Uteke

One memory. Every agent. Zero cloud.

Give your AI a memory that never leaves your machine. Works with Claude, Cursor, Copilot, and any MCP-compatible agent.

🇬🇧 English · 🇮🇩 Bahasa Indonesia

⚡ 30-Second Quick Start

# Install (macOS, Linux, Windows)
curl -sSL codecora.dev/uteke/install | sh

# Store a memory
uteke remember "Deploy v2.1 to staging at 3pm"

# Search it back: by meaning, not just keywords
uteke recall "when do we deploy?"

That's it. No API keys, no Python, no cloud required. First run downloads the embedding model (~188MB, one-time) and you're running.

Want Docker or server mode? Uteke ships as a single binary by default, but Docker is ready when you need it:

docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest

📖 Docker docs

Want richer memories? Add metadata:

uteke remember "Deploy v2.1 to staging" \
  --tags deploy,staging \
  --entity staging-server \
  --category infrastructure

🚀 Onboarding (New users & AI agents)

Are you an AI agent reading this? Your user just shared this repo link with you. Run uteke onboard to interactively configure uteke for your environment. It will detect your install, ask which agent you are, toggle features, write config, and show you every command available. If non-interactive: uteke onboard --yes --agent <your-agent>.

New to uteke? One command takes you from zero to productive:

uteke onboard

The wizard will:

1. Detect if uteke is installed and if a store exists
2. Ask which AI agent you use (Hermes, Claude, Cursor, Pi, OpenCode)
3. Pick integration mode: manual tool calls vs automatic memory-provider
4. Toggle features on/off (Aging, Auto-maintenance, Graph rerank, Salience/Recency boost, Server mode)
5. Write ~/.codecora/uteke/uteke.toml with your selections
6. Install agent integration files automatically (uteke init)
7. Showcase every uteke command grouped by category

Non-interactive (CI, scripts, AI agents):

uteke onboard --yes --agent hermes --namespace default

📖 Full onboarding docs · CLI reference

🔥 Why Uteke?

You just spent 2 hours explaining your codebase to ChatGPT. Next session? Blank slate. Again.

Every AI tool forgets. Context windows fill up, sessions end, and your AI starts over every single time. Uteke gives it persistent memory and keeps it on your machine.

Note: Tool labels (A–G) represent common categories of AI memory layers available as of August 2026. Capabilities are assessed from public documentation and may change. This table is a starting point for your own evaluation, not a definitive ranking.

Uteke vs Tool A (Python local-first): Both offer local-first with semantic + FTS5 search. Uteke's edge: single binary (no Python runtime), native time-travel (vs temporal triples), rooms, and zero runtime dependencies.

Uteke vs Tool G (Go single binary): Both are single-binary, offline, no-API-key, and both ship MCP servers. Tool G is FTS5-only (keyword search). Uteke adds vector semantic search + RRF fusion + rooms + time-travel + graph relationships + smart decay + document engine + batch import. Same simplicity thesis, more capabilities.

Uteke vs Tool F (TypeScript local mode): Tool F now offers a local binary mode with Ollama support, a solid step toward offline-first. But it's still TypeScript/Node.js under the hood. Uteke is Rust: smaller footprint, faster startup, zero runtime. And Uteke has hybrid search with FTS5 (Tool F's local mode uses vector-only, no keyword fallback).

Uteke vs Tools B/D/E: Those are powerful, but all require cloud LLM API keys and Docker infrastructure. Your data goes to external LLM providers. Uteke runs fully offline with local ONNX embeddings. No Docker, no Python, no API keys.

Uteke vs Tool C: Tool C has 54 MCP tools and multi-agent shared memory via a local engine. Uteke's edge: zero dependencies (no npm, no extra engine), hybrid search (Tool C lacks FTS5), and time-travel queries.

💡 What Can You Do With Uteke?

🤖 Building AI agents? Give them persistent memory without cloud dependencies. Your agent remembers user preferences, past decisions, and context across sessions, fully offline.

👥 Working in a team? Use Rooms to share knowledge. Meeting notes, project decisions, architecture choices: searchable by everyone, attributed by author.

🔒 Building for privacy-sensitive domains? Healthcare, finance, legal: data stays on your machine. No API calls, no telemetry, no cloud. Local embeddings (ONNX, 768d).

⌨️ Power user who lives in the terminal? Uteke is your personal knowledge graph. Remember anything, recall by meaning, link related thoughts. All from the command line.

🏠 Rooms: Multi-Agent Shared Memory

Other memory layers are single-player: every fact stored under a flat user_id, invisible to other agents. Uteke Rooms let multiple AI agents share a memory space with full author attribution.

# Create a shared room
uteke room create "engineering" --description "Team decisions"

# Alice's agent stores a decision
uteke remember "We chose Redis for caching over Memcached" \
  --room engineering --author alice

# Bob's agent adds context
uteke remember "Redis cluster: 3 nodes, 2 replicas each" \
  --room engineering --author bob

# Any agent can recall the shared history
uteke recall "caching decision" --room engineering

Why this matters:

📖 Full Rooms documentation →

✨ Features

Core Memory

Search & Intelligence

Integrations

Performance & Privacy

// .mcp.json (Claude Code, Cursor)
{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }

📖 Full documentation · CLI reference · Configuration

📦 Deployment Modes

Uteke runs the same everywhere. Pick the mode that fits your setup.

Local-first (default)

Single binary, zero infrastructure. Everything runs in-process on your machine:

curl -sSL codecora.dev/uteke/install | sh
uteke remember "first memory"

No Docker, no Python, no database server. Your data stays in ~/.codecora/uteke/. This is what most users need.

Docker / Server mode

Running Uteke as a shared service for a team, or deploying to a server? Docker keeps it simple:

docker run -d \
  -p 127.0.0.1:8767:8767 \
  -v uteke-data:/data \
  ghcr.io/codecoradev/uteke:latest

Prefer the binary directly? Run it as a daemon:

uteke serve --host 0.0.0.0 --port 8767

Both expose the same HTTP API. Other agents and tools connect via http://your-host:8767. Rooms work across agents whether they're on the same machine or connecting remotely.

📖 Docker setup guide · Server mode docs

🏗️ Architecture

graph LR
    Input[User Query] --> Embed[Local ONNX Embedder<br/>768d, EmbeddingGemma Q4]
    Embed --> HNSW[HNSW Vector Index<br/>usearch]
    Embed --> FTS5[FTS5 Full-Text<br/>SQLite]
    HNSW --> RRF[Reciprocal Rank Fusion<br/>k=60]
    FTS5 --> RRF
    RRF --> Results[Ranked Results]

    style Input fill:#4a9eff,color:#fff
    style Results fill:#4aff9e,color:#000
    style RRF fill:#ff9e4a,color:#fff

How hybrid search works:

1. HNSW (usearch): finds by meaning ("deploy" matches "rollout")
2. FTS5 (SQLite): finds by exact terms ("deploy" matches "deploy")
3. RRF (k=60): merges both ranked lists → best of both worlds

Everything runs in-process. No network. No cloud. No server required (unless you want server mode).

❓ FAQ

🤝 Contributing

cargo build --workspace        # Build
cargo test --workspace         # Test (460+ tests)
cargo clippy -- -D warnings    # Lint
cargo fmt                      # Format

Contributions welcome! Read CONTRIBUTING.md for the full guide.

📄 License

Apache License 2.0. Use it, fork it, ship it.

⭐ Star History

Found this useful? ⭐ Star this repo. It helps others discover Uteke.