Pydantic AI
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.
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`.
Features
Why choose it
Trade-offs
Maintenance check
- Last commit
- 2026-10-25
- Commits, last 90 days
- 100+
- Releases, last 90 days
- 30
- Latest release
- clai2-bleeding (2026-10-06)
- Open issues
- 1490
- Recent issues with at least one comment
- 14/17
Oldest week on the left; the latest four weeks are orange. Source: GitHub commit activity.
Status rule: archived on GitHub = Archived; last commit within 90 days = Active; 91 to 365 days = Slowing; older = Stale. All figures come from the GitHub API on the date shown.
Most discussed open issues
- #5955 [aw] Detection Runs44 comments · 2026-06-16
- #3274 Human in the Loop Approval for Multi Agent Systems43 comments · 2025-10-28
Issue titles are quoted from GitHub. Open the link to check whether a problem still applies to the version you use.
Who it fits
- You want end-to-end type safety in Python, where tools use typed dependency injection and responses are validated Pydantic models.
- You want to switch model providers by changing a string and optionally pair long-running agents with Temporal durable execution.
- You want a multi-language SDK across Python, TypeScript, Go, or Java; Google ADK or Microsoft Agent Framework cover multiple languages.
- You want an out-of-the-box software-company role simulation; MetaGPT is built specifically for that pattern.
How it differs
vs CrewAI★ 60.2kPydantic AI centres on typed Python functions, dependency injection, and Pydantic validation. CrewAI centres on role-based multi-agent Crews and event-driven Flows.
vs adk-python★ 22.0kBoth are code-first Python agent toolkits. Pydantic AI is built by the Pydantic team around strict type validation and Temporal integration; Google ADK adds a built-in evaluation harness, dev UI, and close integration with Vertex AI and Cloud Run.
Compatibility
Quick start
Use cases
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