LogicHive (Professional AI Logic Hub)

🛡️ LogicHive is a high-precision knowledge extraction and logic management system. It enables AI agents to accumulate, verify, and reuse high-quality code assets via the Model Context Protocol (MCP).

> "Stop rebuilding the same logic. Build a long-term intelligence asset."
> 哲学: 巨人の肩の上に乗り、真に価値ある創造に集中せよ。

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🏗️ The Rigor Gate: A Hybrid Approach

LogicHive uses a Hybrid Deterministic Gate to veto non-factual AI opinions:
- Fact (40%): AST analysis. Mandatory veto power (Assertion counting, Hollow logic detection).
- Static (30%): Ruff/Radon metrics for code health.
- AI (20%): Forensic auditing by LLMs.
- Execution (10%): Isolated runtime validation.

> [!IMPORTANT]
> LogicHive values verifiability over correctness. If an AI-generated logic atom lacks assertion tests, it is rejected by the Fact Gate, preventing low-quality code from polluting your knowledge base.

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🌟 Key Features

- Hybrid Knowledge Search: Semantic and exact-match search for code patterns.
- Verification Quality Gate: Automated testing and linting before code is "vaulted".
- MCP Streamable HTTP (SSE) Integration: Centralized deployment serving multiple clients (Cursor, Claude Desktop) concurrently.
- Project Isolation: Manage logic assets across multiple namespaces and projects.

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💼 Business Value & ROI

LogicHive turns transient AI interactions into reusable corporate assets:
1. API Cost Optimization: Drastically reduces LLM input tokens by injecting precise logic atoms instead of massive code context.
2. Preventing Technical Debt: Automatically blocks un-asserted, redundant, or complex code, slashing future maintenance costs.
3. Secure AI Governance: Filters out security vulnerabilities and runs isolated dynamic execution tests before storing assets.
4. Capitalizing Organizational Knowledge: Ensures critical domain logic is preserved and shared, eliminating project silo effects and key-person risks.

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🚀 Workflow

1. Discovery (探索): Find logic atoms via LogicHive MCP.
2. Retrieval (抽出): Inject verified logic into the agent context.
3. Adaptation (適合): AI refactors logic to match current namespaces.
4. Professionalization (資産化): Refined logic is saved back.
5. Stabilization (安定化): Background tools re-verify assets 24/7.

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🐘 Handling Heavy AI Assets (Torch, Sklearn)

Registering code that imports large libraries like torch or sklearn can hit the 20s Quality Gate Timeout. To bypass this and maintain a fast development rhythm, use the following patterns:

1. Lazy Import (Recommended)

Move heavy imports inside your functions. This prevents the library from loading during the initial module-level scan by LogicHive's AST analyzer.

2. Smart Mocking

If you must have top-level imports, use the mock_imports parameter in save_function. LogicHive will inject MagicMock for those modules during verification.

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⚙️ Configuration

LogicHive is configured via environment variables or a .env file, resolving in the following order:

1. Local .env (Primary): Place .env in the same directory as LogicHive-MCP.exe (or project root).
2. User Home (Fallback): ~/.logichive/.env (Global settings across folder moves).
3. OS Environment Variables: Directly set variables override .env values.

Setup Steps:
1. Locate .env.example: Copy this file to .env.
2. Set your API Keys: At minimum, set GEMINI_API_KEY.
3. Place the file: Follow the rules above based on your deployment.

See .env.example and ADR-005 for details.

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🚀 自動起動の設定（タスクスケジューラー）

LogicHive を常駐させたい場合は、以下のコマンドを管理者権限の PowerShell で実行することでタスクスケジューラーに登録できます。

```powershell