Pydantic AI vs LangChain & LangGraph
LangChain is a large Python ecosystem: LangGraph underneath it for graph-based control flow, deepagents for its coding harness, and a large catalogue of integrations. Pydantic AI does it from one typed Agent with plain Python control flow: pydantic-graph when you want an explicit graph, a Harness SDK of ready-made capabilities and complete agents, and validation from the library you already use.
Pydantic AI is one part of a stack: the Harness SDK for capabilities and complete agents, Pydantic Evals, Pydantic Graph, Pydantic Logfire for observability, and Pydantic itself for validation. The tables below cover the whole of it.
Already built on LangChain? The migrating-langchain-to-pydantic-ai skill ports an existing application to Pydantic AI one working path at a time, preserving behavior rather than translating API names.
| LangChain & LangGraph | Pydantic AI and Harness SDK | |
|---|---|---|
| Language | Python | Python |
| License | MIT | MIT |
| Model providers | Many | Many |
| Extensibility | Middleware, callbacks | Capabilities and toolsets; 50+ with the Harness SDK |
| Harnesses | deepagents, or your own on LangGraph | Built-in Coder and Researcher, or compose your own |
| Observability | OpenTelemetry via LangSmith | OpenTelemetry, including Pydantic Logfire |
| Durable execution | Yes | Seven integrations |
| Interfaces | LangSmith Agent Server, Fleet | CLI, web chat, AG-UI, Vercel AI, ACP (experimental) |
| Realtime voice | No | Realtime |
| Evals | Yes | Pydantic Evals |
| Image generation | Provider-hosted tools only | Image Generation |
| LangChain & LangGraph | Pydantic AI and Harness SDK | |
|---|---|---|
| Multi-agent | Yes | Subagents, delegation, or pydantic-graph |
| Planning | Yes | Planning |
| Skills | Yes | Skills |
| Memory | Yes | Memory |
| Compaction | Yes | Compaction |
| Guardrails | Yes | Guardrails |
| Code sandboxes | Yes | Execution environments |
| Browser use | Provider-hosted tools only | Web & research |
Do you have a graph library? Yes. pydantic-graph: typed nodes, edges from return
types, and persistence for pausing and resuming. Reach for it when the control flow is a real state
machine; plain Python and sub-agents cover the rest.