Comparison
Hermes Agent vs LangGraph: Run a Ready Agent or Build a Graph
Hermes Agent is a finished, self-improving agent you install and talk to from your chat apps. LangGraph is a low-level framework for writing stateful agents as code. One is something you use; the other is something you build with. Here is how to tell which one your project needs.
The short answer: pick Hermes when you want a personal or team assistant that remembers context, learns reusable skills and answers in your messaging apps. Pick LangGraph when the agent's control flow is part of software you are shipping and you need to define every step, checkpoint and approval yourself.
Checked October 19, 2026 against the Hermes Agent repository and documentation, and the LangGraph repository and LangChain documentation. For the broader LangChain versus LangGraph question, see our OpenClaw Launch vs LangGraph page; this one focuses on Hermes.
Quick comparison
| Area | Hermes Agent | LangGraph |
|---|---|---|
| Made by | Nous Research | LangChain |
| License | MIT | MIT (open-source library) |
| What it is | A complete agent you install and configure | A low-level orchestration framework for building stateful agents |
| You supply | A model, configuration and channels | Python or JavaScript code: nodes, edges, state and tools |
| Memory | Agent-curated memory plus full-text search over past sessions | Checkpointers for thread state and stores for long-term memory, which you design |
| Learning | Creates skills from completed tasks and refines them during use | No built-in skill learning; behavior is whatever your graph encodes |
| User interface | CLI plus a messaging gateway (Telegram, Discord, Slack, WhatsApp, Signal, email and more) | None built in; you connect your own app or API |
| Tools | 40+ built-in tools, skills and MCP servers | Any tool you bind in code, including MCP via adapters |
| Control flow | Handled by the agent loop, with subagents for parallel work | Explicit graph with cycles, branches and interrupts |
| Durability | Long-running process with persistent memory and a cron scheduler | Durable execution: state checkpointed at every step, resumable after failure |
| Hosting | Self-host, or managed hosting such as OpenClaw Launch | Self-host, or LangSmith Deployment (formerly LangGraph Platform) |
What LangGraph is
LangGraph describes itself as a low-level orchestration framework for building stateful agents. You model an agent as a graph: nodes are functions or model calls, edges decide what runs next, and a typed state object flows through them. Cycles are allowed, so a model can call tools in a loop, and you can branch, fan out or route to specialist sub-graphs.
Its main selling points are production concerns. Checkpointers save state after every step, which gives you durable execution (a run survives a crash and resumes where it stopped), human-in-the-loop interrupts (pause for approval, edit state, continue) and the ability to inspect earlier states. LangGraph reached a stable 1.0 release in October 2025, and there are Python and JavaScript versions.
The commercial pieces sit around it under the LangSmith name. LangSmith provides tracing and evaluation, LangSmith Deployment (renamed from LangGraph Platform in October 2025) hosts long-running agents on Agent Server, and LangSmith Studio (formerly LangGraph Studio) is the visual debugger. LangChain's higher-level agent API and its Deep Agents package are both built on LangGraph.
What Hermes Agent is
Hermes Agent is Nous Research's open-source agent, billed as the agent that grows with you. It is not a library you import. You install it, choose a model provider (Nous Portal, OpenRouter, OpenAI, a custom endpoint and others), and it is ready to work.
Its distinctive feature is a learning loop. After complex tasks it can write a skill describing how it solved the problem, improve that skill on later use, keep its own memory notes, and search past conversations to recall context. It also ships a cron scheduler that delivers results to any connected platform, subagents for parallel work, several terminal backends (local, Docker, SSH, Modal, Daytona and others) and MCP support.
The messaging gateway is what makes it feel like a teammate rather than a script. The documentation lists Telegram, Discord, Slack, WhatsApp, Signal, SMS, email, Matrix, Mattermost, Microsoft Teams, Feishu/Lark, WeCom, Weixin, LINE and more, so you can message the same agent from wherever you already are.
The real difference: who writes the agent
With LangGraph, you are the author. You decide the graph shape, write each node, choose what goes in state, pick the persistence backend and build the interface users reach it through. That work buys exact, testable control, which matters when an agent is a feature inside your product and must behave the same way for every customer.
