by codecoradev
🧠 The Brain for Your AI — Local-first memory engine for AI agents. Store, recall, and search memories with semantic embeddings. Single Rust binary, zero config, fully offline.
# Add to your Claude Code skills
git clone https://github.com/codecoradev/utekeLast scanned: 8/5/2026
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}uteke is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by codecoradev. 🧠 The Brain for Your AI — Local-first memory engine for AI agents. Store, recall, and search memories with semantic embeddings. Single Rust binary, zero config, fully offline. It has 225 GitHub stars.
Yes. uteke passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/codecoradev/uteke" and add it to your Claude Code skills directory (see the Installation section above).
uteke is primarily written in Rust. It is open-source under codecoradev on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh uteke against similar tools.
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# 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
Want richer memories? Add metadata:
uteke remember "Deploy v2.1 to staging" \
--tags deploy,staging \
--entity staging-server \
--category infrastructure
| Method | Command |
|---|---|
| Homebrew | brew install codecoradev/tap/uteke |
| Cargo | cargo install uteke-cli |
| Docker | docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest |
| Binary | GitHub Releases (macOS, Linux, Windows) |
| Windows (PowerShell) | `powershell -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/codecoradev/uteke/main/install.ps1 |
📖 Full install guide · Docker docs
Are you an AI agent reading this? Your user just shared this repo link with you. Run
uteke onboardto 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:
~/.codecora/uteke/uteke.toml with your selectionsuteke init)Non-interactive (CI, scripts, AI agents):
uteke onboard --yes --agent hermes --namespace default
📖 Full onboarding docs · CLI reference
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.
| Uteke | Tool A | Tool B | Tool C | Tool D | Tool E | Tool F | Tool G | |
|---|---|---|---|---|---|---|---|---|
| Language | Rust (single binary) | Python (pip) | Python | TypeScript | TypeScript | Python | TypeScript | Go (single binary) |
| Setup | One binary (curl | sh) |
pip install + venv | pip + Docker + Qdrant | npm + iii-engine | npm (Node.js) | pip + Docker + Neo4j | Cloud or local binary | One binary |
| API keys | ❌ None | ⚠️ For remote embeddings | ✅ OpenAI/LLM | ✅ LLM key | ✅ LLM key | ✅ LLM key | ⚠️ Cloud only | ❌ None |
| Works offline | ✅ Fully | ⚠️ Optional | ❌ Cloud embedding | ❌ Needs LLM | ❌ Needs LLM | ❌ Needs LLM + vector DB | ✅ Local binary + Ollama | ✅ Fully |
| Search | Hybrid (Vector + FTS5 + RRF) | sqlite-vec + FTS5 | Vector + Graph | Vector + Graph | Vector | Hybrid (semantic + keyword + graph) | Vector + rerank | FTS5 only |
| Recall speed | ~45ms | ~50ms+ | Network round-trip | Network round-trip | Network round-trip | Network round-trip | Network round-trip | ~Fast (local) |
| Multi-agent | ✅ Rooms (shared memory, cross-agent recall, author attribution) | ✅ Multi-agent surface | ❌ | ✅ Shared server | ✅ Multi-agent groups | ❌ | ❌ | ⚠️ Shared via MCP |
| Time-travel | ✅ Native point-in-time | ⚠️ Temporal triples | ❌ | ❌ | ❌ | ✅ Temporal graphs | ❌ | ❌ |
| MCP server | ✅ JSON-RPC + HTTP | ✅ stdio + SSE | ❌ | ✅ 54 MCP tools | ❌ | ✅ Graphiti MCP | ✅ Open-source MCP | ✅ stdio MCP |
| Your data | ✅ Never leaves machine | ✅ Local-first | ⚠️ Sent to LLM cloud | ✅ Local (iii-engine) | ⚠️ Sent to LLM cloud | ⚠️ Sent to LLM cloud | ⚠️ Cloudflare-hosted | ✅ Local |
| License | Apache 2.0 | MIT | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 | MIT | MIT |
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.
| Metric | Result | Notes |
|---|---|---|
| Recall latency (10K memories) | 42ms P50, 50ms P95 | Flat from 100 to 10K memories (HNSW O(log N)) |
| Insert throughput | 6-22 ops/s | CPU-bound (ONNX embedding inference) |
| Storage per memory | ~10KB | SQLite + HNSW, scales linearly |
| LongMemEval Recall@5 | 0.958 | 12-question diverse sample, EmbeddingGemma Q4 |
Full benchmarks: uteke bench --counts 100,1000,10000 --json · Benchmark details · LongMemEval results
🤖 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.
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 ali