Bella Tracer v2 - GraphRAG Observability Platform

Available Languages: 🇬🇧 English | 🇹🇷 Türkçe

Overview

Bella Tracer v2 is an advanced observability platform that leverages Graph Retrieval-Augmented Generation (GraphRAG) and Neo4j to analyze and understand complex distributed system traces. The platform synthesizes synthetic logs, builds dynamic knowledge graphs from observability data, and provides intelligent querying capabilities powered by AI agents.

Key Features

🤖 AI-Powered Query System

• LangGraph-based Agent: Intelligent query processing with question optimization and answer ranking
• OpenAI Integration: Advanced LLM and embedding capabilities
• Multi-stage Processing: Query optimization, document retrieval, and semantic reranking

📊 Knowledge Graph Management

• Neo4j Backend: Powerful graph database for relationship mapping
• Dynamic Graph Building: Automatic creation of nodes and relationships from trace data
• Vector Search: Semantic search capabilities with OpenAI embeddings

🔄 Data Pipeline Architecture

• Synthetic Data Generation: Complex trace pattern generation for testing and validation
• Kafka Integration: Real-time data streaming and processing
• Prefect Workflows: Orchestrated data pipelines for ETL operations

📈 Trace Analysis

• Multi-Level Trace Processing: Service, pod, and log entry correlation
• Context Extraction: Intelligent metadata parsing from observability logs
• Relationship Mapping: Automatic discovery of trace hierarchies and dependencies

Technology Stack

• LangChain: AI framework and tool integrations
• LangGraph: Agent orchestration and workflow
• Neo4j GraphRAG: Knowledge graph RAG
• FastAPI: REST API framework
• Prefect: Workflow orchestration
• Kafka: Distributed streaming
• OpenAI: LLM and embeddings
• spaCy: NLP processing
• Pandas: Data manipulation

Quick Start

Prerequisites

• Python 3.12+
• Neo4j 5.x
• Kafka 3.x (or Docker)
• OpenAI API key

Setup

# Install dependencies
uv sync

# Configure environment
cp .env.example .env  # Edit with your credentials

# Start services
docker-compose up -d

# Create Neo4j index
make neo4j-index

Running Pipelines

# Start data generation and knowledge graph pipelines
make run-flows

# Or start API server
uv run api

Querying

curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What services failed recently?"}'

Project Structure

bella-tracer-v2/
├── src/bella_tracer_v2/
│   ├── api/                    # FastAPI application
│   ├── pipelines/              # ETL pipelines
│   ├── services/               # External service integrations
│   ├── agent.py                # LangGraph agent
│   └── models.py               # Data models
├── artifacts/                  # Generated datasets
├── docker-compose.yaml         # Local environment setup
└── pyproject.toml              # Project configuration

Documentation

For detailed information, see:

• 📖 English Documentation
• 📖 Turkish Documentation

License

MIT License - see LICENSE file for details

Support

For questions or issues, please open an issue on the repository.

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Status: Beta - Under active development
Last Updated: December 2025