Great graphs. Production-grade runtime.
LangGraph gives you the best state-machine abstraction in the Python agent ecosystem. FastAgentic gives you the deployment wrapper it's been missing — resumption, cost caps, MCP, auth, and telemetry — without replacing a single line of your graph code.
The LangGraph deployment gap
Out of the box, LangGraph gives you a compiled graph and a .invoke() / .astream() API. Everything else — HTTP surfacing, auth, multi-tenant cost tracking, durable state, MCP exposure — is on you. Teams usually stitch together FastAPI + Celery + Redis + bespoke middleware and hope it holds.
What FastAgentic adds
- LangGraphAdapter — mount a compiled graph as an agent endpoint in one line.
- StepTracker integration — every node in your graph becomes a durable checkpoint. Process restarts don't lose work.
- Streaming intermediate state — node-level events stream to clients via SSE automatically.
- Cost tracking across nodes — token usage attributed per node, per run, per tenant.
- MCP exposure — your graph becomes a tool your MCP clients can call.
If your graph is already broken
Inherited a LangGraph pipeline that silently stalls, loses state on restart, or hemorrhages money? That's a pattern we see almost weekly. Our rescue engagements start with a one-week audit and end with a service that survives.
LangGraph pipeline stuck in production?
Neul Labs — the team behind FastAgentic — takes on a limited number of consulting engagements each quarter. We help teams ship agents to production, fix broken LangGraph pipelines, and design governance for multi-tenant LLM platforms.