--- title: "OpenClaw" icon: 'lobster' description: "Add AI-native memory to OpenClaw" sidebarTitle: 'OpenClaw' --- [OpenClaw](https://openclaw.ai) is a general AI agent that can perform actions on behalf of a user. The Honcho plugin gives OpenClaw memory across every channel — WhatsApp, Telegram, Discord, Slack, and more. Honcho can run entirely locally with OpenClaw — no external API required. Keep your data on your machine while getting full memory capabilities across all channels. See the [self-hosting guide](/v3/contributing/self-hosting) to get started. ## Install the Plugin ```bash openclaw plugins install @honcho-ai/openclaw-honcho openclaw honcho setup openclaw gateway --force ``` `openclaw honcho setup` prompts for your API key, writes the config, and optionally uploads any legacy memory files to Honcho. **Alternative: ClawHub Skill** The `honcho-setup` skill handles installation and migration interactively from a chat session: ```bash npx clawhub install honcho-setup # Restart OpenClaw, then invoke the skill from a session openclaw plugins install @honcho-ai/openclaw-honcho openclaw gateway restart ``` ## Migrating Legacy Memory If you have existing workspace memory files (`USER.md`, `MEMORY.md`, `IDENTITY.md`, `memory/`, `canvas/`, etc.), `openclaw honcho setup` will detect them and offer to migrate them. Migration is **non-destructive** — files are uploaded to Honcho. Originals are never deleted or moved. ### Legacy files **User/owner files** (content describes the user): - `USER.md`, `IDENTITY.md`, `MEMORY.md` - All files in `memory/` and `canvas/` directories **Agent/self files** (content describes the agent): - `SOUL.md`, `AGENTS.md`, `TOOLS.md`, `BOOTSTRAP.md` ### Upload to Honcho Files are uploaded via `session.uploadFile()`. User/owner files go to the owner peer; agent/self files go to the openclaw peer. ## How It Works Once installed, the plugin runs automatically: * **Message Observation** — After every AI turn, the conversation is persisted to Honcho. Both user and agent messages are observed, allowing Honcho to build and refine its models. * **Tool-Based Context Access** — The AI can query Honcho mid-conversation using tools like `honcho_recall`, `honcho_search`, and `honcho_analyze` to retrieve relevant context. Context is injected during OpenClaw's `before_prompt_build` phase, ensuring accurate turn boundaries. * **Dual Peer Model** — Honcho maintains separate representations: one for the user (preferences, facts, communication style) and one for the agent (personality, learned behaviors). Each OpenClaw agent gets its own Honcho peer (`agent-{id}`), so multi-agent workspaces maintain isolated memory. * **Clean Persistence** — Platform metadata (conversation info, sender headers, thread context, forwarded messages) is stripped before saving to Honcho, ensuring only meaningful content is persisted. ## Multi-Agent Support OpenClaw uses a multi-agent architecture where a primary agent can spawn **subagents** to handle specialized tasks. The Honcho plugin is fully aware of this hierarchy: * **Automatic Subagent Detection** — When OpenClaw spawns a subagent, the plugin tracks the parent→child relationship via the `subagent_spawned` hook. Each subagent session records its `parentPeerId` in metadata. * **Parent Observer Peer** — The spawning agent is added as a silent observer in the subagent's Honcho session (`observeMe: false, observeOthers: true`). This gives Honcho visibility into the full agent tree — the parent can see what its subagents are doing without its own messages being attributed to the subagent session. ## AI Tools ### Data Retrieval (fast, no LLM) | Tool | Description | | ---- | ----------- | | `honcho_session` | Conversation history and summaries from the current session. | | `honcho_profile` | User's peer card — key facts (name, preferences, role). | | `honcho_search` | Semantic search over stored observations. | | `honcho_context` | Full user representation across all sessions. | ### Q&A (LLM-powered) | Tool | Description | | ---- | ----------- | | `honcho_recall` | Simple factual question — minimal reasoning. | | `honcho_analyze` | Complex question requiring synthesis — medium reasoning. | ## CLI Commands ```bash openclaw honcho setup # Configure API key and migrate legacy files openclaw honcho status # Connection status openclaw honcho ask # Query Honcho about the user openclaw honcho search [-k N] [-d D] # Semantic search (topK, maxDistance) ``` ## Configuration Run `openclaw honcho setup` to configure interactively, or set values directly in `~/.openclaw/openclaw.json` under `plugins.entries["openclaw-honcho"].config`. | Key | Default | Description | | --- | ------- | ----------- | | `apiKey` | — | Honcho API key (required for managed; omit for self-hosted). | | `workspaceId` | `"openclaw"` | Honcho workspace ID for memory isolation. | | `baseUrl` | `"https://api.honcho.dev"` | API endpoint (for self-hosted instances). | ### Self-Hosted Honcho Point the plugin to your local instance and follow the [self-hosting guide](https://github.com/plastic-labs/honcho?tab=readme-ov-file#local-development) to get started: ```bash openclaw honcho setup # Enter blank API key, set Base URL to http://localhost:8000 ``` ## Local File Search (QMD Integration) The plugin automatically exposes OpenClaw's `memory_search` and `memory_get` tools when a memory backend is configured, allowing both Honcho cloud memory and local file search together. ### Setup 1. Install [QMD](https://github.com/tobi/qmd) on your server 2. Configure OpenClaw in `~/.openclaw/openclaw.json`: ```json { "memory": { "backend": "qmd", "qmd": { "limits": { "timeoutMs": 120000 } } } } ``` 3. Set up QMD collections and restart: ```bash qmd collection add ~/Documents/notes --name notes qmd update openclaw gateway restart ``` ### Available Tools When QMD is configured, you get both Honcho and local file tools: | Tool | Source | Description | | ---- | ------ | ----------- | | `honcho_*` | Honcho | Cross-session memory, user modeling, dialectic reasoning | | `memory_search` | QMD | Search local markdown files | | `memory_get` | QMD | Retrieve file content | ## Next Steps Source code, issues, and README. Learn about peers, sessions, and dialectic reasoning.