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 -- 63 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
• Document ingestion -- upload PDFs / DOCX / images and turn them into memories; optional ColPali multi-vector page search via the embed-server peer
• Repository Planning Graph -- sync code structure, traverse symbols and dependencies, maintain task overlays, and detect file/symbol conflicts
• Skills + MCP registries -- workspace-scoped libraries of agent skills and MCP server entries with 4-tier scope precedence (user / workspace / tenant / global)
• MCP integration -- first-class Model Context Protocol server (25 tools by default, 38 in the full profile) for Claude Code, Claude Desktop, OpenCode, Cursor, and other MCP-compatible tools
• Optional Aether transport -- run behind an Aether mesh for mTLS, signed identity headers, on-behalf-of delegation, durable task scheduling, and cross-datacenter routing

Packages

Package	Install	Description
memorylayer-core-python	pip install memorylayer-server	FastAPI server with SQLite + sqlite-vec storage; optional Turso/libSQL backend
memorylayer-server-rpg-python	pip install memorylayer-server-rpg	Repository Planning Graph plugin: sync, traversal, overlays, conflicts, maintenance, and enrichment
memorylayer-embed-server	pip install "memorylayer-embed-server[local]"	Stateless embedding peer. [local] = CPU (sentence-transformers + ColPali); [gpu] adds vLLM, OCR, transcription
memorylayer-sdk-python	pip install memorylayer-client	Python client SDK (async/sync, optional Aether transport)
memorylayer-sdk-typescript	npm i @scitrera/memorylayer-sdk	TypeScript/JavaScript client SDK
memorylayer-mcp-typescript	npm i @scitrera/memorylayer-mcp-server	MCP server -- 25 tools (default), up to 38 in full
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-opencode-plugin	npm i @scitrera/memorylayer-opencode-plugin	OpenCode plugin -- session briefings, recall hooks, compaction capture
memorylayer-explorer	see README	(Work in Progress) WebUI

Quick Start

1. Start the server

pip install memorylayer-server
memorylayer serve

That's it — no API key, no peer container. The server stores to SQLite under ~/.config/memorylayer-server and is ready at http://localhost:61001.

To add repository code graphs, install the RPG plugin alongside the server:

pip install memorylayer-server-rpg

The server discovers it automatically and exposes /v1/rpg.

It starts on the hash embedding provider, which is lexical, not semantic: it matches on shared words, which is enough to try the API but not for real retrieval quality. The server says so in its startup log. When you want real embeddings:

# Self-hosted on CPU — no GPU, no API key
pip install "memorylayer-embed-server[local]" && memorylayer-embed serve --port 61051 &
export MEMORYLAYER_EMBEDDING_PROVIDER=embed_server
export MEMORYLAYER_EMBED_SERVER_URL=http://localhost:61051
export MEMORYLAYER_EMBEDDING_DIMENSIONS=384      # must match the embed model
memorylayer serve

# Cloud (pick one)
pip install "memorylayer-server[openai]"        # or [google], [all]
export MEMORYLAYER_EMBEDDING_PROVIDER=openai    # or google
export MEMORYLAYER_EMBEDDING_OPENAI_API_KEY=sk-...
memorylayer serve

# Self-hosted on GPU — adds vLLM, OCR, transcription
pip install "memorylayer-embed-server[gpu]" && memorylayer-embed serve --port 61051 &

The CPU option downloads all-MiniLM-L6-v2 (~90 MB, 384-d) on first use and runs in-process — it is the embed server's default provider.

The one-command version

docker compose up

Brings up the server plus a CPU embed-server peer with real semantic embeddings — no GPU, no API key. See docker-compose.yml. The first request is slow while model weights download; they are cached in a volume after that.

Running the containers yourself

The server image ships all optional dependencies, includes the RPG plugin, and exposes /v1/rpg without an additional install. It is pinned to MEMORYLAYER_EMBEDDING_PROVIDER=embed_server, so it expects a memorylayer-embed-server peer — point it at one, or override the provider:

# Cloud embeddings
docker run -d -p 61001:61001 -v memorylayer-data:/data \
  -e MEMORYLAYER_EMBEDDING_PROVIDER=openai \
  -e MEMORYLAYER_EMBEDDING_OPENAI_API_KEY=sk-... \
  scitrera/memorylayer-server

# Self-hosted embed-server peer
docker run -d -p 61001:61001 -v memorylayer-data:/data \
  -e MEMORYLAYER_EMBED_SERVER_URL=http://embed-host:61051 \
  scitrera/memorylayer-server

The embed-server image ships in two variants sharing one repository:

tag	contents
scitrera/memorylayer-embed-server:<version>	CPU. sentence-transformers (384-d) + ColPali. Runs anywhere.
scitrera/memorylayer-embed-server:<version>-cuda13	CUDA 13. Adds vLLM, OCR and transcription. Needs an NVIDIA GPU.

The tag names the CUDA major version because it is not an implementation detail — the wheels and vLLM's JIT-compiled kernels are built against that toolkit.

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 25 tools by default (38 in the full profile) -- remember, recall, reflect, sessions, context sandbox / RLM, chat threads, and skills/MCP-server registry helpers.

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 [email protected]

Enterprise

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

• Scale -- PostgreSQL + Redis backends, hot / warm / cold storage tiering, vector-graph compression
• Security -- OIDC + RBAC via Aether, audit trails, KMS-backed token issuance, custom ontologies
• Multimodal -- unified handling of text, images, audio, video, and documents (visual-tokenizer add-on for the embed-server)
• Data connector pack -- S3, GitHub, Google Drive, Dropbox, Slack, Teams, Discord, web scraper, manual upload, and the VFS-backed local_fs watcher behind the same /v1/data-providers API
• Cross-cluster orchestration -- durable task scheduling, scheduled syncs, multi-DC GPU peer placement
• Advanced sandbox -- state checkpointing, stronger isolation, extended tool libraries
• Collections, datasets, trajectories -- higher-level grouping primitives for managing memory at scale
• Admin API + dashboards -- /v1/users, /v1/applications, /v1/admin/* for tenant-wide visibility and control

Visit memorylayer.ai for details.

Scitrera Forge

Scitrera Forge is a separate, access-list-only product that builds on MemoryLayer's open-source Repository Planning Graph (RPG) to orchestrate sandboxed agent swarms over real codebases. MemoryLayer provides the structural graph and memory substrate; Forge adds managed orchestration and execution. Currently restricted to approved organizations.

License

Apache 2.0 -- see LICENSE for details.