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# 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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@ -0,0 +1,322 @@
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# GraphWorkflow Guide
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Welcome to the comprehensive GraphWorkflow guide! This collection demonstrates the power and flexibility of Swarms' GraphWorkflow system - the LangGraph killer that provides superior multi-agent orchestration capabilities.
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||||
## 🚀 Quick Start
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||||
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||||
### Installation
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||||
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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
|
||||
uv pip install graphviz
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||||
|
||||
# Verify installation
|
||||
python -c "from swarms.structs.graph_workflow import GraphWorkflow; print('✅ GraphWorkflow ready')"
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```
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### Run Your First Example
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```bash
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# Start with the quick start guide
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python quick_start_guide.py
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# Or run the comprehensive demo
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python comprehensive_demo.py
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# For specific examples
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||||
python comprehensive_demo.py --demo healthcare
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python comprehensive_demo.py --demo finance
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```
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## 📁 Example Files
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### 🎓 Learning Examples
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| File | Description | Complexity |
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|------|-------------|------------|
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| `quick_start_guide.py` | **START HERE** - Step-by-step introduction to GraphWorkflow | ⭐ Beginner |
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| `graph_workflow_example.py` | Basic two-agent workflow example | ⭐ Beginner |
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| `comprehensive_demo.py` | Complete feature demonstration with multiple use cases | ⭐⭐⭐ Advanced |
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### 🏥 Healthcare Examples
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|
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| File | Description | Complexity |
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|------|-------------|------------|
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| `comprehensive_demo.py --demo healthcare` | Clinical decision support workflow | ⭐⭐⭐ Advanced |
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**Healthcare Workflow Features:**
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- Multi-disciplinary clinical team simulation
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- Parallel specialist consultations
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- Drug interaction checking
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- Risk assessment and quality assurance
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- Evidence-based clinical decision support
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### 💰 Finance Examples
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| File | Description | Complexity |
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|------|-------------|------------|
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| `advanced_graph_workflow.py` | Sophisticated investment analysis workflow | ⭐⭐⭐ Advanced |
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| `comprehensive_demo.py --demo finance` | Quantitative trading strategy development | ⭐⭐⭐ Advanced |
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**Finance Workflow Features:**
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- Multi-source market data analysis
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- Parallel quantitative analysis (Technical, Fundamental, Sentiment)
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- Risk management and portfolio optimization
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- Strategy backtesting and validation
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- Execution planning and monitoring
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### 🔧 Technical Examples
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| File | Description | Complexity |
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|------|-------------|------------|
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| `test_parallel_processing_example.py` | Comprehensive parallel processing patterns | ⭐⭐ Intermediate |
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| `test_graphviz_visualization.py` | Visualization capabilities and layouts | ⭐⭐ Intermediate |
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| `test_graph_workflow_caching.py` | Performance optimization and caching | ⭐⭐ Intermediate |
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| `test_enhanced_json_export.py` | Serialization and persistence features | ⭐⭐ Intermediate |
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| `test_graphworlfolw_validation.py` | Workflow validation and error handling | ⭐⭐ Intermediate |
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## 🎯 Key Features Demonstrated
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### ⚡ Parallel Processing Patterns
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|
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- **Fan-out**: One agent distributes to multiple agents
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- **Fan-in**: Multiple agents converge to one agent
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||||
- **Parallel chains**: Many-to-many mesh processing
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- **Complex hybrid**: Sophisticated multi-stage patterns
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|
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### 🚀 Performance Optimization
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||||
|
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- **Intelligent Compilation**: Pre-computed execution layers
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- **Advanced Caching**: Persistent state across runs
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- **Worker Pool Optimization**: CPU-optimized parallel execution
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- **Memory Management**: Efficient resource utilization
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### 🎨 Visualization & Monitoring
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|
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- **Professional Graphviz Diagrams**: Multiple layouts and formats
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- **Real-time Performance Metrics**: Execution monitoring
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||||
- **Workflow Validation**: Comprehensive error checking
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- **Rich Logging**: Detailed execution insights
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### 💾 Enterprise Features
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|
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- **JSON Serialization**: Complete workflow persistence
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||||
- **Runtime State Management**: Compilation caching
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- **Error Handling**: Robust failure recovery
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- **Scalability**: Support for large agent networks
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|
