claudetools/imported-conversations/auto-claude-variants/ac-plus/31ddedb4-4691-4399-8c69-528442ba64a8.jsonl

{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:43:22.054Z","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8"}
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","type":"user","message":{"role":"user","content":"## YOUR ROLE - RESEARCH AGENT\n\nYou are the **Research Agent** in the Auto-Build spec creation pipeline. Your ONLY job is to research and validate external integrations, libraries, and dependencies mentioned in the requirements.\n\n**Key Principle**: Verify everything. Trust nothing assumed. Document findings.\n\n---\n\n## YOUR CONTRACT\n\n**Inputs**:\n- `requirements.json` - User requirements with mentioned integrations\n\n**Output**: `research.json` - Validated research findings\n\nYou MUST create `research.json` with validated information about each integration.\n\n---\n\n## PHASE 0: LOAD REQUIREMENTS\n\n```bash\ncat requirements.json\n```\n\nIdentify from the requirements:\n1. **External libraries** mentioned (packages, SDKs)\n2. **External services** mentioned (databases, APIs)\n3. **Infrastructure** mentioned (Docker, cloud services)\n4. **Frameworks** mentioned (web frameworks, ORMs)\n\n---\n\n## PHASE 1: RESEARCH EACH INTEGRATION\n\nFor EACH external dependency identified, research using available tools:\n\n### 1.1: Use Context7 MCP (PRIMARY RESEARCH TOOL)\n\n**Context7 should be your FIRST choice for researching libraries and integrations.**\n\nContext7 provides up-to-date documentation for thousands of libraries. Use it systematically:\n\n#### Step 1: Resolve the Library ID\n\nFirst, find the correct Context7 library ID:\n\n```\nTool: mcp__context7__resolve-library-id\nInput: { \"libraryName\": \"[library name from requirements]\" }\n```\n\nExample for researching \"NextJS\":\n```\nTool: mcp__context7__resolve-library-id\nInput: { \"libraryName\": \"nextjs\" }\n```\n\nThis returns the Context7-compatible ID (e.g., \"/vercel/next.js\").\n\n#### Step 2: Get Library Documentation\n\nOnce you have the ID, fetch documentation for specific topics:\n\n```\nTool: mcp__context7__get-library-docs\nInput: {\n  \"context7CompatibleLibraryID\": \"/vercel/next.js\",\n  \"topic\": \"routing\",  // Focus on relevant topic\n  \"mode\": \"code\"       // \"code\" for API examples, \"info\" for conceptual guides\n}\n```\n\n**Topics to research for each integration:**\n- \"getting started\" or \"installation\" - For setup patterns\n- \"api\" or \"reference\" - For function signatures\n- \"configuration\" or \"config\" - For environment variables and options\n- \"examples\" - For common usage patterns\n- Specific feature topics relevant to your task\n\n#### Step 3: Document Findings\n\nFor each integration, extract from Context7:\n1. **Correct package name** - The actual npm/pip package name\n2. **Import statements** - How to import in code\n3. **Initialization code** - Setup patterns\n4. **Key API functions** - Function signatures you'll need\n5. **Configuration options** - Environment variables, config files\n6. **Common gotchas** - Issues mentioned in docs\n\n### 1.2: Use Web Search (for supplementary research)\n\nUse web search AFTER Context7 to:\n- Verify package exists on npm/PyPI\n- Find very recent updates or changes\n- Research less common libraries not in Context7\n\nSearch for:\n- `\"[library] official documentation\"`\n- `\"[library] python SDK usage\"` (or appropriate language)\n- `\"[library] getting started\"`\n- `\"[library] pypi\"` or `\"[library] npm\"` (to verify package names)\n\n### 1.3: Key Questions to Answer\n\nFor each integration, find answers to:\n\n1. **What is the correct package name?**\n   - PyPI/npm exact name\n   - Installation command\n   - Version requirements\n\n2. **What are the actual API patterns?**\n   - Import statements\n   - Initialization code\n   - Main function signatures\n\n3. **What configuration is required?**\n   - Environment variables\n   - Config files\n   - Required dependencies\n\n4. **What infrastructure is needed?**\n   - Database requirements\n   - Docker containers\n   - External services\n\n5. **What are known issues or gotchas?**\n   - Common mistakes\n   - Breaking changes in recent versions\n   - Platform-specific issues\n\n---\n\n## PHASE 2: VALIDATE ASSUMPTIONS\n\nFor any technical claims in requirements.json:\n\n1. **Verify package names exist** - Check PyPI, npm, etc.\n2. **Verify API patterns** - Match against documentation\n3. **Verify configuration options** - Confirm they exist\n4. **Flag anything unverified** - Mark as \"unverified\" in output\n\n---\n\n## PHASE 3: CREATE RESEARCH.JSON\n\nOutput your findings:\n\n```bash\ncat > research.json << 'EOF'\n{\n  \"integrations_researched\": [\n    {\n      \"name\": \"[library/service name]\",\n      \"type\": \"library|service|infrastructure\",\n      \"verified_package\": {\n        \"name\": \"[exact package name]\",\n        \"install_command\": \"[pip install X / npm install X]\",\n        \"version\": \"[version if specific]\",\n        \"verified\": true\n      },\n      \"api_patterns\": {\n        \"imports\": [\"from X import Y\"],\n        \"initialization\": \"[code snippet]\",\n        \"key_functions\": [\"function1()\", \"function2()\"],\n        \"verified_against\": \"[documentation URL or source]\"\n      },\n      \"configuration\": {\n        \"env_vars\": [\"VAR1\", \"VAR2\"],\n        \"config_files\": [\"config.json\"],\n        \"dependencies\": [\"other packages needed\"]\n      },\n      \"infrastructure\": {\n        \"requires_docker\": true,\n        \"docker_image\": \"[image name]\",\n        \"ports\": [1234],\n        \"volumes\": [\"/data\"]\n      },\n      \"gotchas\": [\n        \"[Known issue 1]\",\n        \"[Known issue 2]\"\n      ],\n      \"research_sources\": [\n        \"[URL or documentation reference]\"\n      ]\n    }\n  ],\n  \"unverified_claims\": [\n    {\n      \"claim\": \"[what was claimed]\",\n      \"reason\": \"[why it couldn't be verified]\",\n      \"risk_level\": \"low|medium|high\"\n    }\n  ],\n  \"recommendations\": [\n    \"[Any recommendations based on research]\"\n  ],\n  \"created_at\": \"[ISO timestamp]\"\n}\nEOF\n```\n\n---\n\n## PHASE 4: SUMMARIZE FINDINGS\n\nPrint a summary:\n\n```\n=== RESEARCH COMPLETE ===\n\nIntegrations Researched: [count]\n- [name1]: Verified ✓\n- [name2]: Verified ✓\n- [name3]: Partially verified ⚠\n\nUnverified Claims: [count]\n- [claim1]: [risk level]\n\nKey Findings:\n- [Important finding 1]\n- [Important finding 2]\n\nRecommendations:\n- [Recommendation 1]\n\nresearch.json created successfully.\n```\n\n---\n\n## CRITICAL RULES\n\n1. **ALWAYS verify package names** - Don't assume \"graphiti\" is the package name\n2. **ALWAYS cite sources** - Document where information came from\n3. **ALWAYS flag uncertainties** - Mark unverified claims clearly\n4. **DON'T make up APIs** - Only document what you find in docs\n5. **DON'T skip research** - Each integration needs investigation\n\n---\n\n## RESEARCH TOOLS PRIORITY\n\n1. **Context7 MCP** (PRIMARY) - Best for official docs, API patterns, code examples\n   - Use `resolve-library-id` first to get the library ID\n   - Then `get-library-docs` with relevant topics\n   - Covers most popular libraries (React, Next.js, FastAPI, etc.)\n\n2. **Web Search** - For package verification, recent info, obscure libraries\n   - Use when Context7 doesn't have the library\n   - Good for checking npm/PyPI for package existence\n\n3. **Web Fetch** - For reading specific documentation pages\n   - Use for custom or internal documentation URLs\n\n**ALWAYS try Context7 first** - it provides structured, validated documentation that's more reliable than web search results.\n\n---\n\n## EXAMPLE RESEARCH OUTPUT\n\nFor a task involving \"Graphiti memory integration\":\n\n**Step 1: Context7 Lookup**\n```\nTool: mcp__context7__resolve-library-id\nInput: { \"libraryName\": \"graphiti\" }\n→ Returns library ID or \"not found\"\n```\n\nIf found in Context7:\n```\nTool: mcp__context7__get-library-docs\nInput: {\n  \"context7CompatibleLibraryID\": \"/zep/graphiti\",\n  \"topic\": \"getting started\",\n  \"mode\": \"code\"\n}\n→ Returns installation, imports, initialization code\n```\n\n**Step 2: Compile Findings to research.json**\n\n```json\n{\n  \"integrations_researched\": [\n    {\n      \"name\": \"Graphiti\",\n      \"type\": \"library\",\n      \"verified_package\": {\n        \"name\": \"graphiti-core\",\n        \"install_command\": \"pip install graphiti-core\",\n        \"version\": \">=0.5.0\",\n        \"verified\": true\n      },\n      \"api_patterns\": {\n        \"imports\": [\n          \"from graphiti_core import Graphiti\",\n          \"from graphiti_core.nodes import EpisodeType\"\n        ],\n        \"initialization\": \"graphiti = Graphiti(graph_driver=driver)\",\n        \"key_functions\": [\n          \"add_episode(name, episode_body, source, group_id)\",\n          \"search(query, limit, group_ids)\"\n        ],\n        \"verified_against\": \"Context7 MCP + GitHub README\"\n      },\n      \"configuration\": {\n        \"env_vars\": [\"OPENAI_API_KEY\"],\n        \"dependencies\": [\"real_ladybug\"]\n      },\n      \"infrastructure\": {\n        \"requires_docker\": false,\n        \"embedded_database\": \"LadybugDB\"\n      },\n      \"gotchas\": [\n        \"Requires OpenAI API key for embeddings\",\n        \"Must call build_indices_and_constraints() before use\",\n        \"LadybugDB is embedded - no separate database server needed\"\n      ],\n      \"research_sources\": [\n        \"Context7 MCP: /zep/graphiti\",\n        \"https://github.com/getzep/graphiti\",\n        \"https://pypi.org/project/graphiti-core/\"\n      ]\n    }\n  ],\n  \"unverified_claims\": [],\n  \"recommendations\": [\n    \"LadybugDB is embedded and requires no Docker or separate database setup\"\n  ],\n  \"context7_libraries_used\": [\"/zep/graphiti\"],\n  \"created_at\": \"2024-12-10T12:00:00Z\"\n}\n```\n\n---\n\n## BEGIN\n\nStart by reading requirements.json, then research each integration mentioned.\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\n\n**Requirements File**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\requirements.json\n**Research Output**: C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus\\.auto-claude\\specs\\001-multi-llm-agentic-development-platform\\research.json\n\nRead the requirements.json to understand what integrations/libraries are needed.\nResearch each external dependency to validate:\n- Correct package names\n- Actual API patterns\n- Configuration requirements\n- Known issues or gotchas\n\nOutput your findings to research.json.\n\n"},"uuid":"cc44f062-dede-4191-b777-a6417f234f1c","timestamp":"2026-01-12T18:43:22.578Z"}
{"parentUuid":"cc44f062-dede-4191-b777-a6417f234f1c","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","message":{"model":"claude-opus-4-5-20251101","id":"msg_013xaWnTPjmFYWwyo9Who5g4","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The user wants me to act as a Research Agent to validate external integrations, libraries, and dependencies mentioned in the requirements. Let me start by reading the requirements.json file to identify what needs to be researched.","signature":"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"},{"type":"text","text":"I'll start by reading the requirements.json file to identify all external integrations that need research."},{"type":"tool_use","id":"toolu_01TpnxvKcrCv1QzPqSHNaMBq","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"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":4064,"cache_read_input_tokens":15912,"cache_creation":{"ephemeral_5m_input_tokens":4064,"ephemeral_1h_input_tokens":0},"output_tokens":174,"service_tier":"standard"}},"type":"assistant","uuid":"91fe9c16-bb5a-44df-ac13-9bbd2317aa67","timestamp":"2026-01-12T18:43:35.322Z"}
{"parentUuid":"91fe9c16-bb5a-44df-ac13-9bbd2317aa67","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TpnxvKcrCv1QzPqSHNaMBq","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":"385518a6-044e-48c3-ac1e-3f4ddb84a372","timestamp":"2026-01-12T18:43:39.912Z","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":"91fe9c16-bb5a-44df-ac13-9bbd2317aa67"}
{"parentUuid":"385518a6-044e-48c3-ac1e-3f4ddb84a372","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_01DADDy5Nf2kaciMQx2XzdU6","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have a clear understanding of the requirements. Let me identify all the external integrations/libraries that need to be researched:\n\n**LLM Providers/SDKs:**\n1. Claude API (Anthropic)\n2. Gemini API (Google)\n3. Grok API (xAI)\n4. Ollama (local LLM)\n\n**Backend Framework:**\n5. FastAPI (Python web framework)\n\n**Frontend Framework:**\n6. React (JavaScript library)\n7. TypeScript\n\n**Version Control:**\n8. Git (command line)\n9. Gitea (Git hosting)\n\n**Other:**\n10. MCP Server (Model Context Protocol)\n\nLet me research each of these integrations systematically. I'll start with Context7 for the major libraries.","signature":"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"},{"type":"text","text":"Now I can identify the external integrations to research from the requirements:\n\n**LLM Provider SDKs:**\n1. Anthropic (Claude) SDK\n2. Google Gemini SDK\n3. Grok (xAI) SDK\n4. Ollama\n\n**Backend Framework:**\n5. FastAPI\n\n**Frontend:**\n6. React\n7. TypeScript\n\n**Version Control:**\n8. Git/Gitea integration\n\n**Protocol:**\n9. MCP (Model Context Protocol)\n\nLet me research each integration. I'll start by using Context7 to look up the primary libraries:"},{"type":"tool_use","id":"toolu_014wpa1d9humnh3TdKBNKqwA","name":"mcp__context7__resolve-library-id","input":{"libraryName":"anthropic","query":"Anthropic Claude Python SDK for LLM API integration"}},{"type":"tool_use","id":"toolu_013m32FL3GF8aVJyqumqL1h2","name":"mcp__context7__resolve-library-id","input":{"libraryName":"fastapi","query":"FastAPI Python web framework for building APIs"}},{"type":"tool_use","id":"toolu_017ArtQHumzSpyf6Jbq7zMNi","name":"mcp__context7__resolve-library-id","input":{"libraryName":"react","query":"React JavaScript library for building user interfaces"}},{"type":"tool_use","id":"toolu_01WC1uVbAsLH4vW68sXD98x6","name":"mcp__context7__resolve-library-id","input":{"libraryName":"ollama","query":"Ollama local LLM server for running models locally"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1410,"cache_read_input_tokens":19976,"cache_creation":{"ephemeral_5m_input_tokens":1410,"ephemeral_1h_input_tokens":0},"output_tokens":627,"service_tier":"standard"}},"type":"assistant","uuid":"16206acf-852e-4678-80b1-01959feb24e8","timestamp":"2026-01-12T18:43:53.176Z"}
{"parentUuid":"16206acf-852e-4678-80b1-01959feb24e8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WC1uVbAsLH4vW68sXD98x6","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: Ollama\n- Context7-compatible library ID: /ollama/ollama\n- Description: Ollama is a tool that allows you to easily run and manage large language models on your local machine, providing a quick way to get started with AI.\n- Code Snippets: 598\n- Source Reputation: High\n- Benchmark Score: 88.1\n----------\n- Title: Ollama\n- Context7-compatible library ID: /llmstxt/ollama_llms-full_txt\n- Description: Ollama is an API platform that allows you to run and interact with large language models programmatically, with support for chat generation, embeddings, model management, and OpenAI API compatibility.\n- Code Snippets: 1464\n- Source Reputation: High\n- Benchmark Score: 89.3\n----------\n- Title: Ollama\n- Context7-compatible library ID: /llmstxt/ollama_llms_txt\n- Description: Ollama is a platform for running and managing large language models locally, with support for chat, embeddings, vision, tool calling, and OpenAI-compatible APIs.\n- Code Snippets: 425\n- Source Reputation: High\n- Benchmark Score: 86.1\n----------\n- Title: Ollama\n- Context7-compatible library ID: /websites/ollama\n- Description: Ollama is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.\n- Code Snippets: 342\n- Source Reputation: High\n- Benchmark Score: 92.1\n----------\n- Title: Ollama Python\n- Context7-compatible library ID: /ollama/ollama-python\n- Description: The Ollama Python library provides an easy way to integrate Python 3.8+ projects with Ollama, supporting chat, generation, streaming, and custom client configurations.\n- Code Snippets: 36\n- Source Reputation: High\n- Benchmark Score: 87.3"}]}]},"uuid":"9e3372eb-b2f4-4b5b-a3d5-564ae2273db7","timestamp":"2026-01-12T18:43:54.410Z","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: Ollama\n- Context7-compatible library ID: /ollama/ollama\n- Description: Ollama is a tool that allows you to easily run and manage large language models on your local machine, providing a quick way to get started with AI.\n- Code Snippets: 598\n- Source Reputation: High\n- Benchmark Score: 88.1\n----------\n- Title: Ollama\n- Context7-compatible library ID: /llmstxt/ollama_llms-full_txt\n- Description: Ollama is an API platform that allows you to run and interact with large language models programmatically, with support for chat generation, embeddings, model management, and OpenAI API compatibility.\n- Code Snippets: 1464\n- Source Reputation: High\n- Benchmark Score: 89.3\n----------\n- Title: Ollama\n- Context7-compatible library ID: /llmstxt/ollama_llms_txt\n- Description: Ollama is a platform for running and managing large language models locally, with support for chat, embeddings, vision, tool calling, and OpenAI-compatible APIs.\n- Code Snippets: 425\n- Source Reputation: High\n- Benchmark Score: 86.1\n----------\n- Title: Ollama\n- Context7-compatible library ID: /websites/ollama\n- Description: Ollama is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.\n- Code Snippets: 342\n- Source Reputation: High\n- Benchmark Score: 92.1\n----------\n- Title: Ollama Python\n- Context7-compatible library ID: /ollama/ollama-python\n- Description: The Ollama Python library provides an easy way to integrate Python 3.8+ projects with Ollama, supporting chat, generation, streaming, and custom client configurations.\n- Code Snippets: 36\n- Source Reputation: High\n- Benchmark Score: 87.3"}],"sourceToolAssistantUUID":"16206acf-852e-4678-80b1-01959feb24e8"}
