Our team is debating between different frameworks for our next MLOps project. We need something that handles state management well. seems much more intuitive for team-based roles, but is it stable enough for a production environment compared to LangGraph? I’d love to hear from anyone who has deployed this at scale recently.
3 answers
We transitioned our data pipeline to back in early 2024 and haven't looked back. While LangGraph offers more granular control over the 'graph' state, is significantly faster to prototype because of its role-playing abstraction. For production, the key is handling the environment variables and API rate limits properly. We use it to coordinate three different agents for lead generation and it handles the hand-offs beautifully. Just make sure you have a robust logging system in place to monitor the agent's thought process.
Does your production use case involve long-running tasks that require persistence? I’ve heard that might struggle with state recovery if a process is interrupted mid-way through a crew execution.
For sheer ease of use, wins. You can get a multi-agent "crew" up and running in under 50 lines of code, which is incredible for rapid deployment cycles
Exactly! The speed of development is why we chose it. It allowed our devs to focus on the prompt engineering rather than the underlying orchestration logic.
You hit the nail on the head, Brian. For long tasks, we actually wrap the crew in a custom Python script that saves the task output to a database at each step as a workaround.