With Hermes, the agent already exists. You shape it through configuration, skills, memory and the tools you connect. You give up node-level control over its reasoning path, and in return you get a working assistant on day one that keeps improving as it is used.
Neither approach removes operations work on its own. A self-hosted Hermes still needs a machine that stays on, and a LangGraph application still needs hosting, secrets, monitoring and upgrades, whether you run it yourself or on LangSmith Deployment.
Choose LangGraph when
- You are building an agent into your own product, API or backend pipeline.
- The control flow must be explicit: specific branches, retries, approvals or multi-agent routing.
- You need resumable, checkpointed runs for long or failure-prone workflows.
- You want to trace, evaluate and replay runs at the level of individual steps.
- You have engineers who will own the code and its deployment.
Choose Hermes Agent when
- You want an assistant to use now, not a codebase to write.
- Chat apps are the interface: Telegram, Discord, Slack, WhatsApp or similar.
- You want memory and learned skills that build up over weeks of use.
- Recurring tasks matter: daily briefings, reports or checks delivered to a chat.
- Configuration and skills give you enough control over behavior.
Using them together
The two fit together cleanly because both speak MCP. When you deploy a LangGraph agent on Agent Server, it is exposed as an MCP tool at the /mcp endpoint over Streamable HTTP. Hermes can connect to remote HTTP MCP servers declared under mcp_servers in its config, with a URL and auth headers. Hermes then becomes the conversational front end with memory and chat channels, and it hands a well-defined job to your graph when one is needed.
A typical split: people talk to Hermes in Telegram or Slack, while a LangGraph workflow handles a structured process such as an approval chain or a multi-step data pipeline that needs checkpointing. Keep authorization inside the LangGraph service itself; the calling agent should not be what enforces access rules.
Running Hermes 24/7 on OpenClaw Launch
Hermes is most useful when it is always online: scheduled jobs only fire if the process is running, and chat messages need somewhere to land at any hour. You can keep a VPS up yourself, or deploy a managed Hermes on OpenClaw Launch from the browser. The bot runs in its own container, you connect your messaging platforms from the dashboard, and you can bring your own model key or use included AI credits. See running Hermes Agent 24/7 and deploying Hermes for the options.
Managed Hermes hosting runs the Hermes assistant. It does not host a separate LangGraph application; if you combine the two, run your graph on your own infrastructure or LangSmith Deployment and point Hermes at its MCP endpoint.
Bottom line
LangGraph and Hermes are not really rivals. LangGraph is a strong foundation for engineers building custom, durable agent workflows into software. Hermes is a ready agent with memory, learned skills and chat access for people who want the assistant itself. If you are unsure, try a small trial of each: one real task through a Hermes chat, and the same task as a minimal LangGraph graph. The amount of code you had to write, and who will maintain it, usually decides the question.
Hermes Agent vs LangGraph FAQ
Is Hermes Agent built on LangGraph?
No. Hermes Agent is a separate open-source project from Nous Research with its own agent loop, memory, skills and messaging gateway. LangGraph is a framework from LangChain for writing your own agents as graphs. Neither depends on the other.
Is LangGraph Platform the same as LangSmith Deployment?
Yes. LangChain renamed LangGraph Platform to LangSmith Deployment in October 2025, and LangGraph Studio is now LangSmith Studio. The open-source LangGraph library itself kept its name and remains MIT licensed.
Which has better memory, Hermes or LangGraph?
They provide different things. Hermes ships agent-curated memory and searchable session history that work as soon as it runs. LangGraph gives you checkpointers for short-term thread state and stores for long-term memory, and you decide what is saved, under which keys, and when it is recalled.
Can Hermes call a LangGraph agent?
Yes, through MCP. A LangGraph agent deployed on Agent Server is exposed as an MCP tool at the /mcp endpoint over Streamable HTTP, and Hermes can connect to remote HTTP MCP servers listed under mcp_servers in its config. Hermes then calls your graph like any other tool.