rate Limiting** - Implement exponential backoff, queue excess requests\n3. **Model Disagreement** - Escalate to higher-capability model for tie-breaking\n4. **Large Context** - Automatically route to models with larger context windows (Gemini)\n5. **Ollama Not Running** - Detect and warn, exclude from routing until available\n6. **Mid-Spec Change** - Version current spec, preserve history, enable diff view\n7. **Git Conflicts** - Detect and surface for manual resolution\n8. **API Key Invalid** - Mark provider as unavailable, notify user\n\n## Implementation Notes\n\n### DO\n\n- Use LiteLLM as unified wrapper instead of managing individual SDKs directly\n- Use `google-genai` package, NOT deprecated `google-generativeai`\n- Use official `xai-sdk` package, not community alternatives\n- Pin MCP to `mcp>=1.25,<2` to avoid v2 breaking changes\n- Use Python 3.11+ for compatibility with all packages (py-gitea requires 3.11+)\n- Use async clients for all LLM operations to enable concurrency\n- Use `fastapi[standard]` to get uvicorn bundled\n- Start Ollama server separately before using Python client\n- Pull Ollama models first: `ollama pull gemma3`\n\n### DON'T\n\n- Don't use GitPython in long-running daemon processes (resource leaks)\n- Don't hardcode API keys - use environment variables\n- Don't assume all models available - always check provider status\n- Don't block on single LLM call when parallel is possible\n- Don't generate code using deprecated google-generativeai patterns\n- Don't use `--reload` flag in production uvicorn\n\n### Database Migrations (Alembic)\n\n```bash\n# Initialize Alembic (one-time setup)\ncd backend\nalembic init migrations\n\n# Generate migration from model changes\nalembic revision --autogenerate -m \"Add model_interactions table\"\n\n# Apply migrations\nalembic upgrade head\n```\n\n**Key Points:**\n- Use autogenerate to create migrations from SQLAlchemy model changes\n- Always review generated migrations before applying\n- Run migrations before starting the application\n\n## Development Environment\n\n### Prerequisites\n\n```bash\n# Python 3.11+\npython --version # Should be 3.11+\n\n# Node.js 18+\nnode --version # Should be 18+\n\n# Ollama (for local LLM)\n# Download from https://ollama.com/download\nollama --version\n```\n\n### Start Services\n\n```bash\n# Terminal 1: Start Ollama server\nollama serve\n\n# Terminal 2: Pull models\nollama pull gemma3\nollama pull codellama\n\n# Terminal 3: Start backend\ncd backend\npip install -r requirements.txt\nuvicorn main:app --reload --port 8000\n\n# Terminal 4: Start frontend\ncd frontend\nnpm install\nnpm run dev\n\n# Terminal 5: Start MCP server (optional)\ncd mcp-server\npython server.py\n```\n\n### Service URLs\n\n- Backend API: http://localhost:8000\n- API Docs: http://localhost:8000/docs\n- Frontend: http://localhost:5173\n- Ollama: http://localhost:11434\n\n### Required Environment Variables\n\nCreate `.env` file from `.env.example`:\n\n```bash\n# LLM Provider Keys\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AI...\nXAI_API_KEY=xai-...\n\n# Ollama (optional, defaults shown)\nOLLAMA_HOST=http://localhost:11434\n\n# Database\nDATABASE_URL=sqlite:///./multi_llm_platform.db\n\n# Git/Gitea (optional)\nGITEA_URL=http://localhost:3000\nGITEA_TOKEN=your_gitea_token\n```\n\n## Success Criteria\n\nThe task is complete when:\n\n1. [ ] Successfully make API calls to all 4 LLM providers (Claude, Gemini, Grok, Ollama)\n2. [ ] Demonstrate intelligent model routing (correct model for task type)\n3. [ ] Cross-model validation working for critical decisions\n4. [ ] Cost tracking visible and accurate in UI\n5. [ ] Spec interview workflow functional (create, modify, version)\n6. [ ] Git integration working (branch, commit, PR creation)\n7. [ ] Project steering possible mid-flight with context preservation\n8. [ ] Skill plugin system loading and executing skills\n9. [ ] Frontend dashboard displaying project state\n10. [ ] No console errors in frontend or backend\n11. [ ] All unit and integration tests passing\n\n## QA Acceptance Criteria\n\n**CRITICAL**: These criteria must be verified by the QA Agent before sign-off.\n\n### Unit Tests\n\n| Test | File | What to Verify |\n|------|------|----------------|\n| LLM Router | `backend/tests/test_llm_router.py` | Correct model selection for task types |\n| Cost Tracker | `backend/tests/test_cost_tracker.py` | Accurate token and cost calculation |\n| Spec Manager | `backend/tests/test_spec_manager.py` | CRUD operations, versioning |\n| Skill Registry | `backend/tests/test_skill_registry.py` | Skill loading, execution, unloading |\n| Git Service | `backend/tests/test_git_service.py` | Branch creation, commits, PR logic |\n| Cross Validator | `backend/tests/test_cross_validate.py` | Multi-model validation logic |\n\n### Integration Tests\n\n| Test | Services | What to Verify |\n|------|----------|----------------|\n| LLM Provider Health | llm-wrapper + all providers | All 4 providers respond correctly |\n| Task Routing E2E | orchestration + llm-wrapper | Tasks routed to correct models |\n| Spec Persistence | spec-management + database | Specs saved and versioned correctly |\n| Git Operations | version-control + local repo | Branch/commit/PR workflow works |\n| MCP Tools | mcp-server + backend | MCP tools execute successfully |\n\n### End-to-End Tests\n\n| Flow | Steps | Expected Outcome |\n|------|-------|------------------|\n| Project Creation | 1. Open dashboard 2. Start interview 3. Answer questions 4. View spec | Spec created and displayed |\n| Task Execution | 1. Create task 2. View routing 3. Check results | Task completed with correct model |\n| Cross Validation | 1. Mark task critical 2. Submit 3. View validation | Multiple models validate before accept |\n| Mid-Project Steer | 1. Modify spec 2. Confirm change 3. View history | Spec versioned, history preserved |\n| Cost Report | 1. Execute tasks 2. View dashboard 3. Check costs | Accurate cost breakdown by provider |\n\n### Browser Verification\n\n| Page/Component | URL | Checks |\n|----------------|-----|--------|\n| Dashboard | `http://localhost:5173/` | Project overview loads, no errors |\n| Spec Editor | `http://localhost:5173/spec` | Interview form works, saves correctly |\n| Task Viewer | `http://localhost:5173/tasks` | Task list displays, status updates |\n| Model Status | `http://localhost:5173/models` | Provider health indicators accurate |\n| Cost Tracker | `http://localhost:5173/costs` | Cost charts render, data accurate |\n\n### API Verification\n\n| Endpoint | Method | Checks |\n|----------|--------|--------|\n| `/health` | GET | Returns 200, all services healthy |\n| `/api/models/status` | GET | Returns status for all 4 providers |\n| `/api/tasks` | POST | Creates task, returns task ID |\n| `/api/specs` | POST | Creates spec from interview data |\n| `/api/specs/{id}/versions` | GET | Returns version history |\n\n### Database Verification\n\n| Check | Query/Command | Expected |\n|-------|---------------|----------|\n| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |\n| Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |\n| Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |\n\n### Security Verification\n\n| Check | Method | Expected |\n|-------|--------|----------|\n| No hardcoded keys | `grep -r \"sk-ant\\|AIza\" backend/` | No matches |\n| Env vars loaded | Check startup logs | Keys loaded from environment |\n| Input validation | Send malformed JSON to endpoints | 422 validation error returned |\n\n### QA Sign-off Requirements\n\n- [ ] All unit tests pass (`pytest backend/tests/`)\n- [ ] All integration tests pass (`pytest backend/tests/integration/`)\n- [ ] All E2E tests pass (Playwright or manual)\n- [ ] Browser verification complete (all pages load, no console errors)\n- [ ] API verification complete (all endpoints respond correctly)\n- [ ] Database state verified (tables exist, data persists)\n- [ ] Security verification complete (no credential exposure)\n- [ ] No regressions in existing functionality\n- [ ] Code follows established patterns (LiteLLM, FastAPI conventions)\n- [ ] No security vulnerabilities introduced\n- [ ] Cost tracking accurate within 5% of actual API costs\n- [ ] All 4 LLM providers successfully integrated and responding\n\n---\n\n## Appendix: Package Versions\n\n### Python Requirements (requirements.txt)\n\n```\n# Core