The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Uteke
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.
Install
$
docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latestClaude Code
claude mcp add uteke -- docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latestCursor / Claude Desktop (mcp.json)
{
"mcpServers": {
"uteke": {
"command": "docker",
"args": [
"run",
"-d",
"-p",
"127.0.0.1:8767:8767",
"-v",
"uteke-data:/data",
"ghcr.io/codecoradev/uteke:latest"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Uteke One memory. Every agent. Local-first.
Give your AI a memory that never leaves your machine. Works with Claude, Cursor, Copilot, and any MCP-compatible agent.
98.2% 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" } } }
📦 More install options & Docker
| Method | Command |
|---|---|
| Homebrew | brew install codecoradev/tap/uteke (macOS Apple Silicon & Linux) |
| 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
📊 Benchmarks: 98.2% 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.
| Metric | uteke v0.19.0 | agentmemory¹ | BM25-only¹ |
|---|---|---|---|
| recall_any@5 (evidence in top-5) | 98.2% | 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 |
Per-release stability (full 500 questions, scored from committed raw — RESULTS.md):
| Release | v0.16.0 | v0.17.0 | v0.19.0 |
|---|---|---|---|
| recall_any@5 | 98.2% | 98.4% | 98.2% |
| strict recall_all@5 | 88.0% | 88.0% | 88.0% |
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):
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