{"parentUuid":"16206acf-852e-4678-80b1-01959feb24e8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014wpa1d9humnh3TdKBNKqwA","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: Anthropic SDK for Python\n- Context7-compatible library ID: /anthropics/anthropic-sdk-python\n- Description: The Anthropic Python library provides convenient access to the Anthropic REST API, offering synchronous and asynchronous clients with type definitions for all request parameters and response fields.\n- Code Snippets: 93\n- Source Reputation: High\n- Benchmark Score: 73.7\n----------\n- Title: Claude API\n- Context7-compatible library ID: /websites/platform_claude_en_api\n- Description: Claude API is Anthropic's AI API platform that provides advanced language model capabilities including extended context windows, tool use, batch processing, and features like extended thinking, citations, and file handling.\n- Code Snippets: 1217\n- Source Reputation: High\n- Benchmark Score: 77.2\n----------\n- Title: Anthropic API and Claude\n- Context7-compatible library ID: /websites/anthropic_en\n- Description: Official documentation for the Anthropic API and Claude, offering guides and resources for developers to integrate powerful AI models and build applications.\n- Code Snippets: 11\n- Source Reputation: High\n- Benchmark Score: 71.1\n----------\n- Title: Anthropic Cookbook\n- Context7-compatible library ID: /anthropics/anthropic-cookbook\n- Description: The Anthropic Cookbook provides code and guides for developers to build with Claude, offering easily integrable code snippets and explanations for various AI tasks.\n- Code Snippets: 1226\n- Source Reputation: High\n- Benchmark Score: 85.6\n----------\n- Title: Anthropic Courses\n- Context7-compatible library ID: /anthropics/courses\n- Description: Anthropic's educational courses provide a comprehensive guide to working with the Claude SDK, prompt engineering techniques, and implementing tool use with Claude models.\n- Code Snippets: 1173\n- Source Reputation: High\n- Benchmark Score: 76.3"}]}]},"uuid":"5b4e2d56-3ede-4e8e-93c2-13b68bdcbb65","timestamp":"2026-01-12T18:43:54.509Z","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: Anthropic SDK for Python\n- Context7-compatible library ID: /anthropics/anthropic-sdk-python\n- Description: The Anthropic Python library provides convenient access to the Anthropic REST API, offering synchronous and asynchronous clients with type definitions for all request parameters and response fields.\n- Code Snippets: 93\n- Source Reputation: High\n- Benchmark Score: 73.7\n----------\n- Title: Claude API\n- Context7-compatible library ID: /websites/platform_claude_en_api\n- Description: Claude API is Anthropic's AI API platform that provides advanced language model capabilities including extended context windows, tool use, batch processing, and features like extended thinking, citations, and file handling.\n- Code Snippets: 1217\n- Source Reputation: High\n- Benchmark Score: 77.2\n----------\n- Title: Anthropic API and Claude\n- Context7-compatible library ID: /websites/anthropic_en\n- Description: Official documentation for the Anthropic API and Claude, offering guides and resources for developers to integrate powerful AI models and build applications.\n- Code Snippets: 11\n- Source Reputation: High\n- Benchmark Score: 71.1\n----------\n- Title: Anthropic Cookbook\n- Context7-compatible library ID: /anthropics/anthropic-cookbook\n- Description: The Anthropic Cookbook provides code and guides for developers to build with Claude, offering easily integrable code snippets and explanations for various AI tasks.\n- Code Snippets: 1226\n- Source Reputation: High\n- Benchmark Score: 85.6\n----------\n- Title: Anthropic Courses\n- Context7-compatible library ID: /anthropics/courses\n- Description: Anthropic's educational courses provide a comprehensive guide to working with the Claude SDK, prompt engineering techniques, and implementing tool use with Claude models.\n- Code Snippets: 1173\n- Source Reputation: High\n- Benchmark Score: 76.3"}],"sourceToolAssistantUUID":"16206acf-852e-4678-80b1-01959feb24e8"}
{"parentUuid":"16206acf-852e-4678-80b1-01959feb24e8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013m32FL3GF8aVJyqumqL1h2","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":"b6a34412-2f51-414e-a915-074bc72a6429","timestamp":"2026-01-12T18:43:54.563Z","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":"16206acf-852e-4678-80b1-01959feb24e8"}
{"parentUuid":"16206acf-852e-4678-80b1-01959feb24e8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017ArtQHumzSpyf6Jbq7zMNi","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: React\n- Context7-compatible library ID: /websites/react_dev\n- Description: React is a JavaScript library for building user interfaces. It allows developers to create interactive web and native applications using reusable components, enabling efficient and scalable UI development.\n- Code Snippets: 4359\n- Source Reputation: High\n- Benchmark Score: 74.5\n----------\n- Title: React\n- Context7-compatible library ID: /websites/react_dev_reference\n- Description: React is a JavaScript library for building user interfaces, featuring a component-based architecture, hooks for managing state and side effects, and a virtual DOM for efficient rendering.\n- Code Snippets: 2330\n- Source Reputation: High\n- Benchmark Score: 77.8\n----------\n- Title: React\n- Context7-compatible library ID: /websites/18_react_dev\n- Description: React is a JavaScript library for building web and native user interfaces out of individual components, designed for creating interactive and dynamic applications.\n- Code Snippets: 3921\n- Source Reputation: High\n- Benchmark Score: 82.6\n----------\n- Title: React\n- Context7-compatible library ID: /reactjs/react.dev\n- Description: React.dev is the official documentation website for React, a JavaScript library for building user interfaces, providing guides, API references, and tutorials.\n- Code Snippets: 3742\n- Source Reputation: High\n- Benchmark Score: 70.5\n----------\n- Title: React\n- Context7-compatible library ID: /websites/react_dev_learn\n- Description: React is a JavaScript library for building user interfaces, focused on declarative components and efficient updates.\n- Code Snippets: 835\n- Source Reputation: High\n- Benchmark Score: 52.9"}]}]},"uuid":"94068214-2b95-4438-a2e0-3d8c8b1bee22","timestamp":"2026-01-12T18:43:54.737Z","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: React\n- Context7-compatible library ID: /websites/react_dev\n- Description: React is a JavaScript library for building user interfaces. It allows developers to create interactive web and native applications using reusable components, enabling efficient and scalable UI development.\n- Code Snippets: 4359\n- Source Reputation: High\n- Benchmark Score: 74.5\n----------\n- Title: React\n- Context7-compatible library ID: /websites/react_dev_reference\n- Description: React is a JavaScript library for building user interfaces, featuring a component-based architecture, hooks for managing state and side effects, and a virtual DOM for efficient rendering.\n- Code Snippets: 2330\n- Source Reputation: High\n- Benchmark Score: 77.8\n----------\n- Title: React\n- Context7-compatible library ID: /websites/18_react_dev\n- Description: React is a JavaScript library for building web and native user interfaces out of individual components, designed for creating interactive and dynamic applications.\n- Code Snippets: 3921\n- Source Reputation: High\n- Benchmark Score: 82.6\n----------\n- Title: React\n- Context7-compatible library ID: /reactjs/react.dev\n- Description: React.dev is the official documentation website for React, a JavaScript library for building user interfaces, providing guides, API references, and tutorials.\n- Code Snippets: 3742\n- Source Reputation: High\n- Benchmark Score: 70.5\n----------\n- Title: React\n- Context7-compatible library ID: /websites/react_dev_learn\n- Description: React is a JavaScript library for building user interfaces, focused on declarative components and efficient updates.\n- Code Snippets: 835\n- Source Reputation: High\n- Benchmark Score: 52.9"}],"sourceToolAssistantUUID":"16206acf-852e-4678-80b1-01959feb24e8"}
{"parentUuid":"94068214-2b95-4438-a2e0-3d8c8b1bee22","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_01UJVLLwbor6EnAFf1kamLiP","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I've identified the Context7 library IDs for:\n- Anthropic: /anthropics/anthropic-sdk-python (best for SDK patterns)\n- FastAPI: /websites/fastapi_tiangolo (highest score)\n- React: /websites/18_react_dev (highest score)\n- Ollama: /ollama/ollama-python (Python SDK)\n\nNow let me also search for:\n- Google Gemini SDK\n- Grok/xAI SDK\n- MCP (Model Context Protocol)\n- TypeScript\n\nLet me get these library IDs and also start querying docs.","signature":"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"},{"type":"text","text":"Excellent! Now let me search for the remaining integrations:"},{"type":"tool_use","id":"toolu_01QhD1jpm2TpDvHyKTEr3GGz","name":"mcp__context7__resolve-library-id","input":{"libraryName":"google gemini","query":"Google Gemini AI SDK for Python LLM integration"}},{"type":"tool_use","id":"toolu_01MNwBRYNZi22pmTcGxKVjv9","name":"mcp__context7__resolve-library-id","input":{"libraryName":"typescript","query":"TypeScript programming language for React frontend"}},{"type":"tool_use","id":"toolu_01PCvdDa5ZgvqTt8GiccaUYb","name":"mcp__context7__resolve-library-id","input":{"libraryName":"model context protocol","query":"MCP Model Context Protocol for AI skill management"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":681,"cache_creation_input_tokens":2523,"cache_read_input_tokens":21386,"cache_creation":{"ephemeral_5m_input_tokens":2523,"ephemeral_1h_input_tokens":0},"output_tokens":396,"service_tier":"standard"}},"type":"assistant","uuid":"691bac1a-4908-42df-8c98-786a738782e9","timestamp":"2026-01-12T18:44:11.100Z"}
{"parentUuid":"691bac1a-4908-42df-8c98-786a738782e9","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MNwBRYNZi22pmTcGxKVjv9","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: TypeScript\n- Context7-compatible library ID: /microsoft/typescript\n- Description: TypeScript is a language for application-scale JavaScript that adds optional types to JavaScript, supporting tools for large-scale JavaScript applications.\n- Code Snippets: 18942\n- Source Reputation: High\n- Benchmark Score: 61.6\n- Versions: v5.9.2, v5.8.3\n----------\n- Title: TypeScript\n- Context7-compatible library ID: /websites/typescriptlang\n- Description: TypeScript is a superset of JavaScript that adds static types, enabling developers to build more robust and scalable applications with enhanced tooling and error checking.\n- Code Snippets: 2391\n- Source Reputation: High\n- Benchmark Score: 91.3\n----------\n- Title: TypeScript Website\n- Context7-compatible library ID: /microsoft/typescript-website\n- Description: The official website for TypeScript, featuring documentation, playground examples, and resources for developers.\n- Code Snippets: 2459\n- Source Reputation: High\n- Benchmark Score: 60\n----------\n- Title: Wildberries API TypeScript SDK\n- Context7-compatible library ID: /salacoste/daytona-wildberries-typescript-sdk\n- Description: A full-featured TypeScript SDK providing type-safe access to all Wildberries marketplace API methods with automatic rate limiting, retry mechanisms, and comprehensive error handling.\n- Code Snippets: 7650\n- Source Reputation: High\n- Benchmark Score: 87.9\n----------\n- Title: OpenRouter SDK\n- Context7-compatible library ID: /openrouterteam/typescript-sdk\n- Description: The OpenRouter SDK is a TypeScript toolkit for building AI-powered features and solutions in any JS or TS based runtime, offering easy and type-safe access to over 300 models across various providers.\n- Code Snippets: 1046\n- Source Reputation: High\n- Benchmark Score: 79.6"}]}]},"uuid":"17e94877-6882-4dec-b034-f078562bdf30","timestamp":"2026-01-12T18:44:12.281Z","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: TypeScript\n- Context7-compatible library ID: /microsoft/typescript\n- Description: TypeScript is a language for application-scale JavaScript that adds optional types to JavaScript, supporting tools for large-scale JavaScript applications.\n- Code Snippets: 18942\n- Source Reputation: High\n- Benchmark Score: 61.6\n- Versions: v5.9.2, v5.8.3\n----------\n- Title: TypeScript\n- Context7-compatible library ID: /websites/typescriptlang\n- Description: TypeScript is a superset of JavaScript that adds static types, enabling developers to build more robust and scalable applications with enhanced tooling and error checking.\n- Code Snippets: 2391\n- Source Reputation: High\n- Benchmark Score: 91.3\n----------\n- Title: TypeScript Website\n- Context7-compatible library ID: /microsoft/typescript-website\n- Description: The official website for TypeScript, featuring documentation, playground examples, and resources for developers.\n- Code Snippets: 2459\n- Source Reputation: High\n- Benchmark Score: 60\n----------\n- Title: Wildberries API TypeScript SDK\n- Context7-compatible library ID: /salacoste/daytona-wildberries-typescript-sdk\n- Description: A full-featured TypeScript SDK providing type-safe access to all Wildberries marketplace API methods with automatic rate limiting, retry mechanisms, and comprehensive error handling.\n- Code Snippets: 7650\n- Source Reputation: High\n- Benchmark Score: 87.9\n----------\n- Title: OpenRouter SDK\n- Context7-compatible library ID: /openrouterteam/typescript-sdk\n- Description: The OpenRouter SDK is a TypeScript toolkit for building AI-powered features and solutions in any JS or TS based runtime, offering easy and type-safe access to over 300 models across various providers.\n- Code Snippets: 1046\n- Source Reputation: High\n- Benchmark Score: 79.6"}],"sourceToolAssistantUUID":"691bac1a-4908-42df-8c98-786a738782e9"}
{"parentUuid":"691bac1a-4908-42df-8c98-786a738782e9","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QhD1jpm2TpDvHyKTEr3GGz","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: Gemini API\n- Context7-compatible library ID: /websites/ai_google_dev_gemini-api\n- Description: The Gemini API provides access to Google's advanced AI models for text, image, speech, and video generation and understanding, enabling developers to build innovative applications.\n- Code Snippets: 3752\n- Source Reputation: High\n- Benchmark Score: 72.6\n----------\n- Title: Gemini Developer API\n- Context7-compatible library ID: /llmstxt/ai_google_dev_gemini-api_llms_txt\n- Description: The Gemini Developer API provides access to Google's advanced AI models for building applications with text generation, image understanding, and more.\n- Code Snippets: 1074\n- Source Reputation: High\n- Benchmark Score: 81.1\n----------\n- Title: Gemini API Live\n- Context7-compatible library ID: /websites/ai_google_dev_api\n- Description: The Gemini API allows developers to integrate Google's advanced AI models into their applications for content generation, semantic retrieval, and more, with real-time capabilities via WebSocket.\n- Code Snippets: 2350\n- Source Reputation: High\n- Benchmark Score: 69.9\n----------\n- Title: Gemini AI\n- Context7-compatible library ID: /gbaptista/gemini-ai\n- Description: A Ruby Gem for interacting with Google's Gemini generative AI services through Vertex AI, Generative Language API, or AI Studio.\n- Code Snippets: 110\n- Source Reputation: High\n----------\n- Title: Gemini PHP\n- Context7-compatible library ID: /google-gemini-php/client\n- Description: A community-maintained PHP API client for interacting with the Gemini AI API, supporting various features like text and image input, multi-turn conversations, and structured output.\n- Code Snippets: 128\n- Source Reputation: Medium\n- Benchmark Score: 83.8"}]}]},"uuid":"860dac60-68f0-4df0-a679-d6e10342b386","timestamp":"2026-01-12T18:44:12.307Z","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: Gemini API\n- Context7-compatible library ID: /websites/ai_google_dev_gemini-api\n- Description: The Gemini API provides access to Google's advanced AI models for text, image, speech, and video generation and understanding, enabling developers to build innovative applications.\n- Code Snippets: 3752\n- Source Reputation: High\n- Benchmark Score: 72.6\n----------\n- Title: Gemini Developer API\n- Context7-compatible library ID: /llmstxt/ai_google_dev_gemini-api_llms_txt\n- Description: The Gemini Developer API provides access to Google's advanced AI models for building applications with text generation, image understanding, and more.\n- Code Snippets: 1074\n- Source Reputation: High\n- Benchmark Score: 81.1\n----------\n- Title: Gemini API Live\n- Context7-compatible library ID: /websites/ai_google_dev_api\n- Description: The Gemini API allows developers to integrate Google's advanced AI models into their applications for content generation, semantic retrieval, and more, with real-time capabilities via WebSocket.\n- Code Snippets: 2350\n- Source Reputation: High\n- Benchmark Score: 69.9\n----------\n- Title: Gemini AI\n- Context7-compatible library ID: /gbaptista/gemini-ai\n- Description: A Ruby Gem for interacting with Google's Gemini generative AI services through Vertex AI, Generative Language API, or AI Studio.\n- Code Snippets: 110\n- Source Reputation: High\n----------\n- Title: Gemini PHP\n- Context7-compatible library ID: /google-gemini-php/client\n- Description: A community-maintained PHP API client for interacting with the Gemini AI API, supporting various features like text and image input, multi-turn conversations, and structured output.\n- Code Snippets: 128\n- Source Reputation: Medium\n- Benchmark Score: 83.8"}],"sourceToolAssistantUUID":"691bac1a-4908-42df-8c98-786a738782e9"}
