Which integrations are available for this server?

Provides MCP server tools to search, recall, and save memories from Claude Code and Gemini CLI, making cross-project insights accessible within GitHub Copilot sessions.

How do I use crossmem?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@crossmem search for credential masking pattern from backend-api" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

crossmem

by jamiemoles

Overview Schema Source Related Servers Discussions Admin

Local

crossmem

One search across all your Claude Code and Gemini CLI memories — every project, every tool.

Before and after crossmem

crossmem demo

The problem

You use AI coding assistants across multiple projects. Each project's memories are locked in a silo — and each tool has its own silo too. You solved credential masking in your backend API three months ago, but when you need it in a new microservice, your AI assistant starts from scratch.

Here's what's happening under the hood:

~/.claude/projects/
├── backend-api/memory/MEMORY.md    ← Claude remembers here
├── mobile-app/memory/MEMORY.md    ← ...but can't see here
└── data-pipeline/memory/MEMORY.md ← ...or here

~/.gemini/GEMINI.md                ← Gemini's memories (separate silo entirely)

Every project is a silo. Every tool is a silo. Knowledge doesn't compound — it resets.

Related MCP server: Context Pilot

The fix

$ crossmem ingest
Ingested: 42 memories across 4 projects (Claude Code + Gemini CLI)

$ crossmem search "credential masking"
Found 3 results for "credential masking":

[1] backend-api / Security
    Source: MEMORY.md
    - Credentials masked in experience_memory before persisting (_mask_actions)...

[2] mobile-app / Security
    Source: MEMORY.md
    - Credentials masked via _mask_context_credentials() + _mask_text()...

[3] backend-api / Security
    Source: GEMINI.md
    - Credential masking pattern: _mask_actions for persistence, _mask_text for logs...

Three results. Two projects. Two AI tools. One query. The pattern was already solved.

How crossmem differs

vs Mem0 — Mem0 is cloud-based and requires an API key. crossmem is local-only with zero accounts.
vs Basic Memory — Basic Memory works within one tool. crossmem aggregates across tools and projects.
vs grep — crossmem parses multiple formats, deduplicates, and runs as an MCP server — your AI assistant queries it automatically at session start.

Install

pip install crossmem
# or
uv pip install crossmem

Quick start

pip install crossmem        # 1. Install
crossmem ingest             # 2. Index all your AI memories
crossmem search "retry"     # 3. Search across every project

That's it. Three commands, zero config. crossmem finds Claude Code and Gemini CLI memory files automatically.

To give your AI tools direct access, add the MCP server to your config (see MCP Server below) — then mem_recall() and mem_search() just work inside your coding sessions.

Usage

# Ingest Claude Code + Gemini CLI memories
crossmem ingest

# Search across every project
crossmem search "JWT token rotation"
crossmem search "retry strategy" -p backend-api
crossmem search "docker compose" -n 5

# Save a discovery
crossmem save "Always use middleware for credential masking" -p backend-api -s Patterns

# Delete stale or wrong memories
crossmem forget 42                   # delete memory #42 (with confirmation)
crossmem forget -p old-app           # delete all memories for a project
crossmem forget 42 --confirm         # skip confirmation prompt

# Sync Claude memories → Gemini CLI
crossmem sync                        # sync everything
crossmem sync -p backend-api        # sync one project + shared patterns

# Watch for changes and auto-sync
crossmem sync-watch                  # polls every 30s
crossmem sync-watch --interval 10    # custom interval

# Visualize the knowledge graph
crossmem graph

# See what's in the database
crossmem stats

How it works

Ingest — Finds Claude Code and Gemini CLI memory files automatically, splits into chunks, deduplicates
Index — Stores everything locally in SQLite — no cloud, no API keys, no accounts
Search — Full-text search with stemming. Multi-word queries use AND logic; quoted phrases for exact matches
Learn — AI tools save new discoveries via mem_save during sessions. Knowledge compounds automatically
Sync — One-way sync from Claude → Gemini, preserving each tool's own memories

How it works with your AI tools

Once the MCP server is configured, your AI assistant automatically uses crossmem:

You: "How should I handle credentials in this new service?"

AI: Let me check crossmem for existing patterns...
    [calls mem_recall → finds credential masking in 3 of your projects]

    Based on your previous work across backend-api, mobile-app, and infra-tools,
    you consistently use a middleware layer for credential masking. Here's the
    pattern from your backend-api project:
    - Credentials stored in Secret Manager, never in env vars
    - API keys masked in logs via _mask_sensitive_headers()
    ...

No copy-pasting. No "I already solved this." Your AI assistant recalls patterns from every project you've worked on — automatically.

MCP Server

crossmem runs as an MCP server so AI coding tools can search, recall, and save memories in real-time.

