## YOUR ROLE - RESEARCH AGENT

You 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.

**Key Principle**: Verify everything. Trust nothing assumed. Document findings.

---

## YOUR CONTRACT

**Inputs**:
- `requirements.json` - User requirements with mentioned integrations

**Output**: `research.json` - Validated research findings

You MUST create `research.json` with validated information about each integration.

---

## PHASE 0: LOAD REQUIREMENTS

```bash
cat requirements.json
```

Identify from the requirements:
1. **External libraries** mentioned (packages, SDKs)
2. **External services** mentioned (databases, APIs)
3. **Infrastructure** mentioned (Docker, cloud services)
4. **Frameworks** mentioned (web frameworks, ORMs)

---

## PHASE 1: RESEARCH EACH INTEGRATION

For EACH external dependency identified, research using available tools:

### 1.1: Use Context7 MCP (PRIMARY RESEARCH TOOL)

**Context7 should be your FIRST choice for researching libraries and integrations.**

Context7 provides up-to-date documentation for thousands of libraries. Use it systematically:

#### Step 1: Resolve the Library ID

First, find the correct Context7 library ID:

```
Tool: mcp__context7__resolve-library-id
Input: { "libraryName": "[library name from requirements]" }
```

Example for researching "NextJS":
```
Tool: mcp__context7__resolve-library-id
Input: { "libraryName": "nextjs" }
```

This returns the Context7-compatible ID (e.g., "/vercel/next.js").

#### Step 2: Get Library Documentation

Once you have the ID, fetch documentation for specific topics:

```
Tool: mcp__context7__get-library-docs
Input: {
  "context7CompatibleLibraryID": "/vercel/next.js",
  "topic": "routing",  // Focus on relevant topic
  "mode": "code"       // "code" for API examples, "info" for conceptual guides
}
```

**Topics to research for each integration:**
- "getting started" or "installation" - For setup patterns
- "api" or "reference" - For function signatures
- "configuration" or "config" - For environment variables and options
- "examples" - For common usage patterns
- Specific feature topics relevant to your task

#### Step 3: Document Findings

For each integration, extract from Context7:
1. **Correct package name** - The actual npm/pip package name
2. **Import statements** - How to import in code
3. **Initialization code** - Setup patterns
4. **Key API functions** - Function signatures you'll need
5. **Configuration options** - Environment variables, config files
6. **Common gotchas** - Issues mentioned in docs

### 1.2: Use Web Search (for supplementary research)

Use web search AFTER Context7 to:
- Verify package exists on npm/PyPI
- Find very recent updates or changes
- Research less common libraries not in Context7

Search for:
- `"[library] official documentation"`
- `"[library] python SDK usage"` (or appropriate language)
- `"[library] getting started"`
- `"[library] pypi"` or `"[library] npm"` (to verify package names)

### 1.3: Key Questions to Answer

For each integration, find answers to:

1. **What is the correct package name?**
   - PyPI/npm exact name
   - Installation command
   - Version requirements

2. **What are the actual API patterns?**
   - Import statements
   - Initialization code
   - Main function signatures

3. **What configuration is required?**
   - Environment variables
   - Config files
   - Required dependencies

4. **What infrastructure is needed?**
   - Database requirements
   - Docker containers
   - External services

5. **What are known issues or gotchas?**
   - Common mistakes
   - Breaking changes in recent versions
   - Platform-specific issues

---

## PHASE 2: VALIDATE ASSUMPTIONS

For any technical claims in requirements.json:

1. **Verify package names exist** - Check PyPI, npm, etc.
2. **Verify API patterns** - Match against documentation
3. **Verify configuration options** - Confirm they exist
4. **Flag anything unverified** - Mark as "unverified" in output

---

## PHASE 3: CREATE RESEARCH.JSON

Output your findings:

```bash
cat > research.json << 'EOF'
{
  "integrations_researched": [
    {
      "name": "[library/service name]",
      "type": "library|service|infrastructure",
      "verified_package": {
        "name": "[exact package name]",
        "install_command": "[pip install X / npm install X]",
        "version": "[version if specific]",
        "verified": true
      },
      "api_patterns": {
        "imports": ["from X import Y"],
        "initialization": "[code snippet]",
        "key_functions": ["function1()", "function2()"],
        "verified_against": "[documentation URL or source]"
      },
      "configuration": {
        "env_vars": ["VAR1", "VAR2"],
        "config_files": ["config.json"],
        "dependencies": ["other packages needed"]
      },
      "infrastructure": {
        "requires_docker": true,
        "docker_image": "[image name]",
        "ports": [1234],
        "volumes": ["/data"]
      },
      "gotchas": [
        "[Known issue 1]",
        "[Known issue 2]"
      ],
      "research_sources": [
        "[URL or documentation reference]"
      ]
    }
  ],
  "unverified_claims": [
    {
      "claim": "[what was claimed]",
      "reason": "[why it couldn't be verified]",
      "risk_level": "low|medium|high"
    }
  ],
  "recommendations": [
    "[Any recommendations based on research]"
  ],
  "created_at": "[ISO timestamp]"
}
EOF
```

