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Mnemosyne

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Provides persistent, graph-based memory for AI agents via MCP, enabling semantic search, wikilink traversal, reminders, and injection protection.

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Описание

Provides persistent, graph-based memory for AI agents via MCP, enabling semantic search, wikilink traversal, reminders, and injection protection.

README

The Context Engine for AI Agents — Give your AI agents persistent, observable, compliant memory.

All memories are plain markdown files you own. Search by meaning, traverse relationships, schedule reminders, and protect against poisoned data.

Python PostgreSQL License


⚠️ Rebrand Notice

This project was formerly known as Mnemosyne. We rebranded to Palimpsest in July 2026 to avoid confusion with an unrelated project that adopted the same name.

Why Palimpsest? A palimpsest is a manuscript on which later writing has been superimposed on earlier writing — yet traces of the original remain. It's the perfect metaphor for layered, persistent, evolving memory.


What is Palimpsest?

Palimpsest is a production-grade memory platform for AI agents. Unlike simple chat history or RAG, it gives agents:

  • Long-term persistent memory — survives restarts, works across sessions
  • Semantic search — find ideas by meaning, not just keywords
  • Graph memory — notes link via [[wiki-links]], traverse relationships
  • Security gates — injection detection, contradiction flagging, near-duplicate checks
  • Prospective memory — "remind me in 3 days" — and it actually happens
  • Sleep consolidation — nightly maintenance: archive stale, merge duplicates
  • MCP server — Claude Code, Cursor, any MCP client can read/write memory
  • Observability — audit trails, contamination detection, memory health dashboard
  • Compliance — GDPR Article 17, EU AI Act ready

Architecture

┌─────────────────────────────────────────────┐
│  Your Question                              │
│  "What did we decide about API rate limit?" │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────▼──────────────────────────┐
│  Palimpsest Memory Platform               │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐   │
│  │ Semantic │ │ Keyword  │ │  Graph   │   │
│  │ Search   │ │ Search   │ │ Search   │   │
│  │(pgvector)│ │(tsvector)│ │(wikilinks│   │
│  └────┬─────┘ └────┬─────┘ └────┬─────┘   │
│       └────────────┬────────────┘          │
│                    │                       │
│            ┌───────▼────────┐             │
│            │ RRF Merge      │             │
│            └───────┬────────┘             │
│                    │                       │
│  ┌─────────────────▼────────────────────┐ │
│  │ Markdown Vault (source of truth)     │ │
│  │ ~/Palimpsest/vault/*.md             │ │
│  └─────────────────────────────────────┘ │
└─────────────────────────────────────────────┘

Quick Start

# Clone the platform
git clone https://github.com/M4F-S/palimpsest
cd palimpsest

# Install (SQLite works out of the box)
pip install -e ".[dev]"

# Or with PostgreSQL + pgvector for production:
docker run -d --name palimpsest-pg -p 15432:5432 \
  -e POSTGRES_USER=palimpsest \
  -e POSTGRES_PASSWORD=palimpsest_secret \
  -e POSTGRES_DB=palimpsest \
  ankane/pgvector:latest
from palimpsest import UnifiedMemorySystem

memory = UnifiedMemorySystem()

# Save a memory
memory.remember(
    title="API Rate Limit Decision",
    content="100 req/min with burst to 200. Alert if p95 > 200ms.",
    tags=["api", "decision"],
    salience=0.9
)

# Search by meaning
results = memory.recall("rate limiting policy", mode="hybrid", top_k=5)

# Schedule a reminder
memory.remind_me("Review API metrics", "2026-07-07T09:00:00", recurring="weekly")

# Run nightly maintenance
memory.consolidate()

Kimi Skill

For the Kimi AI skill (minimal installable version):

👉 github.com/M4F-S/kimi-palimpsest-skill

The skill repo contains only the essential files for Kimi integration: SKILL.md, palimpsest/ package, and tests.

Documentation

Document Purpose
PLATFORM_BUILDER_BRIEF.md Architecture and design decisions
SETUP.md Installation and configuration guide
PALIMPSEST_V3_REBUILD_PLAN.md V3 roadmap and rebuild strategy
PALIMPSEST_STRATEGIC_DECISIONS.md Strategic decisions log
RESEARCH_*.md Research reports and competitive analysis
TEST_REPORT.md Test coverage and results
CHANGELOG.md Version history
CONTRIBUTING.md Contribution guidelines

Environment Variables

Variable Default Purpose
MEMORY_DB_DSN (none) PostgreSQL connection string
MEMORY_SQLITE_PATH ~/.palimpsest/palimpsest.db SQLite database path
MEMORY_VAULT_PATH ~/Documents/Kimi/Workspaces/Palimpsest/vault Markdown vault directory
EMBEDDING_MODEL all-MiniLM-L6-v2 Sentence-transformers model
OLLAMA_URL http://localhost:11434 Ollama server for embeddings

License

Apache 2.0

from github.com/M4F-S/mnemosyne

Установка Mnemosyne

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/M4F-S/mnemosyne

FAQ

Mnemosyne MCP бесплатный?

Да, Mnemosyne MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Mnemosyne?

Нет, Mnemosyne работает без API-ключей и переменных окружения.

Mnemosyne — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Mnemosyne в Claude Desktop, Claude Code или Cursor?

Открой Mnemosyne на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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