uteke

Why Uteke

On Monday you tell your agent how you deploy. By Thursday it has no idea. Every session starts from zero, and every agent keeps its own half-memory.

Uteke gives all of them one long-term memory that runs on your own machine:

• Verifiable recall. 98.2% recall@5 on LongMemEval-S, with the stricter numbers shown next to it and the raw outputs in this repo.
• Fast and cheap to query. ~45 ms warm recall, 0 LLM tokens per query, CPU only.
• One binary. No Python, no Docker, no vector database, no API key. The embedding model (~200 MB) downloads once.
• One memory, every agent. Claude Code, Claude Desktop, Cursor, Hermes and any MCP client share the same store. Rooms record which agent or person wrote what.
• Finds by meaning and by keyword. Local vectors and SQLite full-text search, fused into one ranking (how it works).
• Remembers over time. Point-in-time recall (--at), a relationship graph, a document engine, and decay for stale memories.
• Private by default. No telemetry. Secrets such as API keys are masked before a memory is stored.
• Open source. Apache-2.0, cosign-signed releases.

Quick start

# Install (macOS Apple Silicon, Linux; Windows: see below)
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?"

# Look at what is stored, in a read-only browser page
uteke view

No API keys, no Python, no cloud account. The first run downloads the embedding model (~200 MB, one time).

Give your agent (Claude Code, Cursor, Hermes) the same memory with 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 it up. 📖 Onboarding

Rather not run anything locally? Uteke Cloud runs the same engine for you: add one URL to Claude, sign in, done. Free during the open beta.

📖 Full install guide · Docker docs

Self-host or Cloud

Same engine, two ways to run it.

Choose self-host if your data must stay on your machine, or you want to run Uteke inside your own infrastructure. Choose Cloud if you want one memory across several devices and agents without running a server.

Benchmarks

LongMemEval-S (ICLR 2025) hides the facts an agent needs across ~115 chat sessions per question and checks whether retrieval finds them. 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 Their published numbers on the same benchmark and 500-question split, on their own recall_any@5 basis (head-to-head notes); evaluation setups differ. ² Strict = every gold session in the top 5; 65% of questions have more than one. Max possible 99.4%. ³ agentmemory publishes MRR 88.2%, which is a different metric.

Stable across releases (full 500 questions, scored from committed raw, RESULTS.md):

By question type (recall_any@5, v0.20.0):

Every question's evidence lands in the top 50 (zero misses). What's left is ordering when a question needs several sessions at once: strict recall_all@5 is 88.0% against a 99.4% ceiling, and most misses lose exactly one session.

🎯 Don't trust our benchmark. Run it yourself. Public dataset, committed raw outputs for every validated release, scoring you can recompute in ~20 lines of Python. Verifying the committed results takes a minute and needs no embedder; re-running all 500 questions costs about $10. 👉 benchmarks/longmemeval/REPRODUCING.md · 🧾 Receipts for the headline number (v0.19.0 run)

uteke bench --counts 100,1000,10000 measures latency and throughput on your own machine. 📖 Benchmark docs

Connect your agent