---

## PHASE 4: SUMMARIZE FINDINGS

Print a summary:

```
=== RESEARCH COMPLETE ===

Integrations Researched: [count]
- [name1]: Verified ✓
- [name2]: Verified ✓
- [name3]: Partially verified ⚠

Unverified Claims: [count]
- [claim1]: [risk level]

Key Findings:
- [Important finding 1]
- [Important finding 2]

Recommendations:
- [Recommendation 1]

research.json created successfully.
```

---

## CRITICAL RULES

1. **ALWAYS verify package names** - Don't assume "graphiti" is the package name
2. **ALWAYS cite sources** - Document where information came from
3. **ALWAYS flag uncertainties** - Mark unverified claims clearly
4. **DON'T make up APIs** - Only document what you find in docs
5. **DON'T skip research** - Each integration needs investigation

---

## RESEARCH TOOLS PRIORITY

1. **Context7 MCP** (PRIMARY) - Best for official docs, API patterns, code examples
   - Use `resolve-library-id` first to get the library ID
   - Then `get-library-docs` with relevant topics
   - Covers most popular libraries (React, Next.js, FastAPI, etc.)

2. **Web Search** - For package verification, recent info, obscure libraries
   - Use when Context7 doesn't have the library
   - Good for checking npm/PyPI for package existence

3. **Web Fetch** - For reading specific documentation pages
   - Use for custom or internal documentation URLs

**ALWAYS try Context7 first** - it provides structured, validated documentation that's more reliable than web search results.

---

## EXAMPLE RESEARCH OUTPUT

For a task involving "Graphiti memory integration":

**Step 1: Context7 Lookup**
```
Tool: mcp__context7__resolve-library-id
Input: { "libraryName": "graphiti" }
→ Returns library ID or "not found"
```

If found in Context7:
```
Tool: mcp__context7__get-library-docs
Input: {
  "context7CompatibleLibraryID": "/zep/graphiti",
  "topic": "getting started",
  "mode": "code"
}
→ Returns installation, imports, initialization code
```

**Step 2: Compile Findings to research.json**

```json
{
  "integrations_researched": [
    {
      "name": "Graphiti",
      "type": "library",
      "verified_package": {
        "name": "graphiti-core",
        "install_command": "pip install graphiti-core",
        "version": ">=0.5.0",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from graphiti_core import Graphiti",
          "from graphiti_core.nodes import EpisodeType"
        ],
        "initialization": "graphiti = Graphiti(graph_driver=driver)",
        "key_functions": [
          "add_episode(name, episode_body, source, group_id)",
          "search(query, limit, group_ids)"
        ],
        "verified_against": "Context7 MCP + GitHub README"
      },
      "configuration": {
        "env_vars": ["OPENAI_API_KEY"],
        "dependencies": ["real_ladybug"]
      },
      "infrastructure": {
        "requires_docker": false,
        "embedded_database": "LadybugDB"
      },
      "gotchas": [
        "Requires OpenAI API key for embeddings",
        "Must call build_indices_and_constraints() before use",
        "LadybugDB is embedded - no separate database server needed"
      ],
      "research_sources": [
        "Context7 MCP: /zep/graphiti",
        "https://github.com/getzep/graphiti",
        "https://pypi.org/project/graphiti-core/"
      ]
    }
  ],
  "unverified_claims": [],
  "recommendations": [
    "LadybugDB is embedded and requires no Docker or separate database setup"
  ],
  "context7_libraries_used": ["/zep/graphiti"],
  "created_at": "2024-12-10T12:00:00Z"
}
```

---

## BEGIN

Start by reading requirements.json, then research each integration mentioned.

---

**Spec Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform
**Project Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus

## Context from Previous Phases

### Requirements
# Requirements Phase Summary

## Project Vision
Build 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.

## Key Architectural Decisions

**Multi-Model Strategy** (Core differentiator)
- No single LLM as source of truth - leverage collective intelligence
- Strategic routing by task type:
  - **Ollama**: Local, token-free lightweight tasks (file analysis, transforms)
  - **Claude Sonnet/Opus**: Complex reasoning and architecture
  - **Gemini**: Multimodal analysis, large context windows
  - **Grok**: Real-time information, alternative perspectives
- Cross-model validation for quality assurance
- Cost optimization through intelligent model selection

**Adaptive Requirements** (Not waterfall)
- Specs evolve with project, not frozen at start
- Re-interview capability to clarify ambiguities during development
- Spec versioning to track requirement evolution
- Mid-flight course correction and steering

