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Pydantic AI review

Pydantic AI is a typed Python agent framework where models are swapped with a string, with tools, structured outputs, durable execution and realtime voice.

Updated Nov 1, 2026. Review written Oct 22, 2026 from the project's own sources; usage data refreshed every Monday.

TL;DR

Pydantic AI is a Python AI SDK built around a typed, extensible agent loop that works with many models and providers. It is for Python developers who want validated, typed agent outputs and tools. The companion Pydantic AI Harness adds memory, planning, sub-agents and a coding agent for long-running work.

Pydantic AI at a glance

Category
Agent frameworks
Maintained by
Pydantic (organization, @pydantic on GitHub)
License
MIT
Language
Python, TypeScript, PLpgSQL
Repository
github.com/pydantic/pydantic-ai
Website
pydantic.dev/pydantic-ai
PyPI package
pydantic-ai 2.59.0
Latest release
v2.59.0 on GitHub, Oct 21, 2026
First published
Jun 21, 2024

What Pydantic AI does

  • Pydantic AI runs agents against many providers, including OpenAI, Anthropic, Google, Bedrock, Groq, Mistral and Ollama, swappable with a model string.
  • Pydantic AI validates tool arguments and returns structured outputs typed as Pydantic models, so IDEs and type checkers know the result type.
  • Pydantic AI runs agents inside durable workflows such as Temporal, DBOS and Prefect, so runs survive restarts, failures and long waits.
  • Pydantic AI puts the same agent on live voice sessions using OpenAI Realtime, Gemini Live, Azure and xAI Grok Voice.
  • Pydantic AI generates images through a standalone image API or through provider-native generation inside an agent run.
  • Pydantic AI Harness adds capabilities such as file access, allowlisted shell, planning, memory, guardrails, sub-agents and context management.
  • Pydantic AI emits OpenTelemetry instrumentation that works with Pydantic Logfire or any OpenTelemetry backend.

How it works

You create an Agent in Python with a model string, optional output type, tools and capabilities, then call run() or expose it in the terminal, a web frontend, a voice call, a background queue or GitHub Actions. Capabilities attach features like MCP servers, web search and durable execution. Separate packages cover evals, graph-based control flow and sandboxed code execution.

How to install and start using Pydantic AI

With uv

uv add pydantic-ai

With Harness

uv add pydantic-ai pydantic-ai-harness

With Temporal

uv add "pydantic-ai[temporal]"

Requirements

  • Python 3.11 or later.
  • Access to a model provider such as OpenAI or Anthropic, or the Pydantic AI Gateway.

First steps

  1. Install the package with uv add pydantic-ai.
  2. Define a Pydantic model and create an Agent with a model string and output_type set to that model.
  3. Add tools with the @agent.tool decorator, then call agent.run_sync() and read result.output.
  4. Optionally call logfire.configure() and logfire.instrument_pydantic_ai() to trace every model and tool call.

Who Pydantic AI is for

  • Python developers who need validated, typed outputs from LLMs, such as data extraction.
  • Teams building long-running agents that must survive restarts or wait for human approval.
  • Developers who want to switch between model providers without rewriting agent code.
  • Developers building coding or research agents with workspace, planning and memory.

Limitations to know

  • Pydantic AI is a Python library; it requires Python 3.11 or later.
  • Harness features like memory, planning and the coding agent require the separate pydantic-ai-harness package.
  • Recovering a run that crashes mid-step requires a durable execution engine such as Temporal, DBOS or Prefect.
  • Recent releases fixed security issues in web_fetch HTML conversion and MCP telemetry; users of older versions should upgrade.

Is Pydantic AI growing? Usage data

In the week of Oct 25–31, 2026, Pydantic AI ranked #107 on the Devtoolwatch Board and #12 in Agent frameworks, with a momentum score of 62 out of 100. Its main package had 1.7M downloads that week, +20% against its own 4-week average.

New GitHub stars per week for Pydantic AI12 weeks from Aug 9, 2026: 173, 137, 145, 164, 155, 180, 156, 202, 155, 167, 154, 135 new stars, an average of 160 a week.Aug 9week of Oct 25
New GitHub stars per week for the last 12 weeks, from GitHub's stargazer history (today's stargazers by the week they starred).
Show the weekly numbers
New GitHub stars per week for Pydantic AI
Week ofNew stars
Aug 9, 2026173
Aug 16, 2026137
Aug 23, 2026145
Aug 30, 2026164
Sep 6, 2026155
Sep 13, 2026180
Sep 20, 2026156
Sep 27, 2026202
Oct 4, 2026155
Oct 11, 2026167
Oct 18, 2026154
Oct 25, 2026135

On Hacker News

The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.

