A standardized protocol for enabling collective intelligence by allowing multiple AI agents to share, sync, and inherit knowledge seamlessly.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cross-agent-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 cross-agent-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 Cross-Agent Memory Sharing Protocol?
The Cross-Agent Memory Sharing Protocol is a technical framework designed to eliminate knowledge silos and redundant learning in multi-agent environments. By implementing this protocol within your Openclaw Skills, you enable agents to function as a cohesive collective rather than isolated units. It provides a standardized way to format, synchronize, and resolve conflicts in shared data, ensuring that every agent in your ecosystem has access to the most accurate and up-to-date information.
This protocol is particularly valuable for complex workflows where different agents handle specialized tasks but require a shared context to succeed. Whether using a centralized hub or a decentralized Git-based approach, this system ensures that when one agent learns a fact or discovers a solution, that intelligence is immediately propagated to the rest of the team, significantly improving the efficiency of all Openclaw Skills involved.
Cross-Agent Memory Sharing Protocol Use Cases
- Collaborative team workflows where multiple agents contribute to a single project.
- Seamless knowledge inheritance when retiring old agents or onboarding new ones.
- Collective decision-making scenarios where agents vote on solutions based on confidence scores.
- Reducing API costs and latency by preventing redundant learning and research cycles across your Openclaw Skills.
How Cross-Agent Memory Sharing Protocol Works
- Schema Standardization: Agents format their findings into a standardized JSON structure including metadata like agentId, timestamp, and confidence levels.
- Synchronization: Knowledge is shared via Push, Pull, or Subscribe models using a Centralized Hub, P2P network, or Git repository.
- Conflict Resolution: When agents provide conflicting information, the protocol applies a priority hierarchy based on timestamps (latest), confidence (highest), and source (user-input vs. inferred).
- Access Control: Permissions are managed via YAML configurations to define which agents have read or write access to specific knowledge domains.
Cross-Agent Memory Sharing Protocol Setup
To implement the recommended Git-based synchronization for your Openclaw Skills, follow these steps:
- Create a private repository for the shared memory:
gh repo create agent-memory-shared --private
- Initialize the repository in each agent's environment:
git clone https://github.com/team/agent-memory-shared.git
- To share new knowledge, commit and push changes:
echo "New Knowledge" >> shared-memory.md
git add shared-memory.md
git commit -m "Agent A: Learned new technical specification"
git push
- To synchronize and resolve conflicts periodically:
git pull --rebase
node skills/cross-agent-memory/merge-conflicts.mjs
Cross-Agent Memory Sharing Protocol Data Schema & Taxonomy
The protocol uses a standardized JSON schema to ensure interoperability across different Openclaw Skills:
| Field | Type | Description |
|---|---|---|
schema |
string | Protocol version (e.g., openclaw.memory.v1) |
agentId |
string | Unique identifier for the agent creating the memory |
timestamp |
string | ISO 8601 format timestamp of the entry |
entries |
array | List of memory objects containing facts, priorities, and tags |
confidence |
float | A score from 0.0 to 1.0 representing data reliability |
source |
string | Origin of the data (e.g., user-input, agent-inferred) |
Cross-Agent Memory Sharing Protocol Advanced Features
- Multi-mode synchronization support including Centralized Hub (Redis), P2P, and Git-based backends.
- Automated conflict resolution scripts using confidence-weighted logic.
- Granular permission control via ACL (Access Control Lists) for read/write operations.
- Version control and auditing for all memory changes, providing a complete history of agent learning.
- Incremental synchronization and compression to optimize performance for high-frequency Openclaw Skills.
SKILL.md
Loading
Related Openclaw Skills
Causal Graph Auto-Builder
An automated tool for extracting entities, events, and causal relationships from logs and memory files to build dynamic knowledge graphs.
Agent ROS Bridge
A secure, universal communication bridge that enables AI agents to control ROS1 and ROS2 robotic systems with ease.
Fact Check
A systematic verification tool designed to validate claims, statistics, and statements against a hierarchy of trusted global sources.
Moltpad
A comprehensive tool for AI agents to write, publish, and collaborate on literary content within the Moltpad.space community.
Meme Signal Scanner
A real-time aggregator and scoring engine for meme coin signals across major decentralized exchanges and launchpads.
Memory Deduplication
A sophisticated utility for cleaning, merging, and consolidating redundant information in AI agent memory files to improve retrieval speed and accuracy.
Featured*
