Local-first memory for AI coding agents. Memory + CodeGraph + Wiki in one SQLite file.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Your coding agent stops repeating the same mistakes.
Local-first memory that survives context compaction. Learns from every error, injects fixes before the next attempt, and syncs to your git repo so your whole team shares it.
No API key. No cloud. No database server. Just a SQLite file.
Auto-detects Claude Code, Cursor, Devin, Codex. Registers MCP server + hooks. Restart your agent.
That's it. Use your agent normally β memory works automatically.
| When | What |
|---|---|
| Session start | Past errors, decisions, and persona injected into agent context |
| Each prompt | Matching memory injected (you'll see [remem-mcp] at the top) |
| Tool calls | Verbose output offloaded to refs, Mermaid canvas injected (92% token cut) |
| Session end | Worker auto-extracts facts, consolidates summaries, updates persona |
You don't run any commands. The agent calls recall() before answering and capture() after work β the skill tells it to.
No LLM API key needed β rule-based extraction + keyword grouping.
Structural code indexing via tree-sitter. The agent uses codegraph_search instead of grep to find symbols.
9 languages: TS/JS/Python/Go/Rust/Java/C/C++/C#. 6-strategy call resolution (import-map β same-module β unique-name β suffix β fuzzy). Stdlib calls filtered out.
| Repo | Files | Symbols | Calls | Time |
|---|---|---|---|---|
| remem-mcp | 79 | 301 | 6,456 | 3s |
| AZR Go | 455 | 3,417 | 41,603 | 111s |
| Orca TS | 3,000 | 7,632 | 78,981 | 705s |
| remem-mcp | Mem0 | Claude MEMORY.md | Mneme | |
|---|---|---|---|---|
| Survives compaction | Yes | Yes β cloud | No β 200-line cap | Yes |
| Learns from errors | Yes β auto | No | No | No |
| Search | Hybrid BM25 + vector + entities | Vector only | No | Vector + graph |
| Memory links | Hebbian co-retrieval | No | No | Graph |
| Decay/forget | Yes | No | No | No |
| CodeGraph | Yes β 6-strategy call resolution | No | No | No |
| Token offload | Yes β Mermaid canvas | No | No | No |
| Setup | 1 command | API key + cloud | Built-in | Build from source |
| Cost | Free | $19β249/mo | Free | Free |
Add to ~/.codex/config.toml:
Then run npx remem-mcp install-hooks.
All settings have defaults. Config file is optional: ~/.config/remem-mcp/config.json.
| Setting | Env var | Default |
|---|---|---|
| DB path | REMEM_DB_PATH | ~/.local/share/remem-mcp/memory.db |
| Cross-project memory | REMEM_GLOBAL_SESSION_KEY | (unset) |
| Unified flow (F1+F2+F3) | REMEM_FLOW | (unset, set to full) |
| Suppress hook feedback | REMEM_QUIET | (unset, set to 1) |
Global memory policy β set REMEM_GLOBAL_SESSION_KEY to read cross-project memory automatically. Captures stay project-local unless the user explicitly asks to save globally; then use session_key: "global". Do not auto-classify ordinary captures into global.
Team sharing β npx remem-mcp sync-export writes .remem-mcp/memory-export.jsonl. Commit it to git. Team members get the same memory on git pull.
Per-repo capture exclusions β Drop a .remem.toml in any project root:
| Benchmark | remem-mcp | Mem0 | Without memory |
|---|---|---|---|
| AMB (L1/L2/L3) | 100/100/100 | β | β |
| LoCoMo (long conversation QA) | 95 | 92.5 | β |
| PersonaMem (personalization) | 100 | β | 48 |
| LongMemEval (ICLR 2025) | 96 | 94.4 | β |
See ARCHITECTURE.md for full system diagrams, schema, and performance details.
Core based on TencentDB Agent Memory (MIT, Tencent 2026). CodeGraph call resolution adapted from Codebase-Memory (arXiv:2603.27277). Recall boost adapted from ai-memory by Akita On Rails. Contextual retrieval from Anthropic (2024).
MIT. See LICENSE.
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