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.