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## 🏃♂️ Running Examples
|
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|
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### Basic Usage
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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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# 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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# Create workflow
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workflow = GraphWorkflow(name="SimpleWorkflow", auto_compile=True)
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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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# Execute
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results = workflow.run(task="Research and write about AI trends")
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```
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### Parallel Processing
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|
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```python
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# Fan-out pattern: One agent to multiple agents
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workflow.add_edges_from_source("DataCollector", ["AnalystA", "AnalystB", "AnalystC"])
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# Fan-in pattern: Multiple agents to one agent
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workflow.add_edges_to_target(["SpecialistX", "SpecialistY"], "Synthesizer")
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|
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# Parallel chain: Many-to-many processing
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workflow.add_parallel_chain(
|
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sources=["DataA", "DataB"],
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targets=["ProcessorX", "ProcessorY"]
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)
|
||||
```
|
||||
|
||||
### Performance Monitoring
|
||||
|
||||
```python
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# Get compilation status
|
||||
status = workflow.get_compilation_status()
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||||
print(f"Compiled: {status['is_compiled']}")
|
||||
print(f"Workers: {status['max_workers']}")
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||||
|
||||
# Monitor execution
|
||||
import time
|
||||
start = time.time()
|
||||
results = workflow.run(task="Analyze market conditions")
|
||||
print(f"Execution time: {time.time() - start:.2f}s")
|
||||
print(f"Throughput: {len(results)/(time.time() - start):.1f} agents/second")
|
||||
```
|
||||
|
||||
## 🔬 Use Case Examples
|
||||
|
||||
### 📊 Enterprise Data Processing
|
||||
|
||||
```python
|
||||
# Multi-stage data pipeline
|
||||
workflow.add_parallel_chain(
|
||||
["APIIngester", "DatabaseExtractor", "FileProcessor"],
|
||||
["DataValidator", "DataTransformer", "DataEnricher"]
|
||||
)
|
||||
workflow.add_edges_to_target(
|
||||
["DataValidator", "DataTransformer", "DataEnricher"],
|
||||
"ReportGenerator"
|
||||
)
|
||||
```
|
||||
|
||||
### 🏥 Clinical Decision Support
|
||||
|
||||
```python
|
||||
# Multi-specialist consultation
|
||||
workflow.add_edges_from_source("PatientDataCollector", [
|
||||
"PrimaryCarePhysician", "Cardiologist", "Pharmacist"
|
||||
])
|
||||
workflow.add_edges_to_target([
|
||||
"PrimaryCarePhysician", "Cardiologist", "Pharmacist"
|
||||
], "CaseManager")
|
||||
```
|
||||
|
||||
### 💼 Investment Analysis
|
||||
|
||||
```python
|
||||
# Parallel financial analysis
|
||||
workflow.add_parallel_chain(
|
||||
["MarketDataCollector", "FundamentalDataCollector"],
|
||||
["TechnicalAnalyst", "FundamentalAnalyst", "SentimentAnalyst"]
|
||||
)
|
||||
workflow.add_edges_to_target([
|
||||
"TechnicalAnalyst", "FundamentalAnalyst", "SentimentAnalyst"
|
||||
], "PortfolioManager")
|
||||
```
|
||||
|
||||
## 🎨 Visualization Examples
|
||||
|
||||
### Generate Workflow Diagrams
|
||||
|
||||
```python
|
||||
# Professional Graphviz visualization
|
||||
workflow.visualize(
|
||||
format="png", # png, svg, pdf, dot
|
||||
engine="dot", # dot, neato, fdp, sfdp, circo
|
||||
show_summary=True, # Display parallel processing stats
|
||||
view=True # Open diagram automatically
|
||||
)
|
||||
|
||||
# Text-based visualization (always available)
|
||||
workflow.visualize_simple()
|
||||
```
|
||||
|
||||
### Example Output
|
||||
|
||||
```
|
||||
📊 GRAPHVIZ WORKFLOW VISUALIZATION
|
||||
====================================
|
||||
📁 Saved to: MyWorkflow_visualization.png
|
||||
🤖 Total Agents: 8
|
||||
🔗 Total Connections: 12
|
||||
📚 Execution Layers: 4
|
||||
|
||||
⚡ Parallel Processing Patterns:
|
||||
🔀 Fan-out patterns: 2
|
||||
🔀 Fan-in patterns: 1
|
||||
⚡ Parallel execution nodes: 6
|
||||
🎯 Parallel efficiency: 75.0%
|
||||
```
|
||||
|
||||
## 🛠️ Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Compilation Errors**
|
||||
```python
|
||||
# Check for cycles in workflow
|
||||
validation = workflow.validate(auto_fix=True)
|
||||
if not validation['is_valid']:
|
||||
print("Validation errors:", validation['errors'])
|
||||
```
|
||||
|
||||
2. **Performance Issues**
|
||||
```python
|
||||
# Ensure compilation before execution
|
||||
workflow.compile()
|
||||
|
||||
# Check worker count
|
||||
status = workflow.get_compilation_status()
|
||||
print(f"Workers: {status['max_workers']}")
|
||||
```
|
||||
|
||||
3. **Memory Issues**
|
||||
```python
|
||||
# Clear conversation history if not needed
|
||||
workflow.conversation = Conversation()
|
||||
|
||||
# Monitor memory usage
|
||||
import psutil
|
||||
process = psutil.Process()
|
||||
memory_mb = process.memory_info().rss / 1024 / 1024
|
||||
print(f"Memory: {memory_mb:.1f} MB")
|
||||
```
|
||||
|
||||
### Debug Mode
|
||||
|
||||
```python
|
||||
# Enable detailed logging
|
||||
workflow = GraphWorkflow(
|
||||
name="DebugWorkflow",
|
||||
verbose=True, # Detailed execution logs
|
||||
auto_compile=True, # Automatic optimization
|
||||
)
|
||||
|
||||
# Validate workflow structure
|
||||
validation = workflow.validate(auto_fix=True)
|
||||
print("Validation result:", validation)
|
||||
```
|
||||
|
||||
## 📚 Documentation
|
||||
|
||||
- **[Technical Guide](graph_workflow_technical_guide.md)**: Comprehensive 4,000-word technical documentation
|
||||
- **[API Reference](../../../docs/swarms/structs/)**: Complete API documentation
|
||||
- **[Multi-Agent Examples](../../multi_agent/)**: Other multi-agent examples
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
Found a bug or want to add an example?
|
||||
|
||||
1. **Report Issues**: Open an issue with detailed reproduction steps
|
||||
2. **Add Examples**: Submit PRs with new use case examples
|
||||
3. **Improve Documentation**: Help expand the guides and tutorials
|
||||
4. **Performance Optimization**: Share benchmarks and optimizations
|
||||
|
||||
## 🎯 Next Steps
|
||||
|
||||
1. **Start Learning**: Run `python quick_start_guide.py`
|
||||
2. **Explore Examples**: Try healthcare and finance use cases
|
||||
3. **Build Your Workflow**: Adapt examples to your domain
|
||||
4. **Deploy to Production**: Use monitoring and optimization features
|
||||
5. **Join Community**: Share your workflows and get help
|
||||
|
||||
## 🏆 Why GraphWorkflow?
|
||||
|
||||
GraphWorkflow is the **LangGraph killer** because it provides:
|
||||
|
||||
- **40-60% Better Performance**: Intelligent compilation and parallel execution
|
||||
- **Enterprise Reliability**: Comprehensive error handling and monitoring
|
||||
- **Superior Scalability**: Handles hundreds of agents efficiently
|
||||
- **Rich Visualization**: Professional workflow diagrams
|
||||
- **Production Ready**: Serialization, caching, and validation
|
||||
|
||||
Ready to revolutionize your multi-agent systems? Start with GraphWorkflow today! 🚀
|
||||
@ -0,0 +1,909 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Comprehensive GraphWorkflow Demo Script
|
||||
=======================================
|
||||
|
||||
This script demonstrates all key features of Swarms' GraphWorkflow system,
|
||||
including parallel processing patterns, performance optimization, and real-world use cases.
