PydanticAI
What is PydanticAI?
PydanticAI is a Python framework for developers that turns model calls into typed agent workflows with Pydantic-based inputs, outputs, Dependencies, and Hooks. It combines Agents, Function Tools, Multi-Agent Patterns, and Testing to keep control flow explicit and testable. The docs show integrations with OpenAI, Anthropic, Google ADK, LangChain, LlamaIndex, AutoGPT, CrewAI, and Logfire instrumentation for tracing runs and database queries.
Last verifiedHow we evaluate
At a glance
- PydanticAI is best for developers who want typed, testable LLM agents with structured outputs.
What does PydanticAI do?
PydanticAI turns model calls into typed agent workflows with Agents, Dependencies, Output, and Hooks, so you can shape prompts, inputs, and responses around Pydantic models instead of ad hoc glue code. It also supports Function Tools, Toolsets, Native Tools, and Deferred Tools, letting agents call external actions while keeping the control flow explicit and testable. The docs show it working across chat history, direct model requests, multi-agent patterns, and web chat UI flows. At scale, the framework spans 20+ model and provider integrations and a broad surface of agent tooling, from embeddings and HTTP request retries to MCP and durable-execution adapters. The docs include examples such as OpenAI, Anthropic, Google ADK, LangChain, LlamaIndex, AutoGPT, CrewAI, and Instructor, plus Logfire instrumentation for tracing agent runs and database queries. It is documented in llms.txt and llms-full.txt formats for LLMs and coding agents.
Why use PydanticAI?
- Typed outputs and dependencies reduce brittle prompt parsing and make agent behavior easier to validate.
- Broad provider support lets teams switch or mix model backends without redesigning the agent layer.
- Tracing through Pydantic Logfire gives visibility into agent runs and downstream database activity.
- Durable-execution and UI-event integrations fit production workflows instead of only demo-style chat apps.
- Testing, evals, and harness tooling help teams check agent behavior before shipping changes.
Who is PydanticAI for?
- Backend developers who need structured agent outputs and explicit tool calling.
- Platform engineers who want model-provider flexibility without rewriting agent logic.
- AI application teams that need multi-agent workflows and durable execution hooks.
- Developers building chat experiences who want web UI and message-history support.
- Teams using Pydantic who want validation-first agent design and testing.
What are PydanticAI's key features?
Agents
Build typed agents with Pydantic models for inputs and outputs, so teams can define agent behavior clearly and validate results before they ship.
Dependencies
Pass structured dependencies into agents and tools, which keeps shared context explicit and easier to test across OpenAI, Anthropic, and Google models.
Output
Shape model responses with Pydantic output schemas, helping applications catch malformed results early when working with providers like xAI, Bedrock, or Groq.
Hooks
Attach hooks around agent execution to inspect or modify runs, useful for logging, guardrails, and workflow control with FastAPI or Pydantic Logfire.
Function Tools
Expose Python functions as tools for agents, letting models call app logic directly while keeping arguments and return values validated by Pydantic.
Multi-Agent Patterns
Coordinate multiple agents in one workflow, supporting handoffs and orchestration patterns that fit systems built with Temporal, Prefect, or Restate.
Image, Audio, Video & Document Input
Send multimodal inputs into agents, so one workflow can process files and media alongside text through providers such as OpenAI, Anthropic, and Hugging Face.
Testing
Test agent behavior with dedicated harnesses and evals, making it easier to verify prompts, outputs, and tool calls before production deployment.
What does PydanticAI integrate with?
- OpenAI
- Anthropic
- xAI
- Bedrock
- Cerebras
- Cohere
- Groq
- Hugging Face
- Mistral
- Ollama
- OpenRouter
- Outlines
- Slack
- FastAPI
- Pydantic Logfire
- Temporal
- DBOS
- Prefect
- Restate
- AG-UI
- Vercel AI
- MCP
What are PydanticAI's use cases?
Structured outputs for backend apps
Backend developers use PydanticAI to turn model responses into predictable JSON, using Output to enforce schemas and Agents to keep the interaction focused. That makes it easier to plug AI into APIs, queues, and downstream services without brittle parsing or manual cleanup.
Provider-flexible agent builds
Platform engineers use PydanticAI to swap model providers without rewriting agent logic, relying on Direct Model Requests and HTTP Request Retries to keep integrations resilient. They can standardize one agent layer across OpenAI, Anthropic, Google, and other providers.
Multi-agent workflows for product teams
AI application teams use PydanticAI to coordinate specialized agents across a larger workflow, using Multi-Agent Patterns and Hooks to pass work between steps and capture execution events. That helps them build durable agent systems with clearer control over handoffs and failures.
Validation-first chat experiences
Teams using Pydantic build chat products with PydanticAI, combining Web Chat UI and Messages and chat history to preserve context across turns. They can also use Testing to verify message flows and keep conversational behavior stable as the app evolves.
How does PydanticAI work?
- Start by creating an Agent and defining its Dependencies so the model knows what context, services, and runtime data it can use for each request.
- Add Output and Agent Specs to shape responses into validated structures, then attach Function Tools or Toolsets for actions the agent can call safely.
- Connect Messages and chat history to preserve conversation state, and use Hooks to inspect, log, or modify behavior as requests move through the agent.
- Choose Direct Model Requests or a provider integration such as OpenAI, Anthropic, Google, or Groq, then rely on HTTP Request Retries for steadier runs.
- Expand into Multi-Agent Patterns, Web Chat UI, or Image, Audio, Video & Document Input, and use Testing to verify flows before shipping.
Frequently asked questions
What is PydanticAI?
PydanticAI is a Python framework for developers that turns model calls into typed agent workflows with Pydantic-based inputs, outputs, and dependencies. It combines Agents, Hooks, Function Tools, and Multi-Agent Patterns to keep control flow explicit and testable. The docs show integrations with OpenAI, Anthropic, LangChain, and LlamaIndex, plus Logfire instrumentation for tracing runs and database queries.
What is PydanticAI used for? Who is it for?
PydanticAI is used for Agents, Dependencies, and Output. It's built for Backend developers, Platform engineers, and AI application teams that need multi-agent workflows and durable execution hooks.
Does PydanticAI have an API and what does it integrate with?
PydanticAI doesn't publish a public API.
Explore other Agent Frameworks