Framework\nfastapi[standard]>=0.115.0\nuvicorn>=0.30.0\npydantic>=2.0.0\n\n# LLM Providers\nlitellm>=1.0.0\nanthropic>=0.39.0\ngoogle-genai>=1.0.0\nxai-sdk>=0.1.0\nollama>=0.4.0\n\n# MCP\nmcp>=1.25,<2\n\n# Database\nsqlalchemy>=2.0.0\nalembic>=1.13.0\n\n# Git\nGitPython>=3.1.40\npy-gitea>=0.2.10\n\n# Utilities\nhttpx>=0.27.0\npython-dotenv>=1.0.0\n```\n\n### Node Requirements (package.json)\n\n```json\n{\n \"dependencies\": {\n \"react\": \"^18.0.0\",\n \"react-dom\": \"^18.0.0\",\n \"@tanstack/react-query\": \"^5.0.0\"\n },\n \"devDependencies\": {\n \"typescript\": \"^5.0.0\",\n \"vite\": \"^6.0.0\",\n \"@types/react\": \"^18.0.0\",\n \"@types/react-dom\": \"^18.0.0\",\n \"vitest\": \"^2.0.0\",\n \"@testing-library/react\": \"^16.0.0\"\n }\n}\n```\n\n---\n\n## Appendix: Phased Implementation Milestones\n\nBased on complexity assessment, implement in phases:\n\n1. **Phase 1**: Core orchestration layer with single LLM provider (Claude via LiteLLM)\n2. **Phase 2**: Add additional LLM providers with unified wrapper and routing\n3. **Phase 3**: Spec management system and version control integration\n4. **Phase 4**: Skill plugin system and cross-model validation\n5. **Phase 5**: Frontend UI and project steering capabilities\n6. **Phase 6**: Quality assurance, security hardening, and production readiness\n","structuredPatch":[{"oldStart":619,"oldLines":7,"newStart":619,"newLines":7,"lines":[" "," | Check | Query/Command | Expected |"," |-------|---------------|----------|","-| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, tasks, decisions, model_interactions |","+| Tables created | `SELECT name FROM sqlite_master WHERE type='table'` | specs, spec_versions, tasks, decisions, model_interactions |"," | Spec versioning | `SELECT COUNT(*) FROM spec_versions WHERE spec_id=1` | Versions increment on changes |"," | Cost tracking | `SELECT SUM(cost) FROM model_interactions` | Non-zero, matches UI display |"," "]}],"userModified":false,"replaceAll":false},"sourceToolAssistantUUID":"e7c3e181-7b3f-4d44-a1bc-6416da352905"}
|
|
{"parentUuid":"5b0a2c05-e039-4f66-ba8f-26246ca5aa9b","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01E4GtpasPS2EjJiuEQF7QWg","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now let me verify all changes are applied by reading the key sections to make sure the spec is valid:","signature":"Eo0CCkYICxgCKkC7vwA2jLdeVSOvKPU1NxVhJBgaVwoKVLcY9oYRSH5oKb9kk0Dfl/XnIgtvOSWJEvBVSFKDDtHagfvgVrD6mdgXEgyTvVKWejuOXuq2VYUaDHPafdDh5XplxvdgKSIwJTKZcVQII0oYHAhXT1RKFki1CLFMI5K//fDzMXi6gnFb81HQTcIhckA6jFVYsDb7KnWwW1wXSjtyCNhvD0bvfXqyzllTt80NZfavubWi27pYyaN8f3sw/9qMsQqirAgP/273gOjI0+vGW2hlZdIRxx4AHb7WjbwhIOZZSQUDLNaZwvnjZKyg65LHA6lS7YKIdwGo2emgKQPoo+aJkr6Fvw4pxsIGJ+kYAQ=="},{"type":"text","text":"Now let me verify the fixes and check that the spec is still well-formed:"},{"type":"tool_use","id":"toolu_015zwBSyoQXNayYdDFuFk452","name":"Bash","input":{"command":"grep -n \"^## \" \"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\" 2>/dev/null || findstr /n \"^## \" \"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\"","description":"Check that all major sections exist in spec.md"}},{"type":"tool_use","id":"toolu_01FU3st77E7rPRL4tV3zyLAq","name":"Bash","input":{"command":"wc -l \"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\" 2>/dev/null || find /c /v \"\" \"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\"","description":"Count lines in updated spec.md"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":335,"cache_read_input_tokens":50243,"cache_creation":{"ephemeral_5m_input_tokens":335,"ephemeral_1h_input_tokens":0},"output_tokens":424,"service_tier":"standard"}},"type":"assistant","uuid":"3868d4b7-cb16-44e0-9a45-6b923603addd","timestamp":"2026-01-12T19:04:33.828Z"}
|
|
{"parentUuid":"3868d4b7-cb16-44e0-9a45-6b923603addd","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015zwBSyoQXNayYdDFuFk452","type":"tool_result","content":"3:## Overview\n7:## Workflow Type\n13:## Task Scope\n47:## Service Context\n130:## Files to Create\n190:## Patterns to Follow\n372:## Requirements\n436:## Implementation Notes\n478:## Development Environment\n547:## Success Criteria\n563:## QA Acceptance Criteria\n651:## Appendix: Package Versions\n706:## Appendix: Phased Implementation Milestones","is_error":false}]},"uuid":"d66ef5c6-3400-4582-a6fb-cba0c8072845","timestamp":"2026-01-12T19:04:41.391Z","toolUseResult":{"stdout":"3:## Overview\n7:## Workflow Type\n13:## Task Scope\n47:## Service Context\n130:## Files to Create\n190:## Patterns to Follow\n372:## Requirements\n436:## Implementation Notes\n478:## Development Environment\n547:## Success Criteria\n563:## QA Acceptance Criteria\n651:## Appendix: Package Versions\n706:## Appendix: Phased Implementation Milestones\r","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"3868d4b7-cb16-44e0-9a45-6b923603addd"}
|
|
{"parentUuid":"3868d4b7-cb16-44e0-9a45-6b923603addd","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FU3st77E7rPRL4tV3zyLAq","type":"tool_result","content":"715 C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","is_error":false}]},"uuid":"4f8f8ab8-4b74-4552-bfa6-75397a519879","timestamp":"2026-01-12T19:04:49.404Z","toolUseResult":{"stdout":"715 C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md\r","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"3868d4b7-cb16-44e0-9a45-6b923603addd"}
|
|
{"parentUuid":"4f8f8ab8-4b74-4552-bfa6-75397a519879","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01Q5dPs7Map6obYz4q1Qq8RT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, all major sections are present. The spec has grown from 610 lines to 715 lines with the additions. Let me verify the Files to Create section now has the test files:","signature":"EtMCCkYICxgCKkAF8sKWaoYf1te3pQUPWJGpRodPqEFQ5iUHpaz1E1k+eJ9YwiLgk7QjshNSM4M5ll9UP9FifPmEKuky2I+tuFWUEgzmpMnz1SJtGpTX3B0aDLTF/1E0iqkLN/0X0iIwxsaaJXofooMhkaglmz11bJVAWyrZLGxAS+SVMi7J4fcOg3Y5UDCYmQXyuy9rhcvRKroBz2x8Vpci5c3JrE4aElIDm4bjnkBFZiJoEsT5A7+IY6YZdnXzMCkCZsq6JzQ5kh1k+2tLe3MFKsCkST5SyLzZL4TY7ZtbpZS7+L6BQ+McmFco3p0IdRPYkCHBHWPUtj3u7oogEJgWUukobhLA2BQy+M8uLzYNRvKjVQVSE1U9on3ZbTvVkKeefutb0ZHVIMhHlGtComJWKcbr4AX+rdMT1eG1nJ2pfzDXSv/g+eQLZZ7oKu6Bed0C09S0GAE="},{"type":"tool_use","id":"toolu_01Xqqnydi2isFcmhZmwto8kU","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","offset":130,"limit":65}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":746,"cache_read_input_tokens":50578,"cache_creation":{"ephemeral_5m_input_tokens":746,"ephemeral_1h_input_tokens":0},"output_tokens":183,"service_tier":"standard"}},"type":"assistant","uuid":"d184a7c4-c6a6-4c57-ab46-6dba87301223","timestamp":"2026-01-12T19:05:14.634Z"}
|
|
{"parentUuid":"d184a7c4-c6a6-4c57-ab46-6dba87301223","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xqqnydi2isFcmhZmwto8kU","type":"tool_result","content":" 130→## Files to Create\n 131→\n 132→| File | Service | Purpose |\n 133→|------|---------|---------|\n 134→| `backend/main.py` | orchestration | FastAPI application entry point |\n 135→| `backend/requirements.txt` | orchestration | Python dependencies |\n 136→| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n 137→| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n 138→| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n 139→| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n 140→| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n 141→| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n 142→| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n 143→| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n 144→| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n 145→| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n 146→| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n 147→| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n 148→| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n 149→| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n 150→| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n 151→| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n 152→| `backend/models/spec.py` | orchestration | Spec data model |\n 153→| `backend/models/task.py` | orchestration | Task data model |\n 154→| `backend/models/decision.py` | orchestration | Decision tracking model |\n 155→| `backend/git/git_service.py` | version-control | GitPython wrapper |\n 156→| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n 