{"parentUuid":"691bac1a-4908-42df-8c98-786a738782e9","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PCvdDa5ZgvqTt8GiccaUYb","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: Model Context Protocol\n- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification_2025-11-25\n- Description: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools, providing a standardized way to share contextual information, expose tools, and build composable integrations.\n- Code Snippets: 599\n- Source Reputation: High\n- Benchmark Score: 45.7\n----------\n- Title: Microsoft MCP Servers\n- Context7-compatible library ID: /microsoft/mcp\n- Description: Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs), allowing AI applications to connect with various data sources and tools consistently.\n- Code Snippets: 1351\n- Source Reputation: High\n- Benchmark Score: 61\n----------\n- Title: Model Context Protocol Python SDK\n- Context7-compatible library ID: /modelcontextprotocol/python-sdk\n- Description: The MCP Python SDK implements the Model Context Protocol, enabling applications to provide standardized context for LLMs, build MCP clients, and create MCP servers that expose resources, prompts, and tools.\n- Code Snippets: 296\n- Source Reputation: High\n- Benchmark Score: 89.2\n----------\n- Title: Model Context Protocol Typescript SDK\n- Context7-compatible library ID: /modelcontextprotocol/typescript-sdk\n- Description: The MCP TypeScript SDK implements the Model Context Protocol, enabling easy creation of clients and servers for standardized LLM context exchange using resources, tools, and prompts.\n- Code Snippets: 80\n- Source Reputation: High\n- Benchmark Score: 85.3\n----------\n- Title: Model Context Protocol\n- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification\n- Description: The Model Context Protocol (MCP) is a standard for defining and managing context for models, focusing on versioning, interoperability, and client-server communication.\n- Code Snippets: 611\n- Source Reputation: High\n- Benchmark Score: 46.3"}]}]},"uuid":"9a600054-bca3-454b-a8aa-61bd30647f98","timestamp":"2026-01-12T18:44:12.360Z","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: Model Context Protocol\n- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification_2025-11-25\n- Description: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools, providing a standardized way to share contextual information, expose tools, and build composable integrations.\n- Code Snippets: 599\n- Source Reputation: High\n- Benchmark Score: 45.7\n----------\n- Title: Microsoft MCP Servers\n- Context7-compatible library ID: /microsoft/mcp\n- Description: Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs), allowing AI applications to connect with various data sources and tools consistently.\n- Code Snippets: 1351\n- Source Reputation: High\n- Benchmark Score: 61\n----------\n- Title: Model Context Protocol Python SDK\n- Context7-compatible library ID: /modelcontextprotocol/python-sdk\n- Description: The MCP Python SDK implements the Model Context Protocol, enabling applications to provide standardized context for LLMs, build MCP clients, and create MCP servers that expose resources, prompts, and tools.\n- Code Snippets: 296\n- Source Reputation: High\n- Benchmark Score: 89.2\n----------\n- Title: Model Context Protocol Typescript SDK\n- Context7-compatible library ID: /modelcontextprotocol/typescript-sdk\n- Description: The MCP TypeScript SDK implements the Model Context Protocol, enabling easy creation of clients and servers for standardized LLM context exchange using resources, tools, and prompts.\n- Code Snippets: 80\n- Source Reputation: High\n- Benchmark Score: 85.3\n----------\n- Title: Model Context Protocol\n- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification\n- Description: The Model Context Protocol (MCP) is a standard for defining and managing context for models, focusing on versioning, interoperability, and client-server communication.\n- Code Snippets: 611\n- Source Reputation: High\n- Benchmark Score: 46.3"}],"sourceToolAssistantUUID":"691bac1a-4908-42df-8c98-786a738782e9"}
{"parentUuid":"9a600054-bca3-454b-a8aa-61bd30647f98","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_01SHNYJnbTEdLdbyo4fFQuN1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have library IDs for most major integrations. Let me now query the docs for each of these to get the API patterns, installation, etc. I'll query multiple in parallel.\n\nFor Anthropic SDK, FastAPI, React, Ollama, Gemini, TypeScript, and MCP.\n\nI still need to research Grok/xAI SDK - let me search for that too.","signature":"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"},{"type":"text","text":"Now let me query the detailed documentation for these key libraries and search for Grok/xAI:"},{"type":"tool_use","id":"toolu_01WvmnZfYu7V2c7774Tj1gy5","name":"mcp__context7__query-docs","input":{"libraryId":"/anthropics/anthropic-sdk-python","query":"Installation, initialization, basic usage, async client, streaming responses"}},{"type":"tool_use","id":"toolu_01Go45ka2gWcSahNofokeBWS","name":"mcp__context7__query-docs","input":{"libraryId":"/websites/fastapi_tiangolo","query":"Installation, getting started, async endpoints, dependency injection, request validation"}},{"type":"tool_use","id":"toolu_019mapEYQjqViHNMQPQNvrJX","name":"mcp__context7__query-docs","input":{"libraryId":"/ollama/ollama-python","query":"Installation, Python client, chat, generation, async usage, streaming"}},{"type":"tool_use","id":"toolu_01TsnsLwNciHxgfUu8bwePBa","name":"mcp__context7__resolve-library-id","input":{"libraryName":"xai grok","query":"xAI Grok API SDK for Python LLM integration"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3088,"cache_read_input_tokens":23909,"cache_creation":{"ephemeral_5m_input_tokens":3088,"ephemeral_1h_input_tokens":0},"output_tokens":457,"service_tier":"standard"}},"type":"assistant","uuid":"ad187484-6f72-4773-8c9f-e3f15f8cda46","timestamp":"2026-01-12T18:44:24.415Z"}
{"parentUuid":"ad187484-6f72-4773-8c9f-e3f15f8cda46","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","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_01WvmnZfYu7V2c7774Tj1gy5"}]},"uuid":"234efab0-1b5b-442b-b342-fb21412c6e63","timestamp":"2026-01-12T18:44:24.596Z","toolUseResult":"Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","sourceToolAssistantUUID":"ad187484-6f72-4773-8c9f-e3f15f8cda46"}
{"parentUuid":"ad187484-6f72-4773-8c9f-e3f15f8cda46","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","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_01Go45ka2gWcSahNofokeBWS"}]},"uuid":"df739a65-2eac-4841-8564-617b52e273c0","timestamp":"2026-01-12T18:44:24.596Z","toolUseResult":"Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","sourceToolAssistantUUID":"ad187484-6f72-4773-8c9f-e3f15f8cda46"}
{"parentUuid":"ad187484-6f72-4773-8c9f-e3f15f8cda46","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","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_019mapEYQjqViHNMQPQNvrJX"}]},"uuid":"4688a1d9-a3b2-4678-a17a-ca3acedf1c0b","timestamp":"2026-01-12T18:44:24.596Z","toolUseResult":"Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.","sourceToolAssistantUUID":"ad187484-6f72-4773-8c9f-e3f15f8cda46"}
{"parentUuid":"ad187484-6f72-4773-8c9f-e3f15f8cda46","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TsnsLwNciHxgfUu8bwePBa","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: xAI API\n- Context7-compatible library ID: /websites/x_ai\n- Description: The xAI API allows developers to integrate Grok's intelligence into their projects, offering features like chat, image understanding, and function calling with models like grok-code-fast-1.\n- Code Snippets: 814\n- Source Reputation: High\n- Benchmark Score: 83.4\n----------\n- Title: Groq Python\n- Context7-compatible library ID: /groq/groq-python\n- Description: The Groq Python library provides convenient access to the Groq REST API for Python 3.8+ applications, offering both synchronous and asynchronous clients with type definitions for requests and responses.\n- Code Snippets: 107\n- Source Reputation: High\n- Benchmark Score: 91.8\n----------\n- Title: Groq API Cookbook\n- Context7-compatible library ID: /groq/groq-api-cookbook\n- Description: A collection of example code and guides for the Groq API, covering tutorials on guardrails, tool use, RAG, JSON mode, multimodal capabilities, and various integrations.\n- Code Snippets: 808\n- Source Reputation: High\n- Benchmark Score: 62\n----------\n- Title: Groq Typescript\n- Context7-compatible library ID: /groq/groq-typescript\n- Description: A TypeScript/JavaScript library for convenient access to the Groq REST API, enabling server-side integration with AI models.\n- Code Snippets: 81\n- Source Reputation: High\n- Benchmark Score: 89.9\n----------\n- Title: Groq PHP\n- Context7-compatible library ID: /lucianotonet/groq-php\n- Description: A high-performance PHP client and comprehensive SDK that simplifies interaction with the GroqCloud API, enabling PHP developers to easily integrate high-performance LLM inference models into any application.\n- Code Snippets: 76\n- Source Reputation: High\n- Benchmark Score: 89"}]}]},"uuid":"6e9a3af5-872d-47bb-aec3-d482e21df878","timestamp":"2026-01-12T18:44:25.666Z","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: xAI API\n- Context7-compatible library ID: /websites/x_ai\n- Description: The xAI API allows developers to integrate Grok's intelligence into their projects, offering features like chat, image understanding, and function calling with models like grok-code-fast-1.\n- Code Snippets: 814\n- Source Reputation: High\n- Benchmark Score: 83.4\n----------\n- Title: Groq Python\n- Context7-compatible library ID: /groq/groq-python\n- Description: The Groq Python library provides convenient access to the Groq REST API for Python 3.8+ applications, offering both synchronous and asynchronous clients with type definitions for requests and responses.\n- Code Snippets: 107\n- Source Reputation: High\n- Benchmark Score: 91.8\n----------\n- Title: Groq API Cookbook\n- Context7-compatible library ID: /groq/groq-api-cookbook\n- Description: A collection of example code and guides for the Groq API, covering tutorials on guardrails, tool use, RAG, JSON mode, multimodal capabilities, and various integrations.\n- Code Snippets: 808\n- Source Reputation: High\n- Benchmark Score: 62\n----------\n- Title: Groq Typescript\n- Context7-compatible library ID: /groq/groq-typescript\n- Description: A TypeScript/JavaScript library for convenient access to the Groq REST API, enabling server-side integration with AI models.\n- Code Snippets: 81\n- Source Reputation: High\n- Benchmark Score: 89.9\n----------\n- Title: Groq PHP\n- Context7-compatible library ID: /lucianotonet/groq-php\n- Description: A high-performance PHP client and comprehensive SDK that simplifies interaction with the GroqCloud API, enabling PHP developers to easily integrate high-performance LLM inference models into any application.\n- Code Snippets: 76\n- Source Reputation: High\n- Benchmark Score: 89"}],"sourceToolAssistantUUID":"ad187484-6f72-4773-8c9f-e3f15f8cda46"}
{"parentUuid":"6e9a3af5-872d-47bb-aec3-d482e21df878","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_01R7LkjYJxVYcqr9Dh3xb2No","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The query-docs function isn't available without permission. Let me use web search and web fetch to gather the detailed API information instead. I now have identified:\n\n- Anthropic SDK: /anthropics/anthropic-sdk-python\n- FastAPI: /websites/fastapi_tiangolo\n- React: /websites/18_react_dev\n- Ollama Python: /ollama/ollama-python\n- Gemini API: /websites/ai_google_dev_gemini-api or /llmstxt/ai_google_dev_gemini-api_llms_txt\n- TypeScript: /websites/typescriptlang\n- MCP Python SDK: /modelcontextprotocol/python-sdk\n- xAI/Grok API: /websites/x_ai\n\nNote: There's also Groq (different from Grok!) - the xAI one is what they need.\n\nLet me use web search to gather the detailed information about these packages.","signature":"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"},{"type":"text","text":"Let me use web search to gather detailed API information for each SDK:"},{"type":"tool_use","id":"toolu_018t7etduANPkWgNd5JsUCR5","name":"WebSearch","input":{"query":"anthropic python SDK pip install anthropic getting started 2025"}},{"type":"tool_use","id":"toolu_01G4gCGQZfKJ4dV2YYkhQQhc","name":"WebSearch","input":{"query":"google gemini python SDK pip install google-generativeai 2025"}},{"type":"tool_use","id":"toolu_01LnfaajaHUW7GqDTVY6263h","name":"WebSearch","input":{"query":"xAI Grok API Python SDK pip install 2025"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1297,"cache_read_input_tokens":26997,"cache_creation":{"ephemeral_5m_input_tokens":1297,"ephemeral_1h_input_tokens":0},"output_tokens":406,"service_tier":"standard"}},"type":"assistant","uuid":"8c67ab57-6c6e-46a0-8969-3b4991eda661","timestamp":"2026-01-12T18:44:44.853Z"}
{"parentUuid":"8c67ab57-6c6e-46a0-8969-3b4991eda661","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018t7etduANPkWgNd5JsUCR5","type":"tool_result","content":"Web search results for query: \"anthropic python SDK pip install anthropic getting started 2025\"\n\nLinks: [{\"title\":\"Anthropic Python API library\",\"url\":\"https://pypi.org/project/anthropic/\"},{\"title\":\"GitHub - anthropics/anthropic-sdk-python\",\"url\":\"https://github.com/anthropics/anthropic-sdk-python\"},{\"title\":\"Anthropic Python SDK\",\"url\":\"https://pypi.org/project/anthropic/0.2.8/\"},{\"title\":\"Client SDKs - Claude Docs\",\"url\":\"https://platform.claude.com/docs/en/api/client-sdks\"},{\"title\":\"Getting started with Anthropic Claude Agent SDK — Python | by namusanga | Medium\",\"url\":\"https://medium.com/@aiablog/getting-started-with-anthropic-claude-agent-sdk-python-826a2216381d\"},{\"title\":\"Releases · anthropics/anthropic-sdk-python\",\"url\":\"https://github.com/anthropics/anthropic-sdk-python/releases\"},{\"title\":\"`pip install anthropic` currently fails on Python 3.13 · Issue #718 · anthropics/anthropic-sdk-python\",\"url\":\"https://github.com/anthropics/anthropic-sdk-python/issues/718\"},{\"title\":\"Installation and Setup | anthropics/anthropic-sdk-python | DeepWiki\",\"url\":\"https://deepwiki.com/anthropics/anthropic-sdk-python/2-installation-and-setup\"},{\"title\":\"Anthropic Claude with tools using Python SDK - DEV Community\",\"url\":\"https://dev.to/thomastaylor/anthropic-claude-with-tools-using-python-sdk-2fio\"},{\"title\":\"anthropic - anaconda | Anaconda.org\",\"url\":\"https://anaconda.org/anaconda/anthropic\"}]\n\nHere are the search results for getting started with the Anthropic Python SDK:\n\n## Installation\n\nThe official Python library for the Anthropic API can be installed using `pip install anthropic`. The latest version was released on November 24, 2025.(no content)The SDK requires Python 3.9 or higher.\n\n## Basic Usage\n\nThe Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.9+ application. It includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.\n\n### Synchronous Client\nYou can use the client with a context manager: `from anthropic import Anthropic with Anthropic() as client:` to make requests, and the HTTP client is automatically closed.\n\n### Asynchronous Client\nFor async usage, simply import `AsyncAnthropic` instead of `Anthropic` and use `await` with each API call.\n\n## Additional Installation Options\n\nFor cloud platform integrations:\n\n- The library provides support for the Anthropic Bedrock API if you install with the bedrock extra: `pip install -U anthropic[bedrock]`.\n\n- For Google Cloud Vertex AI support, install with the vertex extra: `pip install -U anthropic[vertex]`.\n\n## Setting Up Your API Key\n\nYou need to export your API key as an environment variable: `export ANTHROPIC_API_KEY=<insert token here>`\n\n## Documentation\n\nThe REST API documentation can be found on docs.anthropic.com.(no content)For basic usage instructions, see the API reference. For detailed usage instructions, refer to each SDK's GitHub repository.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"c102a72d-ac3b-4496-b6ea-3924eab6b6fa","timestamp":"2026-01-12T18:45:03.814Z","toolUseResult":{"query":"anthropic python SDK pip install anthropic getting started 2025","results":[{"tool_use_id":"srvtoolu_01M3efet7LudKZJATTf7hm9U","content":[{"title":"Anthropic Python API library","url":"https://pypi.org/project/anthropic/"},{"title":"GitHub - anthropics/anthropic-sdk-python","url":"https://github.com/anthropics/anthropic-sdk-python"},{"title":"Anthropic Python SDK","url":"https://pypi.org/project/anthropic/0.2.8/"},{"title":"Client SDKs - Claude Docs","url":"https://platform.claude.com/docs/en/api/client-sdks"},{"title":"Getting started with Anthropic Claude Agent SDK — Python | by namusanga | Medium","url":"https://medium.com/@aiablog/getting-started-with-anthropic-claude-agent-sdk-python-826a2216381d"},{"title":"Releases · anthropics/anthropic-sdk-python","url":"https://github.com/anthropics/anthropic-sdk-python/releases"},{"title":"`pip install anthropic` currently fails on Python 3.13 · Issue #718 · anthropics/anthropic-sdk-python","url":"https://github.com/anthropics/anthropic-sdk-python/issues/718"},{"title":"Installation and Setup | anthropics/anthropic-sdk-python | DeepWiki","url":"https://deepwiki.com/anthropics/anthropic-sdk-python/2-installation-and-setup"},{"title":"Anthropic Claude with tools using Python SDK - DEV Community","url":"https://dev.to/thomastaylor/anthropic-claude-with-tools-using-python-sdk-2fio"},{"title":"anthropic - anaconda | Anaconda.org","url":"https://anaconda.org/anaconda/anthropic"}]},"Here are the search results for getting started with the Anthropic Python SDK:\n\n## Installation\n\nThe official Python library for the Anthropic API can be installed using `pip install anthropic`. The latest version was released on November 24, 2025.(no content)The SDK requires Python 3.9 or higher.\n\n## Basic Usage\n\nThe Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.9+ application. It includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.\n\n### Synchronous Client\nYou can use the client with a context manager: `from anthropic import Anthropic with Anthropic() as client:` to make requests, and the HTTP client is automatically closed.\n\n### Asynchronous Client\nFor async usage, simply import `AsyncAnthropic` instead of `Anthropic` and use `await` with each API call.\n\n## Additional Installation Options\n\nFor cloud platform integrations:\n\n- The library provides support for the Anthropic Bedrock API if you install with the bedrock extra: `pip install -U anthropic[bedrock]`.\n\n- For Google Cloud Vertex AI support, install with the vertex extra: `pip install -U anthropic[vertex]`.\n\n## Setting Up Your API Key\n\nYou need to export your API key as an environment variable: `export ANTHROPIC_API_KEY=<insert token here>`\n\n## Documentation\n\nThe REST API documentation can be found on docs.anthropic.com.(no content)For basic usage instructions, see the API reference. For detailed usage instructions, refer to each SDK's GitHub repository."],"durationSeconds":18.779816300000007},"sourceToolAssistantUUID":"8c67ab57-6c6e-46a0-8969-3b4991eda661"}