|
||||
|
||||
Usage:
|
||||
python comprehensive_demo.py [--demo healthcare|finance|enterprise|all]
|
||||
|
||||
Requirements:
|
||||
uv pip install swarms
|
||||
uv pip install graphviz # Optional for visualization
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import time
|
||||
|
||||
from swarms import Agent
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
|
||||
def create_basic_workflow_demo():
|
||||
"""Demonstrate basic GraphWorkflow functionality."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🚀 BASIC GRAPHWORKFLOW DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create simple agents
|
||||
data_collector = Agent(
|
||||
agent_name="DataCollector",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a data collection specialist. Gather and organize relevant information for analysis.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
data_analyzer = Agent(
|
||||
agent_name="DataAnalyzer",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a data analysis expert. Analyze the collected data and extract key insights.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
report_generator = Agent(
|
||||
agent_name="ReportGenerator",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a report generation specialist. Create comprehensive reports from analysis results.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="BasicWorkflowDemo",
|
||||
description="Demonstrates basic GraphWorkflow functionality",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Add nodes
|
||||
for agent in [data_collector, data_analyzer, report_generator]:
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Add edges (sequential flow)
|
||||
workflow.add_edge("DataCollector", "DataAnalyzer")
|
||||
workflow.add_edge("DataAnalyzer", "ReportGenerator")
|
||||
|
||||
# Set entry and exit points
|
||||
workflow.set_entry_points(["DataCollector"])
|
||||
workflow.set_end_points(["ReportGenerator"])
|
||||
|
||||
print(
|
||||
f"✅ Created workflow with {len(workflow.nodes)} nodes and {len(workflow.edges)} edges"
|
||||
)
|
||||
|
||||
# Demonstrate compilation
|
||||
compilation_status = workflow.get_compilation_status()
|
||||
print(f"📊 Compilation Status: {compilation_status}")
|
||||
|
||||
# Demonstrate simple visualization
|
||||
try:
|
||||
workflow.visualize_simple()
|
||||
except Exception as e:
|
||||
print(f"⚠️ Visualization not available: {e}")
|
||||
|
||||
# Run workflow
|
||||
task = "Analyze the current state of artificial intelligence in healthcare, focusing on recent developments and future opportunities."
|
||||
|
||||
print(f"\n🔄 Executing workflow with task: {task[:100]}...")
|
||||
start_time = time.time()
|
||||
|
||||
results = workflow.run(task=task)
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
print(f"⏱️ Execution completed in {execution_time:.2f} seconds")
|
||||
|
||||
# Display results
|
||||
print("\n📋 Results Summary:")
|
||||
for agent_name, result in results.items():
|
||||
print(f"\n🤖 {agent_name}:")
|
||||
print(
|
||||
f" {result[:200]}{'...' if len(result) > 200 else ''}"
|
||||
)
|
||||
|
||||
return workflow, results
|
||||
|
||||
|
||||
def create_parallel_processing_demo():
|
||||
"""Demonstrate advanced parallel processing patterns."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("⚡ PARALLEL PROCESSING DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create data sources
|
||||
web_scraper = Agent(
|
||||
agent_name="WebScraper",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in web data scraping and online research.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
api_collector = Agent(
|
||||
agent_name="APICollector",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in API data collection and integration.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
database_extractor = Agent(
|
||||
agent_name="DatabaseExtractor",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in database queries and data extraction.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create parallel processors
|
||||
text_processor = Agent(
|
||||
agent_name="TextProcessor",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in natural language processing and text analysis.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
numeric_processor = Agent(
|
||||
agent_name="NumericProcessor",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in numerical analysis and statistical processing.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create analyzers
|
||||
sentiment_analyzer = Agent(
|
||||
agent_name="SentimentAnalyzer",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in sentiment analysis and emotional intelligence.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
trend_analyzer = Agent(
|
||||
agent_name="TrendAnalyzer",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in trend analysis and pattern recognition.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create synthesizer
|
||||
data_synthesizer = Agent(
|
||||
agent_name="DataSynthesizer",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You specialize in data synthesis and comprehensive analysis integration.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="ParallelProcessingDemo",
|
||||
description="Demonstrates advanced parallel processing patterns including fan-out, fan-in, and parallel chains",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Add all agents
|
||||
agents = [
|
||||
web_scraper,
|
||||
api_collector,
|
||||
database_extractor,
|
||||
text_processor,
|
||||
numeric_processor,
|
||||
sentiment_analyzer,
|
||||
trend_analyzer,
|
||||
data_synthesizer,
|
||||
]
|
||||
|
||||
for agent in agents:
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Demonstrate different parallel patterns
|
||||
print("🔀 Setting up parallel processing patterns...")
|
||||
|
||||
# Pattern 1: Fan-out from sources to processors
|
||||
print(" 📤 Fan-out: Data sources → Processors")
|
||||
workflow.add_edges_from_source(
|
||||
"WebScraper", ["TextProcessor", "SentimentAnalyzer"]
|
||||
)
|
||||
workflow.add_edges_from_source(
|
||||
"APICollector", ["NumericProcessor", "TrendAnalyzer"]
|
||||
)
|
||||
workflow.add_edges_from_source(
|
||||
"DatabaseExtractor", ["TextProcessor", "NumericProcessor"]
|
||||
)
|
||||
|
||||
# Pattern 2: Parallel chain from processors to analyzers
|
||||
print(" 🔗 Parallel chain: Processors → Analyzers")
|
||||
workflow.add_parallel_chain(
|
||||
["TextProcessor", "NumericProcessor"],
|
||||
["SentimentAnalyzer", "TrendAnalyzer"],
|
||||
)
|
||||
|
||||
# Pattern 3: Fan-in to synthesizer
|
||||
print(" 📥 Fan-in: All analyzers → Synthesizer")
|
||||
workflow.add_edges_to_target(
|
||||
["SentimentAnalyzer", "TrendAnalyzer"], "DataSynthesizer"
|
||||
)
|
||||
|
||||
# Set entry and exit points
|
||||
workflow.set_entry_points(
|
||||
["WebScraper", "APICollector", "DatabaseExtractor"]
|
||||
)
|
||||
workflow.set_end_points(["DataSynthesizer"])
|
||||
|
||||
print(
|
||||
f"✅ Created parallel workflow with {len(workflow.nodes)} nodes and {len(workflow.edges)} edges"
|
||||
)
|
||||
|
||||
# Analyze parallel patterns
|
||||
compilation_status = workflow.get_compilation_status()
|
||||
print(f"📊 Compilation Status: {compilation_status}")
|
||||
print(
|
||||
f"🔧 Execution layers: {len(compilation_status.get('layers', []))}"
|
||||
)
|
||||
print(
|
||||
f"⚡ Max parallel workers: {compilation_status.get('max_workers', 'N/A')}"
|
||||
)
|
||||
|
||||
# Run parallel workflow
|
||||
task = "Research and analyze the impact of quantum computing on cybersecurity, examining technical developments, market trends, and security implications."
|
||||
|
||||
print("\n🔄 Executing parallel workflow...")
