CrewAI
Multi-agent automation framework & studio to build and run AI crews
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
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.
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.
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.
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.
When teams consider Pydantic AI, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
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.
While optional, defining a Pydantic model enables automatic validation and type‑safe retries, which is recommended for production use.
Pydantic AI emits OpenTelemetry spans that can be collected by Logfire, Jaeger, Prometheus, or any OTel‑compatible backend.
Agents can be run synchronously via `run_sync` or asynchronously with `await agent.run(...)`, fitting both traditional and async Python codebases.
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
ActiveLast synced 4 days ago
