LangGraph vs Pydantic AI
A side-by-side comparison of capabilities, autonomy, integrations, and pricing to help you choose.
Short answer: choose LangGraph if you want low-level framework for stateful, durable, graph-based llm agents (Supervised agent, freemium); choose Pydantic AI if you want type-safe python framework for building production ai agents (Supervised agent, free).
| LangGraph | Pydantic AI | |
|---|---|---|
| What it is | Low-level framework for stateful, durable, graph-based LLM agents | Type-safe Python framework for building production AI agents |
| Type | framework | framework |
| Autonomy | Supervised agent | Supervised agent |
| Pricing | freemium · Framework free (MIT); LangGraph Platform via LangSmith (free Developer tier) | free · Free (open source; pay underlying model usage) |
| Best for | developers, enterprise, mid-market | developers |
| Deployment | self-hosted, api, saas | self-hosted, api |
| Modalities | text, code, api | text, code, api |
| Models | model-agnostic, gpt, claude, gemini, open-source | model-agnostic |
| Protocols | function-calling, mcp, rest-api | mcp, function-calling, rest-api |
| Integrations | OpenAI, Anthropic, Google, AWS Bedrock, LangSmith | OpenAI, Anthropic, Google Gemini, Mistral, Amazon Bedrock, Ollama |
| Capabilities | 4 documented | 3 documented |
LangGraph
- +Explicit graph model makes complex agent control flow (loops, branching, multi-agent routing) inspectable and controllable
- +Production-grade primitives: durable execution, checkpointing/time-travel, and first-class human-in-the-loop interrupts
- +Open source and model-agnostic, with a hosted LangGraph Platform and LangSmith observability for deployment
- -Lower-level and more verbose than higher-level agent libraries; a steeper learning curve
- -Framework, not a product: autonomy and quality depend entirely on what the developer builds
Pydantic AI
- +Strong type safety and schema-validated outputs from the Pydantic team
- +Built-in reflection and self-correction on invalid output
- +Model-agnostic across most major providers; integrates with Logfire observability
- -Python-only
- -A framework, not a product: you build, host, and secure your agents
Which should you choose?
LangGraph is low-level framework for stateful, durable, graph-based llm agents, best for developers, enterprise, mid-market. Pydantic AI is type-safe python framework for building production ai agents, best for developers. The right choice depends on the autonomy level you want, your existing integrations, and your budget, all compared above.
This comparison is generated from the sourced LangGraph and Pydantic AI profiles. Open either profile to review its evidence and last-reviewed date.