{"parentUuid":"8c67ab57-6c6e-46a0-8969-3b4991eda661","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G4gCGQZfKJ4dV2YYkhQQhc","type":"tool_result","content":"Web search results for query: \"google gemini python SDK pip install google-generativeai 2025\"\n\nLinks: [{\"title\":\"Google Gen AI SDK | Generative AI on Vertex AI | Google Cloud Documentation\",\"url\":\"https://docs.cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview\"},{\"title\":\"Gemini API libraries | Google AI for Developers\",\"url\":\"https://ai.google.dev/gemini-api/docs/libraries\"},{\"title\":\"google-generativeai · PyPI\",\"url\":\"https://pypi.org/project/google-generativeai/\"},{\"title\":\"Gemini API quickstart | Google AI for Developers\",\"url\":\"https://ai.google.dev/gemini-api/docs/quickstart\"},{\"title\":\"GitHub - googleapis/python-genai: Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.\",\"url\":\"https://github.com/googleapis/python-genai\"},{\"title\":\"Google Gen AI SDK documentation\",\"url\":\"https://googleapis.github.io/python-genai/\"},{\"title\":\"deprecated-generative-ai-python/docs/api/google/generativeai.md at main · google-gemini/deprecated-generative-ai-python\",\"url\":\"https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai.md\"},{\"title\":\"The Google GenAI SDK: A guide with a Python tutorial\",\"url\":\"https://wandb.ai/byyoung3/gemini-genai/reports/The-Google-GenAI-SDK-A-guide-with-a-Python-tutorial--VmlldzoxMzE2NDIwNA\"},{\"title\":\"GitHub - google-gemini/deprecated-generative-ai-python: This SDK is now deprecated, use the new unified Google GenAI SDK.\",\"url\":\"https://github.com/google-gemini/deprecated-generative-ai-python\"},{\"title\":\"Google Gen AI Python SDK: A Complete Guide\",\"url\":\"https://www.analyticsvidhya.com/blog/2025/08/google-gen-ai-python-sdk-guide/\"}]\n\nBased on my search, here's the current information about the Google Gemini Python SDK:\n\n## Important: Package Deprecation Notice\n\nThe `google-generativeai` package has reached End-of-Life. All support for this repository ended permanently on November 30, 2025. Users are encouraged to begin migrating to the Google Generative AI SDK to ensure continued access to the latest capabilities and support.\n\n## New Recommended SDK: `google-genai`\n\nAs of May 2025, the Google GenAI SDK has reached General Availability (GA) across all supported platforms and is the recommended library to access the Gemini API.(no content)The new SDK is stable, fully supported for production use, and actively maintained. It provides access to the latest features and offers the best performance working with Gemini.\n\n### Installation\n\nYou run `pip install google-genai`. If you want to use the SDK asynchronously, run `pip install google-genai[aiohttp]`.\n\n### Basic Usage\n\nGoogle Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs.(no content)To create a client for the Gemini Developer API:\n```python\nfrom google import genai\nclient = genai.Client(api_key='GEMINI_API_KEY')\n```\n\nIf you set your API key as the environment variable `GEMINI_API_KEY`, it will be picked up automatically by the client when using the Gemini API libraries. Otherwise you will need to pass your API key as an argument when initializing the client.\n\n### Legacy Libraries\n\nThe legacy libraries don't provide access to recent features (such as Live API and Veo) and are deprecated as of November 30th, 2025.(no content)Note: Generative models (e.g., Gemini, Claude) and AI-powered IDEs (e.g., Cursor) may produce code for the Gemini API using outdated or deprecated libraries due to their training data cutoff. For the generated code to use the latest, recommended libraries, provide version and usage guidance directly in your prompts.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"3b25645c-6080-4dba-b59e-d0c7b4fc213b","timestamp":"2026-01-12T18:45:05.821Z","toolUseResult":{"query":"google gemini python SDK pip install google-generativeai 2025","results":[{"tool_use_id":"srvtoolu_01LNZH3H3M3pF9RGRE2H5M5v","content":[{"title":"Google Gen AI SDK | Generative AI on Vertex AI | Google Cloud Documentation","url":"https://docs.cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview"},{"title":"Gemini API libraries | Google AI for Developers","url":"https://ai.google.dev/gemini-api/docs/libraries"},{"title":"google-generativeai · PyPI","url":"https://pypi.org/project/google-generativeai/"},{"title":"Gemini API quickstart | Google AI for Developers","url":"https://ai.google.dev/gemini-api/docs/quickstart"},{"title":"GitHub - googleapis/python-genai: Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.","url":"https://github.com/googleapis/python-genai"},{"title":"Google Gen AI SDK documentation","url":"https://googleapis.github.io/python-genai/"},{"title":"deprecated-generative-ai-python/docs/api/google/generativeai.md at main · google-gemini/deprecated-generative-ai-python","url":"https://github.com/google-gemini/generative-ai-python/blob/main/docs/api/google/generativeai.md"},{"title":"The Google GenAI SDK: A guide with a Python tutorial","url":"https://wandb.ai/byyoung3/gemini-genai/reports/The-Google-GenAI-SDK-A-guide-with-a-Python-tutorial--VmlldzoxMzE2NDIwNA"},{"title":"GitHub - google-gemini/deprecated-generative-ai-python: This SDK is now deprecated, use the new unified Google GenAI SDK.","url":"https://github.com/google-gemini/deprecated-generative-ai-python"},{"title":"Google Gen AI Python SDK: A Complete Guide","url":"https://www.analyticsvidhya.com/blog/2025/08/google-gen-ai-python-sdk-guide/"}]},"Based on my search, here's the current information about the Google Gemini Python SDK:\n\n## Important: Package Deprecation Notice\n\nThe `google-generativeai` package has reached End-of-Life. All support for this repository ended permanently on November 30, 2025. Users are encouraged to begin migrating to the Google Generative AI SDK to ensure continued access to the latest capabilities and support.\n\n## New Recommended SDK: `google-genai`\n\nAs of May 2025, the Google GenAI SDK has reached General Availability (GA) across all supported platforms and is the recommended library to access the Gemini API.(no content)The new SDK is stable, fully supported for production use, and actively maintained. It provides access to the latest features and offers the best performance working with Gemini.\n\n### Installation\n\nYou run `pip install google-genai`. If you want to use the SDK asynchronously, run `pip install google-genai[aiohttp]`.\n\n### Basic Usage\n\nGoogle Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs.(no content)To create a client for the Gemini Developer API:\n```python\nfrom google import genai\nclient = genai.Client(api_key='GEMINI_API_KEY')\n```\n\nIf you set your API key as the environment variable `GEMINI_API_KEY`, it will be picked up automatically by the client when using the Gemini API libraries. Otherwise you will need to pass your API key as an argument when initializing the client.\n\n### Legacy Libraries\n\nThe legacy libraries don't provide access to recent features (such as Live API and Veo) and are deprecated as of November 30th, 2025.(no content)Note: Generative models (e.g., Gemini, Claude) and AI-powered IDEs (e.g., Cursor) may produce code for the Gemini API using outdated or deprecated libraries due to their training data cutoff. For the generated code to use the latest, recommended libraries, provide version and usage guidance directly in your prompts."],"durationSeconds":20.787366299999995},"sourceToolAssistantUUID":"8c67ab57-6c6e-46a0-8969-3b4991eda661"}
{"parentUuid":"8c67ab57-6c6e-46a0-8969-3b4991eda661","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LnfaajaHUW7GqDTVY6263h","type":"tool_result","content":"Web search results for query: \"xAI Grok API Python SDK pip install 2025\"\n\nLinks: [{\"title\":\"GitHub - xai-org/xai-sdk-python: The official Python SDK for the xAI API\",\"url\":\"https://github.com/xai-org/xai-sdk-python\"},{\"title\":\"Complete Guide to xAI's Grok: API Documentation and Implementation\",\"url\":\"https://latenode.com/blog/ai-technology-language-models/xai-grok-grok-2-grok-3/complete-guide-to-xais-grok-api-documentation-and-implementation\"},{\"title\":\"GitHub - moesmufti/xai_grok_sdk: A lightweight Python library for seamless integration with xAI's Grok API. Features minimal dependencies, robust API key management, and supports the latest xAI models for text generation and function calling.\",\"url\":\"https://github.com/moesmufti/xai_grok_sdk\"},{\"title\":\"xai-sdk · PyPI\",\"url\":\"https://pypi.org/project/xai-sdk/\"},{\"title\":\"The Hitchhiker's Guide to Grok | xAI\",\"url\":\"https://docs.x.ai/docs/tutorial\"},{\"title\":\"xai-grok · PyPI\",\"url\":\"https://pypi.org/project/xai-grok/\"},{\"title\":\"GitHub - tiovikram/grok-community-xdk: Community-maintained SDK (XDK lol) for XAI Grok models\",\"url\":\"https://github.com/tiovikram/grok-community-xdk\"},{\"title\":\"xai-grok-sdk · PyPI\",\"url\":\"https://pypi.org/project/xai-grok-sdk/\"},{\"title\":\"piwheels - xai-grok-sdk-advanced\",\"url\":\"https://www.piwheels.org/project/xai-grok-sdk-advanced/\"},{\"title\":\"AI SDK Providers: xAI Grok\",\"url\":\"https://ai-sdk.dev/providers/ai-sdk-providers/xai\"}]\n\nHere's what I found about the xAI Grok API Python SDK and installation:\n\n## Official xAI Python SDK\n\nThe xAI Python SDK is a gRPC-based Python library for interacting with xAI's APIs.(no content)Built for Python 3.10 and above, it offers both synchronous and asynchronous clients. Whether you're generating text, images, or structured outputs, the xAI SDK is designed to be intuitive, robust, and developer-friendly.\n\n### Installation\n\nInstall from PyPI with pip. Python 3.10 or higher is required to use the xAI SDK:\n\n```bash\npip install xai-sdk\n```\n\nAfter installation, configure your environment with the `XAI_API_KEY` environment variable. Simply update the base URL to https://api.x.ai/v1 and install the SDK.\n\n### Optional Telemetry Dependencies\n\nThe telemetry feature requires additional dependencies based on your export needs:\n```bash\n# For HTTP OTLP export\npip install xai-sdk[telemetry-http]\n\n# For gRPC OTLP export\npip install xai-sdk[telemetry-grpc]\n```\n\n### Features\n\n- The xAI SDK supports both synchronous (`xai_sdk.Client`) and asynchronous (`xai_sdk.AsyncClient`) clients. For a complete set of examples demonstrating the SDK's capabilities, including authentication, chat, image generation, function calling, and more, refer to the examples folder.\n- Agentic Tool Calling: Let Grok autonomously decide when to search the web, 𝕏, or execute code to answer your questions with real-time information.\n- Comprehensive documentation is available at docs.x.ai.\n\n---\n\n## Community-Maintained Alternatives\n\nThere are also community-maintained SDKs available:\n\n1. **xai-grok-sdk** - Lightweight xAI SDK with minimal dependencies\n   ```bash\n   pip install xai-grok-sdk\n   ```\n\n2. **xai-grok** - A community-maintained SDK (Standard Developer Kit) for XAI Grok models. This library provides convenient access to the xAI REST API from Python 3.11+ applications, with type definitions for all request parameters and response fields.\n   ```bash\n   pip install xai-grok\n   ```\n\n3. **xai-grok-sdk-advanced** - Advanced SDK for xAI Grok API with analytics and rate-limit handling\n   ```bash\n   pip install xai-grok-sdk-advanced\n   ```\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"a8af7e70-4890-4fcc-8154-f7a3ff222c80","timestamp":"2026-01-12T18:45:09.549Z","toolUseResult":{"query":"xAI Grok API Python SDK pip install 2025","results":[{"tool_use_id":"srvtoolu_01AUGkC9zJsb4RZaLpwBvEWf","content":[{"title":"GitHub - xai-org/xai-sdk-python: The official Python SDK for the xAI API","url":"https://github.com/xai-org/xai-sdk-python"},{"title":"Complete Guide to xAI's Grok: API Documentation and Implementation","url":"https://latenode.com/blog/ai-technology-language-models/xai-grok-grok-2-grok-3/complete-guide-to-xais-grok-api-documentation-and-implementation"},{"title":"GitHub - moesmufti/xai_grok_sdk: A lightweight Python library for seamless integration with xAI's Grok API. Features minimal dependencies, robust API key management, and supports the latest xAI models for text generation and function calling.","url":"https://github.com/moesmufti/xai_grok_sdk"},{"title":"xai-sdk · PyPI","url":"https://pypi.org/project/xai-sdk/"},{"title":"The Hitchhiker's Guide to Grok | xAI","url":"https://docs.x.ai/docs/tutorial"},{"title":"xai-grok · PyPI","url":"https://pypi.org/project/xai-grok/"},{"title":"GitHub - tiovikram/grok-community-xdk: Community-maintained SDK (XDK lol) for XAI Grok models","url":"https://github.com/tiovikram/grok-community-xdk"},{"title":"xai-grok-sdk · PyPI","url":"https://pypi.org/project/xai-grok-sdk/"},{"title":"piwheels - xai-grok-sdk-advanced","url":"https://www.piwheels.org/project/xai-grok-sdk-advanced/"},{"title":"AI SDK Providers: xAI Grok","url":"https://ai-sdk.dev/providers/ai-sdk-providers/xai"}]},"Here's what I found about the xAI Grok API Python SDK and installation:\n\n## Official xAI Python SDK\n\nThe xAI Python SDK is a gRPC-based Python library for interacting with xAI's APIs.(no content)Built for Python 3.10 and above, it offers both synchronous and asynchronous clients. Whether you're generating text, images, or structured outputs, the xAI SDK is designed to be intuitive, robust, and developer-friendly.\n\n### Installation\n\nInstall from PyPI with pip. Python 3.10 or higher is required to use the xAI SDK:\n\n```bash\npip install xai-sdk\n```\n\nAfter installation, configure your environment with the `XAI_API_KEY` environment variable. Simply update the base URL to https://api.x.ai/v1 and install the SDK.\n\n### Optional Telemetry Dependencies\n\nThe telemetry feature requires additional dependencies based on your export needs:\n```bash\n# For HTTP OTLP export\npip install xai-sdk[telemetry-http]\n\n# For gRPC OTLP export\npip install xai-sdk[telemetry-grpc]\n```\n\n### Features\n\n- The xAI SDK supports both synchronous (`xai_sdk.Client`) and asynchronous (`xai_sdk.AsyncClient`) clients. For a complete set of examples demonstrating the SDK's capabilities, including authentication, chat, image generation, function calling, and more, refer to the examples folder.\n- Agentic Tool Calling: Let Grok autonomously decide when to search the web, 𝕏, or execute code to answer your questions with real-time information.\n- Comprehensive documentation is available at docs.x.ai.\n\n---\n\n## Community-Maintained Alternatives\n\nThere are also community-maintained SDKs available:\n\n1. **xai-grok-sdk** - Lightweight xAI SDK with minimal dependencies\n   ```bash\n   pip install xai-grok-sdk\n   ```\n\n2. **xai-grok** - A community-maintained SDK (Standard Developer Kit) for XAI Grok models. This library provides convenient access to the xAI REST API from Python 3.11+ applications, with type definitions for all request parameters and response fields.\n   ```bash\n   pip install xai-grok\n   ```\n\n3. **xai-grok-sdk-advanced** - Advanced SDK for xAI Grok API with analytics and rate-limit handling\n   ```bash\n   pip install xai-grok-sdk-advanced\n   ```"],"durationSeconds":24.5139914},"sourceToolAssistantUUID":"8c67ab57-6c6e-46a0-8969-3b4991eda661"}
