Mycel vs LangGraph
A framework for building agents, versus a harness for running them as a business.
What LangGraph is good at
LangGraph is the best vocabulary going for an agent's control flow: cycles, branches, state machines, human-in-the-loop interrupts. If you're designing how an agent thinks, it hands you the words for it.
The actual difference
LangGraph is a library you build with. Mycel is a service you run on. The graph is not the hard part of an AI service business — tenancy, credential custody, an audit a client's accountant would accept, scheduling, and a customer portal are. Mycel takes an opinion on all of those and comparatively few opinions about how your agent reasons; it drives a coding agent in a sandbox rather than composing nodes.
Side by side
| LangGraph | Mycel | |
|---|---|---|
| Shape | Library | Runtime + product |
| Multi-tenancy | You build it | Org → project → member, tested |
| Audit | You build it | Hash-chained, verifiable |
| Customer portal | You build it | Included, separate credential plane |
| Scheduling | You build it | Cadences + a Postgres queue |
| Agent control flow | Rich and explicit | Delegated to a coding agent |
Choose LangGraph
- The agent's reasoning structure is the interesting problem
- You're embedding an agent in an existing product
- You want full control of the graph
Choose Mycel
- The interesting problem is running a business, not the control flow
- You need multi-tenancy, audit and approvals without building them
- Your customers need somewhere to log in
Other comparisons: ChatGPT, Claude, or whichever tab is already open, Grok Bot and the AI-employee platforms, Hiring an account manager, Building it yourself, Zapier & n8n, Temporal, CrewAI & AutoGen