Remem MCP Server

remem-mcp

Your coding agent stops repeating the same mistakes.

Local 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.

One command setup. No API key. No cloud. No database server. Just a SQLite file.

See it in action

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Install

npx remem-mcp setup

That's it. Auto-detects Claude Code, Cursor, Devin, Codex. Registers MCP server + hooks. Restart your agent.

npx remem-mcp demo     # Live demo: real build, real errors, real hooks
npx remem-mcp demo-codegraph  # Live CodeGraph demo on facebook/react
npx remem-mcp status   # One dashboard: everything at a glance

The demo creates a real TypeScript project, runs real npm run build, captures real TS2307 errors, and shows the full learning loop — capture → inject → fix → zero retries. No hardcoded strings.

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Why it's different

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Per-agent install

claude mcp add remem-mcp --scope user -- npx -y remem-mcp
npx remem-mcp install-hooks

Or add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "remem-mcp": { "command": "npx", "args": ["-y", "remem-mcp"] }
  }
}

devin mcp add remem-mcp --scope user -- npx -y remem-mcp
npx remem-mcp install-hooks

Add to ~/.codex/config.toml:

[mcp_servers.remem-mcp]
command = "npx"
args = ["-y", "remem-mcp"]

[mcp_servers.remem-mcp.env]
REMEM_GLOBAL_SESSION_KEY = "global"

Then run npx remem-mcp install-hooks.

MCP tools require sandbox_mode = "danger-full-access".

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How it works

Memory lives in a local SQLite database — outside the agent's context window. When the agent compacts or starts a new session, memory is re-injected automatically. No more re-explaining what you already told it yesterday.

PreCompact hook: when the agent is about to compact context, remem-mcp saves a checkpoint (decisions made, approaches tried, what's verified working) to the DB. After compaction, the agent recalls it — so the compact doesn't destroy your session's learnings.

Two layers: automatic (runs via hooks, zero tool calls) and on-demand (you call when you need deeper context).

Automatic — three learning loops + compaction survival

All run via lifecycle hooks. The agent doesn't need to call any tool.

1. Error learning — command fails → capture → inject fix before next attempt → succeed → upvote.

2. Decision learning — npm install, git commit, config → auto-capture → inject past decisions before similar commands.

3. Pattern learning — Write/Edit → auto-capture code patterns → inject same-language patterns before editing.

4. Compaction survival — PreCompact hook fires before context compaction → saves checkpoint → agent recalls after compact. Memory survives.

On-demand — CodeGraph, Wiki, Search

When the automatic loops aren't enough, use these for deeper code navigation.

CodeGraph — symbol search, callers/callees, impact analysis. Auto-indexes on first use — just call codegraph_search and it indexes src/ automatically. No manual codegraph_index needed.

# Search symbols (auto-indexes src/ on first call)
npx remem-mcp search-code --query "parseTar"
# → parseTar  at  src/parse.ts:22

# List symbols in a file
npx remem-mcp list-code src/reporters/fancy.ts
# → Class    L49-135  FancyReporter
# → Method   L86-134  formatLogObj

# Trace callers / callees / impact (use symbol ID from search)
npx remem-mcp callers 01KZXPPHF93TS4HV8FWCSSK36A
npx remem-mcp impact  01KZXPPHF93TS4HV8FWCSSK36A

# Manual re-index (only needed after major changes)
npx remem-mcp index --path src --repo .

# Wiki + viewer
npx remem-mcp wiki ingest --path docs      # Index markdown docs + ADRs
npx remem-mcp wiki outdated                 # Find outdated wiki pages
npx remem-mcp viewer                        # Web UI at localhost:7331

• CodeGraph — symbol search, callers/callees, impact analysis. Auto-indexes on first codegraph_search call. Auto-scoped to your project.
• Wiki — markdown docs, ADRs, outdated detection.
• Search — hybrid BM25 + sqlite-vec vector search with RRF fusion. explain_recall shows scores.

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Daily commands

npx remem-mcp status           # Everything at a glance
npx remem-mcp viewer           # Web UI at localhost:7331
npx remem-mcp errors           # Error dashboard
npx remem-mcp decisions        # Decision dashboard
npx remem-mcp patterns         # Pattern dashboard
npx remem-mcp recent [N]       # Recent captures
npx remem-mcp help all         # Full list of 40+ subcommands

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Configuration

All settings have defaults. Config file is optional: ~/.config/remem-mcp/config.json.

Team sharing — npx remem-mcp sync-export writes .remem-mcp/memory-export.jsonl. Commit it to git. Team members get the same memory on git pull (auto-imports on startup).

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TypeScript SDK

import { Memory } from "remem-mcp";

const memory = new Memory();
await memory.capture("We chose SQLite for storage.", "decision", ["arch"]);
const results = await memory.recall("storage decision");

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Benchmark

remem-mcp is evaluated against the same benchmarks as TencentDB Agent Memory and Mem0, plus the Agent Memory Benchmark (AMB) suite.

Note: LoCoMo, PersonaMem, and LongMemEval scores use custom adapters with keyword-heuristic scoring (not official LLM-as-judge runners). AMB uses the official CLI. See scripts/bench-all.sh for methodology.

• PersonaMem — bowen-upenn/PersonaMem (588 questions, 20 personas, multiple-choice QA). TencentDB reports 76% with memory enabled, 48% without. remem-mcp scores 100% using a search-recall proxy (no LLM API key needed).
• LoCoMo — long conversation multi-hop QA (19 sessions, 400+ turns). Mem0 reports 92.5%. remem-mcp scores 95% with keyword + semantic-similarity scoring.
• AMB — Agent Memory Benchmark (L1: 56 recall tests, L2: 5 multi-session scenarios, L3: 1K+ memories with distractors). remem-mcp scores 100/100/100 using the official AMB CLI.
• LongMemEval — xiaowu0162/LongMemEval (ICLR 2025, 500 questions, 5 memory abilities: temporal reasoning, multi-session, knowledge update, single-session recall, abstention). Mem0 reports 94.4%. remem-mcp scores 96% on the oracle variant.

Run the benchmarks:

bash scripts/bench-all.sh           # Full: AMB + LoCoMo + PersonaMem (~5 min)
bash scripts/bench-all.sh --quick   # AMB only (~2 min)

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Credits

Core based on TencentDB Agent Memory (MIT, Tencent 2026). Replaces the cloud backend with embedded SQLite + sqlite-vec + FTS5. Adds error/decision/pattern learning loops and lifecycle hooks.

License

MIT. See LICENSE.