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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}See how uteke compares with popular alternatives.
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 259 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 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" } } }
| 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
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
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.
| Metric | uteke v0.18.2 | agentmemory¹ | BM25-only¹ |
|---|---|---|---|
| recall_any@5 (evidence in top-5) | 98.4% | 95.2% | 86.2% |
| recall_any@10 | 98.8% | 98.6% | 94.6% |
| recall_all@5 (all evidence, strict) | **88.0%**² | 88.2% MRR³ | n/a |
| LLM tokens / query | 0 | 0 | 0 |
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):
| knowledge-update | single-session | temporal | multi-session |
|---|---|---|---|
| 100% | 96.7–98.2% | 99.2% | 98.3% |
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
🤖 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.
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 | Fusion (weighted RRF of vector + hybrid; hybrid = HNSW + 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 dat