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Pydantic AI

by Pydantic Services Inc. · Framework · also known as PydanticAI, pydantic-ai (PyPI)

Type-safe Python agent framework from the Pydantic team with validated structured outputs, dependency injection, swappable model providers, MCP, durable execution and evals

MIT licenseOpen sourceFreeLocalSelf-hostedSelf-hostable
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Link verified Nov 2, 2026

Overview

Pydantic AI is an MIT-licensed Python agent framework built by the team behind the Pydantic validation library. Its design goal is to make agent code feel like ordinary typed Python: an Agent is declared with a model string (OpenAI, Anthropic, Google, Bedrock, Groq, Ollama and many others are supported, with a gateway option for one key across providers), an output type that is a Pydantic model, and tools whose arguments are validated before the function runs. Dependencies such as database connections are injected through a typed RunContext, which keeps agents testable. Capabilities bundle tools, instructions and settings into reusable units, with MCP and web search included.

Beyond the agent loop, the project covers durable execution through Temporal, DBOS or Prefect so runs survive restarts, a graph library for typed control flow, Pydantic Evals for scoring outputs and tool-call trajectories, realtime voice sessions, image generation and embeddings, and OpenTelemetry-based observability with Pydantic Logfire as one backend. Documentation moved from ai.pydantic.dev to pydantic.dev/docs/ai in 2026. The framework is Python-only, and teams already deep in LangGraph or a vendor SDK may not need its abstractions, but for Python shops that value static typing it is one of the more disciplined options.

Capabilities

  • Agents with validated structured outputs typed as Pydantic models
  • Model-agnostic provider interface with gateway and failover
  • Typed tools and dependency injection via RunContext
  • MCP client support and built-in web search capability
  • Durable execution via Temporal, DBOS or Prefect
  • Pydantic Graph, Pydantic Evals and OpenTelemetry observability
  • Realtime voice, image generation and embeddings

Typical workflows

  1. Extract structured records from documents with guaranteed schema
  2. Build a customer-support agent with injected database dependencies and unit tests
  3. Run long agent workflows durably on Temporal

Strengths

  • Strong typing and IDE support throughout
  • Small conceptual surface compared with larger frameworks
  • MIT license from a well-maintained foundation library

Limitations

  • Python only
  • Younger ecosystem of integrations than LangChain
  • Observability features are deepest with the vendor's Logfire

Prerequisites

  • Python 3.10+
  • API key for a model provider or a local runtime

Who it is for

  • Python developers who value type safety
  • Teams building structured-extraction or tool-calling agents

Alternatives & comparison

  • AutoGen

    Microsoft's multi-agent framework (Core, AgentChat, Extensions, Studio) for Python and .NET, now archived with users directed to Microsoft Agent Framework

    Open sourceOpen source
  • CrewAI

    Open-source Python framework for role-based multi-agent crews and event-driven flows, with a commercial enterprise console for deploying, monitoring and triggering automations

    MixedOpen source
  • DSPy

    Python framework for programming language-model pipelines with typed signatures and modules, then optimizing prompts and weights automatically against a metric

    Open sourceOpen source
  • Instructor

    Python library for extracting validated structured data from LLM responses using Pydantic models, with automatic retries, streaming partials and support for many providers

    Open sourceOpen source
  • LangChain

    Open-source Python and JavaScript framework whose create_agent harness builds tool-using agents on the LangGraph runtime with swappable model providers and middleware

    Open sourceOpen source
  • LangGraph

    Low-level orchestration framework and runtime for stateful, long-running agents expressed as graphs, with durable execution, checkpointing, human-in-the-loop and streaming

    Open sourceOpen source
  • LlamaIndex

    Open-source Python framework for retrieval-augmented agents over your data, with connectors, indexes, query engines and event-driven workflows, alongside the commercial LlamaParse document platform

    Open sourceOpen source
  • OpenAI Agents SDK

    Lightweight open-source Python and TypeScript framework for agents, handoffs, guardrails, sessions and tracing, defaulting to OpenAI's Responses API with adapters for other providers

    Open sourceOpen source
  • Pydantic

    Python data-validation library using type hints to parse, validate and serialize models, with a Rust core; the schema layer behind FastAPI and many LLM SDKs

    Open sourceOpen source
Compare Pydantic AI with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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