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PyPI version License: AGPL v3 Python 3.10+ MCP Protocol


Memento is a local-first, open-source MCP middleware that gives your AI agents (Cursor, Claude Desktop, Trae, etc.) persistent memory, proactive goal enforcement, and autonomous intelligence — all running on a zero-cost SQLite temporal graph with Reciprocal Rank Fusion (RRF) retrieval.

No cloud databases. No API calls for storage. Everything stays on your machine.


Architecture

Temporal Graph Memory (RRF)

Built on SQLite FTS5 (full-text search) and cosine similarity (vector embeddings). Fuses keyword matches and semantic meaning via Reciprocal Rank Fusion. WAL-mode enabled for concurrency.

Tri-State Goal Enforcer

Keep your AI aligned with project objectives at three escalation levels:

  • Level 1 — Context Injection: Automatically injects active goals into every search result. Active by default.

  • Level 2 — Strict Mentor: Forces the AI to submit code/plans for goal alignment evaluation via LLM.

  • Level 3 — Daemon Push: File-watcher monitors your workspace and proactively flags goal drift.

Active Coercion (Code Immune System)

Deterministic regex/tree-sitter rules that block anti-patterns at commit time and in the IDE. 100% deterministic — zero LLM hallucination risk during enforcement.

Autonomous Agent

Background cognitive loop with four levels:

  • off: No background behavior (default).

  • passive: Observe health and patterns every 5 min. No modifications.

  • active: Consolidate memories, extract KG, warm caches, detect anomalies every 2 min.

  • autonomous: All of the above plus dream synthesis, goal drift detection, task generation, health reports every 1 min.

Workspace Isolation

Each project gets its own .memento/ directory with an isolated SQLite database. No context bleeding between projects. Configure via MEMENTO_DIR or per-project .cursor/mcp.json.

Session Continuity

  • Auto-checkpoints every 25 tool calls with full L1 working memory snapshot.

  • Auto-resume restores goals and context from the previous session.

  • LLM-agnostic handoff prompts for session transfer between agents.

Project Memory Graph

Semantic entity-relationship graph on top of the Knowledge Graph. Track files, components, decisions, and their dependencies. Impact analysis shows what breaks when you change something.


Unified Tool API (v0.3.x)

Memento exposes 14 action-based tools via MCP. Each tool uses an action parameter instead of separate tools per operation:

Tool

Actions

Purpose

memento

(main router)

Primary proactive memory interface

memento_project

set_state, get_state, delete_state, set_goals, list_goals, summary

Vision, milestones, blockers, goals

memento_session

begin, resume, handoff, status, list

Session lifecycle and handoff

memento_graph

add_entity, add_relation, query, impact, summary

Project Memory Graph

memento_search

basic, advanced, explain

FTS, vNext pipeline, routing trace

memento_remember

add, consolidate, share, evaluate, hit

Memory write operations

memento_configure

enforcement, coercion, daemon, autonomy, consolidation_scheduler, kg_scheduler, dependency_tracker, superpowers, access

All configuration

memento_cognitive

dream, align, warnings, tasks

Cognitive engine operations

memento_health

status, health, memory, kg, quality, relevance, cache, explain

Diagnostics

memento_coercion

list_presets, apply_preset, list_rules, add_rule, remove_rule, install_hooks

Active Coercion management

memento_kg

extract, health, cross_workspace_stats

Knowledge Graph operations

memento_notifications

configure, list, dismiss

Proactive notifications

memento_audit_dependencies

(standalone)

Dependency audit

memento_migrate_workspace_memories

(standalone)

Workspace memory migration


Quick Start

Install

pip install memento-mcp

Or run without installing:

uvx memento-mcp

Configure (Cursor / Claude Desktop / Trae)

Add to your global mcp.json (e.g. ~/.cursor/mcp.json):

{
  "mcpServers": {
    "memento": {
      "command": "memento-mcp",
      "env": {
        "OPENAI_API_KEY": "your-api-key",
        "OPENAI_BASE_URL": "https://api.openai.com/v1",
        "MEM0_MODEL": "openai/gpt-4o-mini"
      }
    }
  }
}

For per-project workspace isolation, add to .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "memento": {
      "command": "memento-mcp",
      "env": {
        "OPENAI_API_KEY": "your-api-key",
        "OPENAI_BASE_URL": "https://api.openai.com/v1",
        "MEM0_MODEL": "openai/gpt-4o-mini",
        "MEMENTO_DIR": "${workspaceFolder}"
      }
    }
  }
}

Add .cursor/ to your .gitignore to avoid committing API keys.

Verify

memento-mcp --help
memento --help

Set MEMENTO_EMBEDDING_BACKEND=none to disable embeddings. Memento falls back to FTS5-only search — no API key needed.

MEMENTO_EMBEDDING_BACKEND=none memento-mcp

Environment Variables

Variable

Description

OPENAI_API_KEY

Required for embeddings and cognitive features

OPENAI_BASE_URL

Optional OpenAI-compatible endpoint (e.g. OpenRouter)

MEM0_MODEL

LLM model for cognitive features

MEM0_EMBEDDING_MODEL

Embeddings model for hybrid search

MEMENTO_EMBEDDING_BACKEND

Set to none for FTS5-only (no API key)

MEMENTO_DIR

Workspace root for .memento/ state

MEMENTO_UI

Enable local web UI (1/true)

MEMENTO_UI_PORT

Local UI port (default 8089)

MEMENTO_HANDOFF_AUTO_CHECKPOINT_EVERY_N_EVENTS

Auto-checkpoint frequency (default 25)


CLI Usage

Memento works from the terminal too:

# Auto-capture git context as a memory
memento capture --auto

# Save a free-form note
memento capture --text "Resolved auth timeout by increasing JWT expiry"

# Search memories
memento search "how did I fix the promise bug"

# Show workspace status
memento status

How Proactivity Works

Memento operates at two levels:

Always-on (zero configuration)

  • Goal awareness: Every tool call is checked against active goals. If work drifts, a warning is appended.

  • Auto-resume: L1 working memory (goals, context) restores from the previous session's checkpoint.

  • Auto-checkpoint: Every 25 events, a full session snapshot is saved with project state and handoff prompt.

  • Session diff: Each checkpoint computes the delta from the previous session (goals changed, files touched).

Activatable (via memento_configure)

  • L2 enforcement: Goal alignment checks via LLM on explicit request.

  • L3 daemon: File-watcher with proactive goal drift notifications.

  • Autonomous agent: Background consolidation, KG extraction, dream synthesis, task generation.

  • Active coercion: Deterministic code pattern enforcement.

  • Consolidation/KG schedulers: Background deduplication and knowledge extraction.

Example activation sequence:

memento_project(action="set_goals", goals=["Implement auth flow", "Refactor DB layer"])
memento_configure(action="enforcement", level="level2", enabled=true)
memento_configure(action="consolidation_scheduler", enabled=true, interval_minutes=30)
memento_configure(action="autonomy", level="active")

License

Memento is released under the GNU Affero General Public License v3.0 (AGPL-3.0). If you modify Memento and offer it as a network service, you must release your modified source code under the same license.

See LICENSE for details.

A
license - permissive license
-
quality - not tested
B
maintenance

Maintenance

Maintainers
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Release cycle
1Releases (12mo)

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