Local RAG Engine
SQLite + sqlite-vec vector search. Every interaction is semantically indexed for O(log n) retrieval. Cross-mode: use a cloud AI for chat while a local model handles embeddings.
Memo is a private, local-first AI second brain. It runs models locally, remembers everything in a vector store, and never phones home. Chat through external APIs when you need more power, or stay 100% offline — your call.
one-click installer · llama.cpp bundled · no terminal required
Zero telemetry, no training on your chats, no cloud dependency. Optional encrypted backup only if you turn it on.
Every interaction is embedded and indexed in a local vector store. Relevant context is retrieved automatically on each turn.
Run chat through a powerful external API while a tiny local model handles embeddings — or stay 100% offline. Your call, per model.
A real Flutter desktop app on Windows, Linux, and macOS, with a mobile companion. It feels like software you own.
A real RAG memory and a tool-calling agent, paired with an interface a first-time user can navigate.
SQLite + sqlite-vec vector search. Every interaction is semantically indexed for O(log n) retrieval. Cross-mode: use a cloud AI for chat while a local model handles embeddings.
12+ built-in tools with a sandboxed execution pipeline. File read/write, shell commands, web search, symlink protection, per-tool timeouts, and a 6-policy permission system.
A Claude Code-style terminal chat with 15+ slash commands, persistent sessions, and an autonomous CEO/Worker task loop. Permission prompts surface inline — no need to leave the terminal.
A chief model plans the task, 8 specialist roles execute in parallel, and the chief synthesizes the result. Assign different models per role — Claude for reasoning, Gemini for speed.
OpenAI, Gemini, Claude, Grok, Groq, OpenRouter, and Ollama — with automatic fallback chain. Switch mid-conversation with /model. API keys encrypted with AES-256-GCM.
Fully managed server lifecycle: auto-download, GPU offloading (NVIDIA/AMD/Metal), VRAM detection. No Docker, no terminal — everything from one native desktop app.
QR pairing with whatsmeow. Read, search, and reply to messages. AI drafts responses, summarizes threads. Profile photos, group names, auto-reconnect. Everything stays local.
E2E-encrypted Google Drive sync (AES-256-GCM, PBKDF2). Auto-sync every N messages. Full .memo archive export. Double-confirm wipe. Your data is encrypted before it leaves your machine.
Thin Flutter companion for Android/iOS over LAN or built-in ngrok tunnel. Built-in Tailscale tunnel for private networking. Access your brain from anywhere with token auth.
Every message flows through a six-stage pipeline — embedding, search, context construction, and response — all on your machine, in milliseconds.
Typed, spoken, or from WhatsApp.
Converted into a 768-dimension vector by a local embedding model.
Cosine similarity against thousands of memories. Top‑K in milliseconds.
Relevant context silently placed in the system prompt.
LLM streams back an informed response via SSE.
Message and response asynchronously embedded. Memory grows.
Memo adapts to how you work — whether you are shipping code, writing a thesis, or just want an AI that respects your privacy.
Drop in your codebase. Ask the agent to refactor modules, write tests, or explain a complex function. Orchestra mode splits work across specialist models — one writes React, another writes Go. All offline.
Upload papers, notes, and references. RAG memory connects ideas across weeks of work. Ask "what was that citation about reinforcement learning?" and get the exact paragraph — no keyword search needed.
Your conversations, files, and memories never leave your machine. No account, no cloud dependency. Use external APIs when you need more power — keys are AES-256 encrypted on disk. You hold all the cards.
One click installs Memo with llama.cpp bundled. Open the app, pick a model from Discover, and start chatting.