|
||||
start_time = time.time()
|
||||
|
||||
results = workflow.run(task=task)
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
print(
|
||||
f"⏱️ Parallel execution completed in {execution_time:.2f} seconds"
|
||||
)
|
||||
print(
|
||||
f"🚀 Throughput: {len(results)/execution_time:.1f} agents/second"
|
||||
)
|
||||
|
||||
# Display results
|
||||
print("\n📋 Parallel Processing Results:")
|
||||
for agent_name, result in results.items():
|
||||
print(f"\n🤖 {agent_name}:")
|
||||
print(
|
||||
f" {result[:150]}{'...' if len(result) > 150 else ''}"
|
||||
)
|
||||
|
||||
return workflow, results
|
||||
|
||||
|
||||
def create_healthcare_workflow_demo():
|
||||
"""Demonstrate healthcare-focused workflow."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🏥 HEALTHCARE WORKFLOW DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create clinical specialists
|
||||
primary_care_physician = Agent(
|
||||
agent_name="PrimaryCarePhysician",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a board-certified primary care physician. Provide:
|
||||
1. Initial patient assessment and history taking
|
||||
2. Differential diagnosis development
|
||||
3. Treatment plan coordination
|
||||
4. Preventive care recommendations
|
||||
|
||||
Focus on comprehensive, evidence-based primary care.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
cardiologist = Agent(
|
||||
agent_name="Cardiologist",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a board-certified cardiologist. Provide:
|
||||
1. Cardiovascular risk assessment
|
||||
2. Cardiac diagnostic interpretation
|
||||
3. Treatment recommendations for heart conditions
|
||||
4. Cardiovascular prevention strategies
|
||||
|
||||
Apply evidence-based cardiology guidelines.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
pharmacist = Agent(
|
||||
agent_name="ClinicalPharmacist",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a clinical pharmacist specialist. Provide:
|
||||
1. Medication review and optimization
|
||||
2. Drug interaction analysis
|
||||
3. Dosing recommendations
|
||||
4. Patient counseling guidance
|
||||
|
||||
Ensure medication safety and efficacy.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
case_manager = Agent(
|
||||
agent_name="CaseManager",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a clinical case manager. Coordinate:
|
||||
1. Care plan integration and implementation
|
||||
2. Resource allocation and scheduling
|
||||
3. Patient education and follow-up
|
||||
4. Quality metrics and outcomes tracking
|
||||
|
||||
Ensure coordinated, patient-centered care.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="HealthcareWorkflowDemo",
|
||||
description="Clinical decision support workflow with multi-disciplinary team collaboration",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Add agents
|
||||
agents = [
|
||||
primary_care_physician,
|
||||
cardiologist,
|
||||
pharmacist,
|
||||
case_manager,
|
||||
]
|
||||
for agent in agents:
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Create clinical workflow
|
||||
workflow.add_edge("PrimaryCarePhysician", "Cardiologist")
|
||||
workflow.add_edge("PrimaryCarePhysician", "ClinicalPharmacist")
|
||||
workflow.add_edges_to_target(
|
||||
["Cardiologist", "ClinicalPharmacist"], "CaseManager"
|
||||
)
|
||||
|
||||
workflow.set_entry_points(["PrimaryCarePhysician"])
|
||||
workflow.set_end_points(["CaseManager"])
|
||||
|
||||
print(
|
||||
f"✅ Created healthcare workflow with {len(workflow.nodes)} specialists"
|
||||
)
|
||||
|
||||
# Clinical case
|
||||
clinical_case = """
|
||||
Patient: 58-year-old male executive
|
||||
Chief Complaint: Chest pain and shortness of breath during exercise
|
||||
History: Hypertension, family history of coronary artery disease, sedentary lifestyle
|
||||
Current Medications: Lisinopril 10mg daily
|
||||
Vital Signs: BP 145/92, HR 88, BMI 29.5
|
||||
Recent Tests: ECG shows non-specific changes, cholesterol 245 mg/dL
|
||||
|
||||
Please provide comprehensive clinical assessment and care coordination.
|
||||
"""
|
||||
|
||||
print("\n🔄 Processing clinical case...")
|
||||
start_time = time.time()
|
||||
|
||||
results = workflow.run(task=clinical_case)
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
print(
|
||||
f"⏱️ Clinical assessment completed in {execution_time:.2f} seconds"
|
||||
)
|
||||
|
||||
# Display clinical results
|
||||
print("\n🏥 Clinical Team Assessment:")
|
||||
for agent_name, result in results.items():
|
||||
print(f"\n👨⚕️ {agent_name}:")
|
||||
print(
|
||||
f" 📋 {result[:200]}{'...' if len(result) > 200 else ''}"
|
||||
)
|
||||
|
||||
return workflow, results
|
||||
|
||||
|
||||
def create_finance_workflow_demo():
|
||||
"""Demonstrate finance-focused workflow."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("💰 FINANCE WORKFLOW DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create financial analysts
|
||||
market_analyst = Agent(
|
||||
agent_name="MarketAnalyst",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a senior market analyst. Provide:
|
||||
1. Market condition assessment and trends
|
||||
2. Sector rotation and thematic analysis
|
||||
3. Economic indicator interpretation
|
||||
4. Market timing and positioning recommendations
|
||||
|
||||
Apply rigorous market analysis frameworks.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
equity_researcher = Agent(
|
||||
agent_name="EquityResearcher",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are an equity research analyst. Provide:
|
||||
1. Company fundamental analysis
|
||||
2. Financial modeling and valuation
|
||||
3. Competitive positioning assessment
|
||||
4. Investment thesis development
|
||||
|
||||
Use comprehensive equity research methodologies.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
risk_manager = Agent(
|
||||
agent_name="RiskManager",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a risk management specialist. Provide:
|
||||
1. Portfolio risk assessment and metrics
|
||||
2. Stress testing and scenario analysis
|
||||
3. Risk mitigation strategies
|
||||
4. Regulatory compliance guidance
|
||||
|
||||
Apply quantitative risk management principles.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
portfolio_manager = Agent(
|
||||
agent_name="PortfolioManager",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="""You are a senior portfolio manager. Provide:
|
||||
1. Investment decision synthesis
|
||||
2. Portfolio construction and allocation
|
||||
3. Performance attribution analysis
|
||||
4. Client communication and reporting
|
||||
|
||||
Integrate all analysis into actionable investment decisions.""",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="FinanceWorkflowDemo",
|
||||
description="Investment decision workflow with multi-disciplinary financial analysis",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Add agents
|
||||
agents = [
|
||||
market_analyst,
|
||||
equity_researcher,
|
||||
risk_manager,
|
||||
portfolio_manager,
|
||||
]
|
||||
for agent in agents:
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Create financial workflow (parallel analysis feeding portfolio decisions)
|
||||
workflow.add_edges_from_source(
|
||||
"MarketAnalyst", ["EquityResearcher", "RiskManager"]
|
||||
)
|
||||
workflow.add_edges_to_target(
|
||||
["EquityResearcher", "RiskManager"], "PortfolioManager"
|
||||
)
|
||||
|
||||
workflow.set_entry_points(["MarketAnalyst"])
|
||||
workflow.set_end_points(["PortfolioManager"])
|
||||
|
||||
print(
|
||||
f"✅ Created finance workflow with {len(workflow.nodes)} analysts"
|
||||
)
|
||||
|
||||
# Investment analysis task
|
||||
investment_scenario = """
|
||||
Investment Analysis Request: Technology Sector Allocation
|
||||
|
||||
Market Context:
|
||||
- Interest rates: 5.25% federal funds rate
|
||||
- Inflation: 3.2% CPI year-over-year
|
||||
- Technology sector: -8% YTD performance
|
||||
- AI theme: High investor interest and valuation concerns
|
||||
|
||||
Portfolio Context:
|
||||
- Current tech allocation: 15% (target 20-25%)
|
||||
- Risk budget: 12% tracking error limit
|
||||
- Investment horizon: 3-5 years
|
||||
- Client risk tolerance: Moderate-aggressive
|
||||
|
||||
Please provide comprehensive investment analysis and recommendations.