## Critical Components to Implement

1. **Model Orchestration Layer**
   - Unified API wrapper supporting all providers
   - Task decomposition with model-specific assignments
   - Parallel execution engine with dependency management
   - Fallback strategies for model failures

2. **Version Control Integration**
   - Git/Gitea native support
   - Auto-branching strategies
   - Conventional commit message generation
   - PR creation with comprehensive descriptions

3. **Spec Management System**
   - Initial spec-app style interview
   - Spec versioning and history tracking
   - Re-interview workflow for clarification
   - Context preservation across changes

4. **Extensible Skill Architecture**
   - Plugin system for specialized capabilities
   - Model-specific and model-agnostic skills
   - Skill composition for complex workflows
   - Custom skill creation framework

5. **Quality Assurance Pipeline**
   - Multi-model consensus for critical decisions
   - Automated testing integration
   - Security scanning, performance profiling
   - Code quality metrics

## Technical Stack

- **Backend**: Python (FastAPI) orchestration layer
- **Frontend**: React/TypeScript for project management UI
- **State**: Track project state, agent activities, decisions
- **Database**: Store specs, decisions, task history, model interactions
- **MCP Server**: Feature graph and skill management

## Critical Constraints

- Minimum **3 LLM providers** working in concert
- Must demonstrate **cost savings** through model routing
- Must handle **mid-project requirement changes** gracefully
- Full **transparency**: visibility into which models did what and why
- Support **local-first** operation via Ollama

## Success Metrics

1. Complete full lifecycle: spec → implementation → steering → completion
2. Prove cost optimization through intelligent routing
3. Show quality improvements via cross-model validation
4. Handle evolving requirements without restart
5. Support at least 3 providers simultaneously

## Workflow Classification
**Type**: Refactoring - building on existing Autocoder 2.0/Auto-Claude patterns

**Requirements File**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform\requirements.json
**Research Output**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform\research.json

Read the requirements.json to understand what integrations/libraries are needed.
Research each external dependency to validate:
- Correct package names
- Actual API patterns
- Configuration requirements
- Known issues or gotchas

Output your findings to research.json.


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.

I'll start by reading the requirements.json file to identify all external integrations that need research.


     1→{
     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",
     3→  "workflow_type": "refactoring"
     4→}

<system-reminder>
Whenever 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.
</system-reminder>

Multi-LLM Agentic Development Platform

  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.

  Core Features

  1. Multi-LLM Provider Support
  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others
  - Strategic model assignment - route tasks to models best suited for them:
    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)
    - Claude Sonnet/Opus for complex reasoning and architecture
    - Gemini for multimodal analysis and large context windows
    - Grok for real-time information and alternative perspectives
  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance
  - Cost optimization: intelligently balance between cloud and local models

  2. Version Control Integration
  - Native Git and Gitea support
  - Automatic branching strategies for features/tasks
  - Commit message generation aligned with conventional commits
  - PR creation with comprehensive descriptions
  - Support for reviewing and iterating on code in branches

  3. Adaptive Spec System
  - Initial spec-app style interview to gather requirements
  - Re-interview capability: as the project evolves, conduct follow-up interviews to:
    - Clarify ambiguous requirements that emerged during development
    - Gather feedback on implemented features
    - Adjust scope based on discoveries
    - Validate assumptions with the user
  - Spec versioning: track how requirements evolve over time
  - Continuous alignment: ensure implementation stays true to user intent

  4. Agent Skill System
  - Extensible skill/addon architecture for specialized agent capabilities
  - Skills can be model-specific or model-agnostic
  - Examples: code review, security scanning, performance optimization, documentation generation
  - Custom skill creation framework
  - Skill composition: chain skills together for complex workflows

  5. Project Steering & Course Correction
  - Mid-flight adjustments: ability to redirect project direction after work has begun
  - Checkpoint system: review progress at milestones and adjust plans
  - Context preservation: maintain full project history when steering changes
  - Impact analysis: understand implications of direction changes
  - Graceful pivoting: refactor or adapt existing work rather than starting over

  6. Intelligent Orchestration
  - Task decomposition with model-specific assignments
  - Parallel execution where possible
  - Dependency management between tasks
  - Progressive refinement: iterative improvement cycles
  - Fallback strategies when a model fails or produces poor results

  7. Quality Assurance
  - Multi-model consensus for critical decisions
  - Automated testing integration
  - Code quality metrics and reporting
  - Security vulnerability scanning
  - Performance profiling

  Technical Architecture

  - Backend: Python (FastAPI) for orchestration layer
  - Frontend: React/TypeScript UI for project management and monitoring
  - Model Integration: Unified API wrapper supporting all providers
  - State Management: Track project state, agent activities, and decisions
  - Database: Store specs, decisions, task history, and model interactions
  - MCP Server: Feature graph and skill management

