Edition Intelligence Platform
Japan Operations OS for AI agents — 14 knowledge domains covering regulations, protocols, calendar, travel, food culture, language, disaster safety, daily life, and persistent memory. 31 MCP tools via REST + Streamable HTTP.
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EDITION Intelligence Platform
The missing infrastructure for AI agents operating in Japan.
Memory API + Regulation Check API + Procedural Knowledge + MCP Server — purpose-built for Japanese business context.
The Problem
AI agents working with Japanese businesses hit walls that generic tools can't solve:
- Keigo (敬語): A sentence like "ワインをお持ちすれば喜ばれるかと存じます" hides the subject, uses layered honorifics, and expresses uncertainty — generic NLP treats this as noise
- Implicit agreements: Japanese business communication rarely states things directly
- Regulatory maze: 10+ industries with overlapping national/prefectural regulations, most documentation only in Japanese
- No persistent context: Agents forget everything between sessions
What This Does
1. Memory API — Japanese-aware persistent memory
Store episodes, auto-extract structured facts with keigo analysis, social hierarchy detection, and confidence scoring.
Input: "佐藤部長にはワインをお持ちすれば喜ばれるかと存じます"
Output:
Subject: 佐藤 (役職: 部長)
Predicate: 好む
Object: ワイン
Keigo: Level 2 (尊敬語)
Hierarchy: superior
Confidence: 0.7 (推測 — not stated as fact)
Tense: present
Three-layer architecture:
- Episodes — raw conversation logs
- Facts — structured knowledge (auto-extracted via LLM)
- Context — summarized state per entity/topic
2. Regulation API — 10 industries + tourist rules
Pre-built regulatory database covering:
- EC sites, Real estate, Staffing, Food service, Construction
- Healthcare, Finance, Transport, Education, Accommodation
- Tourist categories: Visa, Tax-free, Transit, Medical, Manners
All 10 industries include step-by-step procedural guides (65 total steps) — covering what to do, how, where, required documents, costs, timelines, and common pitfalls.
curl -X POST /api/v1/regulation/check \
-d '{"industry": "food_service", "query": "What licenses do I need to open a restaurant in Tokyo?"}'
3. MCP Server — 8 tools for Claude, Cursor, etc.
| Tool | Description |
|---|---|
memory_store | Store episode + auto-extract facts |
memory_recall | Semantic search across episodes |
memory_facts | List structured facts |
memory_context | Get context summary |
memory_extract | Extract facts from text |
regulation_check | Check regulations by industry |
regulation_industries | List covered industries |
regulation_tourist | Tourist regulation lookup |
Quick Start
Backend
git clone https://github.com/hiroshic9-png/edition.git
cd edition
python3 -m venv venv && source venv/bin/activate
pip install fastapi 'uvicorn[standard]' pydantic sqlalchemy aiosqlite chromadb python-dotenv google-genai
# Set your LLM key (any one of these)
echo 'GEMINI_API_KEY=your_key' > .env
# or ANTHROPIC_API_KEY or OPENAI_API_KEY
python -m uvicorn backend.api.main:app --reload
# → http://localhost:8000/docs
MCP Server (for Claude Desktop / Cursor)
cd mcp-server && npm install && npm run build && npm start
Add to claude_desktop_config.json:
{
"mcpServers": {
"edition": {
"command": "node",
"args": ["/path/to/mcp-server/dist/index.js"],
"env": {
"EDITION_API_URL": "http://localhost:8000",
"EDITION_API_KEY": "your_api_key"
}
}
}
}
API Endpoints
Memory
| Method | Endpoint | Description |
|---|---|---|
| POST | /api/v1/memory/episodes | Store episode (set auto_extract=true for auto fact extraction) |
| POST | /api/v1/memory/episodes/search | Semantic search |
| POST | /api/v1/memory/facts | Add fact |
| GET | /api/v1/memory/facts | List facts |
| GET | /api/v1/memory/context | Context summary |
| POST | /api/v1/memory/extract | Extract facts from text |
Regulation
| Method | Endpoint | Description |
|---|---|---|
| POST | /api/v1/regulation/check | Check regulations (10 industries + LLM RAG) |
| GET | /api/v1/regulation/industries | List industries |
| GET | /api/v1/regulation/tourist | Tourist categories |
Tech Stack
| Layer | Technology |
|---|---|
| API | FastAPI (Python) |
| Memory Store | SQLite + ChromaDB (vector search) |
| MCP | TypeScript SDK v1.29 |
| LLM | Gemini / Claude / GPT (fact extraction + RAG) |
Why Not Mem0 / Letta / Zep?
Those are excellent general-purpose memory tools. But they don't:
- Parse Japanese keigo levels (丁寧語 / 尊敬語 / 謙譲語)
- Detect implicit social hierarchy from honorific patterns
- Score confidence based on Japanese speech patterns (断定 vs 推測 vs 伝聞)
- Include a Japanese regulatory database
This project exists because Japanese business context is structurally different, and agents need purpose-built infrastructure to navigate it.
License
MIT
