Head-to-Head Comparison

LangGraph vs Pydantic AI

Quick answer: LangGraph leads this head-to-head in 2026, scoring 94 on the Hyland Score against Pydantic AI's 83. It scores higher on the Hyland Score (94 vs 83). Choose Pydantic AI instead if your work fits the "Single Agent" category better.

HIGHER SCORE

LangGraph

LangChain

94HYLAND SCORE

Stateful, controllable agent orchestration as graphs.

Try LangGraph

Pydantic AI

Pydantic

83HYLAND SCORE

Type-safe agents from the FastAPI team’s lineage.

Try Pydantic AI

Spec-by-spec

SPEC

LANGGRAPH

PYDANTIC AI

Hyland Score

Category

Orchestration

Single Agent

Pricing

Open Source

Open source

Yes

Self-hostable

Yes

Language

Python, TypeScript

Python

Architecture

Graph / state machine

Typed agent loop

License

MIT

GitHub stars

~12K

~11K

LangGraph: strengths & weaknesses

Higher Hyland Score (94 vs 83)
Open Source pricing lowers the cost of getting started

Pydantic AI: strengths & weaknesses

Open Source pricing lowers the cost of getting started
Lower Hyland Score (83 vs 94)

The Verdict

Choose LangGraph for most teams

LangGraph comes out ahead because it scores higher on the Hyland Score (94 vs 83).

Choose Pydantic AI instead if your work fits the "Single Agent" category better.

Try LangGraph Browse all AI Agent Frameworks

LangGraph vs Pydantic AI: FAQ

Which is better, LangGraph or Pydantic AI?

Based on the Hyland Score, LangGraph leads with 94 vs 83, because it scores higher on the Hyland Score (94 vs 83). However, Choose Pydantic AI instead if your work fits the "Single Agent" category better.

Is LangGraph free to use?

LangGraph uses a Open Source pricing model. It is open source. It can be self-hosted.

Is Pydantic AI free to use?

Pydantic AI uses a Open Source pricing model. It is open source. It can be self-hosted.

Can I use LangGraph and Pydantic AI together?

Yes — many teams combine them. LangGraph strengths: python, typescript, stateful. Pydantic AI strengths: python, type-safe, structured output. Evaluate both against your actual workload before standardizing on one.