157→| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n 158→| `mcp-server/server.py` | mcp | FastMCP server entry |\n 159→| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n 160→| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n 161→| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n 162→| `frontend/package.json` | frontend | NPM dependencies |\n 163→| `frontend/vite.config.ts` | frontend | Vite configuration |\n 164→| `frontend/src/main.tsx` | frontend | React entry point |\n 165→| `frontend/src/App.tsx` | frontend | Main application component |\n 166→| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n 167→| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n 168→| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n 169→| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n 170→| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n 171→| `frontend/src/router.tsx` | frontend | React Router configuration |\n 172→| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n 173→| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n 174→| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n 175→| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n 176→| `backend/alembic.ini` | orchestration | Alembic migration config |\n 177→| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n 178→| `backend/tests/__init__.py` | orchestration | Test package init |\n 179→| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n 180→| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n 181→| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n 182→| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n 183→| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n 184→| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n 185→| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n 186→| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n 187→| `docker-compose.yml` | infrastructure | Container orchestration |\n 188→| `.env.example` | infrastructure | Environment variable template |\n 189→\n 190→## Patterns to Follow\n 191→\n 192→### LiteLLM Unified Interface Pattern\n 193→\n 194→Use LiteLLM as the unified API wrapper for all LLM providers:\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"77814275-203b-4b75-8464-13b3e9ceb7b7","timestamp":"2026-01-12T19:05:21.042Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":"## Files to Create\n\n| File | Service | Purpose |\n|------|---------|---------|\n| `backend/main.py` | orchestration | FastAPI application entry point |\n| `backend/requirements.txt` | orchestration | Python dependencies |\n| `backend/api/routes/__init__.py` | orchestration | API route aggregation |\n| `backend/api/routes/tasks.py` | orchestration | Task management endpoints |\n| `backend/api/routes/specs.py` | orchestration | Spec management endpoints |\n| `backend/api/routes/models.py` | orchestration | LLM provider status endpoints |\n| `backend/services/orchestrator.py` | orchestration | Task decomposition and routing |\n| `backend/services/spec_manager.py` | spec-management | Spec CRUD and versioning |\n| `backend/services/skill_registry.py` | skill-service | Skill loading and execution |\n| `backend/llm/unified_client.py` | llm-wrapper | LiteLLM wrapper with routing logic |\n| `backend/llm/providers/claude.py` | llm-wrapper | Claude-specific optimizations |\n| `backend/llm/providers/gemini.py` | llm-wrapper | Gemini-specific optimizations |\n| `backend/llm/providers/grok.py` | llm-wrapper | Grok-specific optimizations |\n| `backend/llm/providers/ollama.py` | llm-wrapper | Ollama local model integration |\n| `backend/llm/routing/router.py` | llm-wrapper | Task-to-model routing logic |\n| `backend/llm/routing/cost_tracker.py` | llm-wrapper | Token usage and cost tracking |\n| `backend/llm/validation/cross_validate.py` | llm-wrapper | Cross-model validation logic |\n| `backend/models/database.py` | orchestration | SQLAlchemy database setup |\n| `backend/models/spec.py` | orchestration | Spec data model |\n| `backend/models/task.py` | orchestration | Task data model |\n| `backend/models/decision.py` | orchestration | Decision tracking model |\n| `backend/git/git_service.py` | version-control | GitPython wrapper |\n| `backend/git/gitea_service.py` | version-control | Gitea API integration |\n| `backend/git/commit_generator.py` | version-control | Conventional commit messages |\n| `mcp-server/server.py` | mcp | FastMCP server entry |\n| `mcp-server/tools/spec_tools.py` | mcp | Spec management MCP tools |\n| `mcp-server/tools/task_tools.py` | mcp | Task management MCP tools |\n| `mcp-server/resources/project_state.py` | mcp | Project state MCP resources |\n| `frontend/package.json` | frontend | NPM dependencies |\n| `frontend/vite.config.ts` | frontend | Vite configuration |\n| `frontend/src/main.tsx` | frontend | React entry point |\n| `frontend/src/App.tsx` | frontend | Main application component |\n| `frontend/src/pages/Dashboard.tsx` | frontend | Project dashboard |\n| `frontend/src/pages/SpecEditor.tsx` | frontend | Spec editing interface |\n| `frontend/src/pages/TaskViewer.tsx` | frontend | Task status viewer |\n| `frontend/src/components/ModelStatus.tsx` | frontend | LLM provider status display |\n| `frontend/src/components/CostTracker.tsx` | frontend | Cost tracking visualization |\n| `frontend/src/router.tsx` | frontend | React Router configuration |\n| `frontend/src/__tests__/App.test.tsx` | frontend | Frontend component tests |\n| `backend/api/routes/health.py` | orchestration | Health check endpoint |\n| `backend/api/routes/git.py` | version-control | Git operations endpoints |\n| `backend/models/model_interaction.py` | orchestration | Model interaction tracking |\n| `backend/alembic.ini` | orchestration | Alembic migration config |\n| `backend/migrations/env.py` | orchestration | Alembic environment setup |\n| `backend/tests/__init__.py` | orchestration | Test package init |\n| `backend/tests/test_llm_router.py` | orchestration | LLM router unit tests |\n| `backend/tests/test_cost_tracker.py` | orchestration | Cost tracker tests |\n| `backend/tests/test_spec_manager.py` | orchestration | Spec manager tests |\n| `backend/tests/test_skill_registry.py` | orchestration | Skill registry tests |\n| `backend/tests/test_git_service.py` | orchestration | Git service tests |\n| `backend/tests/test_cross_validate.py` | orchestration | Cross-validation tests |\n| `backend/tests/integration/__init__.py` | orchestration | Integration test package |\n| `backend/tests/integration/test_providers.py` | orchestration | Provider integration tests |\n| `docker-compose.yml` | infrastructure | Container orchestration |\n| `.env.example` | infrastructure | Environment variable template |\n\n## Patterns to Follow\n\n### LiteLLM Unified Interface Pattern\n\nUse LiteLLM as the unified API wrapper for all LLM providers:","numLines":65,"startLine":130,"totalLines":716}},"sourceToolAssistantUUID":"d184a7c4-c6a6-4c57-ab46-6dba87301223"}
|
|
{"parentUuid":"77814275-203b-4b75-8464-13b3e9ceb7b7","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_019GTk3iNU5Gjyd5v7Two7Mm","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The files are now properly added. Let me also verify the new patterns were added (Health Check, Docker Compose, Alembic):","signature":"EqICCkYICxgCKkARe4yDfBkqU6gh5NQFZAsjBySj0v6nhaMvUUyumrDYaWiMJVh79e9btfGvMYXf+MTvVPJmbwzXD8V7z+tMiH+QEgyBklCl90spdE1vS6gaDNVFcY1XxJc4MpkrESIwmQALBNpGqafrnO4InGxuRZ85KWLLash4o3qq7EWvXoZOizjvmSYoP6MjBmVw75suKokBbAaiwU3EV65kitDwgGrAPkXwj5W5BllbgF+YSlZeCJGbsS9kLbKKjAAdXgFz7iblJcYRj7bktKFrwIL2xg+hRULenz516ld8ktBfzvwdwzE24YeEWSVyoGHhskQeaaUd6pd+sDN9gHnF0FmoVld3sxz/JtSzidudO+eCCvUJTE5Kauix50a/8tsYAQ=="},{"type":"tool_use","id":"toolu_01YAtuVAVhHM6vUQEqX6Su95","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","offset":228,"limit":80}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1781,"cache_read_input_tokens":51324,"cache_creation":{"ephemeral_5m_input_tokens":1781,"ephemeral_1h_input_tokens":0},"output_tokens":172,"service_tier":"standard"}},"type":"assistant","uuid":"e516e299-4e09-46a9-bb2c-cb512b25395b","timestamp":"2026-01-12T19:05:28.056Z"}
|
|
