Pydantic AI
Typed Python agent framework from the Pydantic team for building AI agents with structured outputs across any model provider.
Works with AI agents:llms.txtAsk your AI about this, with this page as the source:ChatGPT ↗Claude ↗Perplexity ↗
Pydantic AI is an open-source Python agent framework from the team behind the Pydantic validation library. It brings the same type-checked style to LLM code: tool arguments and agent outputs are defined as typed Python and validated, instead of parsed out of model text by hand.
You create an Agent with a model string such as anthropic:<model> or openai:<model>, instructions, tools (decorated Python functions whose signatures become the tool schema), and an output type, often a Pydantic model. Dependency injection passes typed services such as a database connection into tools and instructions, and lets tests swap them out. You run an agent with run, run_sync or streaming. It needs Python 3.10 or later, and pydantic-ai-slim installs only the provider extras you use.
Switching providers is a string change across OpenAI, Anthropic, Google, Bedrock, Vertex, Azure, OpenAI-compatible services and local servers. Instrumentation emits standard OpenTelemetry, so traces go to Pydantic Logfire or any OTLP backend. Durable execution integrates with Temporal, DBOS, Prefect and Restate so a run survives crashes, adapters stream to frontends built on the Vercel AI SDK or AG-UI, and TestModel lets unit tests run without real model calls. It runs in your own process; the optional Pydantic AI Gateway is hosted or self-hosted.
It is Python only. It deliberately leaves storage to you: saving a conversation means serializing the message history into your own database. Cross-conversation memory, guardrails and coding-agent tools sit in a separate Pydantic AI Harness package.
Where it fits
How Pydantic AI itself is built
5 tools, from its own code, website and Product Hunt page.
Astro
Claude Code
LangChain
LangSmith
MongoDBWho uses it
No maker's live product on record yet, and 35 open-source projects that declare it in their code.
Declared in code 35How evidence is collected →
We haven't found a maker's live product that uses Pydantic AI yet — only the open-source projects below, which declare it in their code.
Arize PhoenixSource-available (Elastic License 2.0) LLM observability platform for tracing, evaluatin…AI SDK & Agent FrameworkIn its code · source ↗
DeepEvalOpen-source Python framework for unit-testing LLM apps with pytest-style test cases and…AI SDK & Agent FrameworkIn its code · source ↗Open source: a project that declares Pydantic AI as a dependency in its public code — verifiable, but not necessarily a live product.
Loved and watch-outs
Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.
Most loved
- Handles the agent and tool-calling loop for you and makes switching between model providers easy. HNHN 2HN 3HN 4
- Lighter and less abstracted than LangChain or Google ADK, and familiar to anyone already using Pydantic. HNHN 2HN 3
- Durable execution through DBOS or Temporal and OpenTelemetry-based observability are supported. HNHN 2HN 3
- Capabilities and hooks make it a solid base for other libraries and custom harnesses. HNHN 2HN 3HN 4
Reliability and open issues
Most wanted on GitHub
- support batch processing👍 34 · opened May 2025 · active Sep 2026
- Prompt management, versioning, and optimization👍 22 · opened Feb 2025 · active May 2026
- Support Anthropic and OpenAI Skills built-in tool👍 14 · opened Nov 2025 · active Oct 2026
Alternatives to Pydantic AI
All alternatives by situation →
LlamaIndex
Mastra
OpenAI Agents SDK
AI SDK
Claude Agent SDK
LangGraph
CrewAIQuestions makers ask about Pydantic AI
Which model providers does it support?
You pick a provider with a <provider>:<model> string: OpenAI, Anthropic, Google, AWS Bedrock, Vertex AI, Azure and others, plus OpenAI-compatible services and local model servers. Each has a setup guide covering its options. source ↗
Do I have to use Logfire for observability?
No. Instrumentation emits standard OpenTelemetry spans for every model and tool call, and any OTLP backend works. Logfire is the easiest option, not a requirement. source ↗
How do I save a chat and continue it later?
Serialize the message history to JSON with ModelMessagesTypeAdapter and store it in your own database, for example in a jsonb column. The framework leaves the schema to you. source ↗
Can a run survive a crash or restart?
Yes, with durable execution. Integrations are co-maintained for Temporal, DBOS, Prefect, Restate and AWS Lambda, and a builder lets you connect another engine. source ↗
Can I use it behind a frontend built with the Vercel AI SDK?
Yes. VercelAIAdapter speaks the AI SDK data stream protocol that useChat expects; with FastAPI or another Starlette app, one call handles the request and streams the response. source ↗
How do I test agents without calling an LLM?
Swap in TestModel or FunctionModel with Agent.override, and set ALLOW_MODEL_REQUESTS=False so tests can't hit a real model by accident. source ↗
Which Python versions are supported?
Python 3.10 and later. The pydantic-ai-slim package lets you install only the extras for the providers you use. source ↗
Is Pydantic AI free?
Yes — it is free, open-source software.
Is Pydantic AI open source or self-hostable?
Open source, and you can self-host it. source ↗
Can AI coding agents work with Pydantic AI?
It serves an llms.txt docs index.
Who uses Pydantic AI?
No maker's live product we track yet; 35 open-source projects declare it in their code. source ↗