Mnemosyne OS
Mnemosyne OS is a free, local-first memory layer for AI applications. It runs on a user's machine and provides persistent, encrypted memory that any connected model can draw on across sessions, projects, and years. The desktop application is available for Windows, macOS, and Linux, and requires no account or mandatory server. Users control what enters, moves, and leaves memory; making a memory permanent requires explicit user action, and no agent can persist memory from the network. Under the hood, Mnemosyne OS uses a memory graph with nineteen selectable topological surfaces, such as Trefoil, Klein, Möbius, Lorenz, Dini, and Clifford, to organize and recompose memories. It supports multi-model configurations, with separate models for memory and intelligence, and can run 100% locally, in the cloud, or in hybrid mode. Compatible providers include Gemini, Mistral, DeepSeek, and fully local GGUF models. Every SDK and MCP request carries a signed token, and memory is encrypted at rest once armed. The product includes a Neural Map interface for navigating memories as a graph, a Sovereign Notes editor for local classified notes, and a cartridge hub (MnemoHub) for extending functionality. It also offers importers for notes from AFFiNE, Obsidian, Logseq, Markdown, and Notion, plus integrations such as memory for Claude and Vertex AI. The project maintains a measured benchmark of 77.1% for memory performance.
- Category
- Model Serving
- Pricing
- Free
Problem solved
AI assistants and agents typically lack persistent, user-controlled memory across sessions. Mnemosyne OS solves this by providing a local-first memory layer that lives on the user's machine, is encrypted at rest, and is governed by explicit user decisions about what enters, moves, and leaves. It lets any connected model draw on long-term personal or professional knowledge while preserving privacy and data sovereignty.
Use cases
- Give a local AI assistant long-term memory of your documents and notes across sessions
- Let lawyers query case files offline under privilege
- Chat with your documents using a local memory graph
- Build a local second brain with notes imported from Obsidian, Logseq, or Markdown
- Connect multiple models such as Gemini, Mistral, DeepSeek, or local GGUF models to a shared memory layer
- Use MnemoHub cartridges to extend memory for specific workflows
Audience
- Developers
- Data Scientists
- Researchers
- Business Users
- Hobbyists
Pros
- Local-first: memory stays on your machine and works offline
- No account or mandatory server required
- User controls memory permanence; agents cannot persist memory from the network
- Multi-model support: local, cloud, or hybrid with Gemini, Mistral, DeepSeek, and GGUF
- Free to download and use
Cons
- Requires manual setup of models and API keys
- Desktop app only; no mobile or web version
- Memory graph interface may have a learning curve
- Ecosystem and cartridges are still evolving
- No built-in cloud sync due to local-first design
Official website: https://mnemosyne-os.io/
Canonical LambdaBase page: https://www.lambdabase.com/tools/mnemosyne-os