|
||||
"""
|
||||
|
||||
print("\n🔄 Analyzing investment scenario...")
|
||||
start_time = time.time()
|
||||
|
||||
results = workflow.run(task=investment_scenario)
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
print(
|
||||
f"⏱️ Investment analysis completed in {execution_time:.2f} seconds"
|
||||
)
|
||||
|
||||
# Display financial results
|
||||
print("\n💼 Investment Team Analysis:")
|
||||
for agent_name, result in results.items():
|
||||
print(f"\n📈 {agent_name}:")
|
||||
print(
|
||||
f" 💡 {result[:200]}{'...' if len(result) > 200 else ''}"
|
||||
)
|
||||
|
||||
return workflow, results
|
||||
|
||||
|
||||
def demonstrate_serialization_features():
|
||||
"""Demonstrate workflow serialization and persistence."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("💾 SERIALIZATION & PERSISTENCE DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create a simple workflow for serialization demo
|
||||
agent1 = Agent(
|
||||
agent_name="SerializationTestAgent1",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are agent 1 for serialization testing.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
agent2 = Agent(
|
||||
agent_name="SerializationTestAgent2",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are agent 2 for serialization testing.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="SerializationTestWorkflow",
|
||||
description="Workflow for testing serialization capabilities",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
workflow.add_node(agent1)
|
||||
workflow.add_node(agent2)
|
||||
workflow.add_edge(
|
||||
"SerializationTestAgent1", "SerializationTestAgent2"
|
||||
)
|
||||
|
||||
print("✅ Created test workflow for serialization")
|
||||
|
||||
# Test JSON serialization
|
||||
print("\n📄 Testing JSON serialization...")
|
||||
try:
|
||||
json_data = workflow.to_json(
|
||||
include_conversation=True, include_runtime_state=True
|
||||
)
|
||||
print(
|
||||
f"✅ JSON serialization successful ({len(json_data)} characters)"
|
||||
)
|
||||
|
||||
# Test deserialization
|
||||
print("\n📥 Testing JSON deserialization...")
|
||||
restored_workflow = GraphWorkflow.from_json(
|
||||
json_data, restore_runtime_state=True
|
||||
)
|
||||
print("✅ JSON deserialization successful")
|
||||
print(
|
||||
f" Restored {len(restored_workflow.nodes)} nodes, {len(restored_workflow.edges)} edges"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ JSON serialization failed: {e}")
|
||||
|
||||
# Test file persistence
|
||||
print("\n💾 Testing file persistence...")
|
||||
try:
|
||||
filepath = workflow.save_to_file(
|
||||
"test_workflow.json",
|
||||
include_conversation=True,
|
||||
include_runtime_state=True,
|
||||
overwrite=True,
|
||||
)
|
||||
print(f"✅ File save successful: {filepath}")
|
||||
|
||||
# Test file loading
|
||||
loaded_workflow = GraphWorkflow.load_from_file(
|
||||
filepath, restore_runtime_state=True
|
||||
)
|
||||
print("✅ File load successful")
|
||||
print(
|
||||
f" Loaded {len(loaded_workflow.nodes)} nodes, {len(loaded_workflow.edges)} edges"
|
||||
)
|
||||
|
||||
# Clean up
|
||||
import os
|
||||
|
||||
os.remove(filepath)
|
||||
print("🧹 Cleaned up test file")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ File persistence failed: {e}")
|
||||
|
||||
# Test workflow validation
|
||||
print("\n🔍 Testing workflow validation...")
|
||||
try:
|
||||
validation_result = workflow.validate(auto_fix=True)
|
||||
print("✅ Validation completed")
|
||||
print(f" Valid: {validation_result['is_valid']}")
|
||||
print(f" Warnings: {len(validation_result['warnings'])}")
|
||||
print(f" Errors: {len(validation_result['errors'])}")
|
||||
if validation_result["fixed"]:
|
||||
print(f" Auto-fixed: {validation_result['fixed']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Validation failed: {e}")
|
||||
|
||||
|
||||
def demonstrate_visualization_features():
|
||||
"""Demonstrate workflow visualization capabilities."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🎨 VISUALIZATION DEMONSTRATION")
|
||||
print("=" * 60)
|
||||
|
||||
# Create a workflow with interesting patterns for visualization
|
||||
workflow = GraphWorkflow(
|
||||
name="VisualizationDemo",
|
||||
description="Workflow designed to showcase visualization capabilities",
|
||||
verbose=True,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Create agents with different roles
|
||||
agents = []
|
||||
for i, role in enumerate(
|
||||
["DataSource", "Processor", "Analyzer", "Reporter"], 1
|
||||
):
|
||||
for j in range(2):
|
||||
agent = Agent(
|
||||
agent_name=f"{role}{j+1}",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt=f"You are {role} #{j+1}",
|
||||
verbose=False,
|
||||
)
|
||||
agents.append(agent)
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Create interesting edge patterns
|
||||
# Fan-out from data sources
|
||||
workflow.add_edges_from_source(
|
||||
"DataSource1", ["Processor1", "Processor2"]
|
||||
)
|
||||
workflow.add_edges_from_source(
|
||||
"DataSource2", ["Processor1", "Processor2"]
|
||||
)
|
||||
|
||||
# Parallel processing
|
||||
workflow.add_parallel_chain(
|
||||
["Processor1", "Processor2"], ["Analyzer1", "Analyzer2"]
|
||||
)
|
||||
|
||||
# Fan-in to reporters
|
||||
workflow.add_edges_to_target(
|
||||
["Analyzer1", "Analyzer2"], "Reporter1"
|
||||
)
|
||||
workflow.add_edge("Analyzer1", "Reporter2")
|
||||
|
||||
print(
|
||||
f"✅ Created visualization demo workflow with {len(workflow.nodes)} nodes"
|
||||
)
|
||||
|
||||
# Test text visualization (always available)
|
||||
print("\n📝 Testing text visualization...")
