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KYVAutoResearch2MCP

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KYVAutoResearch v2

项目制的自动研究 Agent 框架。让 AI 围绕研究主题把证据穷尽到可以下判断的程度；证据不足时，如实说出来。 Project-based Auto-Research AI Agent Framework. Exhausts evidence until a judgment can be made; honestly states "insufficient evidence" when there isn't enough.

English | 中文

Release Python License MCP

📖 目录 | Table of Contents

项目简介 | Introduction

KYVAutoResearch 是一个项目制的自动研究 Agent 框架。它不同于普通的对话式 AI，而是围绕研究主题建立问题树，用问题 × 证据矩阵追踪证据，按证据等级判定。它的核心承诺是：证据不足时，如实输出“无法判断”，绝不编造。

KYVAutoResearch is a project-based auto-research AI agent framework. Unlike standard conversational AI, it builds a problem tree around a research topic, tracks evidence using a Problem × Evidence Matrix, and makes judgments based on evidence levels. Its core promise: when evidence is insufficient, it honestly outputs "Unable to determine" instead of hallucinating.

核心概念 | Core Concepts

  • 问题 × 证据矩阵 | Problem × Evidence Matrix：追踪每一条证据与问题的对应关系，确保没有无证据的结论。

  • 双通道判定 | Dual-channel Judgment：确保证据充分且逻辑自洽。

  • 证据分级与缺口识别 | Evidence Grading & Gap Identification：自动识别证据不足的环节，拒绝在覆盖率未满 100% 时给出“已解决”的建议。

  • MCP Server + Dual Skill：包含 KYVAutoResearch2MCP，支持与 Claude Desktop 等客户端无缝集成，并配有双 Skill 设计。

快速开始 | Quick Start

(如果你已经发布到 PyPI，请把下方替换为 pip install 你的包名) If not published to PyPI, install from source:

git clone https://github.com/kyvjyf/KYVAutoResearch-v2.git
cd KYVAutoResearch-v2

👉 查看完整使用说明 | See Full Documentation

项目结构 | Project Structure

KYVAutoResearch-v2/
├── core/               # 核心逻辑与引擎
├── schema_migrations/  # 数据库模式迁移
├── scripts/            # 辅助脚本
├── skills/             # 双 Skill 设计
├── tools/              # 工具集
├── server.py           # MCP Server 入口
├── pyproject.toml      # 项目依赖配置
└── 使用说明.md         # 完整使用文档

设计文档 | Design Documentation

关于系统的详细设计理念和架构说明，请参阅：

边界说明 | Notes

本分享版不包含测试套件、演示脚本与研究数据。 This shared version does not include the test suite, demo scripts, or research data.

反馈 | Feedback

欢迎在 Issues 中反馈问题或建议： Feel free to provide feedback or suggestions in Issues: https://github.com/kyvjyf/KYVAutoResearch-v2/issues

License: MIT

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