Pydantic AI: Framework de agentes Python para aplicaciones GenAI - Video-IA.net

Pydantic AI: GenAI Agent Framework, the Pydantic way

Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI. Built by the Pydantic team, it brings the same innovative and ergonomic design philosophy that made FastAPI revolutionary to the world of GenAI app and agent development.

Key Features & Benefits:

• Built by the Pydantic Team: Pydantic Validation is the validation layer of the OpenAI SDK, the Google SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more. Why use the derivative when you can go straight to the source?
• Model-agnostic: Supports virtually every model and provider: OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, and Perplexity; Azure AI Foundry, Amazon Bedrock, Google Vertex AI, Ollama, LiteLLM, Groq, OpenRouter, Together AI, Fireworks AI, Cerebras, Hugging Face, GitHub, Heroku, Vercel. If your favorite model or provider is not listed, you can easily implement a custom model.
• Seamless Observability: Tightly integrates with Pydantic Logfire, their general-purpose OpenTelemetry observability platform, for real-time debugging, evals-based performance monitoring, and behavior, tracing, and cost tracking. If you already have an observability platform that supports OTel, you can use that too.
• Fully Type-safe: Designed to give your IDE or AI coding agent as much context as possible for auto-completion and type checking, moving entire classes of errors from runtime to write-time for a bit of that Rust "if it compiles, it works" feel.
• Powerful Evals: Enables you to systematically test and evaluate the performance and accuracy of the agentic systems you build, and monitor the performance over time in Pydantic Logfire.
• MCP, A2A, and AG-UI: Integrates the Model Context Protocol, Agent2Agent, and AG-UI standards to give your agent access to external tools and data, let it interoperate with other agents, and build interactive applications with streaming event-based communication.
• Human-in-the-Loop Tool Approval: Easily lets you flag that certain tool calls require approval before they can proceed, possibly depending on tool call arguments, conversation history, or user preferences.
• Durable Execution: Enables you to build durable agents that can preserve their progress across transient API failures and application errors or restarts, and handle long-running, asynchronous, and human-in-the-loop workflows with production-grade reliability.
• Streamed Outputs: Provides the ability to stream structured output continuously, with immediate validation, ensuring real time access to generated data.
• Graph Support: Provides a powerful way to define graphs using type hints, for use in complex applications where standard control flow can degrade to spaghetti code.

Use Cases:

• Production AI Applications: Build robust, scalable AI applications with proper error handling and monitoring.
• Agent Development: Create intelligent agents that can interact with external tools and data sources.
• Multi-Agent Systems: Develop systems where multiple agents can communicate and collaborate.
• Enterprise AI Workflows: Implement complex AI workflows with human-in-the-loop capabilities.
• AI Evaluation and Testing: Systematically test and evaluate AI system performance.

Pricing:

Pydantic AI is open-source and available under the Apache 2.0 license. The framework itself is free to use, though you'll need to pay for the underlying AI model providers (OpenAI, Anthropic, etc.) based on your usage.

Integrations:

• AI Models: OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, Perplexity
• Cloud Providers: Azure AI Foundry, Amazon Bedrock, Google Vertex AI
• Observability: Pydantic Logfire, OpenTelemetry-compatible platforms
• Standards: Model Context Protocol (MCP), Agent2Agent (A2A), AG-UI
• Development Tools: FastAPI, Pydantic Validation, Python type hints

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