memorysqlitecoding-agentsknowledge-management
Local-first MCP memory server that stores and retrieves project knowledge in SQLite across coding-agent sessions.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.
Manual Client & Custom JSON ConfigExpand JSON ▾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for lfrmonteiro99/memento-mcp, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Overview
Use cases
Store architectural decisions and project conventions
Retrieve debugging notes and known pitfalls
Import memory from CLAUDE.md, AGENTS.md, Cursor rules, and related files
Summarize coding sessions for later retrieval
Sync selected team memories through git
Key features
Local SQLite persistent memory
Token-aware progressive-disclosure search
Import from common AI instruction files
Optional OpenAI semantic embeddings
Git synchronization for team-scoped memories
Optional Obsidian vault integration
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Memento MCP.
Extracted Tool Capabilities
Local SQLite persistent memory
Token-aware progressive-disclosure search
Import from common AI instruction files
Optional OpenAI semantic embeddings
Git synchronization for team-scoped memories
Optional Obsidian vault integration
Documentation Overview
memento-mcp
Persistent memory for AI coding agents.
A local-first MCP server that gives Claude Code, Codex, Cursor, and any stdio-MCP client durable project memory: facts, decisions, patterns, architecture notes, pitfalls, session summaries, and team-shared knowledge — while cutting thousands of tokens of repeated context out of every single session.
[!NOTE] AI coding agents are powerful, but they forget. They forget why a decision was made, which migration broke production, which convention your project follows, and which workaround saved you three hours last week.
memento-mcpfixes that — and stops your agent burning thousands of tokens re-reading the same context every session.
It stores structured memories locally in SQLite, retrieves the right context when your agent needs it, and can sync selected team memories through git. No hosted vector database. No mandatory cloud account. No mystery SaaS quietly eating your project history.
Quick start
Table of contents
- Documentation
- Requirements
- Package
- What it does
- Why use it
- Installation
- 60-second tour
- Core features
- Knowledge model
- Example use cases
- Testing
- License
Documentation
|
Getting started
|
Features |
Reference |
Requirements
- Node.js 20 or newer (20.x, 22.x, 24.x — Node 18 is EOL and no longer supported)
- npm
- An MCP-compatible client, such as Claude Code, Codex, Cursor, or another stdio-MCP client
Optional dependencies
- Obsidian vault for curated Markdown knowledge
- OpenAI key for semantic embeddings
- Anthropic or OpenAI key for LLM-assisted session summaries
- git repo for team memory sync
Package
Published on npm as:
Install globally:
What it does
memento-mcp gives your AI coding tools a memory layer that survives across sessions, machines, and teammates.
It can remember:
| Architectural decisions | Project conventions |
| Known pitfalls | Implementation patterns |
| Debugging notes | User/team preferences |
| Session summaries | Reusable context from CLAUDE.md, AGENTS.md, Cursor rules, Copilot instructions, … |
| Curated notes from an Obsidian vault |
Then it injects the relevant context back into your agent at the right time, without forcing you to paste the same project explanation into every new chat like a medieval scribe with npm installed.
Why use it
Save tokens. A lot of them.
Without persistent memory, every new session starts blind:
CLAUDE.md/AGENTS.md/.cursor/rules// Copilot instructions get re-pasted or stuffed into the system prompt- Architectural decisions are re-explained mid-conversation
- Last week's pitfall is re-discovered the hard way
- "What were we doing yesterday?" eats hundreds of tokens before any actual work happens
memento-mcp imports that context once — from any of the major LLM instruction files — and serves back only the slice the current prompt needs, using progressive disclosure that prefers cheap index/summary layers over full bodies.
Conservative per-session savings
| Where the tokens go | Without memento | With memento | Saved |
|---|---|---|---|
CLAUDE.md / AGENTS.md / .cursorrules re-injected | ~1,500 t | imported once → 0 t | ~1,500 t |
| Architecture re-explained mid-chat | ~500 t | 1 retrieved decision (~80 t) | ~420 t |
| Pitfall re-discovered | ~300 t | 1 retrieved pitfall (~80 t) | ~220 t |
| "What were we doing?" recap | ~400 t | 1 session summary (~200 t) | ~200 t |
| Total prelude per session | ~2,700 t | ~360 t | ~2,340 t |
[!NOTE] Numbers are deliberately conservative. Real-world
CLAUDE.md/AGENTS.md/.cursor/rules/trees routinely reach 3-5k tokens (often more once a team accumulates files across multiple tools), and longer-running projects accumulate dozens of decisions and pitfalls. Savings scale with project age.
Scaled out
| Cadence | Sessions / month | Tokens saved (conservative) |
|---|---|---|
| Solo dev, ~4 sessions/day | ~80 | ~190,000 |
| 5-person team, same cadence | ~400 | ~940,000 |
Three things you get back, for free:
- Latency — the agent stops chewing through thousands of prelude tokens before responding.
- Context budget — that ~2,300 tokens of saved prelude is ~2,300 tokens you can spend on actual code, longer files, or richer reasoning.
- Cost — every saved token is one you don't pay for, on every session, on every machine, on every teammate.
[!TIP] Compounding effect: when embeddings are enabled, write-time dedup keeps the memory store lean, and adaptive ranking surfaces only high-utility memories — so the retrieved tokens are higher-signal too.
Keep decisions close to the code
Log decisions, pitfalls, patterns, and architecture notes as structured memories instead of burying them in old chats, random Markdown files, or the cursed archaeology layer known as “Slack search”.
Share memory with your team
Team-scoped memories are serialized into your repo under:
Commit them, push them, and teammates can pull the same operational knowledge.
Stay local by default
The default setup uses:
- local SQLite
- SQLite FTS5 search
- local config
- local web inspector
- no required cloud account
- no hosted database
[!TIP] Optional embeddings are available, but they are opt-in.
Keep private text private
<private>...</private> regions are excluded from search indexes, injection, embeddings, LLM calls, and sync paths. Secret scrubbing is applied at write time for common credentials such as env-var values, JWTs, GitHub tokens, URL credentials, and authorization headers.
See and control what the agent knows
Run the local inspector:
Browse memories, sessions, projects, sync state, analytics, and drift without opening yet another SaaS dashboard pretending to be “simple”.
Installation
1. Install from npm:
2. Wire it into your MCP client:
This configures supported local clients such as Claude Code, Codex, Cursor, or other stdio-MCP clients.
3. Verify the install:
4. Open the local web UI:
60-second tour
Core features
Typed memories
Store different kinds of project knowledge with different ranking weights and retrieval behavior:
fact · decision · preference · pattern · architecture · pitfall
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
2
Stargazers on the source repository.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.
Reviews
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Frequently Asked Questions about Memento MCP
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CategoryKnowledge & Memory
PricingFree
41Quality signal: Fair · 41/100How this signal is calculated ▾
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