Can a Hermes agent see context I built up in ChatGPT or Claude?

Yes, if those tools are also connected to Vilix. Memory is tied to your Vilix account rather than any one tool, so decisions and context from any connected AI are visible to Hermes through get_context when it next runs.

June 9, 2026 · 7 min read

Hermes agent + Vilix: durable memory for self-hosted agents

Hermes builds skills and learns in-session, but cross-session memory is on you. Connect Vilix once as a custom MCP server and every loop your agent runs reads past context and writes new decisions back, durably, across any tool in your stack.

Give a Hermes agent durable memory by connecting Vilix as a custom MCP connector at api.vilix.ai/mcp, so it reads context before each loop and saves results after. Hermes refines its own skills in a session, but that learning and your task state vanish when the session ends. Vilix is the persistent store that carries both across sessions and across tools.

Why Hermes specifically needs long-term memory

Most agent harnesses have a memory gap. Hermes makes that gap more visible because it is built around skill improvement. The agent is meant to get better at tasks over time, rewriting its own skill definitions based on what worked. That is a compelling loop, right up until the process exits. Without a persistent store, the improved skill lives only in the current run. Start a new session and Hermes reverts to baseline.

There is a related issue with context. Hermes runs inside a sandbox, which means it has limited visibility into what happened outside that sandbox: what you decided in Claude last Tuesday, what task you parked in Cursor, what constraint you set in ChatGPT. If the agent cannot see any of that, it operates in a narrower world than you do. Vilix connects those worlds.

How Vilix works as agent memory

Vilix is an MCP-native persistent memory layer. You connect it at api.vilix.ai/mcp once, and it exposes a small set of tools the agent calls on its own. The two that matter most for an agent loop are:

get_context: loads recent messages, your saved memories, semantically related past conversations, your user_rules, and live project and task state before the agent acts.
save_turn: persists the exchange after the agent acts, so the next session (or the next tool) can see what just happened.

Because the memory is tied to your Vilix account rather than any one process, it is available to every connected tool. Hermes writes, ChatGPT reads. Claude adds to it, Hermes picks it up next run. This is what cross-AI memory looks like when it is wired up correctly: one store, any tool, no copy-pasting.

Read more about the MCP setup in the MCP connector docs and the broader pattern in how to add memory to any agent with Vilix MCP.

Connecting Vilix to Hermes

Hermes supports custom MCP connectors, which is the same mechanism used by every other agent or tool that connects to Vilix. The connection is a single endpoint:

{
  "mcpServers": {
    "vilix": {
      "url": "https://api.vilix.ai/mcp",
      "transport": "sse"
    }
  }
}

Add this to your Hermes MCP configuration, authenticate when prompted, and the memory tools are available to the agent immediately. No additional code, no separate database to maintain, no custom persistence layer to build.

If Hermes is running with a config.json or similar agent config file, add the mcpServers block there. If you are launching it programmatically, pass the server config as part of your agent initialization. The exact path depends on your Hermes setup version, but the JSON shape above is what every Vilix-compatible connector expects.

Skills plus memory: the combined loop

This is where Hermes and Vilix together become more than the sum of their parts. Hermes already has a mechanism for skill improvement: it can rewrite its own skill definitions based on what worked in a run. Pair that with Vilix memory and the loop looks like this:

Session starts: Hermes calls get_context, loads the relevant past context, including skill notes and decisions from prior runs.
Task runs: the agent executes, improves skills, makes decisions.
Session ends: Hermes calls save_turn, persisting what it did and what worked. Updated skill notes live in memory, not just in a local file.
Next session: fresh context is loaded and the agent does not start from zero. It knows what it tried, what succeeded, and what you told it not to do.

This is qualitatively different from the skill-file-only approach. A local skill file improves only within a single instance of Hermes. Vilix-backed memory improves across instances, across machines, and across the other tools you use alongside Hermes. If you tell Claude a constraint about a project, Hermes sees it next run.

Projects and tasks for agent workloads

Vilix includes a lightweight project and task manager whose state is automatically injected into context. For agent workloads this is more useful than it sounds. You can create a task in Vilix (from Claude, from the Vilix dashboard, from any connected tool), and when Hermes calls get_context it sees that task as part of its working context. The agent knows what it is supposed to be doing before it starts, not from a prompt you write every time, but from a persistent task record.

When the agent finishes, it can update the task state. The next tool that looks at that project sees the update. This is the coordination layer that self-hosted agent setups typically have to build from scratch.

How this differs from the OpenClaw setup

The connection mechanism is the same. The design intent is different. OpenClaw is primarily about giving a code-execution agent access to cross-tool memory and rules. Hermes adds skill self-improvement to the picture, which means the memory store is not just a reference layer, it is an active part of the improvement loop. The agent writes to it to get better, not just to remember. See the OpenClaw + Vilix post for the code-execution angle if that is your setup.

Getting started

If you are running Hermes locally and want durable memory without managing your own persistence infrastructure, Vilix is the fastest path. Connect the MCP endpoint, run your first session, and check your Vilix memory afterward to see what was saved. The free plan covers getting started. Try Vilix free and connect it to Hermes in under five minutes.

Frequently asked questions

How do I add persistent memory to a Hermes agent?

Add Vilix as a custom MCP connector by pointing your Hermes MCP config at https://api.vilix.ai/mcp. Hermes will then have access to get_context and save_turn, which load and persist memory automatically each session.

Does Vilix work with self-hosted agents like Hermes?

Yes. Vilix is MCP-native and works with any tool or agent that supports a custom MCP connector, including self-hosted setups. There is no browser extension and no cloud dependency on the agent side, just the MCP endpoint.

What happens to skill improvements between Hermes sessions without persistent memory?

Without a persistent store, improved skill definitions stay in the local run and are lost when the session ends. With Vilix connected, the agent can save skill notes and outcomes via save_turn so that each new session picks up where the last one left off.

Can Hermes see context I built up in ChatGPT or Claude?

Yes, if those tools are also connected to Vilix. Memory is tied to your Vilix account, not the tool, so decisions and context from any connected AI are available to Hermes through get_context the next time it runs.

Is Vilix free to use with Hermes?

There is a free plan that covers getting started. The Pro plan at $19.99 per month includes a 7-day full-Pro trial and higher memory limits. You can create your account and connect Hermes in a few minutes.