{"parentUuid":"a8af7e70-4890-4fcc-8154-f7a3ff222c80","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_015vM9WcmKnPctkQ4o3aarAG","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have good information on the LLM SDKs. Let me search for more details on Ollama Python, FastAPI, MCP, and verify GitPython or similar for version control integration.","signature":"EtUCCkYICxgCKkALci+GtUlVz7SP6Ma8cDYTG5RMhhTzbY3Bh9w2IvBSu6Tbi49aHpeqbpZLhUBDKw0Bpfdtml+FCuCqk0Lx2X3fEgxjJnJi26BlJzTOcowaDPmhrGx9EjqglDlAeyIwfn5RA6ELpnKUHARuwBkdig/0jRs6G0NlkDiDE6uTesnkL36YweOxmlaZljuNrwYrKrwBUjaro8Qe06BYBzpmlgQs4i+tI42HTo9IRJpCozEfoGrWQKvSIY05DwYOgK6ljSckyp/El/xZMqddwnkpYB1M4XqokUFQeMO2RhwYClEiQLZfRz4Y1xt11fBmg5R6kPXRAuihpVGtNGc0RnHJah7yasIvHNyL2O5685kyTC4P1N2yKFakauh96JCYfGFARw0ObtppeG/MtSfiIoMtHsVVR0cKItlfT5NABWtgdeMykeuKfYq1AdY4BtaATtkYAQ=="},{"type":"text","text":"Let me gather more details on Ollama, FastAPI, MCP, and Git integration:"},{"type":"tool_use","id":"toolu_01SK4SEEJX1ARHQbVPKuYV5j","name":"WebSearch","input":{"query":"ollama python pip install ollama chat streaming async 2025"}},{"type":"tool_use","id":"toolu_01CL3Awk59MpNvtwPHCzLMwZ","name":"WebSearch","input":{"query":"MCP model context protocol python SDK pip install mcp 2025"}},{"type":"tool_use","id":"toolu_019DrkG7DrVdkhq3bWZSeZug","name":"WebSearch","input":{"query":"GitPython pip install git python automation commit branch 2025"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3426,"cache_read_input_tokens":28294,"cache_creation":{"ephemeral_5m_input_tokens":3426,"ephemeral_1h_input_tokens":0},"output_tokens":232,"service_tier":"standard"}},"type":"assistant","uuid":"84c88309-d8e6-49bd-be13-6cc669c5f2c1","timestamp":"2026-01-12T18:45:28.409Z"}
{"parentUuid":"84c88309-d8e6-49bd-be13-6cc669c5f2c1","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CL3Awk59MpNvtwPHCzLMwZ","type":"tool_result","content":"Web search results for query: \"MCP model context protocol python SDK pip install mcp 2025\"\n\nLinks: [{\"title\":\"mcp · PyPI\",\"url\":\"https://pypi.org/project/mcp/\"},{\"title\":\"MCP Python SDK\",\"url\":\"https://pypi.org/project/mcp/1.7.1/\"},{\"title\":\"GitHub - modelcontextprotocol/python-sdk: The official Python SDK for Model Context Protocol servers and clients\",\"url\":\"https://github.com/modelcontextprotocol/python-sdk\"},{\"title\":\"Releases · modelcontextprotocol/python-sdk\",\"url\":\"https://github.com/modelcontextprotocol/python-sdk/releases\"},{\"title\":\"fastmcp · PyPI\",\"url\":\"https://pypi.org/project/fastmcp/\"},{\"title\":\"Model context protocol (MCP) - OpenAI Agents SDK\",\"url\":\"https://openai.github.io/openai-agents-python/mcp/\"},{\"title\":\"Build your MCP server\",\"url\":\"https://developers.openai.com/apps-sdk/build/mcp-server/\"},{\"title\":\"mcp-pypi · PyPI\",\"url\":\"https://pypi.org/project/mcp-pypi/\"},{\"title\":\"Build an MCP server - Model Context Protocol\",\"url\":\"https://modelcontextprotocol.io/docs/develop/build-server\"},{\"title\":\"How to Convert a Python Package to Support MCP (Model Context Protocol) - Mervin Praison\",\"url\":\"https://mer.vin/2025/12/how-to-convert-a-python-package-to-support-mcp-model-context-protocol/\"}]\n\nBased on my search, here's information about the MCP (Model Context Protocol) Python SDK:\n\n## MCP Python SDK Installation\n\nThe `mcp` package is available on PyPI with the latest version released on December 19, 2025. It is the official Model Context Protocol SDK authored by Anthropic, PBC and licensed under MIT.\n\n### Installation Options\n\nYou can install using either uv (recommended) or pip:\n\n```bash\n# Using uv (recommended)\nuv add mcp\n\n# Or using pip\npip install mcp\n```\n\nFor CLI features and building servers, you can install with the cli extra: `uv add \"mcp[cli]\" httpx`\n\n### Requirements\n\nThe SDK requires Python >=3.10 and supports Python 3.10, 3.11, 3.12, and 3.13. It provides extras for cli, rich, and ws (websocket) functionality.\n\n## What is MCP?\n\nThe Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions.(no content)The protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. The Python SDK implements the full MCP specification, making it easy to build MCP clients and servers, use standard transports like stdio, SSE, and Streamable HTTP, and handle all MCP protocol messages.\n\n## Quick Start Example\n\nYou can create a FastMCP server like this:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\n# Initialize FastMCP server\nmcp = FastMCP(\"weather\")\n```\n\nMCP servers can expose data through Resources (like GET endpoints), provide functionality through Tools (like POST endpoints), and define interaction patterns through Prompts (reusable templates for LLM interactions).\n\n## Version Information\n\nVersion 1.7.1 was released on May 2, 2025, with a newer version (1.25.0) available.(no content)Users who need to stay on v1.x should pin to `mcp>=1.25,<2`. Version 2 is planned for release in Q1.\n\n## Official Resources\n\n- **PyPI**: https://pypi.org/project/mcp/\n- **GitHub**: github.com/modelcontextprotocol/python-sdk - The official Python SDK for Model Context Protocol servers and clients\n- **Documentation**: Docs live on modelcontextprotocol.io\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"b2a32e4d-63b7-44b6-ad4b-04f370606792","timestamp":"2026-01-12T18:45:48.672Z","toolUseResult":{"query":"MCP model context protocol python SDK pip install mcp 2025","results":[{"tool_use_id":"srvtoolu_01ADi99Rc2jSoJsoSq89Ux3Y","content":[{"title":"mcp · PyPI","url":"https://pypi.org/project/mcp/"},{"title":"MCP Python SDK","url":"https://pypi.org/project/mcp/1.7.1/"},{"title":"GitHub - modelcontextprotocol/python-sdk: The official Python SDK for Model Context Protocol servers and clients","url":"https://github.com/modelcontextprotocol/python-sdk"},{"title":"Releases · modelcontextprotocol/python-sdk","url":"https://github.com/modelcontextprotocol/python-sdk/releases"},{"title":"fastmcp · PyPI","url":"https://pypi.org/project/fastmcp/"},{"title":"Model context protocol (MCP) - OpenAI Agents SDK","url":"https://openai.github.io/openai-agents-python/mcp/"},{"title":"Build your MCP server","url":"https://developers.openai.com/apps-sdk/build/mcp-server/"},{"title":"mcp-pypi · PyPI","url":"https://pypi.org/project/mcp-pypi/"},{"title":"Build an MCP server - Model Context Protocol","url":"https://modelcontextprotocol.io/docs/develop/build-server"},{"title":"How to Convert a Python Package to Support MCP (Model Context Protocol) - Mervin Praison","url":"https://mer.vin/2025/12/how-to-convert-a-python-package-to-support-mcp-model-context-protocol/"}]},"Based on my search, here's information about the MCP (Model Context Protocol) Python SDK:\n\n## MCP Python SDK Installation\n\nThe `mcp` package is available on PyPI with the latest version released on December 19, 2025. It is the official Model Context Protocol SDK authored by Anthropic, PBC and licensed under MIT.\n\n### Installation Options\n\nYou can install using either uv (recommended) or pip:\n\n```bash\n# Using uv (recommended)\nuv add mcp\n\n# Or using pip\npip install mcp\n```\n\nFor CLI features and building servers, you can install with the cli extra: `uv add \"mcp[cli]\" httpx`\n\n### Requirements\n\nThe SDK requires Python >=3.10 and supports Python 3.10, 3.11, 3.12, and 3.13. It provides extras for cli, rich, and ws (websocket) functionality.\n\n## What is MCP?\n\nThe Model Context Protocol (MCP) lets you build servers that expose data and functionality to LLM applications in a secure, standardized way. Think of it like a web API, but specifically designed for LLM interactions.(no content)The protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction. The Python SDK implements the full MCP specification, making it easy to build MCP clients and servers, use standard transports like stdio, SSE, and Streamable HTTP, and handle all MCP protocol messages.\n\n## Quick Start Example\n\nYou can create a FastMCP server like this:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\n\n# Initialize FastMCP server\nmcp = FastMCP(\"weather\")\n```\n\nMCP servers can expose data through Resources (like GET endpoints), provide functionality through Tools (like POST endpoints), and define interaction patterns through Prompts (reusable templates for LLM interactions).\n\n## Version Information\n\nVersion 1.7.1 was released on May 2, 2025, with a newer version (1.25.0) available.(no content)Users who need to stay on v1.x should pin to `mcp>=1.25,<2`. Version 2 is planned for release in Q1.\n\n## Official Resources\n\n- **PyPI**: https://pypi.org/project/mcp/\n- **GitHub**: github.com/modelcontextprotocol/python-sdk - The official Python SDK for Model Context Protocol servers and clients\n- **Documentation**: Docs live on modelcontextprotocol.io"],"durationSeconds":20.072046099999977},"sourceToolAssistantUUID":"84c88309-d8e6-49bd-be13-6cc669c5f2c1"}
{"parentUuid":"84c88309-d8e6-49bd-be13-6cc669c5f2c1","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019DrkG7DrVdkhq3bWZSeZug","type":"tool_result","content":"Web search results for query: \"GitPython pip install git python automation commit branch 2025\"\n\nLinks: [{\"title\":\"GitPython · PyPI\",\"url\":\"https://pypi.org/project/GitPython/\"},{\"title\":\"Automating some git commands with Python - GeeksforGeeks\",\"url\":\"https://www.geeksforgeeks.org/python/automating-some-git-commands-with-python/\"},{\"title\":\"Overview / Install — GitPython 3.1.46 documentation\",\"url\":\"https://gitpython.readthedocs.io/en/stable/intro.html\"},{\"title\":\"PIP Install Git - A quick read - ActiveState\",\"url\":\"https://www.activestate.com/resources/quick-reads/pip-install-git/\"},{\"title\":\"GitHub - gitpython-developers/GitPython: GitPython is a python library used to interact with Git repositories.\",\"url\":\"https://github.com/gitpython-developers/GitPython\"},{\"title\":\"Automate Your Git Workflow with GitPython | by Chelsea Liu | Medium\",\"url\":\"https://zchelseal.medium.com/automate-your-git-workflow-with-gitpython-3320e833c4e2\"},{\"title\":\"Installing GitPython: A Comprehensive Guide - CodeRivers\",\"url\":\"https://coderivers.org/blog/install-git-python/\"},{\"title\":\"How to Use GitPython to Manage Git Repositories?\",\"url\":\"https://www.packetswitch.co.uk/how-to-use-gitpython-to-manage-git-repositories/\"},{\"title\":\"'pip install' From a Git Repository - Adam Johnson\",\"url\":\"https://adamj.eu/tech/2019/03/11/pip-install-from-a-git-repository/\"},{\"title\":\"First Steps with GitPython - Full Stack Python\",\"url\":\"https://www.fullstackpython.com/blog/first-steps-gitpython.html\"}]\n\nHere's what I found about **GitPython** for automating Git operations with Python:\n\n## Installation\n\nInstalling GitPython is easily done using pip. Assuming it is installed, just run the following from the command-line: `pip install GitPython`. This command will download the latest version of GitPython from the Python Package Index and install it to your system.(no content)If you are using Python 3 and have both Python 2 and Python 3 installed on your system, you may need to use `pip3` instead.\n\n## What is GitPython?\n\nGitPython is a Python library that lets you work with Git repositories. It allows you to manage Git tasks using Python code, making it easy to automate things like commits, branches, and pushes without using the command line.\n\n## Common Automation Tasks\n\n### Initializing and Opening Repositories\nTo start automating Git commands with Python, you will first need to install GitPython by running `pip install GitPython`. To initialize a new repository: `from git import Repo; new_repo = Repo.init('/path/to/repo_directory')`\n\n### Creating Branches and Committing Changes\nYou can create a new branch, make changes, stage and commit them, then merge back using methods like `repo.create_head(new_branch_name)`, `new_branch.checkout()`, and `repo.git.merge(new_branch_name)`.\n\n### Checking for Uncommitted Changes\nIn GitPython, you can check if you have any uncommitted changes by using the `is_dirty()` method of the Repo class. This method returns True if there are uncommitted changes in the working directory, including untracked, modified, or staged files.\n\n### Automating Complete Workflows\nFor any local git repository, you can script-ify the following: create a new branch off prod, make some commits, push these, and make a pull request.\n\n## Important Notes\n\nGitPython is not suited for long-running processes (like daemons) as it tends to leak system resources. It was written in a time where destructors (as implemented in the `__del__` method) still ran deterministically.(no content)Another way to assure proper cleanup of resources is to factor out GitPython into a separate process which can be dropped periodically.\n\n## Resources\n\n- **PyPI**: https://pypi.org/project/GitPython/\n- **GitHub Repository**: https://github.com/gitpython-developers/GitPython\n- **Documentation**: https://gitpython.readthedocs.io/en/stable/\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"df19305d-0c05-4c6c-8616-a4d93b39be1e","timestamp":"2026-01-12T18:45:49.925Z","toolUseResult":{"query":"GitPython pip install git python automation commit branch 2025","results":[{"tool_use_id":"srvtoolu_01RDDs7nHRJrWHDjAS9LEPuA","content":[{"title":"GitPython · PyPI","url":"https://pypi.org/project/GitPython/"},{"title":"Automating some git commands with Python - GeeksforGeeks","url":"https://www.geeksforgeeks.org/python/automating-some-git-commands-with-python/"},{"title":"Overview / Install — GitPython 3.1.46 documentation","url":"https://gitpython.readthedocs.io/en/stable/intro.html"},{"title":"PIP Install Git - A quick read - ActiveState","url":"https://www.activestate.com/resources/quick-reads/pip-install-git/"},{"title":"GitHub - gitpython-developers/GitPython: GitPython is a python library used to interact with Git repositories.","url":"https://github.com/gitpython-developers/GitPython"},{"title":"Automate Your Git Workflow with GitPython | by Chelsea Liu | Medium","url":"https://zchelseal.medium.com/automate-your-git-workflow-with-gitpython-3320e833c4e2"},{"title":"Installing GitPython: A Comprehensive Guide - CodeRivers","url":"https://coderivers.org/blog/install-git-python/"},{"title":"How to Use GitPython to Manage Git Repositories?","url":"https://www.packetswitch.co.uk/how-to-use-gitpython-to-manage-git-repositories/"},{"title":"'pip install' From a Git Repository - Adam Johnson","url":"https://adamj.eu/tech/2019/03/11/pip-install-from-a-git-repository/"},{"title":"First Steps with GitPython - Full Stack Python","url":"https://www.fullstackpython.com/blog/first-steps-gitpython.html"}]},"Here's what I found about **GitPython** for automating Git operations with Python:\n\n## Installation\n\nInstalling GitPython is easily done using pip. Assuming it is installed, just run the following from the command-line: `pip install GitPython`. This command will download the latest version of GitPython from the Python Package Index and install it to your system.(no content)If you are using Python 3 and have both Python 2 and Python 3 installed on your system, you may need to use `pip3` instead.\n\n## What is GitPython?\n\nGitPython is a Python library that lets you work with Git repositories. It allows you to manage Git tasks using Python code, making it easy to automate things like commits, branches, and pushes without using the command line.\n\n## Common Automation Tasks\n\n### Initializing and Opening Repositories\nTo start automating Git commands with Python, you will first need to install GitPython by running `pip install GitPython`. To initialize a new repository: `from git import Repo; new_repo = Repo.init('/path/to/repo_directory')`\n\n### Creating Branches and Committing Changes\nYou can create a new branch, make changes, stage and commit them, then merge back using methods like `repo.create_head(new_branch_name)`, `new_branch.checkout()`, and `repo.git.merge(new_branch_name)`.\n\n### Checking for Uncommitted Changes\nIn GitPython, you can check if you have any uncommitted changes by using the `is_dirty()` method of the Repo class. This method returns True if there are uncommitted changes in the working directory, including untracked, modified, or staged files.\n\n### Automating Complete Workflows\nFor any local git repository, you can script-ify the following: create a new branch off prod, make some commits, push these, and make a pull request.\n\n## Important Notes\n\nGitPython is not suited for long-running processes (like daemons) as it tends to leak system resources. It was written in a time where destructors (as implemented in the `__del__` method) still ran deterministically.(no content)Another way to assure proper cleanup of resources is to factor out GitPython into a separate process which can be dropped periodically.\n\n## Resources\n\n- **PyPI**: https://pypi.org/project/GitPython/\n- **GitHub Repository**: https://github.com/gitpython-developers/GitPython\n- **Documentation**: https://gitpython.readthedocs.io/en/stable/"],"durationSeconds":21.324160399999993},"sourceToolAssistantUUID":"84c88309-d8e6-49bd-be13-6cc669c5f2c1"}
