Pydantic AI vs LangGraph: agent frameworks compared

As of 2026-10-14, the cited sources describe Pydantic AI as a model-agnostic Python framework built around type safety and structured outputs, and LangGraph as an orchestration runtime for durable, stateful agent workflows. Both repositories report an MIT license. The sources do not benchmark them against each other.

Quick comparison

The order of the rows is editorial and reflects the query wording, not a ranking of quality.

How the options differ

Positioning

Pydantic AI's site calls it a "model-agnostic Python framework featuring type safety, structured outputs, validation, and seamless observability". Its repository description says "Every model, every interface, typed end to end."

LangGraph's documentation calls it "the orchestration runtime: durable execution, streaming, human-in-the-loop, and persistence." The docs place it beneath LangChain, which is described as the agent framework, and Deep Agents, which is built on top of LangGraph.

Control flow and state

The LangGraph docs show a StateGraph with nodes and edges and describe mixing deterministic and agentic steps in one graph. They also list persistence that lets agents resume after failures, inspection and modification of agent state, and short-term and long-term memory.

The Pydantic AI excerpts do not describe a graph model or persistence features. This is not stated in the cited sources.

Models and surfaces

Pydantic AI's site says it supports OpenAI, Anthropic, Google and more. The repository description mentions agents, realtime voice, image generation and embeddings.

The LangGraph excerpts do not list supported model providers. The LangGraph docs say LangChain provides abstractions and integrations for models and tools.

Evaluation and observability

Pydantic's site mentions Pydantic Evals as open source, for building datasets and comparing runs when prompts, models or tools change. LangGraph's docs point to LangSmith for tracing, evaluation, prompts and deployment, and describe production-ready deployment infrastructure for stateful, long-running workflows. Pricing for these offerings is not stated in the cited sources.

License

Both GitHub repositories report MIT as the license through the GitHub API. This says nothing about the cost of any hosted service.

How we compared

We read the GitHub repository metadata for both projects and the Pydantic AI product page and LangGraph overview docs, all checked 2026-10-14. We did not install, run or benchmark either framework, and we did not test deployment, performance or model compatibility.

Limitations

The sources do not state pricing, performance, adoption figures, or head-to-head capability comparisons. Pydantic AI's persistence and multi-agent features are not described in the excerpts. LangGraph's model support is not detailed in the excerpts. Version-specific behavior should be checked in each project's release notes.

Related: Pydantic AI hub, LangGraph hub, Frameworks category, Pydantic AI 2.55.0 launch, LangGraph 1.2.15 launch, Agno vs LangGraph, Agno vs Pydantic AI.

For the broader framework comparison, see Pydantic AI vs LangChain.