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Compare FastAPI, Django, Flask, and adjacent tooling with production trade-offs, migration advice, and implementation details you can use immediately.
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Learn how to use Pydantic AI's provider-side MCPServerTool with OpenAI Responses, Anthropic, and xAI. Configure auth, allowed tools, connectors, and know when to choose it over MCPServer or FastMCPToolset.
Learn how to use Pydantic AI's standard MCPServer clients: MCPServerStdio, MCPServerStreamableHTTP, and MCPServerSSE. Load multi-server configs, use tool prefixes, read resources, customize TLS, and identify your client cleanly.
Learn how to enable MCP sampling and elicitation in Pydantic AI with MCPServerStdio and related MCPServer clients. Build callback-driven workflows, set a sampling model correctly, and avoid the FastMCPToolset trap for interactive MCP features.
Compare Pydantic AI's three MCP integration paths: MCPServer, FastMCPToolset, and MCPServerTool. Learn when to use agent-side MCP clients, FastMCP extras, or provider-side remote MCP execution.
Learn how to connect a Pydantic AI agent to local and remote MCP servers with FastMCPToolset. Wrap FastMCP instances, Python scripts, Streamable HTTP endpoints, and multi-server MCP configs with clean tool naming.
Learn how to use the OpenAI Realtime API in Python with WebSocket. Send text and audio events, stream responses, mint ephemeral browser tokens, and choose WebSocket or WebRTC.
Learn how to build a Python MCP server with FastMCP. Create tools, resources, prompts, and a Streamable HTTP endpoint, then connect it from a Pydantic AI agent.
Learn how to use uv for Python project management in 2026. Set up pyproject.toml, migrate from requirements.txt, manage lockfiles, and decide when uv is a better fit than pip or Poetry.
Master Python's asyncio library with this guide. Learn async/await fundamentals, performance patterns, and real-world examples. Transform slow I/O operations into fast concurrent code.
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