> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Hermes Agent

> Connect Hermes Agent to Cognee Cloud with a drop-in memory provider.

Give [Hermes Agent](https://github.com/NousResearch/hermes-agent) persistent memory backed by your Cognee Cloud tenant with a drop-in memory provider plugin. Each completed turn is stored in Cognee's session cache and promoted into the permanent knowledge graph at session end — no code required.

## 1. Install the plugin

The `cognee-integration-hermes-agent` package is not yet published on PyPI. Install it locally from the [cognee-integrations](https://github.com/topoteretes/cognee-integrations) repository:

```bash theme={null}
git clone https://github.com/topoteretes/cognee-integrations.git
cd cognee-integrations
mkdir -p ~/.hermes/plugins/cognee
cp -R integrations/hermes-agent/. ~/.hermes/plugins/cognee/
hermes memory setup        # select "cognee" in the memory provider picker
```

<Info>
  Requires Python 3.10+. The plugin declares `cognee>=1.0.0,<2.0.0` as a pip dependency.
</Info>

## 2. Connect to Cognee Cloud

Hermes connects to Cloud in **remote mode**: set `COGNEE_BASE_URL` (and `COGNEE_API_KEY`) to your tenant. There are no silent fallbacks — if the configured mode fails, the failure surfaces.

```bash theme={null}
export COGNEE_BASE_URL="https://your-tenant.aws.cognee.ai"
export COGNEE_API_KEY="<your-api-key>"
```

No LLM key is needed in remote mode — your tenant does the knowledge extraction server-side. Non-secret settings are also saved to `$HERMES_HOME/cognee.json`; secrets go to `$HERMES_HOME/.env`.

## 3. Run and verify

Run Hermes as usual — memory is captured and recalled automatically:

```bash theme={null}
hermes                  # start a session
```

During a session, just talk to the agent:

```
You: Remember that Alice works in engineering.
     (Cognee stores this turn in the session cache)

You: What does Alice do?
     (Hermes recalls from Cognee Cloud memory)
```

The provider exposes three memory tools to the agent:

* `cognee_recall` — session-first recall with graph fallback
* `cognee_remember` — persist content into the knowledge graph
* `cognee_forget` — delete memory

At session end, if `COGNEE_IMPROVE_ON_END=true` (the default), `cognee.improve(...)` promotes the session cache into your permanent graph.

## Configuration

| Variable                | Default  | Description                                     |
| ----------------------- | -------- | ----------------------------------------------- |
| `COGNEE_BASE_URL`       | —        | Cognee API base URL (enables remote/cloud mode) |
| `COGNEE_API_KEY`        | —        | Cognee service API key                          |
| `COGNEE_DATASET`        | `hermes` | Dataset name for stored memory                  |
| `COGNEE_TOP_K`          | `5`      | Max results per recall                          |
| `COGNEE_IMPROVE_ON_END` | `true`   | Run `improve()` at session end                  |

<Info>
  Manage the plugin with `hermes cognee status`, `hermes cognee setup`, `hermes cognee config`, and `hermes cognee install`.
</Info>

***

<CardGroup cols={2}>
  <Card title="Full integration guide" icon="book" href="/integrations/hermes-agent-integration">
    Tools, connection modes, and how it works
  </Card>

  <Card title="Hermes Agent" icon="book" href="https://github.com/NousResearch/hermes-agent">
    Learn about Hermes Agent
  </Card>
</CardGroup>