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

Project description

kioku-agent-kit-lite

Personal memory engine for AI agents — zero Docker, SQLite-everything.

PyPI Python License: MIT

kioku-agent-kit-lite là phiên bản nhẹ của kioku-agent-kit, thiết kế để chạy hoàn toàn local, không cần Docker, không cần server nào. Mọi thứ đều trong SQLite.

Tính năng

  • Tri-hybrid search — BM25 (FTS5) + Vector (sqlite-vec) + Knowledge Graph (SQLite)
  • Zero Docker — không cần ChromaDB, FalkorDB hay Ollama server
  • FastEmbed ONNX — embedding local, offline-capable (intfloat/multilingual-e5-large)
  • Agent-driven KG — agent tự extract entities → kg-index (không cần built-in LLM)
  • CLIkioku-lite save, search, kg-index, setup, init
  • Python API — import trực tiếp KiokuLiteService vào code
  • Multilingual — tiếng Việt, tiếng Anh và 100+ ngôn ngữ khác
  • 🔜 MCP server — planned (v0.2)

Cài đặt

pip install "kioku-lite[cli]"

Quick Start

CLI

# Lưu memory
kioku-lite save "Hôm nay họp với Hùng về dự án Kioku. Rất productive." --mood work

# Tìm kiếm
kioku-lite search "Hùng làm gì gần đây"

# Index knowledge graph (agent tự extract entities)
kioku-lite kg-index <content_hash> \
  --entities '[{"name":"Hùng","type":"PERSON"},{"name":"Kioku","type":"PROJECT"}]' \
  --relationships '[{"source":"Hùng","rel_type":"WORKS_ON","target":"Kioku"}]'

Python API

from kioku_lite.service import KiokuLiteService

svc = KiokuLiteService()

# Save
result = svc.save_memory("Hôm nay gặp Lan và Minh ở cà phê.", mood="happy")
print(result["content_hash"])

# Search (BM25 + Vector + KG)
results = svc.search("Lan gặp ai hôm nay", limit=5)
for r in results:
    print(r["content"], r["score"])

Agent Workflow

Workflow mẫu để AI agent (Claude Code, OpenClaw,...) sử dụng kioku-lite:

1. Agent lưu memory:
   hash = kioku-lite save "..." --mood work

2. Agent extract entities từ context (dùng LLM riêng của agent):
   entities = [{"name": "Hùng", "type": "PERSON"}, ...]
   rels     = [{"source": "Hùng", "rel_type": "WORKS_ON", "target": "Kioku"}]

3. Agent index KG:
   kioku-lite kg-index <hash> --entities '<json>' --relationships '<json>'

4. Khi cần tìm kiếm:
   kioku-lite search "Hùng làm gì" --limit 5

Thiết kế: kioku-lite không tự gọi LLM — agent chịu trách nhiệm extract entities từ context. Điều này tách biệt hoàn toàn memory store khỏi LLM dependencies.

Cấu hình

Cấu hình qua environment variables với prefix KIOKU_LITE_:

Variable Default Mô tả
KIOKU_LITE_USER_ID default User ID để phân tách dữ liệu
KIOKU_LITE_DATA_DIR ~/.kioku-lite/data Thư mục chứa SQLite DB
KIOKU_LITE_MEMORY_DIR ~/.kioku-lite/memory Thư mục chứa markdown files
KIOKU_LITE_EMBED_PROVIDER fastembed fastembed | ollama | fake
KIOKU_LITE_EMBED_MODEL intfloat/multilingual-e5-large Model name
KIOKU_LITE_EMBED_DIM 1024 Embedding dimensions
KIOKU_LITE_OLLAMA_BASE_URL http://localhost:11434 Ollama URL (nếu dùng Ollama)

Hoặc dùng file .env:

KIOKU_LITE_USER_ID=phuc
KIOKU_LITE_EMBED_PROVIDER=fastembed
KIOKU_LITE_EMBED_MODEL=intfloat/multilingual-e5-large

Benchmark

Benchmark so sánh với kioku-agent-kit (full Docker):

Metric kioku full kioku-lite
Search latency ~2–3s ~1.2s lite nhanh hơn
Precision@3 0.60 0.60 Ngang bằng
Recall@5 1.04 0.89 kit nhỉnh
Infrastructure 3 Docker containers Zero lite

Với cùng embedding model (intfloat/multilingual-e5-large) và cùng Claude Haiku cho KG extraction, kioku-lite đạt chất lượng search bằng kioku full trong khi không cần bất kỳ Docker container nào.

Chi tiết: docs/benchmark.md

Architecture

Xem docs/architecture.md để hiểu thiết kế chi tiết.

Development

git clone https://github.com/phuc-nt/kioku-agent-kit-lite
cd kioku-agent-kit-lite
python -m venv .venv && source .venv/bin/activate
pip install -e ".[cli,dev]"
pytest

License

MIT

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