{"parentUuid":"84c88309-d8e6-49bd-be13-6cc669c5f2c1","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SK4SEEJX1ARHQbVPKuYV5j","type":"tool_result","content":"Web search results for query: \"ollama python pip install ollama chat streaming async 2025\"\n\nLinks: [{\"title\":\"GitHub - ollama/ollama-python: Ollama Python library\",\"url\":\"https://github.com/ollama/ollama-python\"},{\"title\":\"Ollama Python Library\",\"url\":\"https://pypi.org/project/ollama/\"},{\"title\":\"ChatOllama - Docs by LangChain\",\"url\":\"https://docs.langchain.com/oss/python/integrations/chat/ollama\"},{\"title\":\"Streaming responses with tool calling · Ollama Blog\",\"url\":\"https://ollama.com/blog/streaming-tool\"},{\"title\":\"ollama · PyPI\",\"url\":\"https://pypi.org/project/ollama/0.0.1/\"},{\"title\":\"ollama-python · PyPI\",\"url\":\"https://pypi.org/project/ollama-python/\"},{\"title\":\"ollama 0.6.0 on PyPI - Libraries.io - security & maintenance data for open source software\",\"url\":\"https://libraries.io/pypi/ollama\"},{\"title\":\"streamlit-ollama-agent · PyPI\",\"url\":\"https://pypi.org/project/streamlit-ollama-agent/\"},{\"title\":\"ollama-instructor 1.1.0 on PyPI - Libraries.io - security & maintenance data for open source software\",\"url\":\"https://libraries.io/pypi/ollama-instructor\"},{\"title\":\"Using Ollama with Python: A Simple Guide | by Jonathan Gastón Löwenstern | Medium\",\"url\":\"https://medium.com/@jonigl/using-ollama-with-python-a-simple-guide-0752369e1e55\"}]\n\nHere are the search results for the Ollama Python library with chat, streaming, and async capabilities:\n\n## Installation\n\nThe official Python client for Ollama can be installed via pip with `pip install ollama`. The latest version was released November 13, 2025.(no content)The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.\n\n### Prerequisites\nOllama should be installed and running. Pull a model to use with the library using `ollama pull <model>`, e.g., `ollama pull gemma3`.\n\n## Basic Chat Usage\n\nYou can use the `chat` function with a `ChatResponse` type for basic chat completions. The response can be accessed via dictionary notation or directly from the response object using `response.message.content`.\n\n## Streaming\n\nResponse streaming can be enabled by setting `stream=True`.(no content)When you use the streaming functionality with Ollama, the response isn't returned all at once. Instead, it's broken into small pieces (chunks) that arrive one at a time as they're generated by the model.\n\n## Async Client\n\nThe `AsyncClient` class is used to make asynchronous requests.(no content)It can be configured with the same fields as the `Client` class.(no content)For async usage, you import `AsyncClient` from ollama and use `await` with the chat method: `response = await AsyncClient().chat(model='gemma3', messages=[message])`.\n\n### Async Streaming\n\nSetting `stream=True` modifies functions to return a Python asynchronous generator. You can iterate with `async for part in await AsyncClient().chat(model='gemma3', messages=[message], stream=True)`.\n\n## Streaming with Tool Calling\n\nOllama now supports streaming responses with tool calling. This enables all chat applications to stream content and also call tools in real time.(no content)With the improvements, developers can now stream chat content and tool calls when using Ollama with MCP (Model Context Protocol).\n\n## Custom Client Configuration\n\nYou can create a custom `Client` with a specific host and headers, like `Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})`.\n\n## LangChain Integration\n\nFor LangChain users, you can install with `pip install -U ollama` and use `ChatOllama` from `langchain_ollama` to instantiate model objects.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"013ab0c7-39f3-4db1-b063-ac6b4bda3cc8","timestamp":"2026-01-12T18:45:50.134Z","toolUseResult":{"query":"ollama python pip install ollama chat streaming async 2025","results":[{"tool_use_id":"srvtoolu_014p7LwUr9foPb2sikQD45qy","content":[{"title":"GitHub - ollama/ollama-python: Ollama Python library","url":"https://github.com/ollama/ollama-python"},{"title":"Ollama Python Library","url":"https://pypi.org/project/ollama/"},{"title":"ChatOllama - Docs by LangChain","url":"https://docs.langchain.com/oss/python/integrations/chat/ollama"},{"title":"Streaming responses with tool calling · Ollama Blog","url":"https://ollama.com/blog/streaming-tool"},{"title":"ollama · PyPI","url":"https://pypi.org/project/ollama/0.0.1/"},{"title":"ollama-python · PyPI","url":"https://pypi.org/project/ollama-python/"},{"title":"ollama 0.6.0 on PyPI - Libraries.io - security & maintenance data for open source software","url":"https://libraries.io/pypi/ollama"},{"title":"streamlit-ollama-agent · PyPI","url":"https://pypi.org/project/streamlit-ollama-agent/"},{"title":"ollama-instructor 1.1.0 on PyPI - Libraries.io - security & maintenance data for open source software","url":"https://libraries.io/pypi/ollama-instructor"},{"title":"Using Ollama with Python: A Simple Guide | by Jonathan Gastón Löwenstern | Medium","url":"https://medium.com/@jonigl/using-ollama-with-python-a-simple-guide-0752369e1e55"}]},"Here are the search results for the Ollama Python library with chat, streaming, and async capabilities:\n\n## Installation\n\nThe official Python client for Ollama can be installed via pip with `pip install ollama`. The latest version was released November 13, 2025.(no content)The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.\n\n### Prerequisites\nOllama should be installed and running. Pull a model to use with the library using `ollama pull <model>`, e.g., `ollama pull gemma3`.\n\n## Basic Chat Usage\n\nYou can use the `chat` function with a `ChatResponse` type for basic chat completions. The response can be accessed via dictionary notation or directly from the response object using `response.message.content`.\n\n## Streaming\n\nResponse streaming can be enabled by setting `stream=True`.(no content)When you use the streaming functionality with Ollama, the response isn't returned all at once. Instead, it's broken into small pieces (chunks) that arrive one at a time as they're generated by the model.\n\n## Async Client\n\nThe `AsyncClient` class is used to make asynchronous requests.(no content)It can be configured with the same fields as the `Client` class.(no content)For async usage, you import `AsyncClient` from ollama and use `await` with the chat method: `response = await AsyncClient().chat(model='gemma3', messages=[message])`.\n\n### Async Streaming\n\nSetting `stream=True` modifies functions to return a Python asynchronous generator. You can iterate with `async for part in await AsyncClient().chat(model='gemma3', messages=[message], stream=True)`.\n\n## Streaming with Tool Calling\n\nOllama now supports streaming responses with tool calling. This enables all chat applications to stream content and also call tools in real time.(no content)With the improvements, developers can now stream chat content and tool calls when using Ollama with MCP (Model Context Protocol).\n\n## Custom Client Configuration\n\nYou can create a custom `Client` with a specific host and headers, like `Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})`.\n\n## LangChain Integration\n\nFor LangChain users, you can install with `pip install -U ollama` and use `ChatOllama` from `langchain_ollama` to instantiate model objects."],"durationSeconds":21.534739100000007},"sourceToolAssistantUUID":"84c88309-d8e6-49bd-be13-6cc669c5f2c1"}
{"parentUuid":"013ab0c7-39f3-4db1-b063-ac6b4bda3cc8","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_0127ksNLGKEzssNQi6WpLRUN","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now let me search for FastAPI details (specific patterns, uvicorn, pydantic) and React/TypeScript patterns.","signature":"EpMCCkYICxgCKkD2tr+s2NMnreiMdVm3lAHvwmHYJJFeoknAJgR8iUq/gjJld6dJ4Q3snt6vIpLsu36m3UaDdOP8O+znAIWSoEKEEgyvX06MvPpqdBNzifgaDHoawzkH/WBl4VrNbSIwTmpTYvc9MpXr6/POq4WW+5SDk94PROtJ4cT4FlAC9QWb0MNhhQLp05bsj/2dZfPvKnuP3tJ+0sRHsA4bKXEZqOttifA/rZfimtwwranDUChTFqohZjjaM8X+9ElJD/BwC5D8OmlQYAYggO78ASFmtuGMwM0CwBfhi7zBEwYkBCVryxGLiJPzjy5FCiHiCpKmOfFby22Outwq+FiYmGw5It6t7fUf59/7MQWLMQIYAQ=="},{"type":"text","text":"Now let me search for FastAPI details and React/TypeScript setup:"},{"type":"tool_use","id":"toolu_01JQTNnxp8fbUHxhfv7gERYa","name":"WebSearch","input":{"query":"FastAPI uvicorn pydantic pip install fastapi async endpoint 2025"}},{"type":"tool_use","id":"toolu_01CpcHSeYnpoQYebXAhqof1k","name":"WebSearch","input":{"query":"React TypeScript Vite project setup npm create 2025"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":2119,"cache_creation_input_tokens":1238,"cache_read_input_tokens":31720,"cache_creation":{"ephemeral_5m_input_tokens":1238,"ephemeral_1h_input_tokens":0},"output_tokens":166,"service_tier":"standard"}},"type":"assistant","uuid":"dd66bef3-e40e-44a0-aaf6-942f52cca652","timestamp":"2026-01-12T18:45:55.415Z"}
{"parentUuid":"dd66bef3-e40e-44a0-aaf6-942f52cca652","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CpcHSeYnpoQYebXAhqof1k","type":"tool_result","content":"Web search results for query: \"React TypeScript Vite project setup npm create 2025\"\n\nLinks: [{\"title\":\"Complete Guide to Setting Up React with TypeScript and Vite (2026) | by Robin Viktorsson | Medium\",\"url\":\"https://medium.com/@robinviktorsson/complete-guide-to-setting-up-react-with-typescript-and-vite-2025-468f6556aaf2\"},{\"title\":\"Getting Started | Vite\",\"url\":\"https://vite.dev/guide/\"},{\"title\":\"How to build a React + TypeScript app with Vite - LogRocket Blog\",\"url\":\"https://blog.logrocket.com/how-to-build-react-typescript-app-vite/\"},{\"title\":\"Creating a Modern React App: Vite + TypeScript + ESLint + Tailwind + shadcn/ui and Zustand - DEV Community\",\"url\":\"https://dev.to/manojspace/creating-a-modern-react-app-a-comprehensive-guide-1plk\"},{\"title\":\"Setting up a React project using Vite + TypeScript + Vitest - DEV Community\",\"url\":\"https://dev.to/janoskocs/setting-up-a-react-project-using-vite-typescript-vitest-2gl2\"},{\"title\":\"Integrate TypeScript into a React Project with Vite 🚀 - DEV Community\",\"url\":\"https://dev.to/nghiemledo/integrate-typescript-into-a-react-project-with-vite-12ah\"},{\"title\":\"Advanced Guide to Using Vite with React in 2025\",\"url\":\"https://codeparrot.ai/blogs/advanced-guide-to-using-vite-with-react-in-2025\"},{\"title\":\"How To Set Up a React Project with Vite for Fast Development | DigitalOcean\",\"url\":\"https://www.digitalocean.com/community/tutorials/how-to-set-up-a-react-project-with-vite\"},{\"title\":\"Getting Started with Vite + React + TypeScript\",\"url\":\"https://www.markcallen.com/getting-started-with-vite-react-typescript/\"},{\"title\":\"Create a React + TypeScript project with Vite in 2025. | by Ye Min Ko | Learn React | Medium\",\"url\":\"https://medium.com/learn-reactjs/create-a-react-typescript-project-with-vite-40ebd59d38d4\"}]\n\nHere are the search results for setting up a React TypeScript Vite project in 2025:\n\n## Quick Start Command\n\nTo scaffold a new project, run: `npm create vite@latest react-ts-vite-app -- --template react-ts` and follow the CLI prompts.\n\n## After Scaffolding\n\nAfter the project is created, run:\n```\ncd react-ts-vite-app\nnpm install\nnpm run dev\n```\nThe Vite development server (v6.3.2) will be ready in about 1120 ms at `http://localhost:5173/`.\n\n## Available Templates\n\nVite supports multiple templates including: vanilla, vanilla-ts, vue, vue-ts, react, react-ts, react-swc, react-swc-ts, preact, preact-ts, lit, lit-ts, svelte, svelte-ts, solid, solid-ts, qwik, and qwik-ts.\n\n## Prerequisites\n\nEnsure that you have Node.js ≥v18 installed on your machine before creating a Vite project.\n\n## Key Features\n\n- Thanks to HMR (Hot Module Replacement), any updates to your TypeScript, CSS, or other assets will instantly be reflected in the browser without manually refreshing the page.\n\n- During development, instead of bundling all your code upfront, Vite serves your React app as native ES modules directly in the browser. This approach significantly reduces startup time because only necessary files are loaded when needed.\n\n## Project Structure\n\nYou'll find a straightforward setup featuring an `index.html` at the root and a `src/` directory containing your main React entry file (main.jsx or main.tsx).\n\n## Adding Testing (Optional)\n\nVitest test runner is optimized for Vite-powered projects. Install it with `npm install -D vitest` along with `@testing-library/react`, `@testing-library/jest-dom`, and `jsdom` for component testing.\n\n## Non-Interactive Mode\n\nTo create a project without interactive prompts, you can use the `--no-interactive` flag.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"39057346-f3b5-4c17-a97e-236d0c3041a6","timestamp":"2026-01-12T18:46:13.895Z","toolUseResult":{"query":"React TypeScript Vite project setup npm create 2025","results":[{"tool_use_id":"srvtoolu_01MVHjHPVYT44orR7J6n9iKV","content":[{"title":"Complete Guide to Setting Up React with TypeScript and Vite (2026) | by Robin Viktorsson | Medium","url":"https://medium.com/@robinviktorsson/complete-guide-to-setting-up-react-with-typescript-and-vite-2025-468f6556aaf2"},{"title":"Getting Started | Vite","url":"https://vite.dev/guide/"},{"title":"How to build a React + TypeScript app with Vite - LogRocket Blog","url":"https://blog.logrocket.com/how-to-build-react-typescript-app-vite/"},{"title":"Creating a Modern React App: Vite + TypeScript + ESLint + Tailwind + shadcn/ui and Zustand - DEV Community","url":"https://dev.to/manojspace/creating-a-modern-react-app-a-comprehensive-guide-1plk"},{"title":"Setting up a React project using Vite + TypeScript + Vitest - DEV Community","url":"https://dev.to/janoskocs/setting-up-a-react-project-using-vite-typescript-vitest-2gl2"},{"title":"Integrate TypeScript into a React Project with Vite 🚀 - DEV Community","url":"https://dev.to/nghiemledo/integrate-typescript-into-a-react-project-with-vite-12ah"},{"title":"Advanced Guide to Using Vite with React in 2025","url":"https://codeparrot.ai/blogs/advanced-guide-to-using-vite-with-react-in-2025"},{"title":"How To Set Up a React Project with Vite for Fast Development | DigitalOcean","url":"https://www.digitalocean.com/community/tutorials/how-to-set-up-a-react-project-with-vite"},{"title":"Getting Started with Vite + React + TypeScript","url":"https://www.markcallen.com/getting-started-with-vite-react-typescript/"},{"title":"Create a React + TypeScript project with Vite in 2025. | by Ye Min Ko | Learn React | Medium","url":"https://medium.com/learn-reactjs/create-a-react-typescript-project-with-vite-40ebd59d38d4"}]},"Here are the search results for setting up a React TypeScript Vite project in 2025:\n\n## Quick Start Command\n\nTo scaffold a new project, run: `npm create vite@latest react-ts-vite-app -- --template react-ts` and follow the CLI prompts.\n\n## After Scaffolding\n\nAfter the project is created, run:\n```\ncd react-ts-vite-app\nnpm install\nnpm run dev\n```\nThe Vite development server (v6.3.2) will be ready in about 1120 ms at `http://localhost:5173/`.\n\n## Available Templates\n\nVite supports multiple templates including: vanilla, vanilla-ts, vue, vue-ts, react, react-ts, react-swc, react-swc-ts, preact, preact-ts, lit, lit-ts, svelte, svelte-ts, solid, solid-ts, qwik, and qwik-ts.\n\n## Prerequisites\n\nEnsure that you have Node.js ≥v18 installed on your machine before creating a Vite project.\n\n## Key Features\n\n- Thanks to HMR (Hot Module Replacement), any updates to your TypeScript, CSS, or other assets will instantly be reflected in the browser without manually refreshing the page.\n\n- During development, instead of bundling all your code upfront, Vite serves your React app as native ES modules directly in the browser. This approach significantly reduces startup time because only necessary files are loaded when needed.\n\n## Project Structure\n\nYou'll find a straightforward setup featuring an `index.html` at the root and a `src/` directory containing your main React entry file (main.jsx or main.tsx).\n\n## Adding Testing (Optional)\n\nVitest test runner is optimized for Vite-powered projects. Install it with `npm install -D vitest` along with `@testing-library/react`, `@testing-library/jest-dom`, and `jsdom` for component testing.\n\n## Non-Interactive Mode\n\nTo create a project without interactive prompts, you can use the `--no-interactive` flag."],"durationSeconds":18.306363100000016},"sourceToolAssistantUUID":"dd66bef3-e40e-44a0-aaf6-942f52cca652"}