Pydantic AI alternatives

Other AI agent frameworks on the Board in the week of Oct 25–31, 2026, by momentum:

Agent frameworks ranked by momentum, the week of Oct 25–31, 2026
RankToolStarsDownloads/wk
#1Harness²Harness² is a Python framework from Google Research for recursive agent harnessing, refining an agent's prompts, tools, memory and…21.7k–
#2HyperFramesHyperFrames is an open-source framework that turns HTML, CSS, media, and seekable animations into deterministic MP4 videos, built for AI…76.6k1.5M
#3AgentScopeAgentScope is a Python agent framework with an SDK for building ReAct agents and an agent service backend with a web UI.35k51.4k
#4AG-UIAG-UI is an open, event-based protocol that standardizes how AI agents connect to user-facing frontend applications.16.9k4.6M
#5LangGraphLangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents in…44.2k18.5M
#6BifrostOpen-source, high-performance AI gateway that works as your LLM gateway, MCP gateway and agent gateway. One OpenAI-compatible API for…9.3k–

All AI agent frameworks, ranked

Recent Pydantic AI releases

Pydantic AI FAQ

Is Pydantic AI free and open source?

Yes. Pydantic AI's source code is on GitHub (pydantic/pydantic-ai) under the MIT license, so you can use it for free. Its website also links to a pricing page (https://pydantic.dev/pricing) for any paid plans.

How do I install Pydantic AI?

Run uv add pydantic-ai. The pydantic-ai package needs Python 3.11 or newer. The install section above has the other options from the project's README.

Is Pydantic AI still maintained?

Yes. The pydantic/pydantic-ai repository was last updated on Oct 22, 2026, and the latest release, v2.59.0 on GitHub, came out on Oct 21, 2026.

How popular is Pydantic AI?

Pydantic AI has 21k GitHub stars as of Nov 1, 2026. It gained 135 new stars in the week of Oct 25–31, 2026, against a 12-week average of 160 a week. Its main package had 1.7M downloads that week, +20% against its own 4-week average. That made it #12 among AI agent frameworks on the Devtoolwatch Board.

Pydantic AI vs LangGraph: how do they compare?

Pydantic AI and LangGraph are both tracked by Devtoolwatch as AI agent frameworks. Pydantic AI has 21k GitHub stars and LangGraph has 44.2k. In the week of Oct 25–31, 2026, Pydantic AI's main package had 1.7M downloads and LangGraph's had 18.5M. LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents in Python or JavaScript. LangGraph review.

Pydantic AI vs LangChain: how do they compare?

Pydantic AI and LangChain are both tracked by Devtoolwatch as AI agent frameworks. Pydantic AI has 21k GitHub stars and LangChain has 148.8k. In the week of Oct 25–31, 2026, Pydantic AI's main package had 1.7M downloads and LangChain's had 41.1M. LangChain review.

What are the best alternatives to Pydantic AI?

Other AI agent frameworks on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: Harness² (#1), HyperFrames (#2), AgentScope (#3), AG-UI (#4), LangGraph (#5). The alternatives table above has their numbers.

What is Pydantic AI used for?

Pydantic AI is used to build typed AI agents in Python. Uses shown include data extraction with validated outputs, terminal coding agents, durable background workflows, realtime voice assistants and image generation.

Is Pydantic AI an agentic framework?

Yes. Pydantic AI describes itself as an AI agent framework and Python AI SDK with a typed, extensible agent loop that supports tools, structured outputs and multi-agent work.

How does Pydantic AI work?

Pydantic AI wraps a model in an Agent with an output type, tools and capabilities. Tool arguments are validated before your code runs, and the run returns the declared type. The same agent can run in a terminal, web frontend, voice session or durable workflow.

What is the difference between Pydantic AI and Pydantic AI Harness?

Pydantic AI Harness is a companion open-source package. It adds capabilities for long-running work, such as memory, guardrails, sub-agents, planning, context management and a complete coding agent, on the same Agent API.

Does Pydantic AI support skills?

Yes. The v2.57.0 release notes mention a Skills feature that honors disable-model-invocation and user-invocable settings.

Is Pydantic AI production ready?

Pydantic AI's homepage lists companies such as Datalayer, Overjoy and Mixam running agents on it. Durable execution, validation and OpenTelemetry tracing are included for production use, and the vendor offers enterprise support.

Sources

  1. pydantic/pydantic-ai on GitHub
  2. Pydantic AI README
  3. pydantic-ai on PyPI
  4. Pydantic AI website
  5. Release clai2-bleeding (Oct 6, 2026)
  6. Release v2.59.0 (Oct 21, 2026)
  7. Release v1.107.9 (Oct 21, 2026)
  8. Hacker News: The human-in-the-loop is tired
  9. Hacker News: MCP Run Python
  10. Hacker News: Pydantic Logfire
  11. Devtoolwatch Board, the week of Oct 25–31, 2026

Pydantic AI README badge

Maintainers can show the tool's weekly Board rank in their README:

Devtoolwatch badge for Pydantic AI

[![Devtoolwatch](https://index.devtoolwatch.com/badge/pydantic-ai.svg)](https://index.devtoolwatch.com/tools/pydantic-ai/)

How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].