@memorilabs/memori-mcp

Memori MCP

Persistent AI memory for any MCP-compatible agent — no SDK required.

memori-mcp is the official Memori MCP server. Connect it to your AI agent to give it long-term memory: recall relevant facts before answering, store durable preferences after responding, and maintain context across sessions.

Why Memori

Without persistent memory, every session starts from zero. With Memori, your agent:

• Remembers preferences — "I prefer Python and use uv for dependency management" is recalled in future sessions automatically
• Personalizes responses — past context shapes every answer without manual re-prompting
• Isolates memory by user and workflow — scoped per entity_id and process_id so preferences never bleed across users or projects
• Works with any MCP client — no SDK, no code changes, just config

LoCoMo Benchmark

Memori was evaluated on the LoCoMo benchmark for long-conversation memory and achieved 81.95% overall accuracy while using an average of 1,294 tokens per query. That is just 4.97% of the full-context footprint, showing that structured memory can preserve reasoning quality without forcing large prompts into every request.

Compared with other retrieval-based memory systems, Memori outperformed Zep, LangMem, and Mem0 while reducing prompt size by roughly 67% vs. Zep and lowering context cost by more than 20x vs. full-context prompting.

Read the benchmark overview or download the paper.

How It Works

The server exposes two tools:

Tool	When to call	What it does
recall	Start of each user turn	Fetches relevant memories for the current query
advanced_augmentation	After composing a response	Stores durable facts and preferences for future sessions

Example Agent Flow

Given the message: "I prefer Python and use uv for dependency management."

• Agent calls recall with the user message as query
• Agent uses any returned facts to compose a response
• Agent calls advanced_augmentation with the user message and response

On a later turn — "Write a hello world script" — the agent recalls the Python + uv preference and personalizes its response automatically.

Prerequisites

• A Memori API key from app.memorilabs.ai
• An entity_id to identify the end user (e.g. user_123)
• An optional process_id to identify the agent or workflow (e.g. my_agent)

Export these in your shell or replace the placeholders directly in your config:

export MEMORI_API_KEY="your-memori-api-key"
export MEMORI_ENTITY_ID="user_123"
export MEMORI_PROCESS_ID="my_agent"   # optional

Server Details

Property	Value
Endpoint	https://api.memorilabs.ai/mcp/
Transport	Stateless HTTP
Auth	API key via request headers

Headers

Header	Required	Description
X-Memori-API-Key	Yes	Your Memori API key
X-Memori-Entity-Id	Yes	Stable end-user identifier (e.g. user_123)
X-Memori-Process-Id	No	Process, app, or workflow identifier for memory isolation

session_id is derived automatically as <entity_id>-<UTC year-month-day:hour> — you do not need to provide it.

Verifying the Connection

After configuring any client:

• Confirm the MCP server shows as connected in your client's UI
• Check that recall and advanced_augmentation appear in the tools list
• Send a test message — recall should return a response (even if empty for new entities)
• Verify advanced_augmentation returns memory being created

If you receive 401 errors, double-check your X-Memori-API-Key value. See the Troubleshooting guide for more help.

Links

• Memori Cloud
• Get an API key
• MCP Overview docs
• Client Setup docs