angrysky56/neocoder-neo4j-ai-workflow

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An MCP server allowing AI assistants to use a Neo4j knowledge graph as their primary, dynamic instruction manual and long term project memory with adaptive templating and autonomous tool development...

Обзор

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. 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. NeoCoder implements a revolutionary system that goes far beyond traditional RAG (Retrieval-Augmented Generation) by combining: 1. - Authoritative structured facts, relationships, and workflows 2. - Semantic search, similarity detection, and contextual understanding 3. - Intelligent routing between data sources with synthesis and citation 4.

README

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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

Открыть полный README на GitHub

Установка

docker run -p 6333:6333 -p 6334:6334 \

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Конфигурация

{
  "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"
      }
    }
  }
}