{"parentUuid":"e516e299-4e09-46a9-bb2c-cb512b25395b","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YAtuVAVhHM6vUQEqX6Su95","type":"tool_result","content":" 228→\n 229→@app.post(\"/tasks/\", response_model=Task)\n 230→async def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n 231→ \"\"\"Create a new task with automatic model routing.\"\"\"\n 232→ return await orchestrator.create_task(task, db)\n 233→```\n 234→\n 235→**Key Points:**\n 236→- Use Pydantic models for request/response validation\n 237→- Dependency injection for database sessions\n 238→- Async endpoints for non-blocking LLM calls\n 239→\n 240→---\n 241→\n 242→### Health Check Pattern\n 243→\n 244→```python\n 245→from fastapi import FastAPI\n 246→from pydantic import BaseModel\n 247→\n 248→class HealthResponse(BaseModel):\n 249→ status: str\n 250→ services: dict[str, str]\n 251→\n 252→@app.get(\"/health\", response_model=HealthResponse)\n 253→async def health_check():\n 254→ \"\"\"Check health of all services and LLM providers.\"\"\"\n 255→ services = {\n 256→ \"database\": \"healthy\",\n 257→ \"ollama\": await check_ollama_status(),\n 258→ \"claude\": await check_claude_status(),\n 259→ \"gemini\": await check_gemini_status(),\n 260→ \"grok\": await check_grok_status(),\n 261→ }\n 262→ overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n 263→ return HealthResponse(status=overall, services=services)\n 264→```\n 265→\n 266→---\n 267→\n 268→### Async LLM Client Pattern\n 269→\n 270→```python\n 271→from anthropic import AsyncAnthropic\n 272→from google import genai\n 273→from xai_sdk import AsyncClient as XAIClient\n 274→from ollama import AsyncClient as OllamaClient\n 275→\n 276→# All providers support async clients for concurrent operations\n 277→async def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n 278→ tasks = [call_llm(model, prompt) for model in models]\n 279→ responses = await asyncio.gather(*tasks, return_exceptions=True)\n 280→ return [r for r in responses if not isinstance(r, Exception)]\n 281→```\n 282→\n 283→---\n 284→\n 285→### MCP Server Pattern\n 286→\n 287→```python\n 288→from mcp.server.fastmcp import FastMCP\n 289→\n 290→mcp = FastMCP(\"multi-llm-platform\")\n 291→\n 292→@mcp.tool()\n 293→async def create_spec(description: str) -> dict:\n 294→ \"\"\"Create a new project specification.\"\"\"\n 295→ return await spec_service.create(description)\n 296→\n 297→@mcp.resource(\"project://state\")\n 298→async def get_project_state() -> str:\n 299→ \"\"\"Get current project state.\"\"\"\n 300→ return json.dumps(state_manager.get_state())\n 301→```\n 302→\n 303→---\n 304→\n 305→### Git Integration Pattern\n 306→\n 307→```python\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"0db45368-cca2-4631-bd11-dd486eafacf4","timestamp":"2026-01-12T19:05:33.349Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":"\n@app.post(\"/tasks/\", response_model=Task)\nasync def create_task(task: TaskCreate, db: Session = Depends(get_db)):\n \"\"\"Create a new task with automatic model routing.\"\"\"\n return await orchestrator.create_task(task, db)\n```\n\n**Key Points:**\n- Use Pydantic models for request/response validation\n- Dependency injection for database sessions\n- Async endpoints for non-blocking LLM calls\n\n---\n\n### Health Check Pattern\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\nclass HealthResponse(BaseModel):\n status: str\n services: dict[str, str]\n\n@app.get(\"/health\", response_model=HealthResponse)\nasync def health_check():\n \"\"\"Check health of all services and LLM providers.\"\"\"\n services = {\n \"database\": \"healthy\",\n \"ollama\": await check_ollama_status(),\n \"claude\": await check_claude_status(),\n \"gemini\": await check_gemini_status(),\n \"grok\": await check_grok_status(),\n }\n overall = \"healthy\" if all(s == \"healthy\" for s in services.values()) else \"degraded\"\n return HealthResponse(status=overall, services=services)\n```\n\n---\n\n### Async LLM Client Pattern\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom google import genai\nfrom xai_sdk import AsyncClient as XAIClient\nfrom ollama import AsyncClient as OllamaClient\n\n# All providers support async clients for concurrent operations\nasync def parallel_validation(prompt: str, models: list[str]) -> list[str]:\n tasks = [call_llm(model, prompt) for model in models]\n responses = await asyncio.gather(*tasks, return_exceptions=True)\n return [r for r in responses if not isinstance(r, Exception)]\n```\n\n---\n\n### MCP Server Pattern\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\nmcp = FastMCP(\"multi-llm-platform\")\n\n@mcp.tool()\nasync def create_spec(description: str) -> dict:\n \"\"\"Create a new project specification.\"\"\"\n return await spec_service.create(description)\n\n@mcp.resource(\"project://state\")\nasync def get_project_state() -> str:\n \"\"\"Get current project state.\"\"\"\n return json.dumps(state_manager.get_state())\n```\n\n---\n\n### Git Integration Pattern\n\n```python","numLines":80,"startLine":228,"totalLines":716}},"sourceToolAssistantUUID":"e516e299-4e09-46a9-bb2c-cb512b25395b"}
|
|
{"parentUuid":"0db45368-cca2-4631-bd11-dd486eafacf4","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01CicrooXk7jc3ZNcjMfiUav","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, the Health Check Pattern was added. Let me verify the Docker Compose pattern was added too:","signature":"EokCCkYICxgCKkAIqoN6VUgr2m4co+ECuotWSsOrQlJv4X29IVZMz0PrbiKA6HfdjJix/lK8eJ8OHwy4c27qcU45m5ROnxE3RtlBEgyPPeBhJoPfr9/wyCoaDBqhuxMeuUmggfV/4yIwP5qIre2bOFmX82ldVeSLm273wyk7k4BzBAgtyAUp2c8XgI5SLxlydSyfRIuBqsvkKnFQuPRw4bIGhytpLfT7ZjI4pRqtzG/nLkCxJ4gopgnr2c9OnhkPyZezEUqXK4/2WNerHKGhIyNtkAuY/JN4qGOL6FLYUujBmM56BCMRFHnfQ4I5b+nZb8kFebXfOQJg2oggD/MtDiDqXb19NHQqXnuc+xgB"},{"type":"tool_use","id":"toolu_01Vo2bu9Xj3Ct6ehtpLLNigf","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","offset":320,"limit":60}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1121,"cache_read_input_tokens":53105,"cache_creation":{"ephemeral_5m_input_tokens":1121,"ephemeral_1h_input_tokens":0},"output_tokens":164,"service_tier":"standard"}},"type":"assistant","uuid":"60a507c5-b787-4bde-803f-943406ccead0","timestamp":"2026-01-12T19:05:57.277Z"}
|
|
{"parentUuid":"60a507c5-b787-4bde-803f-943406ccead0","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Vo2bu9Xj3Ct6ehtpLLNigf","type":"tool_result","content":" 320→ repo = Repo(repo_path)\n 321→ repo.index.add(files)\n 322→ repo.index.commit(message)\n 323→```\n 324→\n 325→**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n 326→\n 327→---\n 328→\n 329→### Docker Compose Pattern\n 330→\n 331→```yaml\n 332→# docker-compose.yml\n 333→version: '3.8'\n 334→\n 335→services:\n 336→ backend:\n 337→ build: ./backend\n 338→ ports:\n 339→ - \"8000:8000\"\n 340→ environment:\n 341→ - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n 342→ - GEMINI_API_KEY=${GEMINI_API_KEY}\n 343→ - XAI_API_KEY=${XAI_API_KEY}\n 344→ - OLLAMA_HOST=http://ollama:11434\n 345→ - DATABASE_URL=sqlite:///./multi_llm_platform.db\n 346→ depends_on:\n 347→ - ollama\n 348→\n 349→ frontend:\n 350→ build: ./frontend\n 351→ ports:\n 352→ - \"5173:5173\"\n 353→ depends_on:\n 354→ - backend\n 355→\n 356→ ollama:\n 357→ image: ollama/ollama:latest\n 358→ ports:\n 359→ - \"11434:11434\"\n 360→ volumes:\n 361→ - ollama_data:/root/.ollama\n 362→\n 363→volumes:\n 364→ ollama_data:\n 365→```\n 366→\n 367→**Key Points:**\n 368→- Backend depends on Ollama service for local LLM access\n 369→- Environment variables passed from host `.env` file\n 370→- Ollama data persisted via named volume\n 371→\n 372→## Requirements\n 373→\n 374→### Functional Requirements\n 375→\n 376→1. **Multi-LLM Provider Integration**\n 377→ - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n 378→ - Acceptance: Can make requests to all 4 providers and receive responses\n 379→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"847897cb-be42-4899-a6f5-c7210fd092f1","timestamp":"2026-01-12T19:06:01.845Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":" repo = Repo(repo_path)\n repo.index.add(files)\n repo.index.commit(message)\n```\n\n**Gotcha:** GitPython leaks resources in long-running processes. For daemon usage, spawn as subprocess.\n\n---\n\n### Docker Compose Pattern\n\n```yaml\n# docker-compose.yml\nversion: '3.8'\n\nservices:\n backend:\n build: ./backend\n ports:\n - \"8000:8000\"\n environment:\n - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}\n - GEMINI_API_KEY=${GEMINI_API_KEY}\n - XAI_API_KEY=${XAI_API_KEY}\n - OLLAMA_HOST=http://ollama:11434\n - DATABASE_URL=sqlite:///./multi_llm_platform.db\n depends_on:\n - ollama\n\n frontend:\n build: ./frontend\n ports:\n - \"5173:5173\"\n depends_on:\n - backend\n\n ollama:\n image: ollama/ollama:latest\n ports:\n - \"11434:11434\"\n volumes:\n - ollama_data:/root/.ollama\n\nvolumes:\n ollama_data:\n```\n\n**Key Points:**\n- Backend depends on Ollama service for local LLM access\n- Environment variables passed from host `.env` file\n- Ollama data persisted via named volume\n\n## Requirements\n\n### Functional Requirements\n\n1. **Multi-LLM Provider Integration**\n - Description: Support Claude, Gemini, Grok, and Ollama through unified interface\n - Acceptance: Can make requests to all 4 providers and receive responses\n","numLines":60,"startLine":320,"totalLines":716}},"sourceToolAssistantUUID":"60a507c5-b787-4bde-803f-943406ccead0"}
