Add Persistent Memory to Any MCP-Compatible AI Tool
The Model Context Protocol gives AI tools a standard way to call external systems. MemoryLake uses that standard to make structured persistent memory available to any MCP-compatible tool — with no custom integration code required.
Add Persistent Memory to Any MCP-Compatible AI Tool
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The Memory Problem
MCP-compatible tools like Claude Code and OpenClaw are powerful within a session, but that session ends. The next time you open the tool, the accumulated context — decisions made, approaches that worked, project-specific facts — is gone. MCP can connect to external systems, but most tools don't provide a memory system worth connecting to. MemoryLake is that system.
What MemoryLake Does Differently
Six typed memory categories available at session start
When your MCP tool connects to MemoryLake at session open, relevant Background Memory (identity, preferences, role context) and recent Conversation Memory are surfaced immediately, giving the session a warm start with zero manual setup.
Memory accumulation during the session
As work progresses, the tool writes to appropriate memory categories: new facts to Fact Memory, completed approaches to Skill Memory, behavioral patterns to Reflection Memory. Memory compounds with every session.
MCP differs from REST API in scope and trigger
REST API integration is best for application-level memory management — bulk operations, cross-user queries, administrative access. MCP integration is optimized for session-level, real-time memory read/write during a live agent interaction. Both protocols are supported simultaneously.
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How It Works
- Connect — Add MemoryLake as an MCP server in your tool's configuration. Provide your API key and memory store identifier. The MCP server handles authentication and routing.
- Structure — At session start, MemoryLake surfaces relevant memory by type. During the session, the tool writes to memory categories as directed. All writes are versioned and source-attributed automatically.
- Reuse — The next session opens with the accumulated memory from all prior sessions. The tool remembers your preferences, project state, working patterns, and prior decisions without any manual context management.
Before & After
| Without MemoryLake MCP | With MemoryLake MCP | |
|---|---|---|
| Session start | Cold — no prior context | Background + Conversation Memory loaded automatically |
| Project context | Re-explained each session | Stored in Fact and Event Memory, retrieved on demand |
| Working preferences | Re-stated or hardcoded in prompts | Background Memory surfaces them at session open |
| Learned workflows | Rediscovered each time | Skill Memory persists and retrieves them automatically |
| Audit trail | None | Every memory operation versioned with timestamp and source |
Built For
MemoryLake MCP integration is designed for developers and technical users who rely on MCP-compatible tools daily and want those tools to accumulate knowledge rather than reset. It works with Claude Code, OpenClaw, and any agent framework that implements the Model Context Protocol.
Try this use case on your own account
An MCP tool connects to MemoryLake with an API key and a memory store identifier: a boundary, a saved scope of projects, one person and one agent. Fernhill Robotics gives Priya's editor assistant, Tom's editor assistant and the CI review bot one boundary each. The same question returns three different answers, none reaching outside its scope. Then the team changes: a project is deleted, Tom moves on, the bot is replaced. Every boundary still points at what is gone, until an audit finds it and each boundary is repaired in place.
What you will see
- One boundary per tool:
boundary create --projects … --human-actor … --agent …; a boundary for someone not yet on the workspace is refused when it is written - One question, three tools: each boundary expanded into
search --projects … --actors …, every hit labelled with the scope it came from: 3 of 3 answered, 0 hits from outside a boundary - After
proj delete,actor unbindandagent delete,boundary getstill lists all three: the bot's searches fail with HTTP 400, and Tom's and the bot's boundaries refuse any edit, even a rename - An audit checks every reference against what exists today (3 stale),
boundary updaterepairs each one with the same id, and the same question works again; Tom's preferences appear in no answer. Plus a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/mcp-scope-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
How do I register MemoryLake as an MCP server?
How do I register MemoryLake as an MCP server?
Add the MemoryLake MCP server endpoint to your tool's MCP configuration with your API key. The exact configuration format follows the standard MCP server registration pattern. Full setup instructions are in the MemoryLake developer docs.
Can I use MCP and REST API at the same time?
Can I use MCP and REST API at the same time?
Yes. MCP handles real-time, session-level memory operations during live agent interactions. REST API handles administrative, bulk, and cross-session operations. They operate on the same underlying memory store.
Which memory types are accessible via MCP?
Which memory types are accessible via MCP?
All six memory types are accessible via MCP: Background, Fact, Event, Conversation, Reflection, and Skill. The MCP server exposes read and write operations for each type, with role-based access control applied per memory type.