# thedotmack/claude-mem

Persistent Context Across Sessions for Every Agent –  Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

Repository: https://github.com/thedotmack/claude-mem
Canonical: https://ross.abutalabs.com/products/claude-mem
Homepage: https://claude-mem.ai
Language: JavaScript
License: Apache-2.0
License Family: permissive
Topics: ai, ai-agents, ai-memory, anthropic, artificial-intelligence, claude, claude-agent-sdk, claude-agents, claude-code, claude-code-plugin, claude-skills, long-term-memory, mem0, openmemory, supermemory, chromadb, sqlite, embeddings, memory-engine, rag
Last push: 2026-08-26T22:04:47+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 26
- inputs: {"age_days": 367, "days_push": 7, "days_rel": 7, "gap_med": 0.0, "n_releases_24m": 309}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 91957, forks 8085 (observed 2026-08-28T04:12:23.391216+00:00)

## What it is
claude-mem is an open-source persistent memory engine for AI coding agents such as Claude Code, Codex CLI, Gemini CLI, Cursor, and other MCP-capable tools. It captures structured observations of what agents do during sessions, compresses them with AI into a local-first SQLite database with a Chroma vector index, and injects relevant context back into future sessions via hooks, slash commands, and a bundled MCP server, with an optional CMEM Cloud sync layer.

## Use cases
- give my AI coding agent long-term memory across sessions
- stop re-explaining project context to Claude Code every session
- remember decisions and dead ends from previous agent sessions
- sync one agent memory across multiple machines and editors
- search past agent work semantically instead of re-reading the codebase
- share a single memory layer across Claude Code, Cursor, Codex and Gemini
- reduce token costs by reusing compressed agent context

## When to choose
- You use Claude Code or another MCP-aware coding agent and want sessions to pick up where the last one left off
- You want local-first memory stored in SQLite on your own machine with privacy stripping and opt-in cloud sync
- You work across several agents, IDEs, or machines and want one shared memory endpoint
- You want out-of-band observation capture that does not block or slow down your agent

## When to avoid
- You need a general-purpose RAG pipeline over arbitrary documents rather than memory of agent sessions and interactions
- Your environment cannot run Node 20+ or the Chroma vector stack (including Windows without WSL/Git Bash in older versions)
- You need a turnkey hosted multi-tenant memory service with no local component
- Your agent or editor has no MCP support and you are unwilling to use hooks or CLI integration

## Facets
- artifact type: plugin
- maturity: active
- function: rag, mcp, vector-database, database, search-engine
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: cross-platform, self-hosted, cli
- tags: agent-memory, claude-code, mcp-server, long-term-memory, context-compression, session-memory, embeddings, chromadb, sqlite, local-first, slash-commands, hooks, cloud-sync, memory-engine, ai-agents, retrieval-augmented-generation, nodejs, docker

## Member repositories
- thedotmack/claude-mem (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:23.391216+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:13:50.414997+00:00, confidence not recorded.
  - readme: https://github.com/thedotmack/claude-mem (fetched 2026-08-28T04:12:23.391216+00:00, sha c7c8261fd210)
  - homepage: https://claude-mem.ai (fetched 2026-08-28T17:35:38.342612+00:00, sha 7deaa16c4e11)
  - site_page: https://cmem.ai/features (fetched 2026-08-28T17:35:38.346275+00:00, sha 7377d6312172)
  - site_page: https://cmem.ai/about (fetched 2026-08-28T17:35:38.359155+00:00, sha e7441adeb316)
  - site_page: https://cmem.ai/integrations (fetched 2026-08-28T17:35:38.348046+00:00, sha 8f6a70e3a037)
  - site_page: https://cmem.ai/pricing (fetched 2026-08-28T17:35:38.349705+00:00, sha 037bd87fd693)
  - site_page: https://cmem.ai/changelog (fetched 2026-08-28T17:35:38.351615+00:00, sha c43073637d52)
- Data as of 2026-08-30T08:39:29.467469+00:00.