Welcome to FluxGraph Documentation
FluxGraph is an enterprise-grade AI agent orchestration framework for building intelligent, multi-agent systems with advanced memory, caching, and workflow capabilities.
Quick Start
Install FluxGraph:
pip install fluxgraph
Create your first agent:
from fluxgraph import FluxApp
app = FluxApp(title="My AI Agent", version="1.0.0")
@app.agent(name="greeter")
async def greeter(message: str):
return {"response": f"Hello! You said: {message}"}
if __name__ == "__main__":
app.run()
Features
- 🚀 Multi-Agent Orchestration
Build complex agent workflows
Dynamic agent chaining
Parallel execution support
- 🧠 Advanced Memory System
Episodic and semantic memory
PostgreSQL-backed persistence
Context-aware recall
- ⚡ High Performance
Built-in caching layer
Async-first architecture
Real-time analytics dashboard
- 🔌 LLM Provider Support
OpenAI, Anthropic, Google Gemini
Groq, Ollama, HuggingFace
Custom provider integration
- 🛠️ Production Ready
CORS support
Security middleware
Performance monitoring
Error handling & logging
Architecture Overview
FluxGraph follows a modular architecture:
┌─────────────────────────────────────┐
│ FastAPI Application │
├─────────────────────────────────────┤
│ Agent Orchestrator Layer │
├──────────┬──────────┬───────────────┤
│ Memory │ Cache │ Workflows │
├──────────┴──────────┴───────────────┤
│ LLM Provider Adapters │
├─────────────────────────────────────┤
│ Database & External APIs │
└─────────────────────────────────────┘
Use Cases
- ✅ Customer Support Agents
Intelligent chatbots with context retention and escalation handling
- ✅ Lead Generation Systems
Automated sales qualification and CRM integration
- ✅ Data Processing Pipelines
Multi-step workflows with AI-powered decision making
- ✅ Knowledge Management
RAG-enabled question answering systems