I’ve been working with LangGraph agents for some company projects and I’m struggling with the deployment part. Every tutorial and guide I find seems to require LangSmith API keys. I want to host everything on our own infrastructure without depending on external services.
The documentation keeps pointing toward their cloud platform, but I need a standalone solution. I tried a few community solutions I found online but they feel unstable for production use.
Has anyone successfully deployed LangGraph agents to their own servers without using LangSmith? I’m wondering if this framework is designed only for development and testing, or if there’s actually a way to run it independently in production environments.
LangGraph works fine without LangSmith in production. I’ve run it internally for eight months - the dependency on their cloud services is more marketing than technical necessity. The core framework’s solid on its own. I treat LangGraph like any Python service. Wrap the agent logic in Flask and handle orchestration through existing infrastructure. For persistence, I use MongoDB to store conversation state and agent checkpoints. It integrates well with the framework’s serialization. The biggest hurdle was error handling since you lose their built-in monitoring. I implemented custom logging with structured JSON that feeds into our ELK stack. Performance is identical to their cloud offering. Watch out for version compatibility when updating - they sometimes introduce breaking changes that assume LangSmith integration. Pin versions and test thoroughly before upgrades. The framework’s stable for production once you have proper observability.
The whole LangSmith thing is just vendor lock-in. I’ve been running LangGraph on bare metal for months without problems. Treat it like regular Python and ignore their deployment guides completely. The key is handling async properly - wrap everything in asyncio and use Celery for job queues if you need scaling. Works perfectly standalone. Don’t let their marketing convince you that you need their cloud platform.
Yes, running LangGraph agents independently of LangSmith in a production environment is entirely feasible. While their documentation leans heavily towards using their cloud services, it’s not a strict requirement. I’ve personally deployed multiple agents on a Kubernetes cluster for half a year without relying on LangSmith. Their platform mainly aids in monitoring and debugging; instead, you can implement standard solutions like Prometheus and Grafana for those needs. I encapsulate the execution of LangGraph using FastAPI and deploy it as usual containers. A key consideration is managing state persistence if your agents require memory between interactions—I’ve been using Redis for short-term state management and PostgreSQL for logging conversation histories. You’ll have to set up your own logging and handle errors without LangSmith’s support, but the performance remains robust without their cloud. The primary drawback is missing out on their debugging tools, which isn’t typically an issue in a production setting.
Yeah, you can definitely run LangGraph without LangSmith - just gotta handle the monitoring stuff yourself. I’ve had agents running on Google Cloud Run for 6 months now with zero cloud dependencies. Biggest thing is setting up your own checkpointing since you’re managing state without their platform. I threw Cloud SQL behind it for persistence and built custom retry logic for when workflows blow up. Had to roll my own performance monitoring too, but it’s rock solid once you get it dialed in. Docs are pretty terrible for this setup, but the core library splits clean from their cloud stuff. Watch out for concurrent agents though - without proper request isolation they’ll trash each other’s state. Debugging production issues is a pain without LangSmith’s tracing, so definitely spend time on good logging from day one.
Hit this same problem 18 months ago deploying LangGraph for a client. The LangSmith dependency is just them pushing their paid services.
I treated LangGraph like any Python library and ditched their deployment docs. Built a custom pipeline with Docker containers on AWS ECS.
Key is managing the agent lifecycle yourself. I wrote a wrapper service that handles agent instances and state without LangSmith. Used Redis for active sessions, DynamoDB for persistence.
Memory management bit me hard. LangGraph agents eat RAM if you don’t clean up completed workflows properly. Had to write custom garbage collection.
This session helped a ton for building reliable agents without cloud dependencies:
Framework works fine standalone once you rip out LangSmith references. Just means more upfront setup.
Yeah, the constant LangSmith pushing gets old. Went through the same thing deploying agents for our internal stuff last year.
You can definitely run LangGraph standalone, but honestly? Why torture yourself with custom infrastructure? Docker orchestration, Redis management, state handling, custom monitoring - it’s a pain.
I ended up using Latenode for this exact reason. Handles agent deployment without LangSmith, scales properly, and takes care of state persistence automatically. Monitoring’s built in too.
Best part? You just write your LangGraph logic instead of building wrapper services and babysitting infrastructure. Got three agent workflows running in production this way - they’ve been solid.
No vendor lock-in since you can export everything. Way better than building your own deployment pipeline.