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Pydantic AI
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Pydantic AI

Active·★ 20.6k·MIT·Updated 2026-10-14
★ Most Popular★ Trending★ Essential

Pydantic AI, from the team behind Pydantic, is a Python agent framework built around type safety. Its core is a typed, extensible agent loop where switching model providers is a matter of changing a string, tools are plain Python functions with typed dependencies, and results come back as validated Pydantic models. It covers agents, realtime voice, image generation and embeddings.

It suits Python developers who want reliable, structured results: data extraction that always returns a valid schema, tool-using assistants inside existing applications, and long-running agents. Pydantic AI Harness adds ready-made capabilities for complex work, such as a coding agent with file access, an allowlisted shell, repository context and sub-agents; durable execution integrates with Temporal so agents survive restarts; and it can run in GitHub workflows on issues, pull requests or schedules.

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Not affiliated with Anthropic, OpenAI or Microsoft.

Getting started: `uv add pydantic-ai`, define an `Agent` with a model string and an output type, add tools with `@agent.tool`, and run it. Add `pydantic-ai-harness` for the harness capabilities, or try the ready-made coding agent with `clai`.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool. Data as of 2026-10-10.

License
MIT
Latest release
v2.55.0 · 2026-10-09
Last commit
2026-10-10
Contributors
623
Forks
2,900
Open issues and PRs
1,391
First published
2024-06-21

Source: GitHub, npm and PyPI public APIs.

#Python#Generative AI#Agent Framework#Pydantic#LLM#Coding
$ Install
$ uv add pydantic-ai
↗ Visit site★ GitHub
01

Features

01Built by the Pydantic Team and leveraging Pydantic Validation.
02Model-agnostic support for a wide range of LLMs and providers.
03Seamless observability with Pydantic Logfire for real-time debugging and performance monitoring.
04Fully type-safe design for enhanced developer experience and error prevention.
05Powerful evaluation tools for systematic testing and monitoring of agent performance.
02

Why choose it

+Type safety end to end: validated outputs, typed tools and dependency injection.
+Broad model support with minimal code changes when switching providers.
+Durable execution through Temporal and a harness of reusable capabilities for long-running agents.
+MIT licensed and backed by the Pydantic team.
03

Trade-offs

!Full observability is designed around Pydantic Logfire, a separate commercial platform, although OpenTelemetry-based tooling can also be used.
!Python only.
!Fast-moving project; harness capabilities and APIs evolve quickly, so pin versions.
04

Compatibility

OpenAI
Supported
Verified via docs
Anthropic
Supported
Verified via docs
Gemini
Supported
Verified via docs
Azure AI Foundry
Supported
Verified via docs
Ollama
Supported
Verified via docs
05

Quick start

1
$ uv add pydantic-ai
06

Use cases

↳Building production-grade Generative AI applications and workflows.
↳Developing intelligent agents that interact with external tools and data.
↳Creating durable and reliable long-running AI workflows, including human-in-the-loop processes.
07

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Stats
GitHub Stars★ 20.6k
Last commit1d ago
StatusActive
LicenseMIT
CategoryMulti-Agent
Trend (30d)
+0.8k↑ 4.8%
Links
Documentation↗Discussion↗Issues↗Releases↗

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