  Key Differentiators

  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence
  2. Adaptive requirements: Specs evolve with the project, not frozen at start
  3. Local-first option: Can run meaningful work without API costs via Ollama
  4. Course correction: Projects aren't rigid - steer and adapt as you learn
  5. Transparency: Full visibility into which models did what and why

  Use Cases

  - Large greenfield projects with evolving requirements
  - Migrating/refactoring existing codebases with uncertain scope
  - Research projects where requirements emerge through exploration
  - Cost-sensitive projects that need to optimize token usage
  - High-stakes projects requiring multi-model validation

  Success Criteria

  - Successfully complete a full project lifecycle: spec → implementation → steering → completion
  - Demonstrate cost savings through intelligent model routing
  - Show quality improvements through cross-model validation
  - Handle mid-project requirement changes gracefully
  - Support at least 3 different LLM providers working in concert


Now I have a clear understanding of the requirements. Let me identify all the external integrations/libraries that need to be researched:

**LLM Providers/SDKs:**
1. Claude API (Anthropic)
2. Gemini API (Google)
3. Grok API (xAI)
4. Ollama (local LLM)

**Backend Framework:**
5. FastAPI (Python web framework)

**Frontend Framework:**
6. React (JavaScript library)
7. TypeScript

**Version Control:**
8. Git (command line)
9. Gitea (Git hosting)

**Other:**
10. MCP Server (Model Context Protocol)

Let me research each of these integrations systematically. I'll start with Context7 for the major libraries.

Now I can identify the external integrations to research from the requirements:

**LLM Provider SDKs:**
1. Anthropic (Claude) SDK
2. Google Gemini SDK
3. Grok (xAI) SDK
4. Ollama

**Backend Framework:**
5. FastAPI

**Frontend:**
6. React
7. TypeScript

**Version Control:**
8. Git/Gitea integration

**Protocol:**
9. MCP (Model Context Protocol)

Let me research each integration. I'll start by using Context7 to look up the primary libraries:

Question: Anthropic Claude Python SDK for LLM API integration

Question: FastAPI Python web framework for building APIs

Question: React JavaScript library for building user interfaces

Question: Ollama local LLM server for running models locally


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: Ollama
- Context7-compatible library ID: /ollama/ollama
- 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.
- Code Snippets: 598
- Source Reputation: High
- Benchmark Score: 88.1
----------
- Title: Ollama
- Context7-compatible library ID: /llmstxt/ollama_llms-full_txt
- 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.
- Code Snippets: 1464
- Source Reputation: High
- Benchmark Score: 89.3
----------
- Title: Ollama
- Context7-compatible library ID: /llmstxt/ollama_llms_txt
- 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.
- Code Snippets: 425
- Source Reputation: High
- Benchmark Score: 86.1
----------
- Title: Ollama
- Context7-compatible library ID: /websites/ollama
- 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.
- Code Snippets: 342
- Source Reputation: High
- Benchmark Score: 92.1
----------
- Title: Ollama Python
- Context7-compatible library ID: /ollama/ollama-python
- 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.
- Code Snippets: 36
- Source Reputation: High
- Benchmark Score: 87.3


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: Anthropic SDK for Python
- Context7-compatible library ID: /anthropics/anthropic-sdk-python
- 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.
- Code Snippets: 93
- Source Reputation: High
- Benchmark Score: 73.7
----------
- Title: Claude API
- Context7-compatible library ID: /websites/platform_claude_en_api
- 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.
- Code Snippets: 1217
- Source Reputation: High
- Benchmark Score: 77.2
----------
- Title: Anthropic API and Claude
- Context7-compatible library ID: /websites/anthropic_en
- Description: Official documentation for the Anthropic API and Claude, offering guides and resources for developers to integrate powerful AI models and build applications.
- Code Snippets: 11
- Source Reputation: High
- Benchmark Score: 71.1
----------
- Title: Anthropic Cookbook
- Context7-compatible library ID: /anthropics/anthropic-cookbook
- 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.
- Code Snippets: 1226
- Source Reputation: High
- Benchmark Score: 85.6
----------
- Title: Anthropic Courses
- Context7-compatible library ID: /anthropics/courses
- 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.
- Code Snippets: 1173
- Source Reputation: High
- Benchmark Score: 76.3


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: FastAPI
- Context7-compatible library ID: /websites/fastapi_tiangolo
- 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.
- Code Snippets: 12067
- Source Reputation: High
- Benchmark Score: 94.6
----------
- Title: FastAPI
- Context7-compatible library ID: /fastapi/fastapi
- Description: FastAPI framework, high performance, easy to learn, fast to code, ready for production
- Code Snippets: 881
- Source Reputation: High
- Benchmark Score: 87.2
- Versions: 0.115.13, 0_116_1, 0.118.2, 0.122.0, 0.128.0
----------
- Title: FastAPI
- Context7-compatible library ID: /websites/fastapi_tiangolo_learn
- 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.
- Code Snippets: 2
- Source Reputation: High
- Benchmark Score: 8
----------
- Title: Awesome FastAPI
- Context7-compatible library ID: /mjhea0/awesome-fastapi
- Description: A curated list of awesome things related to FastAPI, a modern, high-performance Python web framework for building RESTful APIs.
- Code Snippets: 84
- Source Reputation: High
----------
- Title: FastAPI Boilerplate
- Context7-compatible library ID: /websites/benavlabs_github_io_fastapi-boilerplate
- 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.
- Code Snippets: 715
- Source Reputation: High


