kioku-lite 0.1.22

Personal memory engine for AI agents — zero Docker, SQLite-everything (BM25 + sqlite-vec + SQLite graph)

kioku-lite

Personal memory engine for AI agents. Tri-hybrid search, zero Docker, single SQLite file.

kioku-lite — kioku (記憶) means "memory" in Japanese: 記 (ki) to record, 憶 (oku) to remember. A lightweight, fully local memory engine that gives AI agents long-term memory with causal reasoning, using tri-hybrid search (BM25 + Vector + Knowledge Graph) in a single SQLite file.

Homepage · Docs · The Story · PyPI

Read the intro: Why I built a Knowledge Graph memory engine for my AI agents — also in 日本語 and Tiếng Việt

---

Why kioku-lite

Most agent memory systems store flat text or vectors. They can't answer "Why was I stressed last month?" because they don't track how events, emotions, and decisions connect. kioku-lite solves this with a Knowledge Graph on top of traditional search — running 100% local with no LLM calls.

System	Infrastructure	LLM required	Search	Knowledge Graph
Mem0	Cloud-managed	Yes (every write)	Vector + Graph	✅ Managed
Claude Code	Flat markdown files	No	Context window only	❌
OpenClaw	SQLite per-agent	No	Semantic (embedding)	❌
kioku-lite	Single SQLite file	Agent-driven, zero extra	Tri-hybrid (BM25 + vector + KG)	✅ Agent-driven

vs Mem0: Mem0 targets production apps — managed cloud infra, automatic LLM extraction on every write. kioku-lite targets personal use — fully local, fully offline, the agent is already an LLM so no extra call needed. Full comparison: docs

Features

• Tri-hybrid search — BM25 (FTS5) + Vector (sqlite-vec) + Knowledge Graph
• Zero infrastructure — no Docker, no ChromaDB, no external servers
• Fully offline — FastEmbed ONNX embedding, no API keys needed
• Agent-driven KG — agent extracts entities and indexes them (no built-in LLM)
• CLI + Python API — works with any agent that runs shell commands
• Built-in personas — companion (emotion tracking) and mentor (decision tracking)
• Multilingual — 100+ languages via multilingual-e5-large

Getting started

Install and connect to your agent in 3 commands:

pipx install "kioku-lite[cli]"
kioku-lite init --global
kioku-lite install-profile companion

That's it. Your agent now has long-term memory with Knowledge Graph.

Full setup guides for each agent type:

• Claude Code / Cursor / Windsurf — copy-paste the guide, agent does the rest
• OpenClaw — auto-generates SOUL.md + TOOLS.md

Architecture

Agent (Claude Code, Cursor, …)
  │
  ├─ save "..."          ──→  SQLite FTS5 + sqlite-vec + Markdown backup
  ├─ kg-index <hash>     ──→  GraphStore (nodes, edges, aliases)
  └─ search "..."        ──→  BM25 ∪ Vector ∪ KG → RRF rerank

kioku-lite never calls an LLM. The agent extracts entities from its own context, keeping the memory engine 100% local and LLM-agnostic.

Deep dive: System Architecture · Write Pipeline · Search Pipeline · KG Open Schema

Development

git clone https://github.com/phuc-nt/kioku-agent-kit-lite
cd kioku-agent-kit-lite
pip install -e ".[cli,dev]"
pytest

License

MIT © 2026 Phuc Nguyen