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 179 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/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 Docker. No Python. No cloud.
First run downloads the embedding model (~188MB, one-time) and you're running.
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 | Mnemosyne | Mem0 | AgentMemory | Letta | Zep | Supermemory | Engram | |
|---|---|---|---|---|---|---|---|---|
| Language | Rust (single binary) | Python (pip) | Python | TypeScript | Python | Python | TypeScript | Go (single binary) |
| Setup | One binary (curl | sh) |
pip install + venv | pip + Docker + Qdrant | npm + Docker (iii-engine) | pip + Docker + Postgres | pip + Docker + Neo4j | Cloudflare Workers + Postgres | One binary |
| API keys | ❌ None | ⚠️ For remote embeddings | ✅ OpenAI/LLM | ✅ LLM key | ✅ LLM key | ✅ LLM key | ⚠️ Cloudflare account | ❌ None |
| Works offline | ✅ Fully | ⚠️ Optional | ❌ Cloud embedding | ❌ Needs LLM | ❌ Needs LLM | ❌ Needs LLM + vector DB | ⚠️ Self-hostable but needs infra | ✅ Fully |
| Search | Hybrid (Vector + FTS5 + RRF) | sqlite-vec + FTS5 | Vector + Graph | Vector + Graph | Vector | Temporal 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 (built-in collaboration) | ⚠️ Shared API | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| Time-travel | ✅ Native point-in-time | ⚠️ Temporal triples | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| MCP server | ✅ JSON-RPC + HTTP | ✅ stdio + SSE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| Your data | ✅ Never leaves machine | ✅ Local-first | ⚠️ Sent to LLM cloud | ⚠️ Sent to LLM cloud | ⚠️ 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 | Apache 2.0 | Apache 2.0 |
Uteke vs Mnemosyne: Both are local-first with semantic + FTS5 search. Mnemosyne is the closest competitor (~1.5K stars, Python). Uteke wins on single binary (no Python runtime), rooms, time-travel queries, and zero runtime dependencies.
Uteke vs Engram: Both are single-binary, offline, no-API-key tools. But Engram 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, 10× the features.
Uteke vs Supermemory: Supermemory (28K stars) markets itself as "run fully locally" but requires Cloudflare Workers + Postgres. Uteke is a true single binary with zero infrastructure. No Workers, no Postgres, no Cloudflare account.
Uteke vs AgentMemory/Mem0/Letta/Zep: Those are powerful — but all require cloud LLM API keys and Docker infrastructure. Your data goes to OpenAI/Anthropic. Uteke runs fully offline with local ONNX embeddings. No Docker, no Python, no API keys.
| 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.
| Feature | What it does |
|---|---|
| 🧠 Hybrid Search | Vector similarity + FTS5 full-text search, merged by Reciprocal Rank Fusion (RRF). Finds by meaning AND exact keywords. |
| 🏠 Rooms | Group memories by context (meetings, projects, clients) with author attribution. |
| ⏳ Time-travel | Recall memories as they existed at any point in time. uteke recall "deploy" --at 2025-01-15 |
| 🏷️ Rich Metadata | Tags, entities, categories, key:value pairs on every memory. |
| 🧩 Memory Types | Typed categories (fact, procedure, decision, etc.) with auto-inference. |
| ✏️ Partial Updates | Update content, tags, metadata, importance, or type without full rewrite. |
| 📎 Citations | Source attribution on every memory (URL, file, user, import batch). |
| Feature | What it does |
|---|---|
| 🔗 Relationship Graph | Link memories with typed edges (supersedes, contradicts, references). Auto-backlinks. |
| 🔗 Cross-Entity Linking | Bidirectional memory↔document references via [[doc-slug]] wikilinks. |
| 🤖 Cosine Auto-Linking | Automatically creates similar_to edges between related memories. |
| 📉 Smart Decay | Composite importance scoring. Pin what matters, let stale memories fade. |
| 📈 **Sali |