userType: external

type: user

role: user

content:
Summarize the key findings from the "research" phase in 500 words or less.

Focus on extracting ONLY the most critical information that subsequent phases need:
- Key decisions made and their rationale
- Critical files, components, or patterns identified
- Important constraints or requirements discovered
- Actionable insights for implementation

Be concise and use bullet points. Skip boilerplate and meta-commentary.

## Phase Output:
**research.json**:
```
{
  "integrations_researched": [
    {
      "name": "Anthropic Claude SDK",
      "type": "library",
      "verified_package": {
        "name": "anthropic",
        "install_command": "pip install anthropic",
        "version": "latest (Nov 2025)",
        "python_requires": ">=3.9",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from anthropic import Anthropic",
          "from anthropic import AsyncAnthropic"
        ],
        "initialization": "client = Anthropic()  # Uses ANTHROPIC_API_KEY env var",
        "key_functions": [
          "client.messages.create(model, messages, max_tokens)",
          "await async_client.messages.create(...)"
        ],
        "verified_against": "PyPI + GitHub anthropics/anthropic-sdk-python"
      },
      "configuration": {
        "env_vars": ["ANTHROPIC_API_KEY"],
        "config_files": [],
        "dependencies": ["httpx"]
      },
      "extras": {
        "bedrock": "pip install anthropic[bedrock]",
        "vertex": "pip install anthropic[vertex]"
      },
      "gotchas": [
        "Supports both sync and async clients via Anthropic() and AsyncAnthropic()",
        "Context manager support for automatic HTTP client cleanup",
        "Type definitions for all request/response fields"
      ],
      "research_sources": [
        "https://pypi.org/project/anthropic/",
        "https://github.com/anthropics/anthropic-sdk-python",
        "https://platform.claude.com/docs/en/api/client-sdks"
      ]
    },
    {
      "name": "Google Gemini SDK",
      "type": "library",
      "verified_package": {
        "name": "google-genai",
        "install_command": "pip install google-genai",
        "version": "GA (May 2025)",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from google import genai"
        ],
        "initialization": "client = genai.Client(api_key='GEMINI_API_KEY')  # or uses GEMINI_API_KEY env var",
        "key_functions": [
          "client.models.generate_content(model, contents)",
          "client.models.generate_content_stream(...)"
        ],
        "verified_against": "Google AI for Developers docs"
      },
      "configuration": {
        "env_vars": ["GEMINI_API_KEY"],
        "config_files": [],
        "dependencies": []
      },
      "extras": {
        "async": "pip install google-genai[aiohttp]"
      },
      "gotchas": [
        "CRITICAL: google-generativeai package is DEPRECATED (EOL Nov 30, 2025)",
        "Must use new google-genai package for all new development",
        "Supports both Gemini Developer API and Vertex AI APIs",
        "LLMs may generate code for deprecated library - always use google-genai"
      ],
      "research_sources": [
        "https://pypi.org/project/google-generativeai/",
        "https://ai.google.dev/gemini-api/docs/libraries",
        "https://github.com/googleapis/python-genai"
      ]
    },
    {
      "name": "xAI Grok SDK",
      "type": "library",
      "verified_package": {
        "name": "xai-sdk",
        "install_command": "pip install xai-sdk",
        "version": "latest",
        "python_requires": ">=3.10",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from xai_sdk import Client",
          "from xai_sdk import AsyncClient"
        ],
        "initialization": "client = Client()  # Uses XAI_API_KEY env var",
        "key_functions": [
          "client.chat(...)",
          "client.generate(...)",
          "await async_client.chat(...)"
        ],
        "verified_against": "GitHub xai-org/xai-sdk-python + PyPI"
      },
      "configuration": {
        "env_vars": ["XAI_API_KEY"],
        "config_files": [],
        "base_url": "https://api.x.ai/v1"
      },
      "extras": {
        "telemetry_http": "pip install xai-sdk[telemetry-http]",
        "telemetry_grpc": "pip install xai-sdk[telemetry-grpc]"
      },
      "gotchas": [
        "gRPC-based Python library",
        "Supports both sync and async clients",
        "Agentic tool calling with web search, X search, and code execution",
        "Multiple community SDKs exist (xai-grok, xai-grok-sdk) - use official xai-sdk"
      ],
      "research_sources": [
        "https://github.com/xai-org/xai-sdk-python",
        "https://pypi.org/project/xai-sdk/",
        "https://docs.x.ai/docs/tutorial"
      ]
    },
    {
      "name": "Ollama Python",
      "type": "library",
      "verified_package": {
        "name": "ollama",
        "install_command": "pip install ollama",
        "version": "latest (Nov 2025)",
        "python_requires": ">=3.8",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "import ollama",
          "from ollama import Client",
