---
title: "Hermes Agent + Honcho"
sidebarTitle: "Hermes Agent"
description: "How Hermes Agent uses Honcho for persistent cross-session memory and user modeling"
icon: "message-bot"
---

[Hermes Agent](https://github.com/NousResearch/hermes-agent) is an open-source AI agent from [Nous Research](https://nousresearch.com) with tool-calling, terminal access, a skills system, and multi-platform deployment (Telegram, Discord, Slack, WhatsApp). Honcho gives Hermes persistent cross-session memory and user modeling.

For setup, configuration, and CLI commands, see the [Hermes Agent Honcho docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/honcho).

## What Honcho provides

Honcho acts as a long-term memory and user-model layer alongside Hermes' built-in memory files (`MEMORY.md` and `USER.md`).

It gives Hermes three capabilities:

1. **Prompt-time context injection** -- durable context about a user loaded into the prompt before generating a response.
2. **Cross-session continuity** -- recall of stable preferences, project history, and working context across conversations.
3. **Durable writeback** -- stable facts learned during a conversation stored back for future turns.

These sit alongside Hermes' local session history. Session history remembers the current conversation. Honcho remembers what should still matter later.

## Dual-peer architecture

Both the user and the AI agent have peer representations in Honcho:

- **User peer**: observed from user messages. Learns preferences, goals, communication style.
- **AI peer**: observed from assistant messages. Builds the agent's knowledge representation.

Both representations are injected into the system prompt, giving Hermes awareness of both who it's talking to and what it knows.

## Available tools

Hermes exposes four Honcho tools to the agent:

| Tool | What it does |
|---|---|
| `honcho_profile` | Fast peer card retrieval (no LLM). Returns curated key facts about the user. |
| `honcho_search` | Semantic search over memory. Returns raw excerpts ranked by relevance. |
| `honcho_context` | Dialectic Q&A powered by Honcho's LLM. Synthesizes answers from conversation history. |
| `honcho_conclude` | Writes durable facts to Honcho when the user states preferences, corrections, or important context. |

## Running Honcho locally with Hermes

Follow the [Self-Hosting Guide](/v3/contributing/self-hosting) to get Honcho running locally. Once it's up, point Hermes at your instance:

```bash
hermes memory setup  # select "honcho", enter http://localhost:8000 as the base URL
```

Or manually create/edit the config file (checked in order: `$HERMES_HOME/honcho.json` > `~/.hermes/honcho.json` > `~/.honcho/config.json`):

```json
{
  "baseUrl": "http://localhost:8000",
  "hosts": {
    "hermes": {
      "enabled": true,
      "aiPeer": "hermes",
      "peerName": "your-name",
      "workspace": "hermes"
    }
  }
}
```

For the full list of config fields (`recallMode`, `writeFrequency`, `sessionStrategy`, `dialecticReasoningLevel`, etc.), see the [Hermes memory provider docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory-providers#honcho).

## Verifying the integration

### 1. Check status

```bash
hermes memory status
```

This should show Honcho as the active memory provider with your base URL.

### 2. Store a fact and recall it across sessions

In one conversation, tell Hermes something specific:

```text
My favorite programming language is Rust and I always use dark mode.
```

Start a **new session** (different thread, new CLI invocation, or a different platform). Ask:

```text
What do you know about my preferences?
```

If Hermes mentions Rust and dark mode without being told again, cross-session memory is working. The deriver processed your messages, extracted observations, and the dialectic recalled them.

### 3. Test tool calling directly

Ask Hermes to use a specific Honcho tool:

```text
Use your honcho_search tool to find anything you know about me.
```

If Hermes calls the tool and returns results, the full tool pipeline (API connection, vector search, embedding) is functional.

## Configuration options

| Field | Default | Description |
|---|---|---|
| `recallMode` | `hybrid` | `hybrid` (auto-inject + tools), `context` (inject only), `tools` (tools only) |
| `writeFrequency` | `async` | `async`, `turn`, `session`, or integer N |
| `sessionStrategy` | `per-directory` | `per-directory`, `per-repo`, `per-session`, `global` |
| `dialecticReasoningLevel` | `low` | `minimal`, `low`, `medium`, `high`, `max` |
| `dialecticDynamic` | `true` | Auto-bump reasoning level by query complexity |
| `messageMaxChars` | `25000` | Max chars per message (chunked if exceeded) |

## Next steps

<CardGroup cols={2}>
  <Card title="Hermes Agent Honcho Docs" icon="book" href="https://hermes-agent.nousresearch.com/docs/user-guide/features/honcho">
    Setup, configuration, CLI commands, and all config options.
  </Card>

  <Card title="Hermes Agent Source" icon="github" href="https://github.com/NousResearch/hermes-agent">
    Source code, installation, and full documentation.
  </Card>

  <Card title="Honcho Architecture" icon="sitemap" href="/v3/documentation/core-concepts/architecture">
    Peers, sessions, and how reasoning works.
  </Card>

  <Card title="Self-Hosting Guide" icon="server" href="/v3/contributing/self-hosting">
    Full local environment setup, provider configuration, and troubleshooting.
  </Card>
</CardGroup>
