Phase 2 Complete: Multi-Agent System with Neo4j and LangChain

✅ Implemented Features:
- Neo4j AuraDB integration with knowledge graph
- Assignment Agent with workload balancing algorithm
- Multi-agent coordinator using LangChain patterns
- Enhanced Gradio UI with complete workflow
- Developer expertise modeling and scoring
- Real-time system dashboard

✅ Technical Additions:
- Neo4j Python driver with AuraDB support
- Graph-based developer-component relationships
- Weighted scoring algorithm for assignments
- Agent coordination framework
- Complete end-to-end bug processing pipeline

✅ Testing:
- All agents tested locally and working
- Neo4j queries optimized with indexes
- Multi-agent workflow validated
- UI tested with example bugs

knowledge-graph/neo4j_client.py

@@ -5,6 +5,17 @@ from typing import List, Dict, Any, Optional
 import logging
 from datetime import datetime
 
+# Ensure environment variables from a .env file are loaded when this module is imported.
+# Some callers import this module before calling load_dotenv(); loading here makes the
+# client robust to that ordering. If python-dotenv isn't available, just rely on the
+# existing environment variables.
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    # dotenv not installed or failed to load; continue silently
+    pass
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)