MemoryLayer is a high-performance semantic memory infrastructure that enables AI agents to store and retrieve relevant information with 95% token savings.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memorylayer
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 memorylayer 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 MemoryLayer?
MemoryLayer provides a scalable semantic memory infrastructure designed specifically for AI agents that need to remember context across sessions without bloating prompt windows. By utilizing vector search, it allows agents to find memories based on meaning rather than simple keyword matching, ensuring that only the most relevant context is retrieved.
Integrating this into your workflow through Openclaw Skills helps developers significantly reduce LLM operational costs. It features sub-200ms retrieval times and supports multi-tenancy, making it ideal for production-grade agentic applications that require isolated and secure memory storage per instance.
MemoryLayer Use Cases
- Drastically reducing token usage in LLM prompts by replacing large static files with dynamic semantic retrieval.
- Storing and recalling user-specific preferences, such as UI themes or communication styles, across multiple sessions.
- Maintaining episodic records of agent actions to prevent repetitive errors and improve long-term reasoning.
- Organizing procedural knowledge for complex tasks, such as specific server deployment commands or coding standards.
How MemoryLayer Works
- The agent captures information and uses the
remembermethod to store it in the cloud infrastructure. - MemoryLayer processes the content, generates vector embeddings, and stores it with associated metadata like importance or memory type.
- When the agent requires context, it performs a natural language search through the
searchorget_contextfunctions. - The system identifies the most semantically relevant memories and returns them in a formatted string.
- The retrieved context is injected into the LLM's prompt, allowing for informed responses with minimal token consumption.
MemoryLayer Setup
To begin using MemoryLayer within the ecosystem of Openclaw Skills, follow these installation steps:
- Sign up for an account at the MemoryLayer homepage to obtain your credentials.
- Configure your environment variables for authentication:
# Recommended for production
export MEMORYLAYER_API_KEY=ml_your_api_key_here
- Install the Python SDK to interact with the service:
pip install memorylayer
MemoryLayer Data Schema & Taxonomy
MemoryLayer organizes information into memory objects that include content and taxonomic metadata to facilitate precise retrieval.
| Field | Type | Description |
|---|---|---|
| content | string | The text content to be stored and indexed |
| type | string | Classification: episodic, semantic, or procedural |
| importance | number | A value between 0.0 and 1.0 to weight the memory |
| metadata | object | Custom key-value pairs for advanced filtering (e.g., category, source) |
MemoryLayer Advanced Features
- Advanced Memory Categorization: Separate knowledge into Episodic (events), Semantic (facts), and Procedural (processes) types.
- Multi-tenant Isolation: Ensure data privacy by maintaining isolated memory partitions for different agent instances.
- Prompt Injection Helpers: Use the
get_contextmethod to automatically format retrieved memories for direct use in LLM prompts. - Detailed Usage Analytics: Monitor operations, storage limits, and memory counts via the
stats()API call. - Scalable Infrastructure: Enterprise options available for Openclaw Skills developers requiring self-hosting or unlimited operations.
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