{"parentUuid":"dd66bef3-e40e-44a0-aaf6-942f52cca652","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JQTNnxp8fbUHxhfv7gERYa","type":"tool_result","content":"Web search results for query: \"FastAPI uvicorn pydantic pip install fastapi async endpoint 2025\"\n\nLinks: [{\"title\":\"fastapi · PyPI\",\"url\":\"https://pypi.org/project/fastapi/\"},{\"title\":\"FastAPI Setup and Requirements – Practical Guide for 2026 | Zestminds\",\"url\":\"https://www.zestminds.com/blog/fastapi-requirements-setup-guide-2025/\"},{\"title\":\"Ultimate guide to FastAPI library in Python\",\"url\":\"https://deepnote.com/blog/ultimate-guide-to-fastapi-library-in-python\"},{\"title\":\"FastAPI - Uvicorn - GeeksforGeeks\",\"url\":\"https://www.geeksforgeeks.org/python/fastapi-uvicorn/\"},{\"title\":\"FastAPI\",\"url\":\"https://fastapi.tiangolo.com/\"},{\"title\":\"Installation Steps for creating Async APIs using FastAPI - DEV Community\",\"url\":\"https://dev.to/devhub/installation-for-async-apis-using-fastapi-3p4m\"},{\"title\":\"uvicorn · PyPI\",\"url\":\"https://pypi.org/project/uvicorn/\"},{\"title\":\"Build Your First FastAPI App From Virtual Environment to Uvicorn Server. | by Java Fusion | Medium\",\"url\":\"https://medium.com/@JavaFusion/build-your-first-fastapi-app-from-virtual-environment-to-uvicorn-server-342e1d069900\"},{\"title\":\"Understanding FastAPI Fundamentals: A Guide to FastAPI, Uvicorn, Starlette, Swagger UI, and Pydantic - DEV Community\",\"url\":\"https://dev.to/kfir-g/understanding-fastapi-fundamentals-a-guide-to-fastapi-uvicorn-starlette-swagger-ui-and-pydantic-2fp7\"},{\"title\":\"Tutorial - User Guide - FastAPI\",\"url\":\"https://fastapi.tiangolo.com/tutorial/\"}]\n\nHere are the search results for FastAPI, Uvicorn, Pydantic, and async endpoints:\n\n## Installation\n\n\"When you install FastAPI with `pip install \"fastapi[standard]\"` it comes with the standard group of optional dependencies: ... uvicorn - for the server that loads and serves your application.\"(no content)To install, create and activate a virtual environment and then run: `pip install \"fastapi[standard]\"`. Make sure you put \"fastapi[standard]\" in quotes to ensure it works in all terminals.\n\nFor a more complete async setup with a database, you can use:\n`pip install fastapi \"uvicorn[standard]\" \"sqlalchemy[asyncio]\" asyncpg sqlmodel alembic pydantic-settings`\n\nA sample `requirements.txt` might include:\n`fastapi==0.115.0`, `uvicorn==0.30.0`, `pydantic==2.7.0`, `python-dotenv==1.0.1`, `requests==2.31.0`\n\n## Key Components\n\nFastAPI stands on the shoulders of giants: Starlette for the web parts and Pydantic for the data parts.(no content)Uvicorn is an ASGI web server implementation for Python. The ASGI specification enables building a common set of tooling usable across all async frameworks. Uvicorn supports HTTP/1.1 and WebSockets.\n\n## Async Endpoints\n\nFastAPI is a modern, high-performance web framework for building APIs with Python. It's designed with asynchronous programming in mind, making it extremely fast and able to handle multiple requests concurrently.(no content)The ASGI standard enables asynchronous request handling, allowing FastAPI to efficiently manage many concurrent connections compared to traditional synchronous server interfaces.\n\n## Running Your App\n\nRun your app with `pip install fastapi uvicorn` then `uvicorn main:app --reload`. The `--reload` flag is intended for local development only.(no content)Uvicorn is a lightning-fast ASGI server, optimized for handling asynchronous code. It's essential for running FastAPI applications because it handles incoming HTTP requests and manages the lifecycle of these requests.\n\n## Performance\n\nFastAPI offers very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). It's one of the fastest Python frameworks available.(no content)FastAPI's combination of modern Python features (async/await, type hints), automatic docs, and robust performance has made it a go-to choice for building APIs in Python in 2024–2025.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"875aae9a-9bf1-4d28-99c6-7ec84799fa5c","timestamp":"2026-01-12T18:46:16.541Z","toolUseResult":{"query":"FastAPI uvicorn pydantic pip install fastapi async endpoint 2025","results":[{"tool_use_id":"srvtoolu_019tcN6DWEzcfeN9UEQod4UB","content":[{"title":"fastapi · PyPI","url":"https://pypi.org/project/fastapi/"},{"title":"FastAPI Setup and Requirements – Practical Guide for 2026 | Zestminds","url":"https://www.zestminds.com/blog/fastapi-requirements-setup-guide-2025/"},{"title":"Ultimate guide to FastAPI library in Python","url":"https://deepnote.com/blog/ultimate-guide-to-fastapi-library-in-python"},{"title":"FastAPI - Uvicorn - GeeksforGeeks","url":"https://www.geeksforgeeks.org/python/fastapi-uvicorn/"},{"title":"FastAPI","url":"https://fastapi.tiangolo.com/"},{"title":"Installation Steps for creating Async APIs using FastAPI - DEV Community","url":"https://dev.to/devhub/installation-for-async-apis-using-fastapi-3p4m"},{"title":"uvicorn · PyPI","url":"https://pypi.org/project/uvicorn/"},{"title":"Build Your First FastAPI App From Virtual Environment to Uvicorn Server. | by Java Fusion | Medium","url":"https://medium.com/@JavaFusion/build-your-first-fastapi-app-from-virtual-environment-to-uvicorn-server-342e1d069900"},{"title":"Understanding FastAPI Fundamentals: A Guide to FastAPI, Uvicorn, Starlette, Swagger UI, and Pydantic - DEV Community","url":"https://dev.to/kfir-g/understanding-fastapi-fundamentals-a-guide-to-fastapi-uvicorn-starlette-swagger-ui-and-pydantic-2fp7"},{"title":"Tutorial - User Guide - FastAPI","url":"https://fastapi.tiangolo.com/tutorial/"}]},"Here are the search results for FastAPI, Uvicorn, Pydantic, and async endpoints:\n\n## Installation\n\n\"When you install FastAPI with `pip install \"fastapi[standard]\"` it comes with the standard group of optional dependencies: ... uvicorn - for the server that loads and serves your application.\"(no content)To install, create and activate a virtual environment and then run: `pip install \"fastapi[standard]\"`. Make sure you put \"fastapi[standard]\" in quotes to ensure it works in all terminals.\n\nFor a more complete async setup with a database, you can use:\n`pip install fastapi \"uvicorn[standard]\" \"sqlalchemy[asyncio]\" asyncpg sqlmodel alembic pydantic-settings`\n\nA sample `requirements.txt` might include:\n`fastapi==0.115.0`, `uvicorn==0.30.0`, `pydantic==2.7.0`, `python-dotenv==1.0.1`, `requests==2.31.0`\n\n## Key Components\n\nFastAPI stands on the shoulders of giants: Starlette for the web parts and Pydantic for the data parts.(no content)Uvicorn is an ASGI web server implementation for Python. The ASGI specification enables building a common set of tooling usable across all async frameworks. Uvicorn supports HTTP/1.1 and WebSockets.\n\n## Async Endpoints\n\nFastAPI is a modern, high-performance web framework for building APIs with Python. It's designed with asynchronous programming in mind, making it extremely fast and able to handle multiple requests concurrently.(no content)The ASGI standard enables asynchronous request handling, allowing FastAPI to efficiently manage many concurrent connections compared to traditional synchronous server interfaces.\n\n## Running Your App\n\nRun your app with `pip install fastapi uvicorn` then `uvicorn main:app --reload`. The `--reload` flag is intended for local development only.(no content)Uvicorn is a lightning-fast ASGI server, optimized for handling asynchronous code. It's essential for running FastAPI applications because it handles incoming HTTP requests and manages the lifecycle of these requests.\n\n## Performance\n\nFastAPI offers very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). It's one of the fastest Python frameworks available.(no content)FastAPI's combination of modern Python features (async/await, type hints), automatic docs, and robust performance has made it a go-to choice for building APIs in Python in 2024–2025."],"durationSeconds":20.952515200000022},"sourceToolAssistantUUID":"dd66bef3-e40e-44a0-aaf6-942f52cca652"}
{"parentUuid":"875aae9a-9bf1-4d28-99c6-7ec84799fa5c","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_015JwFYnaDwykvnrtaYUGJKW","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Now I have sufficient information for the main integrations. Let me verify the xAI SDK details a bit more specifically and search for any specific Gitea integration libraries in Python.","signature":"EuICCkYICxgCKkDJ7N7Uk3NEW7Sz16rTWu0urdAxYRb62jzRJ4GrnWUrkD7V7zUiCFQ0s+J4TiPmWJmazS+D8fPM36noQbWVzfciEgxMpR2yQOCIB88fXp0aDDR3yulfDi1o8h943iIwRMmflR0RCYy5Ov6xT6hZK+ylGGg9bADfYweXV3XcRcsb32Io3dKaHmBXMnneGzIjKskBYNG5pKByUgDAYNUGeZOWVxdjkZllcZVBCLpbDZkEQnWOw0+u38pcjTQj21c8hL7i74a85YhqMZ6DwHchMsnfwDWjUrM4W5R66QhAShFnu3j5lpvzPSk1/6OBFYfjFwCGVBaRVyKuAuwQ9ASDzGxWu0tggBP2Y8cJU53GtVvidkbhUCoY8gWEInojzwBV/mnJ3JZR4/Aag/2TGj09MyHWK80akx1p9q8TbZeFqq2PgEUXi9GeE+QHqOgU+rtM+8SUmjzWdceuR5n/GAE="},{"type":"text","text":"Let me verify a couple more details - specifically Gitea API and search for any potential alternative unified LLM wrapper libraries:"},{"type":"tool_use","id":"toolu_01Ct3Gq7Te85dRWqGcs1WCA1","name":"WebSearch","input":{"query":"Gitea Python API client pip install py-gitea python-gitea 2025"}},{"type":"tool_use","id":"toolu_01QEq86M697pwELcwBFXwgBi","name":"WebSearch","input":{"query":"litellm openrouter unified LLM API wrapper Python multiple providers 2025"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1112,"cache_creation_input_tokens":3389,"cache_read_input_tokens":32958,"cache_creation":{"ephemeral_5m_input_tokens":3389,"ephemeral_1h_input_tokens":0},"output_tokens":194,"service_tier":"standard"}},"type":"assistant","uuid":"d52df6a7-d9b6-427f-bf18-556b34a01b42","timestamp":"2026-01-12T18:46:38.964Z"}
{"parentUuid":"d52df6a7-d9b6-427f-bf18-556b34a01b42","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ct3Gq7Te85dRWqGcs1WCA1","type":"tool_result","content":"Web search results for query: \"Gitea Python API client pip install py-gitea python-gitea 2025\"\n\nLinks: [{\"title\":\"py-gitea · PyPI\",\"url\":\"https://pypi.org/project/py-gitea/\"},{\"title\":\"GitHub - Langenfeld/py-gitea: A Gitea-API wrapper in Python\",\"url\":\"https://github.com/Langenfeld/py-gitea\"},{\"title\":\"GitHub - awalker125/gitea-api: A python client for the gitea api\",\"url\":\"https://github.com/awalker125/gitea-api\"},{\"title\":\"giteapy · PyPI\",\"url\":\"https://pypi.org/project/giteapy/\"},{\"title\":\"GitHub - dblueai/giteapy: Python SDK for Gitea - A painless, self-hosted Git service\",\"url\":\"https://github.com/dblueai/giteapy\"},{\"title\":\"py-gitea-opensuse-org · PyPI\",\"url\":\"https://pypi.org/project/py-gitea-opensuse-org/\"},{\"title\":\"PyPI Package Registry | Gitea Documentation\",\"url\":\"https://docs.gitea.com/usage/packages/pypi\"},{\"title\":\"GitHub - arount/pytea: Gitea API wrapper for Python\",\"url\":\"https://github.com/arount/pytea\"},{\"title\":\"vasek/gitea-swagger-client: Automatically generated Python client for Gitea. DO NOT UPDATE MANUALLY!!! - Forgejo (Gitea) at Gimli\",\"url\":\"https://gitea.ks.matfyz.cz/vasek/gitea-swagger-client\"},{\"title\":\"Gitea API Integrations - Pipedream\",\"url\":\"https://pipedream.com/apps/gitea\"}]\n\nHere are the search results for Gitea Python API clients:\n\n## Main Options for Gitea Python API Clients\n\n### 1. **py-gitea** (Most Recently Updated)\nReleased on September 26, 2025, py-gitea is a Python wrapper for the Gitea API that requires Python >=3.11 and is licensed under MIT. It's described as \"A very simple API client for Gitea > 1.16.1.\"(no content)To install, use `pip install py-gitea`.(no content)The implementation is focused on making access and working with Organizations, Teams, Repositories and Users as pain free as possible.(no content)Originally forked from https://github.com/m301/py-gitea.\n\n### 2. **py-gitea-opensuse-org** (December 2025)\nReleased on December 15, 2025, this package is automatically generated by the OpenAPI Generator project with API version 1.25.2 and requires Python >=3.9.(no content)You can install it via: `pip install py-gitea-opensuse-org` or directly using `pip install git+https://github.com/dcermak/py-gitea-opensuse-org.git`.\n\n### 3. **giteapy** (Older)\nReleased on January 14, 2020, giteapy is a Python SDK for Gitea that was automatically generated by the Swagger Codegen project. It supports Python 2.7 and 3.4+.(no content)Install with: `pip install giteapy`.\n\n### 4. **gitea-api** (GitHub)\nThis Python package is automatically generated by the Swagger Codegen project and can be installed directly using `pip install git+https://github.com/awalker125/gitea-api.git`.\n\n### 5. **pytea**\npytea is a Gitea API wrapper for Python 3 only.\n\n---\n\n**Recommendation**: For the most up-to-date options, **py-gitea** (v0.2.10, September 2025) or **py-gitea-opensuse-org** (December 2025) appear to be the most recently maintained packages.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"972c89bd-b2eb-4bc7-b7a6-08195033ec37","timestamp":"2026-01-12T18:46:58.401Z","toolUseResult":{"query":"Gitea Python API client pip install py-gitea python-gitea 2025","results":[{"tool_use_id":"srvtoolu_013YEN9bwZddjTVVfbmZQc9t","content":[{"title":"py-gitea · PyPI","url":"https://pypi.org/project/py-gitea/"},{"title":"GitHub - Langenfeld/py-gitea: A Gitea-API wrapper in Python","url":"https://github.com/Langenfeld/py-gitea"},{"title":"GitHub - awalker125/gitea-api: A python client for the gitea api","url":"https://github.com/awalker125/gitea-api"},{"title":"giteapy · PyPI","url":"https://pypi.org/project/giteapy/"},{"title":"GitHub - dblueai/giteapy: Python SDK for Gitea - A painless, self-hosted Git service","url":"https://github.com/dblueai/giteapy"},{"title":"py-gitea-opensuse-org · PyPI","url":"https://pypi.org/project/py-gitea-opensuse-org/"},{"title":"PyPI Package Registry | Gitea Documentation","url":"https://docs.gitea.com/usage/packages/pypi"},{"title":"GitHub - arount/pytea: Gitea API wrapper for Python","url":"https://github.com/arount/pytea"},{"title":"vasek/gitea-swagger-client: Automatically generated Python client for Gitea. DO NOT UPDATE MANUALLY!!! - Forgejo (Gitea) at Gimli","url":"https://gitea.ks.matfyz.cz/vasek/gitea-swagger-client"},{"title":"Gitea API Integrations - Pipedream","url":"https://pipedream.com/apps/gitea"}]},"Here are the search results for Gitea Python API clients:\n\n## Main Options for Gitea Python API Clients\n\n### 1. **py-gitea** (Most Recently Updated)\nReleased on September 26, 2025, py-gitea is a Python wrapper for the Gitea API that requires Python >=3.11 and is licensed under MIT. It's described as \"A very simple API client for Gitea > 1.16.1.\"(no content)To install, use `pip install py-gitea`.(no content)The implementation is focused on making access and working with Organizations, Teams, Repositories and Users as pain free as possible.(no content)Originally forked from https://github.com/m301/py-gitea.\n\n### 2. **py-gitea-opensuse-org** (December 2025)\nReleased on December 15, 2025, this package is automatically generated by the OpenAPI Generator project with API version 1.25.2 and requires Python >=3.9.(no content)You can install it via: `pip install py-gitea-opensuse-org` or directly using `pip install git+https://github.com/dcermak/py-gitea-opensuse-org.git`.\n\n### 3. **giteapy** (Older)\nReleased on January 14, 2020, giteapy is a Python SDK for Gitea that was automatically generated by the Swagger Codegen project. It supports Python 2.7 and 3.4+.(no content)Install with: `pip install giteapy`.\n\n### 4. **gitea-api** (GitHub)\nThis Python package is automatically generated by the Swagger Codegen project and can be installed directly using `pip install git+https://github.com/awalker125/gitea-api.git`.\n\n### 5. **pytea**\npytea is a Gitea API wrapper for Python 3 only.\n\n---\n\n**Recommendation**: For the most up-to-date options, **py-gitea** (v0.2.10, September 2025) or **py-gitea-opensuse-org** (December 2025) appear to be the most recently maintained packages."],"durationSeconds":19.267765599999983},"sourceToolAssistantUUID":"d52df6a7-d9b6-427f-bf18-556b34a01b42"}
{"parentUuid":"d52df6a7-d9b6-427f-bf18-556b34a01b42","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QEq86M697pwELcwBFXwgBi","type":"tool_result","content":"Web search results for query: \"litellm openrouter unified LLM API wrapper Python multiple providers 2025\"\n\nLinks: [{\"title\":\"OpenRouter | liteLLM\",\"url\":\"https://docs.litellm.ai/docs/providers/openrouter\"},{\"title\":\"LiteLLM - Getting Started | liteLLM\",\"url\":\"https://docs.litellm.ai/docs/\"},{\"title\":\"GitHub - BerriAI/litellm: Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]\",\"url\":\"https://github.com/BerriAI/litellm\"},{\"title\":\"Providers | liteLLM\",\"url\":\"https://docs.litellm.ai/docs/providers\"},{\"title\":\"LiteLLM vs OpenRouter: Which is Best For You ?\",\"url\":\"https://www.truefoundry.com/blog/litellm-vs-openrouter\"},{\"title\":\"Provider Routing | Intelligent Multi-Provider Request Routing | OpenRouter | Documentation\",\"url\":\"https://openrouter.ai/docs/guides/routing/provider-selection\"},{\"title\":\"How to Use Any OpenRouter Model with Google Agent Development Kit (ADK)\",\"url\":\"https://www.bitdoze.com/google-adk-openrouter-models/\"},{\"title\":\"OpenRouter vs LiteLLM: features, pricing, and use cases\",\"url\":\"https://xenoss.io/blog/openrouter-vs-litellm\"},{\"title\":\"OpenRouter vs LiteLLM: Choosing an LLM Gateway - Den's Hub\",\"url\":\"https://denshub.com/en/choosing-llm-gateway/\"},{\"title\":\"LiteLLM and the Rise of the Open-Source LLM Gateway - Tech News\",\"url\":\"https://bizety.com/2025/09/30/litellm-and-the-rise-of-the-open-source-llm-gateway/\"}]\n\nHere's what I found about LiteLLM and OpenRouter as unified LLM API wrappers for Python:\n\n## LiteLLM\n\nLiteLLM is a Python SDK and Proxy Server (AI Gateway) that allows you to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging.(no content)It supports major providers including Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, and NVIDIA NIM.\n\n**Key Features:**\n- You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs).\n- It offers a proxy server component that acts as a central gateway for routing requests across multiple providers, handling load balancing, retries, and fallbacks automatically.\n- Netflix, Lemonade, and RocketMoney are among LiteLLM's enterprise customers, indicating production-scale viability.\n- LiteLLM translates calls to different LLMs into a consistent format, mimicking OpenAI's API structure. This means you write code once and swap models with minimal changes.\n\n**Recent Growth:**\nLiteLLM's proxy tool has surpassed 470,000 downloads, reflecting widespread adoption among developers.(no content)At the time of writing, LiteLLM's GitHub repository has over 28,800 GitHub stars and over 4,000 forks.\n\n## OpenRouter\n\nOpenRouter is a unified API gateway that provides developers with a single endpoint to access a wide range of large language models (LLMs) from multiple providers such as OpenAI, Anthropic, Google's Gemini, Cohere, and Mistral.\n\n**Key Features:**\n- OpenRouter is a unified API gateway that gives engineering teams access to over 500 models. The platform abstracts individual provider APIs into a single interface, enabling teams to switch between models without changing integration code.\n- OpenRouter includes advanced features such as prompt caching, custom data policies for compliance, and traffic-shaping controls that let you set rate limits or prioritize certain providers.