Skip to main content
Glama
anils123
by anils123

Manufacturing Traceability Intelligence Platform

GraphRAG-powered agentic AI platform for manufacturing traceability using Neo4j, Amazon Bedrock, and MCP.

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                    INGESTION & ENRICHMENT LAYER                      │
│  Connectors → Chunking → Embeddings → Entity Extraction →           │
│  Entity Resolution → Graph Construction                              │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────────────┐
│                    KNOWLEDGE GRAPH (Neo4j)                           │
│  Product → Requirement → Component → TestCase → TestRun →           │
│  Defect → ChangeRequest                                              │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────────────┐
│                    RETRIEVAL LAYER                                   │
│  VectorRetriever │ GraphRAGRetriever │ NL2CypherRetriever │ Hybrid  │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────────────┐
│                    AGENT LAYER                                       │
│  LangGraph Agent (plan→retrieve→reason→validate→respond)            │
│  Strands Agent (tool-calling with 5 specialized graph tools)        │
└──────────────────────────┬──────────────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────────────┐
│                    INTERFACE LAYER                                   │
│  FastAPI REST │ MCP Server (Claude Desktop / Cursor compatible)     │
└─────────────────────────────────────────────────────────────────────┘

Related MCP server: Knowledge Graph MCP Server

Quick Start

1. Prerequisites

  • Neo4j 5.x (with APOC plugin)

  • Python 3.11+

  • AWS credentials with Bedrock access (Claude 3.5 Sonnet + Titan Embed v2)

2. Setup

cd manufacturing-graphrag
python -m venv .venv
.venv\Scripts\activate          # Windows
pip install -r requirements.txt
python -m spacy download en_core_web_sm
copy .env.example .env          # Edit with your credentials

3. Start Neo4j (Docker)

docker-compose up neo4j -d

4. Seed the Knowledge Graph

set PYTHONPATH=src
python scripts/seed_data.py

5. Start the Platform

# API server
python main.py api

# MCP server (for Claude Desktop)
python main.py mcp

# Both
python main.py all

6. Ingest Documents

python scripts/ingest.py path/to/spec.pdf path/to/requirements.csv

API Endpoints

Method

Endpoint

Description

GET

/health

Health check

GET

/graph/stats

Node counts by label

POST

/ingest/file

Upload and ingest a document

POST

/ingest/record

Ingest a structured API record

POST

/query

Hybrid GraphRAG query

POST

/agent/langgraph

LangGraph reasoning agent

POST

/agent/strands

Strands tool-calling agent

GET

/traceability/defect/{id}

Full defect traceability chain

GET

/traceability/product/{id}

Product traceability summary

Example Queries

# Hybrid GraphRAG query
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What components are affected by the thermal runaway defect?"}'

# LangGraph agent — multi-step reasoning
curl -X POST http://localhost:8000/agent/langgraph \
  -d '{"question": "Trace the full impact chain of DEF-001 and identify all change requests needed"}'

# Strands agent — tool-calling
curl -X POST http://localhost:8000/agent/strands \
  -d '{"question": "Which critical defects are blocking the EV BMS release?"}'

# Defect traceability
curl http://localhost:8000/traceability/defect/DEF-002

MCP Integration (Claude Desktop)

Copy config/claude_desktop_mcp.json content into your Claude Desktop claude_desktop_config.json.

Available MCP tools:

  • ask_manufacturing_ai — hybrid GraphRAG Q&A

  • semantic_search — vector similarity search

  • graph_trace — graph traversal retrieval

  • natural_language_to_cypher — NL2Cypher

  • get_defect_chain — full defect traceability

  • get_requirement_traceability — requirement coverage

  • product_health_dashboard — product metrics

Knowledge Graph Schema

(Product)-[:HAS_REQUIREMENT]->(Requirement)
(Component)-[:IMPLEMENTS]->(Requirement)
(TestCase)-[:VALIDATES]->(Requirement)
(TestRun)-[:INSTANCE_OF]->(TestCase)
(TestRun)-[:FOUND_IN]->(Defect)
(Defect)-[:AFFECTS]->(Component)
(Defect)-[:TRIGGERS_CHANGE]->(ChangeRequest)
(ChangeRequest)-[:MODIFIES]->(Component)
(Document)-[:CONTAINS_CHUNK]->(Chunk)
(Chunk)-[:MENTIONS]->(any entity)

Running Tests

set PYTHONPATH=src
pytest tests/ -v
F
license - not found
Not graded
quality - not tested
C
maintenance

Maintenance

0Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.
    5
  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.
    2
  • F
    license
    C
    quality
    D
    maintenance
    Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.
    13

View all related MCP servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/anils123/manufacturing-graphrag'

If you have feedback or need assistance with the MCP directory API, please join our Discord server