Pydantic AI

What is Pydantic AI?

Pydantic AI is an open-source Python framework from the Pydantic team for building agents and applications on top of large language models. You define an agent with an output type, dependencies and tools, and each run returns validated, typed results, so your editor, type checker and the model agree on the shape of the data. It works with many providers, including OpenAI, Anthropic, Google, AWS Bedrock, Mistral, Groq, Ollama and OpenRouter, and switching models is a change of string. The framework covers function tools, toolsets, MCP servers, streaming to frontends, multi-agent workflows with graphs, and durable execution through Temporal, DBOS, Prefect, Restate and AWS Lambda. Testing uses Pydantic Evals, and observability connects to Pydantic Logfire or any OpenTelemetry backend. The library is MIT-licensed and free to install with pip or uv. The Logfire platform has separate plans: Personal is free, Team is $49 per month and Growth is $249 per month.

Use cases

• Build typed Python agents with tools
• Extract validated structured data from text
• Connect agents to MCP servers
• Run fault-tolerant agents with durable execution
• Stream agent events to a web frontend
• Evaluate agent outputs on datasets
• Route requests across model providers

Who it's for

• Python developers
• AI engineers
• Backend teams
• Startups building agents

Why use Pydantic AI

• Validation is built in, so a run is guaranteed to return the output type you declared.
• Changing the model string moves an agent between providers without rewriting the agent code.
• Durable execution integrations let long-running or human-in-the-loop agents survive restarts and API failures.
• The library is MIT-licensed with no usage fees, and observability through OpenTelemetry works with the backend you already run.

Pydantic AI features

• Typed agents with dependency injection
• Validated structured outputs built on Pydantic
• Model-agnostic provider support with one-string model switching
• Function tools registered with decorators and schemas from type hints
• Built-in Model Context Protocol (MCP) support
• Streaming with AG-UI and Vercel AI Data Stream protocol support
• Durable execution with Temporal, DBOS, Prefect, Restate and AWS Lambda
• Multi-agent workflows with graph-based coordination
• Pydantic Evals for datasets and evaluations
• Logfire and OpenTelemetry instrumentation
• Optional Pydantic AI Gateway for single-key model routing

Pydantic AI integrations

• OpenAI
• Anthropic
• Google
• AWS Bedrock
• Groq
• Temporal
• DBOS
• Prefect
• Restate
• Pydantic Logfire

How to get started with Pydantic AI

1. Install the package with pip or uv and set credentials for your chosen model provider.
2. Create an Agent with a model string, instructions, an output type and any dependencies.
3. Register function tools with decorators; schemas are generated from type hints and docstrings.
4. Run the agent synchronously, asynchronously or as a stream, and receive validated output.
5. Add Logfire or OpenTelemetry instrumentation, Evals and a durable execution layer as you move toward production.

Pydantic AI limitations

• It is a code-first Python library, so it requires programming
• You need credentials for each model provider you call
• Logfire and the AI Gateway are separate paid services, not required for the library
• Free Logfire Personal plan is hard-capped at 10 million records per month

Pydantic AI pricing

Pydantic AI is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

Pydantic AI alternatives

The main Pydantic AI alternatives listed on Debuly are LangGraph, CrewAI, OpenAI Agents SDK, LlamaIndex, Haystack and Instructor. The table compares pricing and platforms; order is A–Z.

1. 

   CrewAI

   Multi-agent framework and platform for building AI agent crews in Python or a visual editor, then deploying and monitoring them.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit CrewAI (official site)
2. 

   Haystack

   Open-source Python framework from deepset for building RAG pipelines, AI agents and multimodal apps from modular components.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Haystack (official site)
3. 

   Instructor

   Open-source library that gets validated, typed structured data out of LLMs using Pydantic models and automatic retries.

   Developer platformsCoding
   Open sourceVisit Instructor (official site)
4. 

   LangGraph

   Low-level open-source orchestration framework and runtime for building stateful, long-running AI agents as graphs.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit LangGraph (official site)
5. 

   LlamaIndex

   Open-source framework plus the LlamaParse platform for parsing, extracting and indexing documents so LLM apps can use them.

   Developer platformsResearch and searchAutomation and agents
   FreemiumVisit LlamaIndex (official site)
6. 

   OpenAI Agents SDK

   Open-source Python framework for building multi-agent workflows with tools, handoffs, guardrails, tracing, sandboxes and voice agents.

   Developer platformsAutomation and agentsCoding
   Open sourceVisit OpenAI Agents SDK (official site)

Pydantic AI FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

Image credits

• Logo: from pydantic.dev · fetched 24 Oct 2026 · Vendor trademark, used only to identify the product; removed on request
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