Python + LangGraph
for ML teams.
Python + LangGraph is the most mature stack for production agentic workflows — graph-shaped, persistent, observable. For ML teams: Pythonic environment for ML-adjacent agents.
This stack, applied to you.
For ML teams, Python + LangGraph is the most mature stack for production agentic workflows. Python has the data ecosystem (NumPy, Pandas, scikit-learn) and most ML model serving tools. LangGraph's Python SDK is more mature than the JS version. LangSmith integration for tracing is best-in-class. State persistence via Postgres or other backends is straightforward.
ML teams-specific gotchas
- Async Python adds complexity vs sync
- Checkpoint storage choice matters at scale
- Tool execution lives in the graph — separate concerns carefully
- Streaming requires SSE or WebSockets
- Pydantic v2 changed many APIs — pin versions carefully
An ML team builds a research agent in Python + LangGraph. The agent runs 30+ tool calls per task; checkpointing prevents loss on errors. LangSmith traces every call. Average task: 8 minutes; reliability: 95%.
Common ML teams questions.
What if our team is TypeScript-fluent?
LangGraph JS works but lags Python on features. For TS teams that aren't Python-allergic, mixing is OK.
How do we serve fine-tuned models alongside?
Modal, Replicate, or self-hosted vLLM. Pair with LangGraph for orchestration.
We've shipped this.
When the client team is Python-fluent. If you're a ML teams shipping on this stack, we can save you a quarter.
Brief us