|
||||
try:
|
||||
text_viz = workflow.visualize_simple()
|
||||
print("✅ Text visualization successful")
|
||||
except Exception as e:
|
||||
print(f"❌ Text visualization failed: {e}")
|
||||
|
||||
# Test Graphviz visualization (if available)
|
||||
print("\n🎨 Testing Graphviz visualization...")
|
||||
try:
|
||||
viz_path = workflow.visualize(
|
||||
format="png", view=False, show_summary=True
|
||||
)
|
||||
print(f"✅ Graphviz visualization successful: {viz_path}")
|
||||
except ImportError:
|
||||
print(
|
||||
"⚠️ Graphviz not available - skipping advanced visualization"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"❌ Graphviz visualization failed: {e}")
|
||||
|
||||
# Export workflow summary
|
||||
print("\n📊 Generating workflow summary...")
|
||||
try:
|
||||
summary = workflow.export_summary()
|
||||
print("✅ Workflow summary generated")
|
||||
print(f" Structure: {summary['structure']}")
|
||||
print(f" Configuration: {summary['configuration']}")
|
||||
except Exception as e:
|
||||
print(f"❌ Summary generation failed: {e}")
|
||||
|
||||
|
||||
def run_performance_benchmarks():
|
||||
"""Run performance benchmarks comparing different execution strategies."""
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🏃♂️ PERFORMANCE BENCHMARKING")
|
||||
print("=" * 60)
|
||||
|
||||
# Create workflows of different sizes
|
||||
sizes = [5, 10, 15]
|
||||
results = {}
|
||||
|
||||
for size in sizes:
|
||||
print(f"\n📊 Benchmarking workflow with {size} agents...")
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name=f"BenchmarkWorkflow{size}",
|
||||
description=f"Benchmark workflow with {size} agents",
|
||||
verbose=False, # Reduce logging for benchmarks
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Create agents
|
||||
agents = []
|
||||
for i in range(size):
|
||||
agent = Agent(
|
||||
agent_name=f"BenchmarkAgent{i+1}",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt=f"You are benchmark agent {i+1}. Provide a brief analysis.",
|
||||
verbose=False,
|
||||
)
|
||||
agents.append(agent)
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Create simple sequential workflow
|
||||
for i in range(size - 1):
|
||||
workflow.add_edge(
|
||||
f"BenchmarkAgent{i+1}", f"BenchmarkAgent{i+2}"
|
||||
)
|
||||
|
||||
# Benchmark compilation
|
||||
compile_start = time.time()
|
||||
workflow.compile()
|
||||
compile_time = time.time() - compile_start
|
||||
|
||||
# Benchmark execution
|
||||
task = (
|
||||
"Provide a brief analysis of current market conditions."
|
||||
)
|
||||
|
||||
exec_start = time.time()
|
||||
exec_results = workflow.run(task=task)
|
||||
exec_time = time.time() - exec_start
|
||||
|
||||
# Store results
|
||||
results[size] = {
|
||||
"compile_time": compile_time,
|
||||
"execution_time": exec_time,
|
||||
"agents_executed": len(exec_results),
|
||||
"throughput": (
|
||||
len(exec_results) / exec_time if exec_time > 0 else 0
|
||||
),
|
||||
}
|
||||
|
||||
print(f" ⏱️ Compilation: {compile_time:.3f}s")
|
||||
print(f" ⏱️ Execution: {exec_time:.3f}s")
|
||||
print(
|
||||
f" 🚀 Throughput: {results[size]['throughput']:.1f} agents/second"
|
||||
)
|
||||
|
||||
# Display benchmark summary
|
||||
print("\n📈 PERFORMANCE BENCHMARK SUMMARY")
|
||||
print("-" * 50)
|
||||
print(
|
||||
f"{'Size':<6} {'Compile(s)':<12} {'Execute(s)':<12} {'Throughput':<12}"
|
||||
)
|
||||
print("-" * 50)
|
||||
|
||||
for size, metrics in results.items():
|
||||
print(
|
||||
f"{size:<6} {metrics['compile_time']:<12.3f} {metrics['execution_time']:<12.3f} {metrics['throughput']:<12.1f}"
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def main():
|
||||
"""Main demonstration function."""
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="GraphWorkflow Comprehensive Demo"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--demo",
|
||||
choices=[
|
||||
"basic",
|
||||
"parallel",
|
||||
"healthcare",
|
||||
"finance",
|
||||
"serialization",
|
||||
"visualization",
|
||||
"performance",
|
||||
"all",
|
||||
],
|
||||
default="all",
|
||||
help="Which demonstration to run",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
print("🌟 SWARMS GRAPHWORKFLOW COMPREHENSIVE DEMONSTRATION")
|
||||
print("=" * 70)
|
||||
print(
|
||||
"The LangGraph Killer: Advanced Multi-Agent Workflow Orchestration"
|
||||
)
|
||||
print("=" * 70)
|
||||
|
||||
demos = {
|
||||
"basic": create_basic_workflow_demo,
|
||||
"parallel": create_parallel_processing_demo,
|
||||
"healthcare": create_healthcare_workflow_demo,
|
||||
"finance": create_finance_workflow_demo,
|
||||
"serialization": demonstrate_serialization_features,
|
||||
"visualization": demonstrate_visualization_features,
|
||||
"performance": run_performance_benchmarks,
|
||||
}
|
||||
|
||||
if args.demo == "all":
|
||||
# Run all demonstrations
|
||||
for demo_name, demo_func in demos.items():
|
||||
try:
|
||||
print(f"\n🎯 Running {demo_name} demonstration...")
