LangGraph vs 
Pydantic AI
LangGraph vs Pydantic AI compared for 2026 — features, license, ease of use, performance and which one to choose. Stateful, controllable agent graphs vs Type-safe agents for production.
Updated regularly · curated by olud.ai
LangGraph vs Pydantic AI at a glance
| Spec | LangGraph | Pydantic AI |
|---|---|---|
| Category | AI agent framework | AI agent framework |
| Type | Agent orchestration (graphs) | Agent framework (typed) |
| License | MIT | MIT |
| Runs locally | Cloud-optional | Cloud-optional |
| Primary language | Python / JS | Python |
| Ease of use | Advanced | Intermediate |
| Best for | developers needing controllable agent workflows | production agents with typed outputs |
| GitHub stars | 37.8k | 18.7k |
How LangGraph and Pydantic AI score
| Criterion | LangGraph | Pydantic AI |
|---|---|---|
| Popularity | 4.0 | 3.5 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 2.5 | 3.5 |
| Privacy | 3.5 | 3.5 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
LangGraph
LangGraph is a library for building stateful, controllable agents as graphs, giving you fine-grained control over loops, branching and persistence.
- Explicit, controllable agent state machines
- Persistence and human-in-the-loop built in
- Integrates with the LangChain ecosystem
Pydantic AI
Pydantic AI brings the ergonomics and type-safety of Pydantic to agent development, with structured outputs, dependency injection and model-agnostic support.
- Type-safe, structured agent outputs
- Familiar Pydantic developer experience
- Model-agnostic with great tooling
Key differences
LangGraph is agent orchestration (graphs), while Pydantic AI is agent framework (typed). LangGraph leans more advanced-friendly, whereas Pydantic AI is more suited to intermediate users. In short, LangGraph fits developers needing controllable agent workflows, and Pydantic AI fits production agents with typed outputs.
Which should you choose?
Choose LangGraph for developers needing controllable agent workflows. Choose Pydantic AI for production agents with typed outputs.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is LangGraph or Pydantic AI easier to use?
Pydantic AI is generally the easier of the two to get started with, while LangGraph rewards more setup with more control.
Are LangGraph and Pydantic AI free?
LangGraph is free and open source (MIT), and Pydantic AI is free and open source (MIT). Neither charges for the core software.
Can I run LangGraph and Pydantic AI locally?
LangGraph: cloud-optional · Pydantic AI: cloud-optional. Both can be used without sending your data to a third-party cloud where their setup allows.
LangGraph vs Pydantic AI — which should I pick in 2026?
Choose LangGraph for developers needing controllable agent workflows. Choose Pydantic AI for production agents with typed outputs.
People also compare
Explore more open-source AI
Browse thousands of open-source AI tools, models and projects — all curated in one place, updated daily.
Explore the directory →