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: React
- Context7-compatible library ID: /websites/react_dev
- 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.
- Code Snippets: 4359
- Source Reputation: High
- Benchmark Score: 74.5
----------
- Title: React
- Context7-compatible library ID: /websites/react_dev_reference
- 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.
- Code Snippets: 2330
- Source Reputation: High
- Benchmark Score: 77.8
----------
- Title: React
- Context7-compatible library ID: /websites/18_react_dev
- Description: React is a JavaScript library for building web and native user interfaces out of individual components, designed for creating interactive and dynamic applications.
- Code Snippets: 3921
- Source Reputation: High
- Benchmark Score: 82.6
----------
- Title: React
- Context7-compatible library ID: /reactjs/react.dev
- Description: React.dev is the official documentation website for React, a JavaScript library for building user interfaces, providing guides, API references, and tutorials.
- Code Snippets: 3742
- Source Reputation: High
- Benchmark Score: 70.5
----------
- Title: React
- Context7-compatible library ID: /websites/react_dev_learn
- Description: React is a JavaScript library for building user interfaces, focused on declarative components and efficient updates.
- Code Snippets: 835
- Source Reputation: High
- Benchmark Score: 52.9


I've identified the Context7 library IDs for:
- Anthropic: /anthropics/anthropic-sdk-python (best for SDK patterns)
- FastAPI: /websites/fastapi_tiangolo (highest score)
- React: /websites/18_react_dev (highest score)
- Ollama: /ollama/ollama-python (Python SDK)

Now let me also search for:
- Google Gemini SDK
- Grok/xAI SDK
- MCP (Model Context Protocol)
- TypeScript

Let me get these library IDs and also start querying docs.

Excellent! Now let me search for the remaining integrations:

Question: Google Gemini AI SDK for Python LLM integration

Question: TypeScript programming language for React frontend

Question: MCP Model Context Protocol for AI skill management


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: TypeScript
- Context7-compatible library ID: /microsoft/typescript
- Description: TypeScript is a language for application-scale JavaScript that adds optional types to JavaScript, supporting tools for large-scale JavaScript applications.
- Code Snippets: 18942
- Source Reputation: High
- Benchmark Score: 61.6
- Versions: v5.9.2, v5.8.3
----------
- Title: TypeScript
- Context7-compatible library ID: /websites/typescriptlang
- 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.
- Code Snippets: 2391
- Source Reputation: High
- Benchmark Score: 91.3
----------
- Title: TypeScript Website
- Context7-compatible library ID: /microsoft/typescript-website
- Description: The official website for TypeScript, featuring documentation, playground examples, and resources for developers.
- Code Snippets: 2459
- Source Reputation: High
- Benchmark Score: 60
----------
- Title: Wildberries API TypeScript SDK
- Context7-compatible library ID: /salacoste/daytona-wildberries-typescript-sdk
- 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.
- Code Snippets: 7650
- Source Reputation: High
- Benchmark Score: 87.9
----------
- Title: OpenRouter SDK
- Context7-compatible library ID: /openrouterteam/typescript-sdk
- 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.
- Code Snippets: 1046
- Source Reputation: High
- Benchmark Score: 79.6


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: Gemini API
- Context7-compatible library ID: /websites/ai_google_dev_gemini-api
- 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.
- Code Snippets: 3752
- Source Reputation: High
- Benchmark Score: 72.6
----------
- Title: Gemini Developer API
- Context7-compatible library ID: /llmstxt/ai_google_dev_gemini-api_llms_txt
- Description: The Gemini Developer API provides access to Google's advanced AI models for building applications with text generation, image understanding, and more.
- Code Snippets: 1074
- Source Reputation: High
- Benchmark Score: 81.1
----------
- Title: Gemini API Live
- Context7-compatible library ID: /websites/ai_google_dev_api
- 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.
- Code Snippets: 2350
- Source Reputation: High
- Benchmark Score: 69.9
----------
- Title: Gemini AI
- Context7-compatible library ID: /gbaptista/gemini-ai
- Description: A Ruby Gem for interacting with Google's Gemini generative AI services through Vertex AI, Generative Language API, or AI Studio.
- Code Snippets: 110
- Source Reputation: High
----------
- Title: Gemini PHP
- Context7-compatible library ID: /google-gemini-php/client
- 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.
- Code Snippets: 128
- Source Reputation: Medium
- Benchmark Score: 83.8


