Memento

Give your AI tools a persistent memory — so every session starts where the last one left off.

Your AI starts fresh every session. Memento fixes that.

It runs on your machine, connects to any MCP-compatible AI tool, and builds a persistent knowledge graph from your conversations — entities, relationships, decisions, and context that survive every session restart.

No cloud. No API keys required. No subscriptions. Your data stays on your machine.

Quick Start

Prerequisites: Docker — or — Go 1.23+ + Node.js 18+ + Ollama

git clone https://github.com/scrypster/memento.git
cd memento
./launch.sh

The script detects your environment, runs preflight checks, builds everything, and prints the exact command to connect your AI tool at the end. First run downloads Ollama models (~5 GB). After that, starts in seconds.

Your first memory

Once connected, try this in Claude:

"We're using PostgreSQL — chose it for pgvector support."

Close the tab. Open a new session. Ask:

"What database are we using?"

Your AI already knows. No re-explaining. No context window tricks.

Close the tab. Open a new session.

You: "What database are we using?"

→ Your AI already knows: "PostgreSQL — you chose it for pgvector support."
  No re-explaining. No context window tricks. It just remembers.

Behind the scenes, Memento built this automatically:

Every entity gets wired into a knowledge graph — people, tools, projects, decisions — with confidence scores and timestamps.

Connect Your Tools

Open http://localhost:6363/integrations — the web UI generates configs, download buttons, and connection testing for every client:

The integrations page generates ready-to-paste configs with your actual binary paths and data directories. It also has connection testing, troubleshooting, and per-project workspace scoping.

Make Claude Code proactive (recommended)

The MCP connection makes tools available, but Claude won't use them automatically. Add this to ~/.claude/CLAUDE.md to make Claude store decisions and recall context without being asked:

## Memento MCP — Persistent Memory

The `memento` MCP server provides persistent cross-session memory. Use these tools proactively — don't wait to be asked.

**Store** (`store_memory`) when the user:
- States a preference or working style ("I prefer X", "always use Y format")
- Makes an architectural or technical decision
- Establishes project context that should survive session restarts
- Explicitly says "remember this" or similar

**Recall** (`recall_memory` or `find_related`) when:
- Starting a session for a known project — query for relevant context before diving in
- About to make a recommendation — check for existing preferences first
- The user asks about past decisions, choices, or "what did we decide about X"
- Something seems like it may have been discussed in a prior session

**Don't store:** transient debug output, in-progress exploration, or anything session-specific that won't matter next time.

Memories are searchable immediately after storing. Enrichment (entity/relationship extraction) runs asynchronously via local Ollama.

The web UI at Integrations → Claude Code → Make it proactive generates a version with your specific paths and connection settings, plus a download button.

See the full integration guides: Claude Code | Claude Desktop | Cursor & Windsurf | OpenClaw

Team memory — shared knowledge across your whole engineering team

Point everyone's AI tools at the same Memento instance and your team's decisions, conventions, and context become shared knowledge — queryable by anyone, attributable to anyone.

Every memory is tagged with who stored it. Memento auto-detects this from your git config, or you can set it explicitly:

export MEMENTO_USER=alice   # or set in your shell profile

Or in your MCP config:

"env": { "MEMENTO_USER": "alice" }

Once set, you can ask:

What did Bob decide about the auth service this week?
recall_memory(created_by="bob", created_after="2024-01-14T00:00:00Z")

Setup: Each teammate runs Memento pointing at the same PostgreSQL database. Personal context stays personal (use a separate personal connection). Shared architectural decisions, conventions, and project context go into the shared connection.

See the team setup guide for full PostgreSQL configuration.

What Your AI Gets

Once connected, your AI has 20 tools it can call — no prompting required:

Core memory operations

Search and intelligence

Memory lifecycle

Soft delete and recovery

Project management

Store returns in <10ms. Enrichment — entity extraction, relationship mapping, embedding generation — runs asynchronously. Your AI is never blocked.

What It Looks Like

Auto-extracted entities — zero manual input

People, projects, tools, organizations, languages, APIs — extracted automatically from your AI conversations. No tagging required.

Relationship intelligence

Your AI knows who works_on what, which tools depend_on which services, and what the current state of each decision is — with confidence scores and timestamps.

The dashboard

Live enrichment queue, entity browser, relationship explorer, and graph visualizer — all in the web UI.

Why Memento

vs. Mem0

Mem0 requires cloud API keys and a paid plan for production use. Memento runs entirely on your machine with Ollama — no API keys, no cloud, no per-memory pricing. Memento also ships a full web UI with graph visualization, entity browser, and one-click integration setup. Mem0 has no web interface.

vs. Zep / Graphiti

Zep requires Neo4j or FalkorDB for its knowledge graph. Memento uses SQLite (zero deps) or PostgreSQL — no graph database to manage. Zep's open-source version is limited; the full feature set requires Zep Cloud.

vs. Built-in AI memory (ChatGPT, Claude)

Built-in memory is a flat list of facts with no relationships, no search, no graph, and no way to export or control your data. Memento gives you a structured knowledge graph you own, with hybrid search and full lifecycle management.

vs. Writing docs or wikis