memorylayer-server

memorylayer.ai

Persistent, queryable memory for stateless LLMs.

Website · Docs · GitHub

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LLMs forget everything between sessions. MemoryLayer fixes that.

Store memories with a single call, recall them with semantic search, and let the knowledge graph surface connections that vector similarity alone can't find. Works with any LLM framework or directly via REST API.

from memorylayer import sync_client

with sync_client() as memory:
    memory.remember("User prefers dark mode and TypeScript")

    results = memory.recall("What are the user's preferences?")

Why MemoryLayer

• Cognitive memory types -- episodic, semantic, procedural, and working memory mirror how humans organize knowledge
• Knowledge graph -- 60+ typed relationships across 11 categories enable multi-hop causal queries
• Semantic tiering -- memories are progressively summarized so you retrieve the right detail level without wasting context
• Context sandbox -- process hundreds of memories server-side in a persistent Python sandbox without consuming your context window
• Recursive reasoning -- inspired by RLM, the server iteratively executes code and LLM queries over memory data
• Smart extraction -- every memory stored automatically extracts facts, builds associations, deduplicates, and categorizes
• Adaptive decay -- memory importance adjusts over time based on usage and feedback
• MCP integration -- first-class Model Context Protocol server for Claude Code, Claude Desktop, Cursor, and other MCP-compatible tools

Packages

Package	Install	Description
memorylayer-core-python	pip install memorylayer-server	FastAPI server with SQLite + sqlite-vec storage
memorylayer-sdk-python	pip install memorylayer-client	Python client SDK (async/sync)
memorylayer-sdk-typescript	npm i @scitrera/memorylayer-sdk	TypeScript/JavaScript client SDK
memorylayer-mcp-typescript	npm i @scitrera/memorylayer-mcp-server	MCP server -- 21 tools for LLM agents
memorylayer-sdk-langchain-python	pip install memorylayer-langchain	LangChain integration
memorylayer-sdk-llamaindex-python	pip install memorylayer-llamaindex	LlamaIndex integration
memorylayer-cc-plugin	see README	Claude Code plugin -- captures memory before compaction
memorylayer-explorer	see README	(Work in Progress) WebUI

Quick Start

1. Start the server

pip install memorylayer-server[local]
memorylayer serve

Or with Docker (no setup required):

docker run -d -p 61001:61001 -v memorylayer-data:/data scitrera/memorylayer-server

2. Connect a client

Python:

from memorylayer import MemoryLayerClient, MemoryType

async with MemoryLayerClient(base_url="http://localhost:61001") as client:
    # Store
    await client.remember(
        content="User prefers Python for backend development",
        type=MemoryType.SEMANTIC,
        importance=0.8,
        tags=["preferences", "programming"]
    )

    # Recall
    results = await client.recall(
        query="What programming languages does the user like?",
        limit=5
    )

TypeScript:

import { MemoryLayerClient } from "@scitrera/memorylayer-sdk";

const client = new MemoryLayerClient({
  baseUrl: "http://localhost:61001",
  workspaceId: "my-project"
});

await client.remember("User prefers TypeScript for new projects", {
  type: "semantic",
  importance: 0.8
});

3. Or use with Claude Code (MCP)

Add .mcp.json to your project root:

{
  "mcpServers": {
    "memorylayer": {
      "command": "npx",
      "args": ["@scitrera/memorylayer-mcp-server"],
      "env": {
        "MEMORYLAYER_URL": "http://localhost:61001"
      }
    }
  }
}

The MCP server auto-detects your workspace from the git repo name. Claude gets 21 memory tools -- remember, recall, reflect, associate, graph queries, sessions, and a full context sandbox.

For the full Claude Code experience, also install the MemoryLayer plugin which adds pre-compaction memory capture, session briefings, and automatic memory triggers:

# Add the marketplace (one-time setup)
claude plugin marketplace add scitrera/memorylayer

# Install the plugin
claude plugin install memorylayer@memorylayer.ai

Enterprise

MemoryLayer also offers an enterprise edition that builds on the open source core:

• Scale -- PostgreSQL + Redis backends, hot/warm/cold data tiering, vector-graph compression
• Security -- RBAC, audit trails, custom ontologies
• Multimodal -- unified handling of text, images, audio, video, and documents
• Advanced sandbox -- state checkpointing, stronger isolation, extended tool libraries

Visit memorylayer.ai for details.

License

Apache 2.0 -- see LICENSE for details.