mcp-memori

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

mcp-memori 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

Client Setup

Claude Code

Via CLI:

claude mcp add --transport http memori https://api.memorilabs.ai/mcp/ \
  --header "X-Memori-API-Key: ${MEMORI_API_KEY}" \
  --header "X-Memori-Entity-Id: ${MEMORI_ENTITY_ID}" \
  --header "X-Memori-Process-Id: ${MEMORI_PROCESS_ID}"

Via .mcp.json (project root):

{
  "mcpServers": {
    "memori": {
      "type": "http",
      "url": "https://api.memorilabs.ai/mcp/",
      "headers": {
        "X-Memori-API-Key": "${MEMORI_API_KEY}",
        "X-Memori-Entity-Id": "${MEMORI_ENTITY_ID}",
        "X-Memori-Process-Id": "${MEMORI_PROCESS_ID}"
      }
    }
  }
}

Run /mcp inside Claude Code to verify the server status.

Cursor

Create ~/.cursor/mcp.json (global) or .cursor/mcp.json (project-level):

{
  "mcpServers": {
    "memori": {
      "url": "https://api.memorilabs.ai/mcp/",
      "headers": {
        "X-Memori-API-Key": "${MEMORI_API_KEY}",
        "X-Memori-Entity-Id": "${MEMORI_ENTITY_ID}",
        "X-Memori-Process-Id": "${MEMORI_PROCESS_ID}"
      }
    }
  }
}

Restart Cursor after saving.

OpenAI Codex

Add to ~/.codex/config.toml:

[mcp_servers.memori]
enabled = true
url = "https://api.memorilabs.ai/mcp/"

[mcp_servers.memori.http_headers]
X-Memori-API-Key = "${MEMORI_API_KEY}"
X-Memori-Entity-Id = "${MEMORI_ENTITY_ID}"
X-Memori-Process-Id = "${MEMORI_PROCESS_ID}"

You can also add the server from the Codex UI: Settings > MCP Servers > + Add Server.

Warp

Add to your Warp MCP configuration:

{
  "memori": {
    "serverUrl": "https://api.memorilabs.ai/mcp/",
    "headers": {
      "X-Memori-API-Key": "your-memori-api-key",
      "X-Memori-Entity-Id": "user_123",
      "X-Memori-Process-Id": "my_agent"
    }
  }
}

Antigravity

Open Manage MCP Servers and edit mcp_config.json:

{
  "mcpServers": {
    "memori": {
      "serverUrl": "https://api.memorilabs.ai/mcp/",
      "headers": {
        "X-Memori-API-Key": "your-memori-api-key",
        "X-Memori-Entity-Id": "user_123",
        "X-Memori-Process-Id": "my_agent"
      }
    }
  }
}

Save and restart Antigravity to refresh the tools list.

LangChain

from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient({
    "memori": {
        "transport": "streamable_http",
        "url": "https://api.memorilabs.ai/mcp/",
        "headers": {
            "X-Memori-API-Key": "your-memori-api-key",
            "X-Memori-Entity-Id": "user_123",
            "X-Memori-Process-Id": "langchain_agent"
        }
    }
})

tools = await client.get_tools()

Slack

Set headers dynamically per request using the Slack user ID from the event payload:

const memoriHeaders = {
  "X-Memori-API-Key": process.env.MEMORI_API_KEY,
  "X-Memori-Entity-Id": slackEvent.user,   // e.g. "U04ABCDEF"
  "X-Memori-Process-Id": "supportbot",
};

Pass these headers in every MCP tool call. Use process_id to isolate memories by workspace so preferences from personal workspaces don't bleed into team ones.

Notion

Set entity and process IDs from the Notion API user object:

const memoriHeaders = {
  "X-Memori-API-Key": process.env.MEMORI_API_KEY,
  "X-Memori-Entity-Id": notionUser.id,
  "X-Memori-Process-Id": "notion_writing_assistant",
};

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.

mcp-memori

mcp-memori

Why Memori

LoCoMo Benchmark

How It Works

Example Agent Flow

Prerequisites

Client Setup

Server Details

Headers

Verifying the Connection

Links

Reviews