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Pydantic AI
Build type-safe, observable GenAI agents the FastAPI way
A Python framework that lets you create production‑grade generative‑AI agents with full type safety, model‑agnostic support, built‑in observability, and durable execution.
Overview
Overview
Pydantic AI is a Python framework that brings the ergonomic, type‑driven experience of FastAPI to generative‑AI agent development. It targets developers and teams who need production‑grade reliability, validation, and observability when building LLM‑powered applications.
Core capabilities
The library is model‑agnostic, supporting OpenAI, Anthropic, Gemini, Azure, Bedrock, Ollama and many others, while allowing custom providers. Full Pydantic validation moves many runtime errors to static type checking, and integrated Logfire/OpenTelemetry gives real‑time tracing, cost tracking, and systematic evals. Features such as durable execution, streamed structured outputs, human‑in‑the‑loop tool approval, and the Model Context Protocol enable complex, interactive workflows and multi‑agent orchestration.
Deployment
Agents are defined with typed dependencies and output models, then run synchronously or asynchronously in any Python environment. Because observability hooks follow OpenTelemetry standards, they can be exported to existing monitoring stacks, making Pydantic AI suitable for cloud services, on‑premise deployments, or serverless functions.
Highlights
Model‑agnostic support for all major LLM providers
Full type‑safety with Pydantic validation moving errors to compile time
Integrated observability via Pydantic Logfire and OpenTelemetry
Durable, streamed, and human‑in‑the‑loop execution with tool approval
Pros
- Type‑safe API reduces runtime bugs
- Seamless integration with existing Pydantic ecosystem
- Rich observability and evals out of the box
- Supports custom models and providers
Considerations
- Requires familiarity with Pydantic and type hints
- May add overhead for simple scripts
- Observability integration assumes OpenTelemetry compatible platform
- Learning curve for advanced features like MCP and A2A
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Fit guide
Great for
- Teams building production‑grade AI assistants
- Developers needing strict validation of LLM outputs
- Projects that require multi‑model or provider flexibility
- Applications with complex tool usage and human‑in‑the‑loop workflows
Not ideal when
- Quick prototypes where type safety is unnecessary
- Environments without OpenTelemetry support
- Use cases limited to a single static model without tooling
- Developers unfamiliar with Pydantic's typing system
How teams use it
Bank support chatbot
Generates risk‑rated advice, can block cards after human approval, and returns structured support output.
Content generation pipeline
Streams validated article drafts from multiple LLMs, enabling real‑time editing and cost tracking.
Multi‑agent orchestration
Agents communicate via A2A to coordinate complex tasks, sharing context through the Model Context Protocol.
Evaluation dashboard
Runs systematic evals, tracks latency and cost in Logfire, and visualizes performance trends over time.
Tech snapshot
Python100%
Makefile1%
TypeScript1%
Frequently asked questions
Can I use Pydantic AI with a single LLM provider?
Yes, you can configure the Agent with any supported provider or a custom model; the framework works equally well with a single or multiple providers.
Do I need to write Pydantic models for every agent output?
While optional, defining a Pydantic model enables automatic validation and type‑safe retries, which is recommended for production use.
How does observability integrate with existing monitoring tools?
Pydantic AI emits OpenTelemetry spans that can be collected by Logfire, Jaeger, Prometheus, or any OTel‑compatible backend.
Is the framework compatible with async environments?
Agents can be run synchronously via `run_sync` or asynchronously with `await agent.run(...)`, fitting both traditional and async Python codebases.
Can I add custom tools that require human approval?
Yes, the human‑in‑the‑loop feature lets you flag tool calls for manual review based on arguments or conversation context.
Project at a glance
Active- Stars
- 14,393
- Watchers
- 14,393
- Forks
- 1,558
LicenseMIT
Repo age1 year old
Last commit3 hours ago
Primary languagePython
Last synced 3 hours ago