Skip to main content

Cross-project memory for AI coding agents

Project description

crossmem

PyPI Downloads Python License

Your AI tools forget. crossmem doesn't.

Cross-tool memory for AI coding agents. One pip install, zero cloud, zero accounts — your Claude Code, GitHub Copilot, and Gemini CLI sessions remember everything, across every project, automatically.

Before and after crossmem

Quick start

pip install crossmem        # 1. Install
crossmem setup              # 2. Done. All tools configured.

That's it. Every AI coding session now starts with cross-project context.

crossmem demo

Why crossmem?

Every AI coding session starts cold. The model re-asks the same setup questions, re-derives the same patterns, re-reads the same files — because it has no memory of what you worked through last time.

We measured the impact using tokenxray across real Claude Code sessions before and after installing crossmem:

Metric Delta
Avg input tokens / session −74%
Avg cost / session −83%
Cache hit rate +18%
Avg turns to reach result −60%

Measured across real sessions. Results vary by project size and task type.

crossmem fixes this by injecting remembered context before the AI starts thinking — not as a suggestion it can ignore, but as enforced context.

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

AI: [crossmem recalls patterns from 3 of your projects]
    Based on your backend-api, mobile-app, and infra-tools projects,
    you use a middleware layer for credential masking — keys in
    Secret Manager, never env vars, masked in logs via
    _mask_sensitive_headers(). Applying the same pattern here.

No copy-pasting. No "I already solved this." Your AI remembers — across every project.

How it differs

crossmem Mem0 Letta Zep
Install pip install + SQLite Cloud API key or self-hosted Qdrant Server + Docker Postgres + Go server
Cross-tool Claude + Copilot + Gemini Single app Single app Single app
Cross-project All projects, one index Per-app scoped Per-agent scoped Per-session scoped
Protocol MCP-native REST API Custom framework SDK
Infrastructure None. Local SQLite. Cloud or Qdrant + server Letta server Postgres + server
Enforcement Hook injects context before generation LLM decides to call API LLM self-manages memory LLM calls SDK

Works with

Tool Auto-recall How
Claude Code SessionStart hook (startup/resume/compact) + UserPromptSubmit hook (every prompt) crossmem install-hook
GitHub Copilot Injects memories into copilot-instructions.md crossmem install-hook --tool copilot
VS Code Agent Mode SessionStart + UserPromptSubmit hooks (Preview) crossmem install-hook --tool copilot-agent
Gemini CLI Instruction in GEMINI.md crossmem install-instructions

What happens under the hood

cd ~/any-project
claude                    # Claude Code: hook fires, memories injected automatically
code .                    # Copilot: reads context pre-injected into copilot-instructions.md
gemini                    # Gemini: calls mem_recall via instruction in GEMINI.md
  1. Auto-ingest — pulls latest memories from Claude, Copilot, and Gemini native files
  2. Auto-init — first time in a project? Indexes README.md, CLAUDE.md, etc.
  3. Tiered recall — returns most relevant context within a token budget: curated memories > tool memories > CLAUDE.md > CONTRIBUTING.md > README.md
  4. Mid-session recall (Claude Code + VS Code Agent Mode) — every prompt is searched against your memories. Relevant context is injected before the model responds — no manual mem_recall needed.
  5. Learn — AI saves new discoveries via mem_save during sessions. Knowledge compounds. Each memory is written as a durable .md file to ~/.crossmem/memories/<project>/ — the DB is rebuilt from these files on startup, so memories survive a clean install.
  6. Freshness tracking (v1.3.0) — every memory carries a last_verified timestamp. mem_recall and mem_search surface [verified: YYYY-MM-DD] or [unverified] next to each result so agents can judge trust level at a glance. Use mem_verify(id) to stamp a memory as confirmed without changing its content.
  7. Smart search (v1.1.0) — a two-layer noise filter separates signal tokens from noise before every FTS query. Layer 1 excludes 168 linguistically fixed closed-class words (prepositions, pronouns, auxiliaries, etc.) in O(1). Layer 2 applies corpus-adaptive IDF via FTS5 — tokens that appear in more than 40% of your documents are treated as project-specific noise. Zero additional dependencies.
  8. Scope model (v1.2.0) — memories are either project-scoped (visible only within their project) or global (surfaced everywhere). Memories saved identically across 2+ projects are automatically promoted to global via auto_promote_patterns().

MCP Server

Add to your tool's MCP config so AI assistants can search, recall, and save memories in real-time:

Claude Code (~/.mcp.json)
{
  "mcpServers": {
    "crossmem": {
      "command": "crossmem-server"
    }
  }
}
GitHub Copilot (.vscode/mcp.json)
{
  "servers": {
    "crossmem": {
      "command": "uvx",
      "args": ["--from", "crossmem", "crossmem-server"]
    }
  }
}
Gemini CLI (~/.gemini/settings.json)
{
  "mcpServers": {
    "crossmem": {
      "command": "crossmem-server"
    }
  }
}

If crossmem-server isn't on PATH, use uvx --from crossmem crossmem-server instead.