          "from ollama import AsyncClient"
        ],
        "initialization": "client = Client(host='http://localhost:11434')",
        "key_functions": [
          "ollama.chat(model, messages)",
          "ollama.generate(model, prompt)",
          "await AsyncClient().chat(model, messages, stream=True)"
        ],
        "verified_against": "GitHub ollama/ollama-python"
      },
      "configuration": {
        "env_vars": [],
        "config_files": [],
        "host": "http://localhost:11434 (default)"
      },
      "infrastructure": {
        "requires_docker": false,
        "requires_ollama_server": true,
        "install_ollama": "https://ollama.com/download"
      },
      "gotchas": [
        "Ollama server must be running separately before using Python client",
        "Pull models first with 'ollama pull <model>' e.g., 'ollama pull gemma3'",
        "Supports streaming with stream=True parameter",
        "Custom client with headers: Client(host=..., headers={...})",
        "Now supports streaming with MCP tool calling"
      ],
      "research_sources": [
        "https://github.com/ollama/ollama-python",
        "https://pypi.org/project/ollama/",
        "https://ollama.com/blog/streaming-tool"
      ]
    },
    {
      "name": "FastAPI",
      "type": "framework",
      "verified_package": {
        "name": "fastapi",
        "install_command": "pip install \"fastapi[standard]\"",
        "version": "0.115.0+",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from fastapi import FastAPI",
          "from fastapi import Depends, HTTPException",
          "from pydantic import BaseModel"
        ],
        "initialization": "app = FastAPI()",
        "key_functions": [
          "@app.get('/path')",
          "@app.post('/path')",
          "async def endpoint(): ...",
          "Depends() for dependency injection"
        ],
        "verified_against": "Official FastAPI docs"
      },
      "configuration": {
        "env_vars": [],
        "config_files": [],
        "dependencies": ["uvicorn", "pydantic", "starlette"]
      },
      "infrastructure": {
        "run_command": "uvicorn main:app --reload",
        "default_port": 8000
      },
      "gotchas": [
        "Use fastapi[standard] to get uvicorn and other standard dependencies",
        "ASGI server (uvicorn) required to run the app",
        "Built on Starlette (web) + Pydantic (data validation)",
        "Automatic OpenAPI/Swagger docs at /docs and /redoc",
        "--reload flag for development only"
      ],
      "research_sources": [
        "https://pypi.org/project/fastapi/",
        "https://fastapi.tiangolo.com/",
        "https://fastapi.tiangolo.com/tutorial/"
      ]
    },
    {
      "name": "React with TypeScript (Vite)",
      "type": "framework",
      "verified_package": {
        "name": "vite + react + typescript",
        "install_command": "npm create vite@latest my-app -- --template react-ts",
        "version": "Vite 6.3.2+ (latest)",
        "node_requires": ">=18",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "import React from 'react'",
          "import { useState, useEffect } from 'react'"
        ],
        "project_structure": "index.html at root, src/ with main.tsx entry",
        "key_features": [
          "Hot Module Replacement (HMR)",
          "Native ES modules in development",
          "TypeScript support out of box"
        ],
        "verified_against": "Vite official guide"
      },
      "configuration": {
        "config_files": ["vite.config.ts", "tsconfig.json"],
        "commands": {
          "dev": "npm run dev",
          "build": "npm run build",
          "preview": "npm run preview"
        }
      },
      "infrastructure": {
        "dev_server": "http://localhost:5173",
        "build_output": "dist/"
      },
      "gotchas": [
        "Node.js >= v18 required",
        "Available templates: react-ts, react-swc-ts (faster builds)",
        "For testing: npm install -D vitest @testing-library/react",
        "HMR provides instant updates without page refresh"
      ],
      "research_sources": [
        "https://vite.dev/guide/",
        "https://blog.logrocket.com/how-to-build-react-typescript-app-vite/"
      ]
    },
    {
      "name": "MCP Python SDK",
      "type": "library",
      "verified_package": {
        "name": "mcp",
        "install_command": "pip install mcp",
        "version": "1.25.0+ (v2 coming Q1)",
        "python_requires": ">=3.10",
        "verified": true
      },
      "api_patterns": {
        "imports": [
          "from mcp.server.fastmcp import FastMCP",
          "from mcp.client import Client"
        ],
        "initialization": "mcp = FastMCP('server-name')",
        "key_concepts": [
          "Resources - like GET endpoints for data",
          "Tools - like POST endpoints for actions",
          "Prompts - reusable templates for LLM interactions"
        ],
        "verified_against": "GitHub modelcontextprotocol/python-sdk"
      },
      "configuration": {
        "env_vars": [],
        "config_files": [],
        "transport_options": ["stdio", "SSE

[... file truncated ...]
```