|
|
{"parentUuid":"847897cb-be42-4899-a6f5-c7210fd092f1","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_018QbSeUddRAN5QGsbhb3t9Z","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Excellent, the Docker Compose pattern was added. Now let me check the Alembic section was added:","signature":"EogCCkYICxgCKkAT51GDBykmxR6pEC2e9u/Kb3PvTt6DWjpmI1pDm3emTUlGe8m1duUk3fJokEUZpzZGuRWgARBmp7qIufxvN9iTEgwR24QtBITG1OEPiZMaDIUjcGm3vj7AyQ/2JSIwC0uVL9KrZrvNYln0gK/VFgSkcayHuH1FovS4rN89pqWIb0QmHpe42i/NEccSBDqjKnDFb5oLILXtDeo/Nani1yHU4/xbvX7vCWEKfAAIYrDnoRWkGmaRHUVMr7uECLteCZ80tabotlWAGdRZEco3aKUSSc3KJOnjiUGOv7PCALbUExZXC1ITlZjfDFOF2GsvHazRxenY0uvD4H43rsx7Jh0QGAE="},{"type":"tool_use","id":"toolu_01CHSkpRj6jAPsLGska1m4Dq","name":"Grep","input":{"pattern":"Database Migrations.*Alembic","path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","output_mode":"content"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":869,"cache_read_input_tokens":54226,"cache_creation":{"ephemeral_5m_input_tokens":869,"ephemeral_1h_input_tokens":0},"output_tokens":175,"service_tier":"standard"}},"type":"assistant","uuid":"cda9e9a0-ee50-4c3e-af37-6685a9f6878d","timestamp":"2026-01-12T19:06:25.262Z"}
|
|
{"parentUuid":"cda9e9a0-ee50-4c3e-af37-6685a9f6878d","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CHSkpRj6jAPsLGska1m4Dq","type":"tool_result","content":"459:### Database Migrations (Alembic)"}]},"uuid":"34c0b9b0-df4a-4c6f-8475-0552a392c453","timestamp":"2026-01-12T19:06:31.121Z","toolUseResult":{"mode":"content","numFiles":0,"filenames":[],"content":"459:### Database Migrations (Alembic)","numLines":1},"sourceToolAssistantUUID":"cda9e9a0-ee50-4c3e-af37-6685a9f6878d"}
|
|
{"parentUuid":"34c0b9b0-df4a-4c6f-8475-0552a392c453","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_013tuDCKQAtHmGt2KNbYXuNR","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Good, all my changes are in place. Now let me create the critique_report.json to document all issues found and fixed:","signature":"Ep4CCkYICxgCKkCtsK+eBVBr+PHl0H2vlH3AE0uNnbG9594WUSAiRYY05cnu0cWtUeVX4krbjk0/KkuJNSzO+fjMb+OL9kjfofZEEgzlQc0fs3x6mlVqE1AaDCWEJpI/Zz2Lv3eJQyIwpGHJmTJ1xUPKKMNd9ffRc7a/WTd+QOj8fs1dETVO0H/r43FhmoRbqfQ1Do6bY/ARKoUBrG7lr/sNQUt8VA7GK1LtWYOzPmDr/6LLLMWiRehUMwzbReuKErOSOdfYohFhujkDT/1z66Bzhhx0Jiwde3R0wUtFi3yr1P9e11ctBfVtIxVK1Hc2/YZSRiKpCCPqXrPo0kZyTvarWCLZkXLxyWiPIB2zSM4Nw9+jh44XJIv6ZKzKFIXCVRgB"},{"type":"text","text":"All fixes have been applied. Now let me create the critique report:"},{"type":"tool_use","id":"toolu_017ALU5JA5Xsf8qfy8GxLjKY","name":"Write","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json","content":"{\n \"critique_completed\": true,\n \"issues_found\": [\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Test files referenced in QA section were missing from 'Files to Create' table\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added 9 test files: test_llm_router.py, test_cost_tracker.py, test_spec_manager.py, test_skill_registry.py, test_git_service.py, test_cross_validate.py, integration/__init__.py, integration/test_providers.py, and __init__.py\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing health check endpoint file and pattern - QA section required /health but no implementation guidance provided\",\n \"location\": \"Files to Create and Patterns sections\",\n \"fix_applied\": \"Added backend/api/routes/health.py to Files to Create and added complete Health Check Pattern with code example\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing Git API routes file - version-control-service had backend service files but no API route to expose them\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/api/routes/git.py for Git operations endpoints\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing model_interaction.py model file - referenced in Database Verification but not in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table) vs Line 517 (Database Verification)\",\n \"fix_applied\": \"Added backend/models/model_interaction.py to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing Alembic configuration files - requirements.txt listed alembic but no config files specified\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/alembic.ini and backend/migrations/env.py to Files to Create, plus Alembic migration pattern in Implementation Notes\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"accuracy\",\n \"description\": \"LiteLLM model name examples missing xAI/Grok format and using imprecise Claude model name\",\n \"location\": \"Line 188 (LiteLLM pattern code example)\",\n \"fix_applied\": \"Updated model examples from 'claude-3-opus' to 'claude-3-opus-20240229' and added 'xai/grok-beta' to examples\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Docker Compose file listed in Files to Create but no pattern or example provided\",\n \"location\": \"Patterns to Follow section\",\n \"fix_applied\": \"Added complete Docker Compose Pattern with 3-service configuration (backend, frontend, ollama)\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend router configuration file - QA section references multiple routes but no router file listed\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/router.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend test file - package.json lists vitest but no test files in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/__tests__/App.test.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"low\",\n \"category\": \"consistency\",\n \"description\": \"Database Verification table missing spec_versions table - versioning mentioned but table not listed\",\n \"location\": \"Line 517 (Database Verification)\",\n \"fix_applied\": \"Added spec_versions to expected tables list\",\n \"verified\": true\n }\n ],\n \"issues_fixed\": true,\n \"no_issues_found\": false,\n \"critique_summary\": \"Found 10 issues (3 high, 6 medium, 1 low severity). Primary issues were completeness gaps: test files, API routes, database models, and configuration files referenced in QA but missing from Files to Create. Also added missing patterns for Health Check, Docker Compose, and Alembic migrations. Fixed model name accuracy in LiteLLM examples. All issues have been resolved directly in spec.md.\",\n \"confidence_level\": \"high\",\n \"recommendations\": [\n \"Consider adding pytest configuration file (pytest.ini or pyproject.toml) to Files to Create\",\n \"Consider adding ESLint/Prettier config files for frontend code quality\",\n \"Consider documenting the expected directory structure for skills/plugins\",\n \"Frontend router.tsx should use react-router-dom which may need to be added to package.json dependencies\"\n ],\n \"files_modified\": [\n \"spec.md\"\n ],\n \"lines_added\": 105,\n \"original_line_count\": 610,\n \"final_line_count\": 715,\n \"created_at\": \"2026-01-12T12:15:00.000Z\"\n}\n"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":205,"cache_read_input_tokens":55095,"cache_creation":{"ephemeral_5m_input_tokens":205,"ephemeral_1h_input_tokens":0},"output_tokens":1562,"service_tier":"standard"}},"type":"assistant","uuid":"359822b2-81d8-41a6-8987-354c739274ca","timestamp":"2026-01-12T19:07:18.580Z"}
|
|