MCP Tools

Tool Description
mem_recall Load project context + cross-project patterns at session start
mem_search Search across all memories (query, project filter, limit)
mem_save Save a discovery during a session (writes a durable .md file + DB row)
mem_update Update a memory in place (preserves ID, syncs backing file)
mem_forget Delete a memory by ID (removes backing file from disk)
mem_get Get full content of a memory by ID
mem_verify Mark a memory as verified today (no content change)
mem_promote Promote a project-scoped memory to global scope
mem_demote Demote a global memory back to project scope
mem_deduplicate Scan for near-duplicate memories and remove redundant entries
mem_init Index project documentation files
mem_ingest Refresh the index from native tool memory files
CLI reference
# Recall (runs automatically via hook)
crossmem recall                  # auto-detects project from cwd
crossmem recall -p backend-api   # explicit project
crossmem recall --format copilot # marker-wrapped for Copilot injection
crossmem recall --format vscode  # JSON for VS Code agent-mode hooks

# Search
crossmem search "JWT token rotation"
crossmem search "retry strategy" -p backend-api -n 5

# Save / Update / Delete
crossmem save "Always use middleware for credential masking" -p backend-api -s Patterns
crossmem update 42 "corrected content here"
crossmem forget 42

# Index project docs
crossmem init                        # current directory
crossmem init -p my-api --path ~/projects/api

# Hooks
crossmem install-hook                              # Claude Code (SessionStart + UserPromptSubmit)
crossmem install-hook --tool copilot               # Copilot (workspace instructions)
crossmem install-hook --tool copilot --global      # Copilot (all workspaces)
crossmem install-hook --tool copilot --if-stale    # refresh if >30 min old
crossmem install-hook --tool copilot-agent         # VS Code agent mode (.github/hooks/)
crossmem install-instructions                      # Gemini

# Internal (installed as hooks — not run manually)
crossmem prompt-search                             # mid-session recall via UserPromptSubmit

# Diagnose
crossmem doctor       # health check: binary, DB, hooks, Gemini, FTS index

# Other
crossmem ingest       # re-ingest tool memories
crossmem graph        # visualize knowledge graph in browser
crossmem stats        # database stats
crossmem setup        # one-time: Claude hook + Copilot injection + Gemini instructions + ingest

Supported tools

Tool Memory files
Claude Code ~/.claude/projects/*/memory/*.md
Gemini CLI ~/.gemini/GEMINI.md
GitHub Copilot (macOS) ~/Library/Application Support/Code/User/globalStorage/github.copilot-chat/memory-tool/memories/*.md
GitHub Copilot (Linux) ~/.config/Code/User/globalStorage/github.copilot-chat/memory-tool/memories/*.md
GitHub Copilot (Windows) %APPDATA%\Code\User\globalStorage\github.copilot-chat\memory-tool\memories\*.md
crossmem (via mem_save) ~/.crossmem/memories/<project>/*.md

Ingestion is pluggable — PRs welcome for new tools.

Changelog

See CHANGELOG.md for release notes.

Contributing

Found a bug? Want to add support for another AI tool? Open an issue or submit a PR.

If crossmem saves you from re-explaining your codebase to AI, consider giving it a star — it helps others find it.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

crossmem-1.9.1.tar.gz (57.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

crossmem-1.9.1-py3-none-any.whl (64.5 kB view details)

Uploaded Python 3

File details

Details for the file crossmem-1.9.1.tar.gz.

File metadata

  • Download URL: crossmem-1.9.1.tar.gz
  • Upload date:
  • Size: 57.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for crossmem-1.9.1.tar.gz
Algorithm Hash digest
SHA256 2307849b2736c5f463e13e92611adb747cbd29cac2b58a29e655ede42f422b63
MD5 d15c61830e56eee330911e1400b626d3
BLAKE2b-256 23e534c58a4fd64c4925b04c7cb6344a0bf343168daa3a284506dcf5c3ea6027

See more details on using hashes here.

Provenance

The following attestation bundles were made for crossmem-1.9.1.tar.gz:

Publisher: ci.yml on Crack525/crossmem

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file crossmem-1.9.1-py3-none-any.whl.

File metadata

  • Download URL: crossmem-1.9.1-py3-none-any.whl
  • Upload date:
  • Size: 64.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for crossmem-1.9.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e5461da85b2a55d4de0bfe39f39f782b48d0e28169b59a003471ca8a1ab88b74
MD5 3c1fe2a60457fb646e3d2d83e472f6fa
BLAKE2b-256 c26210d33a511267915ab0b0684a2bc9932afaa4097653c54d392d4533759546

See more details on using hashes here.

Provenance

The following attestation bundles were made for crossmem-1.9.1-py3-none-any.whl:

Publisher: ci.yml on Crack525/crossmem

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page