Hey folks! I’ve been working with Langgraph for building AI agents and workflow automation for some time now. It’s been really great because the graph structure gives you good visibility into what your agents are doing step by step. But now I’m hearing more about Pydantic AI and wondering if I should try it out. Has anyone here worked with both frameworks? I’d love to hear your thoughts on how they compare. What are the main advantages and disadvantages of each one? Are there specific use cases where one works better than the other? Any insights would be really helpful!
Switched from Langgraph to Pydantic AI three months ago - here’s what I’ve noticed. Pydantic AI feels way lighter and more intuitive, especially if you already know Pydantic’s validation system. Type safety is rock solid and debugging’s a breeze since it uses standard Python patterns. But I really miss Langgraph’s visual workflow representation. That was a lifesaver for complex multi-step processes where I needed to track state changes. Pydantic AI crushes it for rapid prototyping and keeping code clean, but Langgraph still wins for enterprise stuff where you need serious observability and complex branching. Performance-wise, Pydantic AI’s been noticeably faster in my tests, especially on simpler agent tasks.
I’ve run production agents with both for a year now. Pydantic AI clicks right away if you know Python typing - Langgraph makes you learn graph concepts first. Langgraph crushes it for complex conditional flows and error recovery. The graph structure makes retry logic and fallbacks dead simple. Pydantic AI can handle this stuff but you’re writing way more custom code. When explaining agent behavior to non-tech people, Langgraph’s visual graphs are a lifesaver. But Pydantic AI’s simpler approach gets junior devs up to speed faster without drowning in graph abstractions. Also worth noting - Langgraph’s state persistence beats Pydantic AI for long-running processes.
Been running agents in production for 4 years and just migrated a bunch of systems to test both frameworks side by side.
Biggest difference? How they scale with complexity. Langgraph forces you into nodes and edges from the start - feels like overhead for simple chatbots, but saves you when things get messy. Pydantic AI lets you start simple and add complexity later, but you’ll refactor way more.
Deployment differences are massive and nobody’s talking about it. Pydantic AI agents deploy like regular Python apps. Langgraph needs infrastructure planning because of state management and the graph execution engine. We had to rework our entire CI/CD pipeline for Langgraph. Pydantic AI just dropped into our existing FastAPI setup.
Memory usage is another big one. Langgraph keeps more state in memory for graph execution - gets expensive at scale. Pydantic AI is way more efficient for simple request-response patterns.
If Langgraph’s working for you, don’t rush to switch. But for new projects, Pydantic AI’s worth considering, especially if your team already knows the Pydantic ecosystem.
This breakdown covers real world examples that might help you decide what makes sense for your setup.
depends on what u r used to. if you’ve worked with fastapi/pydantic b4, pydantic ai will feel familiar. but langgraph has way better docs and community support rn. pydantic ai is pretty new, so u won’t find many stack overflow answers when u hit problems.
I switched to Pydantic AI from LangChain and completely skipped Langgraph. The biggest difference? Way easier learning curve. Pydantic AI just uses regular Python knowledge instead of making you learn some new framework mindset. The structured outputs work exactly like normal Pydantic models, so there’s less mental overhead when you’re building agents. But here’s the catch - debugging complex agent stuff gets messy without those visual graphs Langgraph gives you. When your agent logic gets buried in nested function calls, tracing errors becomes a pain. The tool calling approach feels way more natural though. You’re just writing normal Python functions with type hints instead of messing with graph node configs. If your team’s already using Pydantic heavily, it integrates perfectly. You’ll miss some workflow orchestration features that graph frameworks give you right away, but the trade-off’s worth it for most cases.
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