{"parentUuid":"359822b2-81d8-41a6-8987-354c739274ca","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017ALU5JA5Xsf8qfy8GxLjKY","type":"tool_result","content":"File created successfully at: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json"}]},"uuid":"4de3c90f-8fc4-4d1e-872a-a0cbf3f4cd83","timestamp":"2026-01-12T19:07:23.464Z","toolUseResult":{"type":"create","filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json","content":"{\n \"critique_completed\": true,\n \"issues_found\": [\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Test files referenced in QA section were missing from 'Files to Create' table\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added 9 test files: test_llm_router.py, test_cost_tracker.py, test_spec_manager.py, test_skill_registry.py, test_git_service.py, test_cross_validate.py, integration/__init__.py, integration/test_providers.py, and __init__.py\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing health check endpoint file and pattern - QA section required /health but no implementation guidance provided\",\n \"location\": \"Files to Create and Patterns sections\",\n \"fix_applied\": \"Added backend/api/routes/health.py to Files to Create and added complete Health Check Pattern with code example\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing Git API routes file - version-control-service had backend service files but no API route to expose them\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/api/routes/git.py for Git operations endpoints\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing model_interaction.py model file - referenced in Database Verification but not in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table) vs Line 517 (Database Verification)\",\n \"fix_applied\": \"Added backend/models/model_interaction.py to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing Alembic configuration files - requirements.txt listed alembic but no config files specified\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/alembic.ini and backend/migrations/env.py to Files to Create, plus Alembic migration pattern in Implementation Notes\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"accuracy\",\n \"description\": \"LiteLLM model name examples missing xAI/Grok format and using imprecise Claude model name\",\n \"location\": \"Line 188 (LiteLLM pattern code example)\",\n \"fix_applied\": \"Updated model examples from 'claude-3-opus' to 'claude-3-opus-20240229' and added 'xai/grok-beta' to examples\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Docker Compose file listed in Files to Create but no pattern or example provided\",\n \"location\": \"Patterns to Follow section\",\n \"fix_applied\": \"Added complete Docker Compose Pattern with 3-service configuration (backend, frontend, ollama)\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend router configuration file - QA section references multiple routes but no router file listed\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/router.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend test file - package.json lists vitest but no test files in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/__tests__/App.test.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"low\",\n \"category\": \"consistency\",\n \"description\": \"Database Verification table missing spec_versions table - versioning mentioned but table not listed\",\n \"location\": \"Line 517 (Database Verification)\",\n \"fix_applied\": \"Added spec_versions to expected tables list\",\n \"verified\": true\n }\n ],\n \"issues_fixed\": true,\n \"no_issues_found\": false,\n \"critique_summary\": \"Found 10 issues (3 high, 6 medium, 1 low severity). Primary issues were completeness gaps: test files, API routes, database models, and configuration files referenced in QA but missing from Files to Create. Also added missing patterns for Health Check, Docker Compose, and Alembic migrations. Fixed model name accuracy in LiteLLM examples. All issues have been resolved directly in spec.md.\",\n \"confidence_level\": \"high\",\n \"recommendations\": [\n \"Consider adding pytest configuration file (pytest.ini or pyproject.toml) to Files to Create\",\n \"Consider adding ESLint/Prettier config files for frontend code quality\",\n \"Consider documenting the expected directory structure for skills/plugins\",\n \"Frontend router.tsx should use react-router-dom which may need to be added to package.json dependencies\"\n ],\n \"files_modified\": [\n \"spec.md\"\n ],\n \"lines_added\": 105,\n \"original_line_count\": 610,\n \"final_line_count\": 715,\n \"created_at\": \"2026-01-12T12:15:00.000Z\"\n}\n","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"359822b2-81d8-41a6-8987-354c739274ca"}
|
|
{"parentUuid":"4de3c90f-8fc4-4d1e-872a-a0cbf3f4cd83","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01KmCSDcBBfhTL4w2zwPXaJQ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Critique report has been created. Let me do a final verification to ensure everything is in order:","signature":"EooCCkYICxgCKkCXw9J+i8qi5g20ILCruxjY53CbJRvX+6pYBoyt+pJzDlbPcQeiRC98VrsdniOnwmFEVRWj6IZAOuX1/8iY66ATEgwUrJFPL+rCWPZcafoaDCQ89ZpmK4CUnUB7+yIwDp1AnjNosI79c7p4Y+qFU1qdzmz8D/TDi9pqt7geNDi5MpyG4BsMrKjtiU3EMTLUKnLA8Jb8VfKnLHjRTCR4Arye3UrPNb5Ys7k5IAl4/L+MTy5/56Ia8FQYwXld+wFob7dzd+6QVmgEpQy62Exj8vLf2lh15ecqh9CaQITwiAh1tCcRGAv4sinM9H8vuqeLdlU8KQn+Jh8a+EICD5ojC3c2nwcYAQ=="},{"type":"tool_use","id":"toolu_011xVR6guH44bht9QvgYb7WX","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1637,"cache_read_input_tokens":55300,"cache_creation":{"ephemeral_5m_input_tokens":1637,"ephemeral_1h_input_tokens":0},"output_tokens":131,"service_tier":"standard"}},"type":"assistant","uuid":"7d704322-4e1e-4d85-9956-51488553bfc5","timestamp":"2026-01-12T19:07:45.331Z"}
|
|
{"parentUuid":"7d704322-4e1e-4d85-9956-51488553bfc5","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011xVR6guH44bht9QvgYb7WX","type":"tool_result","content":" 1→{\n 2→ \"critique_completed\": true,\n 3→ \"issues_found\": [\n 4→ {\n 5→ \"severity\": \"high\",\n 6→ \"category\": \"completeness\",\n 7→ \"description\": \"Test files referenced in QA section were missing from 'Files to Create' table\",\n 8→ \"location\": \"Lines 130-172 (Files to Create table)\",\n 9→ \"fix_applied\": \"Added 9 test files: test_llm_router.py, test_cost_tracker.py, test_spec_manager.py, test_skill_registry.py, test_git_service.py, test_cross_validate.py, integration/__init__.py, integration/test_providers.py, and __init__.py\",\n 10→ \"verified\": true\n 11→ },\n 12→ {\n 13→ \"severity\": \"high\",\n 14→ \"category\": \"completeness\",\n 15→ \"description\": \"Missing health check endpoint file and pattern - QA section required /health but no implementation guidance provided\",\n 16→ \"location\": \"Files to Create and Patterns sections\",\n 17→ \"fix_applied\": \"Added backend/api/routes/health.py to Files to Create and added complete Health Check Pattern with code example\",\n 18→ \"verified\": true\n 19→ },\n 20→ {\n 21→ \"severity\": \"high\",\n 22→ \"category\": \"completeness\",\n 23→ \"description\": \"Missing Git API routes file - version-control-service had backend service files but no API route to expose them\",\n 24→ \"location\": \"Lines 130-172 (Files to Create table)\",\n 25→ \"fix_applied\": \"Added backend/api/routes/git.py for Git operations endpoints\",\n 26→ \"verified\": true\n 27→ },\n 28→ {\n 29→ \"severity\": \"medium\",\n 30→ \"category\": \"completeness\",\n 31→ \"description\": \"Missing model_interaction.py model file - referenced in Database Verification but not in Files to Create\",\n 32→ \"location\": \"Lines 130-172 (Files to Create table) vs Line 517 (Database Verification)\",\n 33→ \"fix_applied\": \"Added backend/models/model_interaction.py to Files to Create\",\n 34→ \"verified\": true\n 35→ },\n 36→ {\n 37→ \"severity\": \"medium\",\n 38→ \"category\": \"completeness\",\n 39→ \"description\": \"Missing Alembic configuration files - requirements.txt listed alembic but no config files specified\",\n 40→ \"location\": \"Lines 130-172 (Files to Create table)\",\n 41→ \"fix_applied\": \"Added backend/alembic.ini and backend/migrations/env.py to Files to Create, plus Alembic migration pattern in Implementation Notes\",\n 42→ \"verified\": true\n 43→ },\n 44→ {\n 45→ \"severity\": \"medium\",\n 46→ \"category\": \"accuracy\",\n 47→ \"description\": \"LiteLLM model name examples missing xAI/Grok format and using imprecise Claude model name\",\n 48→ \"location\": \"Line 188 (LiteLLM pattern code example)\",\n 49→ \"fix_applied\": \"Updated model examples from 'claude-3-opus' to 'claude-3-opus-20240229' and added 'xai/grok-beta' to examples\",\n 50→ \"verified\": true\n 51→ },\n 52→ {\n 53→ \"severity\": \"medium\",\n 