Comparison
Clavis vs LangGraph
Both run AI agents. They sit at opposite ends of the build-versus-run spectrum. The right pick depends on what your team wants to own.
Home / Clavis vs LangGraph
The short version
| Clavis | LangGraph | |
|---|---|---|
| Category | Complete agent harness | Agent framework |
| You interact through | Dashboard, CLI, chat | Your own code |
| Setup | Install and run | Import and build an app |
| Audience | Whole team | Engineers |
| Orchestration control | High, agent-level | Very high, graph-level |
| Memory and knowledge base | Built in | Build your own |
| Visual workflow builder | Yes | No |
| Scheduler | Yes | Build your own |
| Observability and cost tracking | Built in | Via LangSmith or your own |
| Safety layer | 5-gate, sandboxed | Build your own |
| Time to first running agent | Minutes | Days to a working app |
Where LangGraph wins
LangGraph is the right tool when control is the priority. Its graph model gives engineers precise command over agent state, branching, and recovery. If you are embedding agent logic deep inside an existing application, or your requirements are unusual enough that a finished product would get in the way, a framework is the correct choice and LangGraph is among the best.
That control has a cost. The interface, the memory store, the scheduler, the integrations, the dashboards, and the safety layer are all yours to design, build, run, and maintain. For a strong engineering team with time to invest, that is a fair trade.
Where Clavis wins
Clavis is the right tool when you want the outcome, not the construction project. It ships the entire harness: 76 agent structures, a drag-and-drop workflow builder, six-layer memory with a versioned wiki, a scheduler, a native CRM, multi-channel chat, observability, and a 5-gate security model. You install it, add a key or point it at a local model, and run real agent work the same day.
It also widens who can do that work. LangGraph output lives in code, so only engineers operate it. The Clavis dashboard, chat, and visual builder let a researcher, an operator, or a marketer run agents directly, while the CLI stays available for engineers who want it.
The honest trade
Which should you choose?
Choose LangGraph if you have an engineering team that wants low-level control, you are embedding agents inside a larger codebase, and you have time to build and maintain the surrounding system.
Choose Clavis if you want a finished, self-hosted harness the whole team can run, you would rather operate agents than build infrastructure, and you want memory, scheduling, safety, and observability included on day one.
For a wider view of the field, see the best agent harness 2026 rankings or the full Clavis overview.
The takeaway
Run the harness instead of building it
Clavis ships everything LangGraph leaves to you, in one self-hosted install.