Maestro vs CrewAI, AutoGen, LangGraph and n8n¶
The short version: CrewAI, AutoGen and LangGraph are libraries. n8n is a workflow automation app. Maestro is an agent orchestration app. That difference decides almost everything else, so it is worth being precise about it before comparing features.
The core difference: library or application¶
Most agent tooling ships as a library. You import it, write the crew or the graph in Python, and build everything around it — accounts, key storage, quotas, a UI, some way to watch a run in progress. That is the right shape when agent logic belongs inside a service you are already building.
Maestro ships as the application. You deploy it, users sign in, and a routing layer picks the agent team for each prompt. Nobody writes a crew. The parts you would otherwise build around a library — auth, a BYOK key vault, a quota ledger, live run visibility, a marketplace — are the product.
At a glance¶
| Maestro | CrewAI / AutoGen | LangGraph | n8n | |
|---|---|---|---|---|
| Form factor | Deployable web app (API + UI) | Python library | Python graph runtime | Workflow automation app |
| Who defines the team | Orchestrator routes to one of 15 built-in domain squads | You write the crew in code | You build the graph in code | You wire the nodes by hand |
| Control flow | Plan generated per prompt, bounded | Role and task definitions | Explicit graph you author | Explicit nodes you author |
| Multi-user out of the box | Auth, 2FA, sessions, per-user isolation, quota ledger | — | — | Yes |
| Provider keys | BYOK vault, AES-256-GCM, 67 providers | Env vars in your process | Env vars in your process | Credential store |
| Live run visibility | Architect view over WebSocket, built in | Callbacks and terminal logs | LangSmith (separate hosted service) | Execution view |
| Durable resume | Postgres checkpoints, leases, reconciliation sweep | — | Checkpointers (you wire the store) | Execution retry |
| Agent sharing | Marketplace with security scanning and ratings | Copy the code | Copy the code | Workflow templates |
| Runs with no paid key | Yes, local Ollama end to end | Depends on your setup | Depends on your setup | Depends on your setup |
| License | Fair-code (SUL) | OSI open source | OSI open source | Fair-code |
When the others are the better choice¶
This section is not a formality. Picking the wrong shape costs months.
Use LangGraph or CrewAI if you want to embed agent logic inside your own Python service,
or you need full control of the control-flow graph. Maestro is not a library and does not try
to be — there is no import maestro. If your agents need to be a function call inside an
existing request handler, use a library.
Use n8n if your problem is connector-shaped: "when a row lands in Sheets, call an API, post to Slack". That is deterministic integration work, and an LLM routing layer adds cost, latency and nondeterminism to a problem that had none. n8n has years of connector coverage that Maestro will not match.
Use AutoGen if you are researching multi-agent conversation patterns and want to shape the message-passing protocol itself.
Use Maestro if you want to hand a deployed thing to people who are not going to write Python — where each user brings their own model key, tasks need to be metered and isolated, and somebody needs to watch a run and see why it went wrong.
Where Maestro is weaker¶
Connector breadth. n8n's integration catalog is vast. Maestro has six working connected-API tools; the other stored integrations are staged, not live.
Control flow. LangGraph gives you exact graph semantics — cycles, conditional edges, interrupts you place yourself. Maestro's plan is generated per prompt within bounds. That is the point of the design, but if you need an exact state machine, you want the graph runtime.
Ecosystem. CrewAI and LangGraph have far more tutorials, examples and community answers.
License. Maestro is fair-code, not OSI open source. You can read, run, modify and self-host it including commercially inside your own organization, but you cannot resell it as a hosted service. CrewAI, AutoGen and LangGraph carry no such restriction. If OSI-approved licensing is a hard requirement, that rules Maestro out and no feature comparison changes it.
Maturity. Maestro is pre-1.0 with a single maintainer. The others are older and have far more production mileage.
What Maestro adds on top of a raw agent loop¶
Mostly the unglamorous part — the difference between a demo and something you can leave running:
- Durable execution. Postgres checkpoints so a crashed worker resumes instead of hanging in "running" forever.
- Token budgets. Hierarchical, per wave and per call, with a quota re-check at each step boundary — so a runaway plan costs a bounded amount rather than an unbounded one.
- A weighted review rubric with hard-fail criteria, not a yes/no pass.
- Failure honesty. A blank subagent answer is a failure, not a silent success. A run that could not reach a data source says so in a mandatory coverage section instead of inventing numbers. A run where every member failed reports as failed.
- A BYOK vault. AES-256-GCM at rest, keys never returned to the frontend, decrypted once per run at the engine edge.
- Rate limiting on every route, enforced by a test that fails the build on any endpoint without an explicit limit.
Cost¶
Maestro is free to self-host, permanently, and runs end to end with no paid API key at
all — a local Ollama model for chat and nomic-embed-text for RAG embeddings. That is not
a crippled trial mode; it is the same pipeline with a different adapter. When you do connect a
paid provider, the key is yours and the spend is yours: Maestro takes no cut and proxies
nothing through a third party.
The same is broadly true of the alternatives, all of which are self-hostable. The difference is what you have to build yourself before the first user can log in.
Convinced, or curious enough to run it? Quick Start has the whole stack running in two commands. Architecture explains what happens between the prompt and the answer.