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: Model Context Protocol
- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification_2025-11-25
- 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.
- Code Snippets: 599
- Source Reputation: High
- Benchmark Score: 45.7
----------
- Title: Microsoft MCP Servers
- Context7-compatible library ID: /microsoft/mcp
- 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.
- Code Snippets: 1351
- Source Reputation: High
- Benchmark Score: 61
----------
- Title: Model Context Protocol Python SDK
- Context7-compatible library ID: /modelcontextprotocol/python-sdk
- 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.
- Code Snippets: 296
- Source Reputation: High
- Benchmark Score: 89.2
----------
- Title: Model Context Protocol Typescript SDK
- Context7-compatible library ID: /modelcontextprotocol/typescript-sdk
- 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.
- Code Snippets: 80
- Source Reputation: High
- Benchmark Score: 85.3
----------
- Title: Model Context Protocol
- Context7-compatible library ID: /websites/modelcontextprotocol_io_specification
- Description: The Model Context Protocol (MCP) is a standard for defining and managing context for models, focusing on versioning, interoperability, and client-server communication.
- Code Snippets: 611
- Source Reputation: High
- Benchmark Score: 46.3


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.

For Anthropic SDK, FastAPI, React, Ollama, Gemini, TypeScript, and MCP.

I still need to research Grok/xAI SDK - let me search for that too.

Now let me query the detailed documentation for these key libraries and search for Grok/xAI:

Question: Installation, initialization, basic usage, async client, streaming responses

Question: Installation, getting started, async endpoints, dependency injection, request validation

Question: Installation, Python client, chat, generation, async usage, streaming

Question: xAI Grok API SDK for Python LLM integration


Error: Claude requested permissions to use mcp__context7__query-docs, but you haven't granted it yet.


Available Libraries:

Each result includes:
- Library ID: Context7-compatible identifier (format: /org/project)
- Name: Library or package name
- Description: Short summary
- Code Snippets: Number of available code examples
- Source Reputation: Authority indicator (High, Medium, Low, or Unknown)
- Benchmark Score: Quality indicator (100 is the highest score)
- 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.

For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case.

----------

- Title: xAI API
- Context7-compatible library ID: /websites/x_ai
- 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.
- Code Snippets: 814
- Source Reputation: High
- Benchmark Score: 83.4
----------
- Title: Groq Python
- Context7-compatible library ID: /groq/groq-python
- 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.
- Code Snippets: 107
- Source Reputation: High
- Benchmark Score: 91.8
----------
- Title: Groq API Cookbook
- Context7-compatible library ID: /groq/groq-api-cookbook
- 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.
- Code Snippets: 808
- Source Reputation: High
- Benchmark Score: 62
----------
- Title: Groq Typescript
- Context7-compatible library ID: /groq/groq-typescript
- Description: A TypeScript/JavaScript library for convenient access to the Groq REST API, enabling server-side integration with AI models.
- Code Snippets: 81
- Source Reputation: High
- Benchmark Score: 89.9
----------
- Title: Groq PHP
- Context7-compatible library ID: /lucianotonet/groq-php
- 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.
- Code Snippets: 76
- Source Reputation: High
- Benchmark Score: 89


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:

- Anthropic SDK: /anthropics/anthropic-sdk-python
- FastAPI: /websites/fastapi_tiangolo
- React: /websites/18_react_dev
- Ollama Python: /ollama/ollama-python
- Gemini API: /websites/ai_google_dev_gemini-api or /llmstxt/ai_google_dev_gemini-api_llms_txt
- TypeScript: /websites/typescriptlang
- MCP Python SDK: /modelcontextprotocol/python-sdk
- xAI/Grok API: /websites/x_ai

Note: There's also Groq (different from Grok!) - the xAI one is what they need.

Let me use web search to gather the detailed information about these packages.

Let me use web search to gather detailed API information for each SDK:

WebSearch

Question: anthropic python SDK pip install anthropic getting started 2025

Question: google gemini python SDK pip install google-generativeai 2025

Question: xAI Grok API Python SDK pip install 2025


Web search results for query: "anthropic python SDK pip install anthropic getting started 2025"

Links: [{"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:

## Installation

The 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.

## Basic Usage

The 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.

### Synchronous Client
You 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.

### Asynchronous Client
For async usage, simply import `AsyncAnthropic` instead of `Anthropic` and use `await` with each API call.

## Additional Installation Options

For cloud platform integrations:

- The library provides support for the Anthropic Bedrock API if you install with the bedrock extra: `pip install -U anthropic[bedrock]`.

- For Google Cloud Vertex AI support, install with the vertex extra: `pip install -U anthropic[vertex]`.

