一个MCP服务器，允许AI助手将Neo4j知识图谱作为主用，动态说明书和长期项目记忆，支持自适应模板和自主工具开发......
概览
一个MCP服务器实现，使像Claude这样的AI助手能够将Neo4j知识图谱作为主要的动态“说明手册”和项目内存，用于标准化编码工作流程。 一个先进的MCP服务器实现，结合了Neo4j知识图谱、Qdrant向量数据库和复杂的AI编排，创建了一个用于知识管理、研究分析和标准化工作流程的混合推理系统。 NeoCoder 实现了一个革命性的系统，远远超越了传统的 RAG（检索增强生成），通过结合： 1. - 权威结构化事实、关系与工作流程 2.- 语义搜索、相似性检测与上下文理解 3.- 通过综合和引用实现数据源间智能路由 4.
概述（AI 翻译）
一个MCP服务器实现，使像Claude这样的AI助手能够将Neo4j知识图谱作为主要的动态“说明手册”和项目内存，用于标准化编码工作流程。
NeoCoder：混合人工智能推理与工作流程系统
一个先进的MCP服务器实现，结合了Neo4j知识图谱、Qdrant向量数据库和复杂的AI编排，创建了一个用于知识管理、研究分析和标准化工作流程的混合推理系统。
概述
NeoCoder 实现了一套革命性的 上下文增强推理系统，远超传统的 RAG（检索增强生成），通过结合：
核心架构：
- Neo4j 知识图谱 - 权威的结构化事实、关系和工作流程
- Qdrant 向量数据库 - 语义搜索、相似性检测与上下文理解
- MCP 编排 - 通过综合和引用实现数据源间的智能路由
- F-收缩综合 - 动态知识合并，保持来源归因
关键能力：
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
NeoCoder: Neo4j-Guided AI Coding Workflow
An MCP server implementation that enables AI assistants like Claude to use a Neo4j knowledge graph as their primary, dynamic “instruction manual” and project memory for standardized coding workflows.
NeoCoder: Hybrid AI Reasoning & Workflow System
An advanced MCP server implementation that combines Neo4j knowledge graphs, Qdrant vector databases, and sophisticated AI orchestration to create a hybrid reasoning system for knowledge management, research analysis, and standardized workflows.
Overview
NeoCoder implements a revolutionary Context-Augmented Reasoning system that goes far beyond traditional RAG (Retrieval-Augmented Generation) by combining:
Core Architecture:
- Neo4j Knowledge Graphs - Authoritative structured facts, relationships, and workflows
- Qdrant Vector Databases - Semantic search, similarity detection, and contextual understanding
- MCP Orchestration - Intelligent routing between data sources with synthesis and citation
- F-Contraction Synthesis - Dynamic knowledge merging that preserves source attribution
Key Capabilities:
- Hybrid Knowledge Reasoning: Seamlessly combine structured facts with semantic context
- Dynamic Knowledge Extraction: Process documents, code, and conversations into interconnected knowledge structures
- Citation-Based Analysis: Every claim tracked to its source across multiple databases
- Multi-Incarnation System: Specialized modes for coding, research, decision support, and knowledge management
- Intelligent Workflow Templates: Neo4j-guided procedures with mandatory verification steps
Revolutionary Features:
🧠 Smart Query Routing: AI automatically determines optimal data source (graph, vector, or hybrid) 🔬 Research Analysis Engine: Process academic papers with citation graphs and semantic content ⚡ F-Contraction Processing: Dynamically merge similar concepts while preserving provenance 🎯 Context-Augmented Reasoning: Generate insights impossible with single data sources 📊 Full Audit Trails: Complete tracking of knowledge synthesis and workflow execution 🛡️ Production-Ready Process Management: Automatic cleanup, signal handling, and resource tracking to prevent process leaks 🔧 Enhanced Tool Handling: Robust async initialization with proper background task management
New from an idea I had- Lotka-Volterra Ecological Framework integrated into Knowledge Graph Incarnation
Process Management & Reliability
NeoCoder implements comprehensive process management following MCP best practices:
- Signal Handlers: Proper SIGTERM/SIGINT handling for graceful shutdowns
- Resource Tracking: Automatic tracking of processes, Neo4j connections, and background tasks
安装
docker run -p 6333:6333 -p 6334:6334 \配置
{
"mcpServers": {
"neocoder": {
"command": "uv",
"args": [
"--directory",
"/your-path-to/NeoCoder-neo4j-ai-workflow",
"run",
"mcp_neocoder"
],
"env": {
"NEO4J_URL": "bolt://localhost:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "your-neo4j-password-here",
"NEO4J_DATABASE": "neo4j",
"LOG_LEVEL": "INFO",
"MCP_TRANSPORT": "stdio",
"PYTHONUNBUFFERED": "1"
}
}
}
}