Setup

Add to your tool's MCP config:

Claude Code (~/.mcp.json for global, or .mcp.json in project root):

{
  "mcpServers": {
    "crossmem": {
      "command": "crossmem-server"
    }
  }
}

Gemini CLI (~/.gemini/settings.json):

{
  "mcpServers": {
    "crossmem": {
      "command": "crossmem-server"
    }
  }
}

VS Code / GitHub Copilot (.vscode/mcp.json in project root, or user settings.json):

{
  "servers": {
    "crossmem": {
      "command": "uvx",
      "args": ["--from", "crossmem", "crossmem-server"]
    }
  }
}

Note: For Claude Code and Gemini CLI, if crossmem-server isn't on PATH, use the same uvx command shown in the Copilot config above.

Tools

Tool	Description
`mem_recall`	Load project context + cross-project patterns at session start (auto-detects project from cwd)
`mem_search`	Search across all memories (query, project filter, limit)
`mem_save`	Save a discovery during a session — immediately searchable
`mem_forget`	Delete a memory by ID (find IDs via `mem_search`)
`mem_ingest`	Refresh the index when memory files change (auto-runs on server startup)

Start manually

crossmem serve    # starts MCP server on stdio (same as crossmem-server)

Supported tools

Tool	Ingestion
Claude Code	`~/.claude/projects//memory/.md`
Gemini CLI	`~/.gemini/GEMINI.md`
VS Code / GitHub Copilot	Via MCP server (no direct ingestion — uses the shared index)

Ingestion is pluggable — PRs welcome for new tools.

License

MIT

Available Tools

5 tools

mem_forgetA

Delete a memory by ID.

Use this to remove stale, wrong, or duplicate memories. Find the ID via mem_search first, then pass it here.

Args: memory_id: The ID of the memory to delete (shown in search results)

Inputs: memory_id (required). Returns: result

mem_ingestA

Refresh the memory index by re-ingesting all memory files.

Reads Claude Code and Gemini CLI memory files and updates the searchable index. Run this when you know memory files have changed.

Inputs: no named parameters. Returns: result

mem_recallA

Recall relevant memories for a project at session start.

Returns the project's own memories plus cross-project patterns (knowledge validated across multiple projects). Call this at the beginning of a coding session to load context.

If no project is given, auto-detects from the working directory by matching path segments against known project names.

Args: project: Project name to recall memories for (auto-detected if omitted) cwd: Working directory path for auto-detection (defaults to os.getcwd())

Inputs: cwd, project. Returns: result

mem_saveA

Save a memory during a coding session.

Use this when you discover something worth remembering: patterns, decisions, gotchas, architecture notes, debugging insights, or any knowledge that would help future sessions.

The memory is immediately searchable via mem_search.

Args: content: The memory content to save (be specific and actionable) section: Category heading (e.g. "Security", "Architecture", "Gotchas") project: Project name (auto-detected from cwd if omitted) cwd: Working directory for auto-detection (defaults to os.getcwd())

Inputs: content (required), cwd, project, section. Returns: result

mem_searchA

Search across all project memories.

Use this to find patterns, decisions, and solutions from past projects. Multi-word queries use AND logic. Use quoted phrases for exact matches.

Args: query: Search terms (e.g. "credential masking", "docker sidecar") project: Optional project name to filter results limit: Max number of results (default 10)

Inputs: query (required), limit, project. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

TDQS

A4.4/5.0

Scored 2026-07-30 16:45 across 5 tools

Disambiguation5/5

Each tool targets a distinct memory operation: searching, recalling context, saving, ingesting index, and deleting. No overlaps.

Naming Consistency5/5

All tools follow a consistent 'mem_' prefix with descriptive snake_case verbs (search, recall, save, ingest, forget).

Tool Count5/5

With 5 tools, the set covers core memory lifecycle operations without being excessive or insufficient for the domain.

Completeness4/5

CRUD-like operations are present (save, search, recall, forget), and ingest handles index refresh. Lacks an explicit update tool, but save can potentially overwrite.

Maintenance

ActivityInactive

ResponsivenessNo recent community issues

Resources

Related MCP Connectors

SubstrateB
co.trysubstrate
Shared memory MCP server for Claude Code, Codex, Gemini CLI and Cursor. Cited, editable.
Developer Tools Knowledge & Memory
131
https://app.trysubstrate.co/mcp
WorkingMemoryA
ai.workingmemory
Persistent personal memory for AI assistants — save, search, and recall across every MCP client.
Knowledge & Memory Search
42
https://app.workingmemory.ai/mcp
mcp
ai.butlerbrain
Persistent memory for AI assistants. Save once; recall from Claude, ChatGPT, or any MCP client.
Knowledge & Memory RAG Systems Workplace & Productivity
2
https://api.butlerbrain.ai/mcp
Engram
app.getengram
Persistent, verbatim, searchable memory for AI assistants — one memory across every MCP client.
Knowledge & Memory AI & Machine Learning
122
https://mcp.getengram.app/mcp

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crossmem

The problem

The fix

How crossmem differs

Install

Quick start

Usage

How it works

How it works with your AI tools

MCP Server

Setup

Tools

Start manually

Supported tools

License

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

codemem

Context Pilot

claude-memory

recall