I’m exploring LangGraph as a framework for creating AI agents and want to know if it’s ready for production use. Since I’m just getting started with AI agent development, I’d love to hear from developers who have hands-on experience.
Specifically interested in:
- What development challenges have you encountered?
- How does it perform in terms of stability and scaling for live applications?
- What’s your experience with monitoring and troubleshooting (using LangSmith or other tools)?
- How straightforward is the deployment and ongoing maintenance process?
I’d also welcome suggestions for other frameworks worth considering.
We’ve been running LangGraph for six months on customer support automation. I’m more cautious than others here. The framework’s stable, but agents behave unpredictably at scale. Same inputs would trigger completely different paths, making consistent user experiences nearly impossible. Testing’s a nightmare - you can’t mock the complex state transitions easily. We ended up building massive integration tests against real LLM endpoints, which kills our CI speed. The async model works great but gets messy when you’re juggling conversation context across sessions. Here’s what people don’t talk about: version upgrades constantly break subtle behaviors in ways you won’t catch right away. The docs assume you know graph theory, so expect a brutal learning curve if you’re coming from traditional web dev. If consistency beats flexibility for your use case, try simpler solutions first.
I’ve pushed LangGraph to production twice this year - here’s what I learned. The framework’s solid enough for prod, but you’ll need proper error handling for LLM timeouts and rate limits. Core execution engine’s reliable, though we hit state persistence issues when nodes failed. Had to build custom checkpoint recovery logic. Their docs on error scenarios suck. You’ll spend most time on monitoring. LangSmith’s fine for dev but we built custom dashboards for prod. Agent paths aren’t predictable, so tracking performance issues is way different than normal apps. Updates have been smooth but they ship fast - staying current takes work. Keep your agent logic separate from framework code. Good luck finding devs who know this stuff - budget extra time for training.
Been running LangGraph in prod for 8 months across three projects. Here’s what I’ve learned:
Reality check: It’s production ready, but you need to know what you’re getting into. Framework’s solid, but the ecosystem’s still catching up.
Biggest pain points:
Debugging complex agent flows sucks without proper tooling. LangSmith helps but isn’t perfect. Built custom logging middleware just to track state transitions.
Memory management gets tricky with long conversations. Had one agent slowly eat RAM over days.
Performance: Scales fine horizontally. We handle 10k requests daily without issues. Cache your LLM calls aggressively though.
Deployment: Straightforward if you’re already doing containerized deployments. Dependency management’s annoying - lots of moving parts.
Alternatives worth checking: Want something more battle tested? Try Crew AI or build directly on LangChain. Less fancy but sometimes that’s what you want in prod.
Bottom line: LangGraph works, but budget extra time for monitoring and observability. Agent debugging still sucks across all frameworks.
Honestly, all these manual debugging and monitoring solutions sound like way too much work. I’ve been through similar production nightmares, and automation is what actually fixes this stuff.
Why build custom dashboards and logging middleware from scratch? I just set up automated workflows for LangGraph monitoring, error detection, and self-healing. When agents start hogging RAM or timing out, automated processes restart services, clear memory, and handle the logging.
Everyone’s complaining about unpredictable agent behavior? Automate your regression testing. Run agents through standard scenarios on every deployment - you’ll catch breaking changes before users do.
State persistence and checkpoint recovery issues? Automated backup workflows snapshot agent states regularly and restore them when nodes fail. Way more reliable than trying to code manual recovery logic.
Team training gets easier too when you’ve got automated documentation workflows tracking agent behaviors and generating reports.
LangGraph works fine in production - you just need to automate the operational stuff instead of doing it by hand. Saves months of dev time and makes everything more reliable.
Check out https://latenode.com for setting up these automation workflows.