## Summary:


type: message

role: assistant

type: thinking

thinking:
The user is asking me to summarize the key findings from a research phase output. They want a concise summary (500 words or less) focused on:
- Key decisions made and their rationale
- Critical files, components, or patterns identified
- Important constraints or requirements discovered
- Actionable insights for implementation

The research phase appears to be about investigating various LLM SDKs, frameworks, and the MCP (Model Context Protocol) Python SDK.

Let me extract the most critical information:

1. **Anthropic Claude SDK**: Standard package, uses env vars, supports sync/async
2. **Google Gemini SDK**: CRITICAL - must use google-genai (not google-generativeai which is deprecated)
3. **xAI Grok SDK**: Official package is xai-sdk, gRPC-based
4. **Ollama**: Requires separate server running, must pull models first
5. **FastAPI**: Use fastapi[standard] for all dependencies
6. **React + TypeScript + Vite**: Modern setup, Node >= 18 required
7. **MCP Python SDK**: Key concepts are Resources, Tools, Prompts

I need to focus on actionable insights and critical gotchas that would affect implementation.

service_tier: standard

type: assistant


type: text

text:
# Research Phase Summary

## Critical Decisions & Package Choices

**LLM SDKs Verified:**
- **Anthropic Claude**: `pip install anthropic` - supports sync/async clients via `Anthropic()` and `AsyncAnthropic()`
- **Google Gemini**: `pip install google-genai` ⚠ **CRITICAL**: Old `google-generativeai` package is DEPRECATED (EOL Nov 30, 2025) - must use new `google-genai` for all development
- **xAI Grok**: `pip install xai-sdk` (official package, not community alternatives like `xai-grok` or `xai-grok-sdk`)
- **Ollama**: `pip install ollama` - requires separate Ollama server running locally

**Frameworks:**
- **Backend**: FastAPI - use `pip install "fastapi[standard]"` to get uvicorn + dependencies bundled
- **Frontend**: Vite + React + TypeScript - `npm create vite@latest -- --template react-ts`
- **MCP**: `pip install mcp` (v1.25.0+, v2 coming Q1)

## Critical Constraints & Requirements

**Python Requirements:**
- Python >= 3.10 (MCP SDK and xAI require this)
- Python >= 3.9 (Anthropic)
- Python >= 3.8 (Ollama)

**Node Requirements:**
- Node.js >= 18 (Vite)

**Infrastructure Dependencies:**
- **Ollama**: Separate Ollama server must be running (default: http://localhost:11434) + models must be pre-pulled with `ollama pull <model>`
- **FastAPI**: Requires ASGI server (uvicorn) - run with `uvicorn main:app --reload`
- **Vite**: Dev server runs on http://localhost:5173

## Key API Patterns Identified

**Authentication:**
- All LLM SDKs use environment variables: `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, `XAI_API_KEY`
- Clients auto-detect env vars: `client = Anthropic()` or explicit: `Client(api_key='...')`

**Common Patterns:**
- All support both sync and async clients
- Context manager support for cleanup
- Streaming with `stream=True` parameter

**MCP Core Concepts:**
- **Resources**: GET-like endpoints for data access
- **Tools**: POST-like endpoints for actions
- **Prompts**: Reusable LLM interaction templates
- Transport options: stdio, SSE (Server-Sent Events)

## Critical Gotchas & Actionable Insights

1. **Google Gemini Migration Risk**: LLMs may generate code using deprecated `google-generativeai` - always use `google-genai`

2. **Ollama Workflow**:
   - Start server first: `ollama serve`
   - Pull models: `ollama pull gemma3`
   - Then use Python client

3. **FastAPI Setup**: Use `[standard]` extra to avoid missing uvicorn dependency

4. **xAI SDK**: gRPC-based, supports agentic tool calling with web search, X search, code execution

5. **Ollama Streaming**: Now supports streaming with MCP tool calling (recent update)

6. **Vite Templates**: `react-swc-ts` available for faster builds vs standard `react-ts`

7. **FastAPI Dev**: Use `--reload` flag for development only, not production

## Implementation Recommendations

- Use async clients for all LLM SDKs to enable concurrent operations
- FastAPI automatic OpenAPI docs available at `/docs` and `/redoc`
- Vite HMR provides instant updates without page refresh
- All verified packages have type definitions for better IDE support