An opinionated list of awesome Pydantic AI frameworks, libraries, software and resources.
Pydantic AI is a Python agent framework designed to make it easier to build production-grade applications with Generative AI. It brings the "FastAPI feeling" to AI development with type safety, dependency injection, and structured outputs.
- Official Resources
- Frameworks & Libraries
- Templates & Boilerplates
- Examples & Tutorials
- Observability
- Articles & Blog Posts
- Case Studies
- Contributing
- Pydantic AI Documentation - Official documentation with guides, API reference, and examples.
- Pydantic AI Repository - Official GitHub repository. Agent framework with model-agnostic architecture, type safety, dependency injection, tool integration, and structured outputs.
- pai-agent-sdk - Application framework for building AI agents with Pydantic AI. Provides full developer control without abstraction overhead, featuring environment-based architecture, resumable sessions, hierarchical subagents, and human-in-the-loop approval workflows.
- pydantic-deep - Python framework for building production-grade autonomous agents. Extends Pydantic AI with agent orchestration, task planning, subagent delegation, context management, and multiple backend support.
- pydantic-ai-middleware - Lightweight middleware library providing clean before/after hooks without imposed guardrail structure. Supports input/output processing, tool management, error handling, rate limiting, and audit logging.
- pydantic-ai-backend - File storage, sandbox backends, and console toolset for pydantic-ai agents. Includes LocalBackend, StateBackend, DockerSandbox, and pre-configured Docker runtimes.
- pydantic-ai-todo - Standalone task planning library for pydantic-ai agents. Provides
read_todosandwrite_todostools with flexible storage backends. - subagents-pydantic-ai - Multi-agent orchestration library with dual-mode execution (sync/async), dynamic agent creation, and parent-child communication via
ask_parenttool. - summarization-pydantic-ai - Automatic conversation summarization and context management. Provides
SummarizationProcessorfor LLM-based summaries andSlidingWindowProcessorfor zero-cost message trimming. - pydantic-ai-filesystem-sandbox - Secure filesystem sandbox toolset with LLM-friendly errors. Provides sandboxing, read/write control, granular permissions, and human-in-the-loop approval workflows.
- pydantic-ai-skills - Standardized framework for building and managing Agent Skills. Features progressive disclosure, type-safe design, multi-directory support, and Anthropic Agent Skills compatibility.
- pydantic-collab - Multi-agent orchestration framework for building agent teams. Agents collaborate via handoffs, consultations (tool calls), and shared memory on top of pre-built and custom network topologies, Logfire observability included.
- full-stack-fastapi-nextjs-llm-template - Production-ready project generator for AI/LLM applications. Combines FastAPI backend with Next.js 15 frontend, WebSocket streaming, JWT auth, multiple database options, and 20+ enterprise integrations.
- pydantic-ai-examples - Curated collection of examples demonstrating Pydantic AI usage including direct model requests, sentiment classification, dynamic classification, local models with Ollama, and conversation history management.
- pydantic-ai-rlm - Recursive Language Models with Pydantic AI.
- Pydantic Logfire - Observability platform for Python applications built by the Pydantic team. Provides logging, tracing, metrics with SQL query interface, OpenTelemetry foundation, and native Pydantic integration.
- Building Production-Grade AI Agents: How We Brought Deep Agent Patterns to Pydantic - Introduction to pydantic-deep framework and deep agent architectural patterns for production AI systems.
- Production-Ready Template for AI/LLM Applications: FastAPI + Next.js + 20+ Integrations - Guide to fastapi-fullstack CLI tool that generates complete full-stack AI applications with enterprise integrations.
- Mixam: AI Agent for Order Recommendation - How Mixam, a global self-publishing company, used Pydantic AI to create an AI agent that helps customers navigate complex printing specifications.
- Sophos: Unified Observability with Logfire - How Sophos implemented Pydantic Logfire for unified observability across their AI-powered security solutions, achieving end-to-end visibility and proactive issue detection.
- Boosted.ai: Monitoring AI Investment Research - How Boosted.ai uses Pydantic Logfire to monitor 50,000+ concurrent AI workflows processing billions of tokens daily, reducing issue diagnosis time significantly.
Contributions are welcome! Please read the contribution guidelines first.
This work is licensed under a Creative Commons Attribution 4.0 International License.
