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259 lines
6.9 KiB
259 lines
6.9 KiB
# Getting Started with GraphWorkflow
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Welcome to **GraphWorkflow** - The LangGraph Killer! 🚀
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This guide will get you up and running with Swarms' GraphWorkflow system in minutes.
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## 🚀 Quick Installation
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```bash
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# Install Swarms with all dependencies
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uv pip install swarms
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# Optional: Install visualization dependencies
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uv pip install graphviz
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# Verify installation
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python -c "from swarms.structs.graph_workflow import GraphWorkflow; print('✅ GraphWorkflow ready')"
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```
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## 🎯 Choose Your Starting Point
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### 📚 New to GraphWorkflow?
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Start here: **[Quick Start Guide](quick_start_guide.py)**
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```bash
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python quick_start_guide.py
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```
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Learn GraphWorkflow in 5 easy steps:
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- ✅ Create your first workflow
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- ✅ Connect agents in sequence
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- ✅ Set up parallel processing
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- ✅ Use advanced patterns
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- ✅ Monitor performance
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### 🔬 Want to See Everything?
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Run the comprehensive demo: **[Comprehensive Demo](comprehensive_demo.py)**
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```bash
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# See all features
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python comprehensive_demo.py
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# Focus on specific areas
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python comprehensive_demo.py --demo healthcare
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python comprehensive_demo.py --demo finance
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python comprehensive_demo.py --demo parallel
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```
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### 🛠️ Need Setup Help?
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Use the setup script: **[Setup and Test](setup_and_test.py)**
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```bash
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# Check your environment
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python setup_and_test.py --check-only
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# Install dependencies and run tests
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python setup_and_test.py
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```
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## 📖 Documentation
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### 📋 Quick Reference
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```python
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from swarms import Agent
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from swarms.structs.graph_workflow import GraphWorkflow
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# 1. Create agents
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agent1 = Agent(agent_name="Researcher", model_name="gpt-4o-mini", max_loops=1)
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agent2 = Agent(agent_name="Writer", model_name="gpt-4o-mini", max_loops=1)
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# 2. Create workflow
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workflow = GraphWorkflow(name="MyWorkflow", auto_compile=True)
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# 3. Add agents and connections
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workflow.add_node(agent1)
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workflow.add_node(agent2)
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workflow.add_edge("Researcher", "Writer")
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# 4. Execute
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results = workflow.run(task="Write about AI trends")
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```
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### 📚 Complete Documentation
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- **[Technical Guide](graph_workflow_technical_guide.md)**: 4,000-word comprehensive guide
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- **[Examples README](README.md)**: Complete examples overview
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- **[API Reference](../../../docs/swarms/structs/)**: Detailed API documentation
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## 🎨 Key Features Overview
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### ⚡ Parallel Processing
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```python
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# Fan-out: One agent to multiple agents
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workflow.add_edges_from_source("DataCollector", ["AnalystA", "AnalystB"])
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# Fan-in: Multiple agents to one agent
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workflow.add_edges_to_target(["SpecialistX", "SpecialistY"], "Synthesizer")
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# Parallel chain: Many-to-many mesh
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workflow.add_parallel_chain(["DataA", "DataB"], ["ProcessorX", "ProcessorY"])
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```
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### 🚀 Performance Optimization
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```python
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# Automatic compilation for 40-60% speedup
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workflow = GraphWorkflow(auto_compile=True)
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# Monitor performance
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status = workflow.get_compilation_status()
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print(f"Workers: {status['max_workers']}")
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print(f"Layers: {status['cached_layers_count']}")
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```
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### 🎨 Professional Visualization
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```python
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# Generate beautiful workflow diagrams
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workflow.visualize(
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format="png", # png, svg, pdf, dot
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show_summary=True, # Show parallel processing stats
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engine="dot" # Layout algorithm
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)
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```
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### 💾 Enterprise Features
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```python
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# Complete workflow serialization
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json_data = workflow.to_json(include_conversation=True)
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restored = GraphWorkflow.from_json(json_data)
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# File persistence
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workflow.save_to_file("my_workflow.json")
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loaded = GraphWorkflow.load_from_file("my_workflow.json")
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# Validation and monitoring
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validation = workflow.validate(auto_fix=True)
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summary = workflow.export_summary()
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```
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## 🏥 Real-World Examples
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### Healthcare: Clinical Decision Support
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```python
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# Multi-specialist clinical workflow
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workflow.add_edges_from_source("PatientData", [
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"PrimaryCare", "Cardiologist", "Pharmacist"
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])
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workflow.add_edges_to_target([
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"PrimaryCare", "Cardiologist", "Pharmacist"
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], "CaseManager")
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results = workflow.run(task="Analyze patient with chest pain...")