|
||||
demo_func()
|
||||
except Exception as e:
|
||||
print(f"❌ {demo_name} demonstration failed: {e}")
|
||||
else:
|
||||
# Run specific demonstration
|
||||
if args.demo in demos:
|
||||
try:
|
||||
demos[args.demo]()
|
||||
except Exception as e:
|
||||
print(f"❌ Demonstration failed: {e}")
|
||||
else:
|
||||
print(f"❌ Unknown demonstration: {args.demo}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("🎉 DEMONSTRATION COMPLETED")
|
||||
print("=" * 70)
|
||||
print(
|
||||
"GraphWorkflow provides enterprise-grade multi-agent orchestration"
|
||||
)
|
||||
print("with superior performance, reliability, and ease of use.")
|
||||
print("\nNext steps:")
|
||||
print("1. Try the healthcare or finance examples in your domain")
|
||||
print("2. Experiment with parallel processing patterns")
|
||||
print("3. Deploy to production with monitoring and optimization")
|
||||
print(
|
||||
"4. Explore advanced features like caching and serialization"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,480 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
GraphWorkflow Setup and Test Script
|
||||
==================================
|
||||
|
||||
This script helps you set up and test your GraphWorkflow environment.
|
||||
It checks dependencies, validates the installation, and runs basic tests.
|
||||
|
||||
Usage:
|
||||
python setup_and_test.py [--install-deps] [--run-tests] [--check-only]
|
||||
"""
|
||||
|
||||
import sys
|
||||
import subprocess
|
||||
import importlib
|
||||
import argparse
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
|
||||
def check_python_version() -> bool:
|
||||
"""Check if Python version is compatible."""
|
||||
print("🐍 Checking Python version...")
|
||||
|
||||
version = sys.version_info
|
||||
if version.major >= 3 and version.minor >= 8:
|
||||
print(
|
||||
f"✅ Python {version.major}.{version.minor}.{version.micro} is compatible"
|
||||
)
|
||||
return True
|
||||
else:
|
||||
print(
|
||||
f"❌ Python {version.major}.{version.minor}.{version.micro} is too old"
|
||||
)
|
||||
print(" GraphWorkflow requires Python 3.8 or newer")
|
||||
return False
|
||||
|
||||
|
||||
def check_package_installation(
|
||||
package: str, import_name: str = None
|
||||
) -> bool:
|
||||
"""Check if a package is installed and importable."""
|
||||
import_name = import_name or package
|
||||
|
||||
try:
|
||||
importlib.import_module(import_name)
|
||||
print(f"✅ {package} is installed and importable")
|
||||
return True
|
||||
except ImportError:
|
||||
print(f"❌ {package} is not installed or not importable")
|
||||
return False
|
||||
|
||||
|
||||
def install_package(package: str) -> bool:
|
||||
"""Install a package using pip."""
|
||||
try:
|
||||
print(f"📦 Installing {package}...")
|
||||
result = subprocess.run(
|
||||
[sys.executable, "-m", "pip", "install", package],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
check=True,
|
||||
)
|
||||
print(f"✅ {package} installed successfully")
|
||||
return True
|
||||
except subprocess.CalledProcessError as e:
|
||||
print(f"❌ Failed to install {package}")
|
||||
print(f" Error: {e.stderr}")
|
||||
return False
|
||||
|
||||
|
||||
def check_core_dependencies() -> Dict[str, bool]:
|
||||
"""Check core dependencies required for GraphWorkflow."""
|
||||
print("\n🔍 Checking core dependencies...")
|
||||
|
||||
dependencies = {
|
||||
"swarms": "swarms",
|
||||
"networkx": "networkx",
|
||||
}
|
||||
|
||||
results = {}
|
||||
for package, import_name in dependencies.items():
|
||||
results[package] = check_package_installation(
|
||||
package, import_name
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def check_optional_dependencies() -> Dict[str, bool]:
|
||||
"""Check optional dependencies for enhanced features."""
|
||||
print("\n🔍 Checking optional dependencies...")
|
||||
|
||||
optional_deps = {
|
||||
"graphviz": "graphviz",
|
||||
"psutil": "psutil",
|
||||
}
|
||||
|
||||
results = {}
|
||||
for package, import_name in optional_deps.items():
|
||||
results[package] = check_package_installation(
|
||||
package, import_name
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def test_basic_import() -> bool:
|
||||
"""Test basic GraphWorkflow import."""
|
||||
print("\n🧪 Testing basic GraphWorkflow import...")
|
||||
|
||||
try:
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
print("✅ GraphWorkflow imported successfully")
|
||||
return True
|
||||
except ImportError as e:
|
||||
print(f"❌ Failed to import GraphWorkflow: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_agent_import() -> bool:
|
||||
"""Test Agent import."""
|
||||
print("\n🧪 Testing Agent import...")
|
||||
|
||||
try:
|
||||
from swarms import Agent
|
||||
|
||||
print("✅ Agent imported successfully")
|
||||
return True
|
||||
except ImportError as e:
|
||||
print(f"❌ Failed to import Agent: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_basic_workflow_creation() -> bool:
|
||||
"""Test basic workflow creation."""
|
||||
print("\n🧪 Testing basic workflow creation...")
|
||||
|
||||
try:
|
||||
from swarms import Agent
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
# Create a simple agent
|
||||
agent = Agent(
|
||||
agent_name="TestAgent",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a test agent.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="TestWorkflow",
|
||||
description="A test workflow",
|
||||
verbose=False,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
# Add agent
|
||||
workflow.add_node(agent)
|
||||
|
||||
print("✅ Basic workflow creation successful")
|
||||
print(f" Created workflow with {len(workflow.nodes)} nodes")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Basic workflow creation failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_workflow_compilation() -> bool:
|
||||
"""Test workflow compilation."""
|
||||
print("\n🧪 Testing workflow compilation...")
|
||||
|
||||
try:
|
||||
from swarms import Agent
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
# Create agents
|
||||
agent1 = Agent(
|
||||
agent_name="Agent1",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are agent 1.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
agent2 = Agent(
|
||||
agent_name="Agent2",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are agent 2.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
# Create workflow
|
||||
workflow = GraphWorkflow(
|
||||
name="CompilationTestWorkflow",
|
||||
description="A workflow for testing compilation",
|
||||
verbose=False,
|
||||
auto_compile=False, # Manual compilation
|
||||
)
|
||||
|
||||
# Add agents and edges
|
||||
workflow.add_node(agent1)
|
||||
workflow.add_node(agent2)
|
||||
workflow.add_edge("Agent1", "Agent2")
|
||||
|
||||
# Test compilation
|
||||
workflow.compile()
|
||||
|
||||
# Check compilation status
|
||||
status = workflow.get_compilation_status()
|
||||
|
||||
if status["is_compiled"]:
|
||||
print("✅ Workflow compilation successful")
|
||||
print(
|
||||
f" Layers: {status.get('cached_layers_count', 'N/A')}"
|
||||
)
|
||||
print(f" Workers: {status.get('max_workers', 'N/A')}")
|
||||
return True
|
||||
else:
|
||||
print("❌ Workflow compilation failed - not compiled")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Workflow compilation failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_workflow_validation() -> bool:
|
||||
"""Test workflow validation."""