\n- You buy credits once and use them for any available model. OpenRouter automatically selects providers based on availability and your preferences.\n\n**Recent Funding:**\nIn June 2025, OpenRouter raised $40 million and hit a $500 million valuation.\n\n## Key Differences\n\nLiteLLM is the infrastructure-first answer to the problem that OpenRouter solves commercially. Where OpenRouter monetizes convenience, LiteLLM offers control.(no content)LiteLLM gives you full control over your LLM stack with a self-hosted proxy, policy-as-code via GitOps, and deep integration with existing observability tools, making it ideal for platform teams that need custom governance and on-prem deployments. OpenRouter, by contrast, is a fully managed edge SaaS offering that requires no hosting overhead.\n\n## Using LiteLLM with OpenRouter\n\nLiteLLM supports ALL OpenRouter models - you can send model=openrouter/<your-openrouter-model> to route requests through OpenRouter. This allows you to combine both tools, using LiteLLM as your unified interface while accessing OpenRouter's extensive model catalog.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"uuid":"6ef13f1d-c464-480c-8822-0ccb0b94d490","timestamp":"2026-01-12T18:47:06.523Z","toolUseResult":{"query":"litellm openrouter unified LLM API wrapper Python multiple providers 2025","results":[{"tool_use_id":"srvtoolu_01TLW35BUmh76bo5jMAPQUvt","content":[{"title":"OpenRouter | liteLLM","url":"https://docs.litellm.ai/docs/providers/openrouter"},{"title":"LiteLLM - Getting Started | liteLLM","url":"https://docs.litellm.ai/docs/"},{"title":"GitHub - BerriAI/litellm: Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]","url":"https://github.com/BerriAI/litellm"},{"title":"Providers | liteLLM","url":"https://docs.litellm.ai/docs/providers"},{"title":"LiteLLM vs OpenRouter: Which is Best For You ?","url":"https://www.truefoundry.com/blog/litellm-vs-openrouter"},{"title":"Provider Routing | Intelligent Multi-Provider Request Routing | OpenRouter | Documentation","url":"https://openrouter.ai/docs/guides/routing/provider-selection"},{"title":"How to Use Any OpenRouter Model with Google Agent Development Kit (ADK)","url":"https://www.bitdoze.com/google-adk-openrouter-models/"},{"title":"OpenRouter vs LiteLLM: features, pricing, and use cases","url":"https://xenoss.io/blog/openrouter-vs-litellm"},{"title":"OpenRouter vs LiteLLM: Choosing an LLM Gateway - Den's Hub","url":"https://denshub.com/en/choosing-llm-gateway/"},{"title":"LiteLLM and the Rise of the Open-Source LLM Gateway - Tech News","url":"https://bizety.com/2025/09/30/litellm-and-the-rise-of-the-open-source-llm-gateway/"}]},"Here's what I found about LiteLLM and OpenRouter as unified LLM API wrappers for Python:\n\n## LiteLLM\n\nLiteLLM is a Python SDK and Proxy Server (AI Gateway) that allows you to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging.(no content)It supports major providers including Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, and NVIDIA NIM.\n\n**Key Features:**\n- You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs).\n- It offers a proxy server component that acts as a central gateway for routing requests across multiple providers, handling load balancing, retries, and fallbacks automatically.\n- Netflix, Lemonade, and RocketMoney are among LiteLLM's enterprise customers, indicating production-scale viability.\n- LiteLLM translates calls to different LLMs into a consistent format, mimicking OpenAI's API structure. This means you write code once and swap models with minimal changes.\n\n**Recent Growth:**\nLiteLLM's proxy tool has surpassed 470,000 downloads, reflecting widespread adoption among developers.(no content)At the time of writing, LiteLLM's GitHub repository has over 28,800 GitHub stars and over 4,000 forks.\n\n## OpenRouter\n\nOpenRouter is a unified API gateway that provides developers with a single endpoint to access a wide range of large language models (LLMs) from multiple providers such as OpenAI, Anthropic, Google's Gemini, Cohere, and Mistral.\n\n**Key Features:**\n- OpenRouter is a unified API gateway that gives engineering teams access to over 500 models. The platform abstracts individual provider APIs into a single interface, enabling teams to switch between models without changing integration code.\n- OpenRouter includes advanced features such as prompt caching, custom data policies for compliance, and traffic-shaping controls that let you set rate limits or prioritize certain providers.\n- You buy credits once and use them for any available model. OpenRouter automatically selects providers based on availability and your preferences.\n\n**Recent Funding:**\nIn June 2025, OpenRouter raised $40 million and hit a $500 million valuation.\n\n## Key Differences\n\nLiteLLM is the infrastructure-first answer to the problem that OpenRouter solves commercially. Where OpenRouter monetizes convenience, LiteLLM offers control.(no content)LiteLLM gives you full control over your LLM stack with a self-hosted proxy, policy-as-code via GitOps, and deep integration with existing observability tools, making it ideal for platform teams that need custom governance and on-prem deployments. OpenRouter, by contrast, is a fully managed edge SaaS offering that requires no hosting overhead.\n\n## Using LiteLLM with OpenRouter\n\nLiteLLM supports ALL OpenRouter models - you can send model=openrouter/<your-openrouter-model> to route requests through OpenRouter. This allows you to combine both tools, using LiteLLM as your unified interface while accessing OpenRouter's extensive model catalog."],"durationSeconds":27.39054759999999},"sourceToolAssistantUUID":"d52df6a7-d9b6-427f-bf18-556b34a01b42"}
{"parentUuid":"6ef13f1d-c464-480c-8822-0ccb0b94d490","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_017d3XgtiaBhdZZLR5Lbf9eQ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Excellent! This is very valuable information. LiteLLM appears to be an excellent option for the unified LLM API wrapper that the requirements mention. It can abstract multiple providers (Anthropic, Google, OpenAI, Ollama, xAI, etc.) behind a common interface.\n\nNow I have all the research I need to create the research.json file. Let me compile this into a comprehensive document.\n\nSummary of what I've researched:\n\n1. **Anthropic SDK** - `pip install anthropic`, Python 3.9+\n2. **Google Gemini SDK** - `pip install google-genai` (NEW SDK, google-generativeai is deprecated)\n3. **xAI Grok SDK** - `pip install xai-sdk`, Python 3.10+\n4. **Ollama Python** - `pip install ollama`, Python 3.8+\n5. **FastAPI** - `pip install \"fastapi[standard]\"` (includes uvicorn)\n6. **React/TypeScript/Vite** - `npm create vite@latest -- --template react-ts`\n7. **MCP Python SDK** - `pip install mcp`, Python 3.10+\n8. **GitPython** - `pip install GitPython` for Git operations\n9. **py-gitea** - `pip install py-gitea` for Gitea API\n10. **LiteLLM** (recommendation) - Unified LLM API wrapper for multiple providers\n\nLet me create the research.json file now.","signature":"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"},{"type":"text","text":"Now I have comprehensive research on all the key integrations. Let me create the research.json file with all my findings:"},{"type":"tool_use","id":"toolu_01DBoGdYCQXibLDNjaKDxv7K","name":"Write","input":{"file_path":"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"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":3636,"cache_read_input_tokens":36347,"cache_creation":{"ephemeral_5m_input_tokens":3636,"ephemeral_1h_input_tokens":0},"output_tokens":6330,"service_tier":"standard"}},"type":"assistant","uuid":"fb6dd9bc-d5a8-4bfa-af0b-2bb19b68528b","timestamp":"2026-01-12T18:48:49.495Z"}
{"parentUuid":"fb6dd9bc-d5a8-4bfa-af0b-2bb19b68528b","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DBoGdYCQXibLDNjaKDxv7K","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\\research.json"}]},"uuid":"54d5689d-d68c-4684-9d0d-ef67a51e276d","timestamp":"2026-01-12T18:48:53.828Z","toolUseResult":{"type":"create","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","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"fb6dd9bc-d5a8-4bfa-af0b-2bb19b68528b"}
{"parentUuid":"54d5689d-d68c-4684-9d0d-ef67a51e276d","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_01RJAqRUrvZGfVFX3aPwhv4x","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I've successfully created the research.json file. Now let me print a summary to confirm completion.","signature":"EosCCkYICxgCKkC9/wyHjVlDZkgUOb2yj/Uk47jZk39OlNongRyjduil1oAChNT4gOn1d5ADyIEu5nPt5ux+kj/2n3UjBJbabIv1EgxM3GNBbOvEr65hM+UaDFRBmWNtxddVPnVi2SIw1A0KKUU0zibBshmKRFfESpcgKYLsjaCcK2jzzoQ/vG71j0YzY3qQohtNsfonhkUeKnMMK96o14KLLnmx6KKRLhmP2c+uk/5Mm6J5ips7zAqIDpT7jH7yxKJqpXo3DNp5k3wX4nW4GPEbsq3CgWkd0lequ9VqrKjQGsobzLEZvAwIuXNALorjhpIZYPmz/PpuBm2uWEBmfgwbPFLTyqHKzGV5PmjuGAE="},{"type":"text","text":"Now let me print a summary of the research findings:"},{"type":"tool_use","id":"toolu_01AkhksKDcDAGii8RvwfDBTS","name":"Bash","input":{"command":"echo \"=== RESEARCH COMPLETE ===\" && echo \"\" && echo \"Integrations Researched: 10\" && echo \"- Anthropic Claude SDK: Verified ✓ (pip install anthropic)\" && echo \"- Google Gemini SDK: Verified ✓ (pip install google-genai) [DEPRECATED: google-generativeai]\" && echo \"- xAI Grok SDK: Verified ✓ (pip install xai-sdk)\" && echo \"- Ollama Python: Verified ✓ (pip install ollama)\" && echo \"- FastAPI: Verified ✓ (pip install fastapi[standard])\" && echo \"- React/TypeScript/Vite: Verified ✓ (npm create vite@latest -- --template react-ts)\" && echo \"- MCP Python SDK: Verified ✓ (pip install mcp>=1.25,<2)\" && echo \"- GitPython: Verified ✓ (pip install GitPython)\" && echo \"- py-gitea: Verified ✓ (pip install py-gitea)\" && echo \"- LiteLLM (Unified API): Verified ✓ (pip install litellm)\" && echo \"\" && echo \"Unverified Claims: 2\" && echo \"- Cross-model validation quality improvements: low risk (design pattern)\" && echo \"- Cost optimization through routing: low risk (LiteLLM supports)\" && echo \"\" && echo \"Key Findings:\" && echo \"- CRITICAL: google-generativeai is DEPRECATED - use google-genai instead\" && echo \"- LiteLLM provides unified interface for 100+ LLMs - ideal for multi-provider architecture\" && echo \"- Python 3.11+ required for full compatibility across all packages\" && echo \"- MCP v2 coming Q1 2026 - pin to mcp>=1.25,<2 for stability\" && echo \"- GitPython has resource leaks in long-running processes - use subprocess pattern\" && echo \"\" && echo \"Recommendations:\" && echo \"- Use LiteLLM as the unified LLM API wrapper (already supports all required providers)\" && echo \"- Use official xai-sdk, not community alternatives\" && echo \"- Set minimum Python version to 3.11\" && echo \"- Pin MCP package version for stability\" && echo \"\" && echo \"research.json created successfully.\"","description":"Print research summary"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":6403,"cache_read_input_tokens":39983,"cache_creation":{"ephemeral_5m_input_tokens":6403,"ephemeral_1h_input_tokens":0},"output_tokens":663,"service_tier":"standard"}},"type":"assistant","uuid":"dec4a7a6-1dfb-47c9-976a-54d3d6430c81","timestamp":"2026-01-12T18:49:42.453Z"}
{"parentUuid":"dec4a7a6-1dfb-47c9-976a-54d3d6430c81","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AkhksKDcDAGii8RvwfDBTS","type":"tool_result","content":"=== RESEARCH COMPLETE ===\n\nIntegrations Researched: 10\n- Anthropic Claude SDK: Verified ✓ (pip install anthropic)\n- Google Gemini SDK: Verified ✓ (pip install google-genai) [DEPRECATED: google-generativeai]\n- xAI Grok SDK: Verified ✓ (pip install xai-sdk)\n- Ollama Python: Verified ✓ (pip install ollama)\n- FastAPI: Verified ✓ (pip install fastapi[standard])\n- React/TypeScript/Vite: Verified ✓ (npm create vite@latest -- --template react-ts)\n- MCP Python SDK: Verified ✓ (pip install mcp>=1.25,<2)\n- GitPython: Verified ✓ (pip install GitPython)\n- py-gitea: Verified ✓ (pip install py-gitea)\n- LiteLLM (Unified API): Verified ✓ (pip install litellm)\n\nUnverified Claims: 2\n- Cross-model validation quality improvements: low risk (design pattern)\n- Cost optimization through routing: low risk (LiteLLM supports)\n\nKey Findings:\n- CRITICAL: google-generativeai is DEPRECATED - use google-genai instead\n- LiteLLM provides unified interface for 100+ LLMs - ideal for multi-provider architecture\n- Python 3.11+ required for full compatibility across all packages\n- MCP v2 coming Q1 2026 - pin to mcp>=1.25,<2 for stability\n- GitPython has resource leaks in long-running processes - use subprocess pattern\n\nRecommendations:\n- Use LiteLLM as the unified LLM API wrapper (already supports all required providers)\n- Use official xai-sdk, not community alternatives\n- Set minimum Python version to 3.11\n- Pin MCP package version for stability\n\nresearch.json created successfully.","is_error":false}]},"uuid":"2e4881f6-7582-4154-9a28-2df485ce9ebb","timestamp":"2026-01-12T18:49:48.688Z","toolUseResult":{"stdout":"=== RESEARCH COMPLETE ===\n\nIntegrations Researched: 10\n- Anthropic Claude SDK: Verified ✓ (pip install anthropic)\n- Google Gemini SDK: Verified ✓ (pip install google-genai) [DEPRECATED: google-generativeai]\n- xAI Grok SDK: Verified ✓ (pip install xai-sdk)\n- Ollama Python: Verified ✓ (pip install ollama)\n- FastAPI: Verified ✓ (pip install fastapi[standard])\n- React/TypeScript/Vite: Verified ✓ (npm create vite@latest -- --template react-ts)\n- MCP Python SDK: Verified ✓ (pip install mcp>=1.25,<2)\n- GitPython: Verified ✓ (pip install GitPython)\n- py-gitea: Verified ✓ (pip install py-gitea)\n- LiteLLM (Unified API): Verified ✓ (pip install litellm)\n\nUnverified Claims: 2\n- Cross-model validation quality improvements: low risk (design pattern)\n- Cost optimization through routing: low risk (LiteLLM supports)\n\nKey Findings:\n- CRITICAL: google-generativeai is DEPRECATED - use google-genai instead\n- LiteLLM provides unified interface for 100+ LLMs - ideal for multi-provider architecture\n- Python 3.11+ required for full compatibility across all packages\n- MCP v2 coming Q1 2026 - pin to mcp>=1.25,<2 for stability\n- GitPython has resource leaks in long-running processes - use subprocess pattern\n\nRecommendations:\n- Use LiteLLM as the unified LLM API wrapper (already supports all required providers)\n- Use official xai-sdk, not community alternatives\n- Set minimum Python version to 3.11\n- Pin MCP package version for stability\n\nresearch.json created successfully.\r","stderr":"","interrupted":false,"isImage":false},"sourceToolAssistantUUID":"dec4a7a6-1dfb-47c9-976a-54d3d6430c81"}
{"parentUuid":"2e4881f6-7582-4154-9a28-2df485ce9ebb","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus","sessionId":"31ddedb4-4691-4399-8c69-528442ba64a8","version":"2.1.5","gitBranch":"master","slug":"jaunty-prancing-sunbeam","message":{"model":"claude-opus-4-5-20251101","id":"msg_011J7ruWedeAqsQY9VvaUWgp","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"I've completed the research phase. The research.json file has been created with comprehensive validated information about all the external integrations mentioned in the requirements.\n\nLet me provide a summary to the user.","signature":"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"},{"type":"text","text":"## Research Phase Complete\n\nI've researched and validated all external integrations mentioned in the requirements. Here's what I found:\n\n### Verified Integrations (10 total)\n\n| Integration | Package | Status |\n|------------|---------|--------|\n| Anthropic Claude SDK | `pip install anthropic` | ✓ Verified |\n| Google Gemini SDK | `pip install google-genai` | ✓ Verified |\n| xAI Grok SDK | `pip install xai-sdk` | ✓ Verified |\n| Ollama Python | `pip install ollama` | ✓ Verified |\n| FastAPI | `pip install \"fastapi[standard]\"` | ✓ Verified |\n| React/TypeScript/Vite | `npm create vite@latest -- --template react-ts` | ✓ Verified |\n| MCP Python SDK | `pip install mcp>=1.25,<2` | ✓ Verified |\n| GitPython | `pip install GitPython` | ✓ Verified |\n| py-gitea | `pip install py-gitea` | ✓ Verified |\n| **LiteLLM** (bonus find) | `pip install litellm` | ✓ Verified |\n\n### Critical Findings\n\n1. **Google Gemini SDK DEPRECATED**: The `google-generativeai` package reached EOL on Nov 30, 2025. Must use `google-genai` for all new development.\n\n2. **LiteLLM - Key Discovery**: This package provides a **unified interface for 100+ LLMs** including all required providers (Anthropic, Gemini, xAI, Ollama). It supports:\n   - OpenAI-compatible API format\n   - Cost tracking and budgeting\n   - Automatic fallbacks and load balancing\n   - Enterprise adoption (Netflix, Lemonade, RocketMoney)\n\n3. **Python Version**: Python 3.11+ required for full compatibility (py-gitea requires 3.11+)\n\n4. **MCP Stability**: Pin to `mcp>=1.25,<2` as v2 is planned for Q1 with potential breaking changes\n\n5. **GitPython Warning**: Has resource leak issues in long-running processes - use subprocess pattern for daemons\n\n### Output\n\n**`research.json`** has been created with:\n- Detailed API patterns and import statements for each library\n- Verified package names and installation commands\n- Configuration requirements (env vars, dependencies)\n- Known gotchas and warnings\n- Research sources for verification"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":1256,"cache_read_input_tokens":46386,"cache_creation":{"ephemeral_5m_input_tokens":1256,"ephemeral_1h_input_tokens":0},"output_tokens":659,"service_tier":"standard"}},"type":"assistant","uuid":"7b2c18b3-0f3b-47f8-8718-0468392f35d9","timestamp":"2026-01-12T18:50:13.774Z"}