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.
| 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 | 5+ 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.