Synapse Layer is a high-security persistent memory infrastructure designed to give AI agents long-term recall through an encrypted 4-layer Cognitive Security Pipeline.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install synapse-layer
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 synapse-layer 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 Synapse Layer?
Synapse Layer provides a robust framework for persistent, encrypted memory tailored for the next generation of autonomous agents. By integrating this into Openclaw Skills, developers can ensure their agents retain context across sessions while maintaining the highest standards of data privacy. It solves the volatility of standard LLM contexts by offering a dedicated storage layer that utilizes Trust Quotient scoring to rank and retrieve the most reliable information.
At its core, the skill acts as a bridge between your AI agents and a sophisticated backend, offering both cloud-based (Forge) and local (SQLite) storage options. Whether you are building complex multi-agent systems or a single assistant, Synapse Layer ensures that every piece of data is vetted by a Cognitive Security Pipeline before being committed to long-term storage.
Synapse Layer Use Cases
- Storing and retrieving agent memories across multiple user sessions to maintain long-term context.
- Sharing critical data between different agents using cross-agent memory sharing for collaborative workflows.
- Implementing PII-safe data storage where sensitive information is automatically redacted before persistence.
- Analyzing the reliability of stored information using Trust Quotient scoring for data-driven decision making.
- Integrating persistent memory into existing AI frameworks like LangChain, CrewAI, and AutoGen via Openclaw Skills.
How Synapse Layer Works
- The AI agent sends a memory string or text block to the Synapse Layer skill via the Python SDK.
- The data enters the Semantic Privacy Guard to detect and redact PII, credentials, or secrets.
- The Intelligent Intent Validation layer categorizes the memory and validates the agent's intent.
- Validated content is encrypted using AES-256-GCM and processed with Differential Privacy noise for embedding protection.
- The secure memory is stored in either a PostgreSQL (Forge) or SQLite backend.
- Agents retrieve information using semantic search or keyword matching, with results ranked by their Trust Quotient.
Synapse Layer Setup
Installation
Install the required library for Openclaw Skills integration:
pip install synapse-layer
Configuration
Initialize the client with your API key to start managing agent memories:
from synapse_test import SynapseClient
# Initialize with your Forge API key
client = SynapseClient(api_key="sk_connect_YOUR_KEY")
# Basic memory storage
client.remember("User prefers dark mode", agent="assistant_one")
Synapse Layer Data Schema & Taxonomy
Synapse Layer organizes data through a multi-dimensional taxonomy to ensure fast retrieval and security compliance:
| Attribute | Description | Type |
|---|---|---|
agent_id |
Unique identifier for the agent owning the memory | String |
trust_quotient |
Reliability score between 0.0 and 1.0 | Float |
importance |
User-defined priority level for memory weight | Integer |
metadata |
Automated detection of decisions, milestones, and alerts | Object |
encryption |
AES-256-GCM authenticated encryption | Binary/Blob |
Synapse Layer Advanced Features
- Multi-agent support allowing shared memory pools via common agent_ids in Openclaw Skills.
- Automated event extraction that identifies project milestones and decisions from raw conversational text.
- Self-healing intent validation that corrects categorization errors during the ingestion phase.
- Cross-agent memory search enabling one agent to query knowledge bases populated by another.
- Real-time system health monitoring and capability reporting through the health_check tool.
SKILL.md
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