## Setting Up Your API Key

You need to export your API key as an environment variable: `export ANTHROPIC_API_KEY=<insert token here>`

## Documentation

The 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.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: anthropic python SDK pip install anthropic getting started 2025

Anthropic Python API library

Anthropic Python SDK

Client SDKs - Claude Docs

anthropic - anaconda | Anaconda.org


Web search results for query: "google gemini python SDK pip install google-generativeai 2025"

Links: [{"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:

## Important: Package Deprecation Notice

The `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.

## New Recommended SDK: `google-genai`

As 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.

### Installation

You run `pip install google-genai`. If you want to use the SDK asynchronously, run `pip install google-genai[aiohttp]`.

### Basic Usage

Google 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:
```python
from google import genai
client = genai.Client(api_key='GEMINI_API_KEY')
```

If 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.

### Legacy Libraries

The 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.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: google gemini python SDK pip install google-generativeai 2025

google-generativeai · PyPI

Google Gen AI SDK documentation


Web search results for query: "xAI Grok API Python SDK pip install 2025"

Links: [{"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:

## Official xAI Python SDK

The 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.

### Installation

Install from PyPI with pip. Python 3.10 or higher is required to use the xAI SDK:

```bash
pip install xai-sdk
```

After 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.

### Optional Telemetry Dependencies

The telemetry feature requires additional dependencies based on your export needs:
```bash
# For HTTP OTLP export
pip install xai-sdk[telemetry-http]

# For gRPC OTLP export
pip install xai-sdk[telemetry-grpc]
```

### Features

- 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.
- Agentic Tool Calling: Let Grok autonomously decide when to search the web, 𝕏, or execute code to answer your questions with real-time information.
- Comprehensive documentation is available at docs.x.ai.

---

## Community-Maintained Alternatives

There are also community-maintained SDKs available:

1. **xai-grok-sdk** - Lightweight xAI SDK with minimal dependencies
   ```bash
   pip install xai-grok-sdk
   ```

2. **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.
   ```bash
   pip install xai-grok
   ```

3. **xai-grok-sdk-advanced** - Advanced SDK for xAI Grok API with analytics and rate-limit handling
   ```bash
   pip install xai-grok-sdk-advanced
   ```

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: xAI Grok API Python SDK pip install 2025

xai-sdk · PyPI

The Hitchhiker's Guide to Grok | xAI

xai-grok · PyPI

xai-grok-sdk · PyPI

piwheels - xai-grok-sdk-advanced

AI SDK Providers: xAI Grok


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.

Let me gather more details on Ollama, FastAPI, MCP, and Git integration:

Question: ollama python pip install ollama chat streaming async 2025

Question: MCP model context protocol python SDK pip install mcp 2025

Question: GitPython pip install git python automation commit branch 2025


Web search results for query: "MCP model context protocol python SDK pip install mcp 2025"

Links: [{"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:

## MCP Python SDK Installation

The `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.

### Installation Options

You can install using either uv (recommended) or pip:

```bash
# Using uv (recommended)
uv add mcp

# Or using pip
pip install mcp
```

For CLI features and building servers, you can install with the cli extra: `uv add "mcp[cli]" httpx`

### Requirements

The 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.

## What is MCP?

The 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.

## Quick Start Example

You can create a FastMCP server like this:

```python
from mcp.server.fastmcp import FastMCP

# Initialize FastMCP server
mcp = FastMCP("weather")
```

MCP 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).

## Version Information

Version 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.

## Official Resources

- **PyPI**: https://pypi.org/project/mcp/
- **GitHub**: github.com/modelcontextprotocol/python-sdk - The official Python SDK for Model Context Protocol servers and clients
- **Documentation**: Docs live on modelcontextprotocol.io

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: MCP model context protocol python SDK pip install mcp 2025

mcp · PyPI

MCP Python SDK

fastmcp · PyPI

Build your MCP server

mcp-pypi · PyPI


Web search results for query: "GitPython pip install git python automation commit branch 2025"

Links: [{"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:

## Installation

Installing 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.

## What is GitPython?

GitPython 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.

## Common Automation Tasks

### Initializing and Opening Repositories
To 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')`

### Creating Branches and Committing Changes
You 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)`.

### Checking for Uncommitted Changes
In 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.

### Automating Complete Workflows
For 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.

## Important Notes

GitPython 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.

## Resources

- **PyPI**: https://pypi.org/project/GitPython/
- **GitHub Repository**: https://github.com/gitpython-developers/GitPython
- **Documentation**: https://gitpython.readthedocs.io/en/stable/

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: GitPython pip install git python automation commit branch 2025

GitPython · PyPI


Web search results for query: "ollama python pip install ollama chat streaming async 2025"

Links: [{"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:

## Installation

The 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.

### Prerequisites
Ollama should be installed and running. Pull a model to use with the library using `ollama pull <model>`, e.g., `ollama pull gemma3`.