54→ \"category\": \"completeness\",\n 55→ \"description\": \"Docker Compose file listed in Files to Create but no pattern or example provided\",\n 56→ \"location\": \"Patterns to Follow section\",\n 57→ \"fix_applied\": \"Added complete Docker Compose Pattern with 3-service configuration (backend, frontend, ollama)\",\n 58→ \"verified\": true\n 59→ },\n 60→ {\n 61→ \"severity\": \"medium\",\n 62→ \"category\": \"completeness\",\n 63→ \"description\": \"Missing frontend router configuration file - QA section references multiple routes but no router file listed\",\n 64→ \"location\": \"Lines 130-172 (Files to Create table)\",\n 65→ \"fix_applied\": \"Added frontend/src/router.tsx to Files to Create\",\n 66→ \"verified\": true\n 67→ },\n 68→ {\n 69→ \"severity\": \"medium\",\n 70→ \"category\": \"completeness\",\n 71→ \"description\": \"Missing frontend test file - package.json lists vitest but no test files in Files to Create\",\n 72→ \"location\": \"Lines 130-172 (Files to Create table)\",\n 73→ \"fix_applied\": \"Added frontend/src/__tests__/App.test.tsx to Files to Create\",\n 74→ \"verified\": true\n 75→ },\n 76→ {\n 77→ \"severity\": \"low\",\n 78→ \"category\": \"consistency\",\n 79→ \"description\": \"Database Verification table missing spec_versions table - versioning mentioned but table not listed\",\n 80→ \"location\": \"Line 517 (Database Verification)\",\n 81→ \"fix_applied\": \"Added spec_versions to expected tables list\",\n 82→ \"verified\": true\n 83→ }\n 84→ ],\n 85→ \"issues_fixed\": true,\n 86→ \"no_issues_found\": false,\n 87→ \"critique_summary\": \"Found 10 issues (3 high, 6 medium, 1 low severity). Primary issues were completeness gaps: test files, API routes, database models, and configuration files referenced in QA but missing from Files to Create. Also added missing patterns for Health Check, Docker Compose, and Alembic migrations. Fixed model name accuracy in LiteLLM examples. All issues have been resolved directly in spec.md.\",\n 88→ \"confidence_level\": \"high\",\n 89→ \"recommendations\": [\n 90→ \"Consider adding pytest configuration file (pytest.ini or pyproject.toml) to Files to Create\",\n 91→ \"Consider adding ESLint/Prettier config files for frontend code quality\",\n 92→ \"Consider documenting the expected directory structure for skills/plugins\",\n 93→ \"Frontend router.tsx should use react-router-dom which may need to be added to package.json dependencies\"\n 94→ ],\n 95→ \"files_modified\": [\n 96→ \"spec.md\"\n 97→ ],\n 98→ \"lines_added\": 105,\n 99→ \"original_line_count\": 610,\n 100→ \"final_line_count\": 715,\n 101→ \"created_at\": \"2026-01-12T12:15:00.000Z\"\n 102→}\n 103→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"06fae51d-f016-4bc1-8969-d228e114942a","timestamp":"2026-01-12T19:07:51.250Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\critique_report.json","content":"{\n \"critique_completed\": true,\n \"issues_found\": [\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Test files referenced in QA section were missing from 'Files to Create' table\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added 9 test files: test_llm_router.py, test_cost_tracker.py, test_spec_manager.py, test_skill_registry.py, test_git_service.py, test_cross_validate.py, integration/__init__.py, integration/test_providers.py, and __init__.py\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing health check endpoint file and pattern - QA section required /health but no implementation guidance provided\",\n \"location\": \"Files to Create and Patterns sections\",\n \"fix_applied\": \"Added backend/api/routes/health.py to Files to Create and added complete Health Check Pattern with code example\",\n \"verified\": true\n },\n {\n \"severity\": \"high\",\n \"category\": \"completeness\",\n \"description\": \"Missing Git API routes file - version-control-service had backend service files but no API route to expose them\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/api/routes/git.py for Git operations endpoints\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing model_interaction.py model file - referenced in Database Verification but not in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table) vs Line 517 (Database Verification)\",\n \"fix_applied\": \"Added backend/models/model_interaction.py to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing Alembic configuration files - requirements.txt listed alembic but no config files specified\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added backend/alembic.ini and backend/migrations/env.py to Files to Create, plus Alembic migration pattern in Implementation Notes\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"accuracy\",\n \"description\": \"LiteLLM model name examples missing xAI/Grok format and using imprecise Claude model name\",\n \"location\": \"Line 188 (LiteLLM pattern code example)\",\n \"fix_applied\": \"Updated model examples from 'claude-3-opus' to 'claude-3-opus-20240229' and added 'xai/grok-beta' to examples\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Docker Compose file listed in Files to Create but no pattern or example provided\",\n \"location\": \"Patterns to Follow section\",\n \"fix_applied\": \"Added complete Docker Compose Pattern with 3-service configuration (backend, frontend, ollama)\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend router configuration file - QA section references multiple routes but no router file listed\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/router.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"medium\",\n \"category\": \"completeness\",\n \"description\": \"Missing frontend test file - package.json lists vitest but no test files in Files to Create\",\n \"location\": \"Lines 130-172 (Files to Create table)\",\n \"fix_applied\": \"Added frontend/src/__tests__/App.test.tsx to Files to Create\",\n \"verified\": true\n },\n {\n \"severity\": \"low\",\n \"category\": \"consistency\",\n \"description\": \"Database Verification table missing spec_versions table - versioning mentioned but table not listed\",\n \"location\": \"Line 517 (Database Verification)\",\n \"fix_applied\": \"Added spec_versions to expected tables list\",\n \"verified\": true\n }\n ],\n \"issues_fixed\": true,\n \"no_issues_found\": false,\n \"critique_summary\": \"Found 10 issues (3 high, 6 medium, 1 low severity). Primary issues were completeness gaps: test files, API routes, database models, and configuration files referenced in QA but missing from Files to Create. Also added missing patterns for Health Check, Docker Compose, and Alembic migrations. Fixed model name accuracy in LiteLLM examples. All issues have been resolved directly in spec.md.\",\n \"confidence_level\": \"high\",\n \"recommendations\": [\n \"Consider adding pytest configuration file (pytest.ini or pyproject.toml) to Files to Create\",\n \"Consider adding ESLint/Prettier config files for frontend code quality\",\n \"Consider documenting the expected directory structure for skills/plugins\",\n \"Frontend router.tsx should use react-router-dom which may need to be added to package.json dependencies\"\n ],\n \"files_modified\": [\n \"spec.md\"\n ],\n \"lines_added\": 105,\n \"original_line_count\": 610,\n \"final_line_count\": 715,\n \"created_at\": \"2026-01-12T12:15:00.000Z\"\n}\n","numLines":103,"startLine":1,"totalLines":103}},"sourceToolAssistantUUID":"7d704322-4e1e-4d85-9956-51488553bfc5"}
|
|
{"parentUuid":"06fae51d-f016-4bc1-8969-d228e114942a","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_01DjW6TSqPDrcR484Sybgja1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The critique report is complete. Let me do a final verification of the spec to make sure it's still valid:","signature":"EpICCkYICxgCKkA6UL6SApGAp2DuFdumzXhotXgWJ/vycv86cGgsUJB4OSM255H/m1R8viCjN0Qqez4UA7z+jIq2w92Izo+uLXjzEgwHpJXDTB4alhj6gLYaDHguyox+945p9rzu4SIws0rLE+t+pUEvn06BNDVd32m+JdbQNtF/r5KDNh8XiGg5RqHXGPcPtUi7ONLl/8y0KnrqEcPO3tNrocdUHOg7fa1PgRBzP4wmFS1diZEuOFofdijKn0+bLljAGaMyalr5r5wqYf19CTHcO/fm4Tpo20qI+nKgfYKks8m5iNNHqeYJ5YhgJ9fz/zhucULjR2t99GPYkcG4sUDFzuz0HWGQBlrhOPheTmuSKncVYhgB"},{"type":"tool_use","id":"toolu_018ikeYdePgKxPDutfYcbBkc","name":"Read","input":{"file_path":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","offset":1,"limit":50}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1932,"cache_read_input_tokens":56937,"cache_creation":{"ephemeral_5m_input_tokens":1932,"ephemeral_1h_input_tokens":0},"output_tokens":166,"service_tier":"standard"}},"type":"assistant","uuid":"783c8fd4-a9ba-4eb3-a2ee-05a01e3711d5","timestamp":"2026-01-12T19:08:19.225Z"}
