A production-grade memory pipeline that replaces standard embedding retrieval with reasoned, evolving understanding across multi-agent fleets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install honcho-memory
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install honcho-memory using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Honcho Memory System?
Honcho Memory is a sophisticated persistence layer for AI agents built on the Honcho reasoning engine by Plastic Labs. Unlike traditional RAG systems that rely on simple embedding-based retrieval, this skill utilizes Neuromancer to perform formal logical reasoning over agent interactions. It synthesizes deductive and inductive observations into evolving representations of users and agents, ensuring that context is not just stored, but understood.
This system is designed specifically for complex Openclaw Skills environments where agents face challenges like context compaction amnesia and isolated session blindness. By feeding conversations into a shared reasoning engine, agents can maintain continuity across sessions, share knowledge across a multi-agent fleet, and operate with a deep, reasoned understanding of historical decisions and user priorities.
Honcho Memory System Use Cases
- Providing context for cron jobs and background tasks that spin up fresh, isolated sessions.
- Preventing amnesia when long conversation sessions undergo context compaction.
- Enabling cross-agent coordination where one agent needs to learn from another agent's interactions.
- Accelerating the onboarding of new agents into an existing fleet by providing immediate shared context.
- On-demand querying for specific historical decisions or performance analysis during active agent turns.
How Honcho Memory System Works
- The feed script auto-discovers active agent session transcripts and attributes messages to specific peers.
- The Honcho API processes these messages through Neuromancer to generate background logical reasoning and observations.
- Representations are built per-peer (user or agent), creating a synthesized understanding of all entities.
- Context generation scripts produce token-budgeted Markdown files that agents load at startup.
- Agents can perform on-demand queries using varying reasoning levels to recall specific details not present in their current session.
Honcho Memory System Setup
1. Install the SDK
Install the necessary library via pip:
pip install honcho-ai
2. Configure Credentials
Create a configuration file at ~/.config/honcho/credentials.json with your API key and workspace ID.
3. Initialize Workspace
Run the setup script to register your users and agents as peers:
python3 scripts/setup.py --workspace "your-workspace" --peers "user,agent1,agent2"
Honcho Memory System Data Schema & Taxonomy
The skill organizes data within the Honcho cloud and local workspace using the following structure:
| Level | Description |
|---|---|
| Workspace | High-level isolation for different environments. |
| Peers | Persistent entities including the user and multiple agent roles. |
| Sessions | Individual conversation threads or execution runs. |
| Context Files | HONCHO-CONTEXT.md files generated at both the root and agent-specific directories. |
| Metadata | Sync position tracking to ensure no duplicate messages are processed. |
Honcho Memory System Advanced Features
- Compaction-Proof Architecture: Reasoned context files are treated as workspace files that survive session resets and gateway restarts.
- Five-Level Reasoning Engine: Supports query levels from minimal to max, allowing developers to balance cost and depth of analysis.
- Token-Budgeted Context: Automatically distills weeks of logs into ~1500 tokens of dense, relevant context to avoid prompt bloat.
- Hybrid Memory Support: Designed to complement existing local file-based memory (like MEMORY.md) as a reasoned secondary layer.
- Automated Cron Pipelines: Includes configurations for keeping memory fresh through scheduled background synchronization within Openclaw Skills.
SKILL.md
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