## Basic Chat Usage

You 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`.

## Streaming

Response 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.

## Async Client

The `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])`.

### Async Streaming

Setting `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)`.

## Streaming with Tool Calling

Ollama 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).

## Custom Client Configuration

You can create a custom `Client` with a specific host and headers, like `Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})`.

## LangChain Integration

For LangChain users, you can install with `pip install -U ollama` and use `ChatOllama` from `langchain_ollama` to instantiate model objects.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: ollama python pip install ollama chat streaming async 2025

Ollama Python Library

ChatOllama - Docs by LangChain

ollama · PyPI

ollama-python · PyPI

streamlit-ollama-agent · PyPI


Now let me search for FastAPI details (specific patterns, uvicorn, pydantic) and React/TypeScript patterns.

Now let me search for FastAPI details and React/TypeScript setup:

Question: FastAPI uvicorn pydantic pip install fastapi async endpoint 2025

Question: React TypeScript Vite project setup npm create 2025


Web search results for query: "React TypeScript Vite project setup npm create 2025"

Links: [{"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:

## Quick Start Command

To scaffold a new project, run: `npm create vite@latest react-ts-vite-app -- --template react-ts` and follow the CLI prompts.

## After Scaffolding

After the project is created, run:
```
cd react-ts-vite-app
npm install
npm run dev
```
The Vite development server (v6.3.2) will be ready in about 1120 ms at `http://localhost:5173/`.

## Available Templates

Vite 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.

## Prerequisites

Ensure that you have Node.js ≥v18 installed on your machine before creating a Vite project.

## Key Features

- 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.

- 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.

## Project Structure

You'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).

## Adding Testing (Optional)

Vitest 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.

## Non-Interactive Mode

To create a project without interactive prompts, you can use the `--no-interactive` flag.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: React TypeScript Vite project setup npm create 2025

Getting Started | Vite


Web search results for query: "FastAPI uvicorn pydantic pip install fastapi async endpoint 2025"

Links: [{"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:

## Installation

"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.

For a more complete async setup with a database, you can use:
`pip install fastapi "uvicorn[standard]" "sqlalchemy[asyncio]" asyncpg sqlmodel alembic pydantic-settings`

A sample `requirements.txt` might include:
`fastapi==0.115.0`, `uvicorn==0.30.0`, `pydantic==2.7.0`, `python-dotenv==1.0.1`, `requests==2.31.0`

## Key Components

FastAPI 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.

## Async Endpoints

FastAPI 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.

## Running Your App

Run 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.

## Performance

FastAPI 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.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: FastAPI uvicorn pydantic pip install fastapi async endpoint 2025

fastapi · PyPI

FastAPI - Uvicorn - GeeksforGeeks

FastAPI

uvicorn · PyPI

Tutorial - User Guide - FastAPI


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.

Let me verify a couple more details - specifically Gitea API and search for any potential alternative unified LLM wrapper libraries:

Question: Gitea Python API client pip install py-gitea python-gitea 2025

Question: litellm openrouter unified LLM API wrapper Python multiple providers 2025


Web search results for query: "Gitea Python API client pip install py-gitea python-gitea 2025"

Links: [{"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:

## Main Options for Gitea Python API Clients

### 1. **py-gitea** (Most Recently Updated)
Released 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.

### 2. **py-gitea-opensuse-org** (December 2025)
Released 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`.

### 3. **giteapy** (Older)
Released 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`.

### 4. **gitea-api** (GitHub)
This 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`.

### 5. **pytea**
pytea is a Gitea API wrapper for Python 3 only.

---

**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.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: Gitea Python API client pip install py-gitea python-gitea 2025

py-gitea · PyPI

giteapy · PyPI

py-gitea-opensuse-org · PyPI

Gitea API Integrations - Pipedream


Web search results for query: "litellm openrouter unified LLM API wrapper Python multiple providers 2025"

Links: [{"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:

## LiteLLM

LiteLLM 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.

**Key Features:**
- You can use LiteLLM through either the Proxy Server or Python SDK. Both give you a unified interface to access multiple LLMs (100+ LLMs).
- 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.
- Netflix, Lemonade, and RocketMoney are among LiteLLM's enterprise customers, indicating production-scale viability.
- 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.

**Recent Growth:**
LiteLLM'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.

## OpenRouter

OpenRouter 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.

**Key Features:**
- 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.
- 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.
- You buy credits once and use them for any available model. OpenRouter automatically selects providers based on availability and your preferences.

**Recent Funding:**
In June 2025, OpenRouter raised $40 million and hit a $500 million valuation.

## Key Differences

LiteLLM 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.

## Using LiteLLM with OpenRouter

LiteLLM 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.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: litellm openrouter unified LLM API wrapper Python multiple providers 2025

OpenRouter | liteLLM

LiteLLM - Getting Started | liteLLM

Providers | liteLLM