|
|
{"parentUuid":"783c8fd4-a9ba-4eb3-a2ee-05a01e3711d5","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018ikeYdePgKxPDutfYcbBkc","type":"tool_result","content":" 1→# Specification: Multi-LLM Agentic Development Platform\n 2→\n 3→## Overview\n 4→\n 5→Build an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n 6→\n 7→## Workflow Type\n 8→\n 9→**Type**: Feature (Greenfield Development)\n 10→\n 11→**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n 12→\n 13→## Task Scope\n 14→\n 15→### Services Involved\n 16→\n 17→- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n 18→- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n 19→- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n 20→- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n 21→- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n 22→- **frontend-ui** (integration) - React/TypeScript project management dashboard\n 23→\n 24→### This Task Will:\n 25→\n 26→- [ ] Create Python/FastAPI backend with modular service architecture\n 27→- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n 28→- [ ] Build task orchestration engine with model-specific routing\n 29→- [ ] Implement cross-model validation for quality assurance\n 30→- [ ] Create adaptive spec system with interview and re-interview workflows\n 31→- [ ] Build extensible skill/addon plugin architecture\n 32→- [ ] Integrate Git/Gitea for version control automation\n 33→- [ ] Implement checkpoint system for project steering and course correction\n 34→- [ ] Create React/TypeScript frontend for project management\n 35→- [ ] Design database schema for specs, decisions, task history, model interactions\n 36→- [ ] Build MCP server for feature graph and skill management\n 37→\n 38→### Out of Scope:\n 39→\n 40→- Production deployment infrastructure (Kubernetes, cloud deployment)\n 41→- Enterprise SSO/SAML authentication\n 42→- Multi-tenant user management\n 43→- Billing and subscription management\n 44→- Mobile application\n 45→- Integration with CI/CD pipelines (beyond basic Git operations)\n 46→\n 47→## Service Context\n 48→\n 49→### Backend Orchestration Service\n 50→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"uuid":"b0dce636-2e7f-488f-85b7-8ea0c062ef03","timestamp":"2026-01-12T19:08:24.767Z","toolUseResult":{"type":"text","file":{"filePath":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\spec.md","content":"# Specification: Multi-LLM Agentic Development Platform\n\n## Overview\n\nBuild an advanced agentic development platform that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent software development. The system strategically routes tasks to models best suited for them, implements cross-model validation for quality assurance, and provides cost optimization through intelligent balancing of cloud and local models. This greenfield project extends concepts from Autocoder 2.0/Auto-Claude with multi-model orchestration, adaptive requirements management, and mid-project course correction capabilities.\n\n## Workflow Type\n\n**Type**: Feature (Greenfield Development)\n\n**Rationale**: Despite referencing existing Autocoder 2.0/Auto-Claude patterns, the project index shows an empty codebase with no existing code to refactor. This is building new functionality from scratch, requiring full-stack development across 5 services, 4+ external LLM provider integrations, database schema design, and novel architectural patterns for multi-model orchestration.\n\n## Task Scope\n\n### Services Involved\n\n- **orchestration-service** (primary) - Core task decomposition, model routing, parallel execution engine\n- **llm-wrapper-service** (primary) - Unified API abstraction for all LLM providers via LiteLLM\n- **spec-management-service** (primary) - Adaptive spec system with interview and versioning\n- **skill-service** (integration) - Extensible plugin architecture for specialized capabilities\n- **version-control-service** (integration) - Git/Gitea automation for branching, commits, PRs\n- **frontend-ui** (integration) - React/TypeScript project management dashboard\n\n### This Task Will:\n\n- [ ] Create Python/FastAPI backend with modular service architecture\n- [ ] Implement unified LLM wrapper supporting Claude, Gemini, Grok, and Ollama\n- [ ] Build task orchestration engine with model-specific routing\n- [ ] Implement cross-model validation for quality assurance\n- [ ] Create adaptive spec system with interview and re-interview workflows\n- [ ] Build extensible skill/addon plugin architecture\n- [ ] Integrate Git/Gitea for version control automation\n- [ ] Implement checkpoint system for project steering and course correction\n- [ ] Create React/TypeScript frontend for project management\n- [ ] Design database schema for specs, decisions, task history, model interactions\n- [ ] Build MCP server for feature graph and skill management\n\n### Out of Scope:\n\n- Production deployment infrastructure (Kubernetes, cloud deployment)\n- Enterprise SSO/SAML authentication\n- Multi-tenant user management\n- Billing and subscription management\n- Mobile application\n- Integration with CI/CD pipelines (beyond basic Git operations)\n\n## Service Context\n\n### Backend Orchestration Service\n","numLines":50,"startLine":1,"totalLines":716}},"sourceToolAssistantUUID":"783c8fd4-a9ba-4eb3-a2ee-05a01e3711d5"}
|
|
{"parentUuid":"b0dce636-2e7f-488f-85b7-8ea0c062ef03","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"e565d885-2a02-4c73-9dee-2807029038dd","version":"2.1.5","gitBranch":"master","slug":"serialized-greeting-volcano","message":{"model":"claude-opus-4-5-20251101","id":"msg_013pEs4GzdfcRHSwk9AfGUUa","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The spec.md header and structure look good. All sections are in place and the document is valid Markdown. The critique is complete!","signature":"EqwCCkYICxgCKkBP+MSH2H0NeloDgxZA3fOliqqLuzrnX4fU/l3jnY02DDYAvZynQHW2wV44NLB5mDBEHME9PyfB8jBIgbU4Q13DEgx5HP9iRnwYkxMCGS0aDC5w0lrU/uFxNK52rCIwd6Me/pyaK/lDrYOEV61awjI2lwUyQ29Fg7zlKNOGI+n+tc52xmsaqoqh5GQiks64KpMBP3uwc5uAuBsLeRN3nsDdYcccXqHZQ3r+mRqgZ5oBP0SXMFSOCvERbKW7nYUt9t9zFqsxuqgA0sshBcRh1YNuLEd8svKp4th7AmxVVDWNdBa3aQ+wiu4WBG5rNrOdNWbmBj3jdkYo9Cg1/F+OwErZV0wNUx82JFm79tbpZ3DObVOv92qhnzK4uDKA0AcuTdCORCiNGAE="},{"type":"text","text":"---\n\n## === SPEC CRITIQUE COMPLETE ===\n\n**Issues Found: 10**\n- High severity: 3\n- Medium severity: 6\n- Low severity: 1\n\n**Fixes Applied: 10**\n**Confidence Level: HIGH**\n\n### Summary\n\nI performed a deep analysis of spec.md against research.json and requirements.json, identifying completeness gaps as the primary issue category.\n\n**Critical Fixes Applied:**\n\n1. **Test Files Gap (HIGH)** - Added 9 test files that were referenced in QA section but missing from Files to Create\n2. **Health Endpoint Gap (HIGH)** - Added health.py route file AND complete Health Check Pattern with code example\n3. **Git API Routes Gap (HIGH)** - Added git.py API route to expose version-control-service functionality\n\n**Additional Fixes:**\n- Added model_interaction.py data model\n- Added Alembic configuration files (alembic.ini, migrations/env.py) with migration pattern\n- Fixed LiteLLM model name examples to include xAI/Grok format (`xai/grok-beta`)\n- Added complete Docker Compose Pattern with 3-service configuration\n- Added frontend router.tsx and App.test.tsx files\n- Fixed Database Verification table to include spec_versions\n\n**Technical Accuracy Verified:**\n- Package names match research.json ✓\n- Import statements match documented API patterns ✓\n- Environment variables correctly specified ✓\n- Port numbers match framework defaults ✓\n- All requirements from requirements.json covered ✓\n\n**Files Modified:**\n- `spec.md` (610 → 715 lines)\n\n**critique_report.json** created successfully.\n**spec.md** has been updated with fixes."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1056,"cache_read_input_tokens":58869,"cache_creation":{"ephemeral_5m_input_tokens":1056,"ephemeral_1h_input_tokens":0},"output_tokens":437,"service_tier":"standard"}},"type":"assistant","uuid":"64e9a39d-69d9-4a4a-af2a-75fd9ba9fd8c","timestamp":"2026-01-12T19:09:01.573Z"}
|