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.

98.4% LongMemEval-S recall@5 · ~45 ms warm query · 0 LLM tokens per query · CPU-only · fully offline

🇬🇧 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 AND by keyword
uteke recall "when do we deploy?"

That's it. No API keys, no Python, no cloud required. First run downloads the embedding model (~200MB, one-time) and you're running.

Want your agent (Claude Code, Cursor, Hermes) to use it? One line:

// .mcp.json
{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }

New to uteke (or you are an AI agent)? Run uteke onboard. It detects your setup, asks which agent you use, and wires everything up. 📖 Onboarding docs

📊 Benchmarks: 98.4% recall on LongMemEval-S

LongMemEval-S (ICLR 2025) hides the facts an agent needs across ~115 chat sessions per question and checks whether retrieval finds the evidence. 500 hand-curated questions, five memory abilities. Uteke runs the full suite with zero LLM calls in the retrieval path: local embeddings, one CPU, deterministic.

_{¹ agentmemory's published numbers, same benchmark, same 500-question split (their recall_any@5 basis; verified apples-to-apples in our head-to-head). ² Strict = every gold session in top-5; 65% of questions have multiple gold sessions. Mathematical ceiling 99.4%. ³ MRR, not recall_all (not directly comparable; shown for completeness).}

By question category (recall_any@5: the category-level story most tools don't show):

The hard part isn't finding a needle; every question's evidence lands in the top-50 (zero misses). The residual gap is ordering when a question needs several sessions at once: strict recall_all@5 is 88.0% against a 99.4% ceiling.

🎯 Don't trust our benchmark. Run it yourself. The full harness is in this repo: public dataset, committed raw outputs for both releases, deterministic scoring you can recompute in ~20 lines of Python. No embedder needed to verify, ~$10 to re-run the whole 500 questions yourself. 👉 benchmarks/longmemeval/REPRODUCING.md

Also built in: uteke bench --counts 100,1000,10000 for latency/throughput on your own machine. 📖 Full benchmark docs · RESULTS.md

💡 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.

🔥 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.

✨ Features

Core Memory

Search & Intelligence

Integrations

Performance & Privacy

🚀 How It Works

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).

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

docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest
# or: uteke serve --host 0.0.0.0 --port 8767

# 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

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

📚 Documentation

❓ FAQ

🗺️ Roadmap & Editions

The open-source engine stays full-capability. Nothing about the benchmark results above is paywalled. Cloud adds hosted convenience on top.

🤝 Contributing

cargo build --workspace        # Build
cargo test --workspace         # Test (200+ 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.

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