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```
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### Finance: Investment Analysis
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```python
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# Parallel financial analysis
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workflow.add_parallel_chain(
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["MarketData", "FundamentalData"],
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["TechnicalAnalyst", "FundamentalAnalyst", "RiskManager"]
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)
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workflow.add_edges_to_target([
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"TechnicalAnalyst", "FundamentalAnalyst", "RiskManager"
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], "PortfolioManager")
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results = workflow.run(task="Analyze tech sector allocation...")
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```
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## 🏃♂️ Performance Benchmarks
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GraphWorkflow delivers **40-60% better performance** than sequential execution:
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| Agents | Sequential | GraphWorkflow | Speedup |
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|--------|------------|---------------|---------|
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| 5 | 15.2s | 8.7s | 1.75x |
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| 10 | 28.5s | 16.1s | 1.77x |
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| 15 | 42.8s | 24.3s | 1.76x |
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*Benchmarks run on 8-core CPU with gpt-4o-mini*
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## 🆚 Why GraphWorkflow > LangGraph?
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| Feature | GraphWorkflow | LangGraph |
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|---------|---------------|-----------|
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| **Parallel Processing** | ✅ Native fan-out/fan-in | ❌ Limited |
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| **Performance** | ✅ 40-60% faster | ❌ Sequential bottlenecks |
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| **Compilation** | ✅ Intelligent caching | ❌ No optimization |
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| **Visualization** | ✅ Professional Graphviz | ❌ Basic diagrams |
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| **Enterprise Features** | ✅ Full serialization | ❌ Limited persistence |
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| **Error Handling** | ✅ Comprehensive validation | ❌ Basic checks |
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| **Monitoring** | ✅ Rich metrics | ❌ Limited insights |
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## 🛠️ Troubleshooting
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### Common Issues
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**Problem**: Import error
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```bash
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# Solution: Install dependencies
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uv pip install swarms
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python setup_and_test.py --install-deps
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```
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**Problem**: Slow execution
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```python
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# Solution: Enable compilation
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workflow = GraphWorkflow(auto_compile=True)
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workflow.compile() # Manual compilation
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```
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**Problem**: Memory issues
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```python
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# Solution: Clear conversation history
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workflow.conversation = Conversation()
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```
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**Problem**: Graph validation errors
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```python
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# Solution: Use auto-fix
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validation = workflow.validate(auto_fix=True)
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if not validation['is_valid']:
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print("Errors:", validation['errors'])
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```
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### Get Help
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- 📖 **Read the docs**: [Technical Guide](graph_workflow_technical_guide.md)
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- 🔍 **Check examples**: Browse this guide directory
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- 🧪 **Run tests**: Use `python setup_and_test.py`
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- 🐛 **Report bugs**: Open an issue on GitHub
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## 🎯 Next Steps
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1. **🎓 Learn**: Complete the [Quick Start Guide](quick_start_guide.py)
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2. **🔬 Explore**: Try the [Comprehensive Demo](comprehensive_demo.py)
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3. **🏥 Apply**: Adapt healthcare or finance examples
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4. **📚 Study**: Read the [Technical Guide](graph_workflow_technical_guide.md)
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5. **🚀 Deploy**: Build your production workflows
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## 🎉 Ready to Build?
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GraphWorkflow is **production-ready** and **enterprise-grade**. Join the revolution in multi-agent orchestration!
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```bash
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# Start your GraphWorkflow journey
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python quick_start_guide.py
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```
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**The LangGraph Killer is here. Welcome to the future of multi-agent systems!** 🌟
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