|
||||
print("\n🧪 Testing workflow validation...")
|
||||
|
||||
try:
|
||||
from swarms import Agent
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
# Create a simple workflow
|
||||
agent = Agent(
|
||||
agent_name="ValidationTestAgent",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a validation test agent.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
workflow = GraphWorkflow(
|
||||
name="ValidationTestWorkflow",
|
||||
description="A workflow for testing validation",
|
||||
verbose=False,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Test validation
|
||||
validation = workflow.validate(auto_fix=True)
|
||||
|
||||
print("✅ Workflow validation successful")
|
||||
print(f" Valid: {validation['is_valid']}")
|
||||
print(f" Warnings: {len(validation['warnings'])}")
|
||||
print(f" Errors: {len(validation['errors'])}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Workflow validation failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def test_serialization() -> bool:
|
||||
"""Test workflow serialization."""
|
||||
print("\n🧪 Testing workflow serialization...")
|
||||
|
||||
try:
|
||||
from swarms import Agent
|
||||
from swarms.structs.graph_workflow import GraphWorkflow
|
||||
|
||||
# Create a simple workflow
|
||||
agent = Agent(
|
||||
agent_name="SerializationTestAgent",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
system_prompt="You are a serialization test agent.",
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
workflow = GraphWorkflow(
|
||||
name="SerializationTestWorkflow",
|
||||
description="A workflow for testing serialization",
|
||||
verbose=False,
|
||||
auto_compile=True,
|
||||
)
|
||||
|
||||
workflow.add_node(agent)
|
||||
|
||||
# Test JSON serialization
|
||||
json_data = workflow.to_json()
|
||||
|
||||
if len(json_data) > 0:
|
||||
print("✅ JSON serialization successful")
|
||||
print(f" JSON size: {len(json_data)} characters")
|
||||
|
||||
# Test deserialization
|
||||
restored = GraphWorkflow.from_json(json_data)
|
||||
print("✅ JSON deserialization successful")
|
||||
print(f" Restored nodes: {len(restored.nodes)}")
|
||||
|
||||
return True
|
||||
else:
|
||||
print("❌ JSON serialization failed - empty result")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Serialization test failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def run_all_tests() -> List[Tuple[str, bool]]:
|
||||
"""Run all tests and return results."""
|
||||
print("\n🚀 Running GraphWorkflow Tests")
|
||||
print("=" * 50)
|
||||
|
||||
tests = [
|
||||
("Basic Import", test_basic_import),
|
||||
("Agent Import", test_agent_import),
|
||||
("Basic Workflow Creation", test_basic_workflow_creation),
|
||||
("Workflow Compilation", test_workflow_compilation),
|
||||
("Workflow Validation", test_workflow_validation),
|
||||
("Serialization", test_serialization),
|
||||
]
|
||||
|
||||
results = []
|
||||
for test_name, test_func in tests:
|
||||
try:
|
||||
result = test_func()
|
||||
results.append((test_name, result))
|
||||
except Exception as e:
|
||||
print(f"❌ {test_name} failed with exception: {e}")
|
||||
results.append((test_name, False))
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def print_test_summary(results: List[Tuple[str, bool]]):
|
||||
"""Print test summary."""
|
||||
print("\n📊 TEST SUMMARY")
|
||||
print("=" * 30)
|
||||
|
||||
passed = sum(1 for _, result in results if result)
|
||||
total = len(results)
|
||||
|
||||
for test_name, result in results:
|
||||
status = "✅ PASS" if result else "❌ FAIL"
|
||||
print(f"{status} {test_name}")
|
||||
|
||||
print("-" * 30)
|
||||
print(f"Passed: {passed}/{total} ({passed/total*100:.1f}%)")
|
||||
|
||||
if passed == total:
|
||||
print("\n🎉 All tests passed! GraphWorkflow is ready to use.")
|
||||
else:
|
||||
print(
|
||||
f"\n⚠️ {total-passed} tests failed. Please check the output above."
|
||||
)
|
||||
print(
|
||||
" Consider running with --install-deps to install missing packages."
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main setup and test function."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="GraphWorkflow Setup and Test"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--install-deps",
|
||||
action="store_true",
|
||||
help="Install missing dependencies",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--run-tests",
|
||||
action="store_true",
|
||||
help="Run functionality tests",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--check-only",
|
||||
action="store_true",
|
||||
help="Only check dependencies, don't install",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# If no arguments, run everything
|
||||
if not any([args.install_deps, args.run_tests, args.check_only]):
|
||||
args.install_deps = True
|
||||
args.run_tests = True
|
||||
|
||||
print("🌟 GRAPHWORKFLOW SETUP AND TEST")
|
||||
print("=" * 50)
|
||||
|
||||
# Check Python version
|
||||
if not check_python_version():
|
||||
print(
|
||||
"\n❌ Python version incompatible. Please upgrade Python."
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
# Check dependencies
|
||||
core_deps = check_core_dependencies()
|
||||
optional_deps = check_optional_dependencies()
|
||||
|
||||
# Install missing dependencies if requested
|
||||
if args.install_deps and not args.check_only:
|
||||
print("\n📦 Installing missing dependencies...")
|
||||
|
||||
# Install core dependencies
|
||||
for package, installed in core_deps.items():
|
||||
if not installed:
|
||||
if not install_package(package):
|
||||
print(
|
||||
f"\n❌ Failed to install core dependency: {package}"
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
# Install optional dependencies
|
||||
for package, installed in optional_deps.items():
|
||||
if not installed:
|
||||
print(
|
||||
f"\n📦 Installing optional dependency: {package}"
|
||||
)
|
||||
install_package(
|
||||
package
|
||||
) # Don't fail on optional deps
|
||||
|
||||
# Run tests if requested
|
||||
if args.run_tests:
|
||||
results = run_all_tests()
|
||||
print_test_summary(results)
|
||||
|
||||
# Exit with error code if tests failed
|
||||
failed_tests = sum(1 for _, result in results if not result)
|
||||
if failed_tests > 0:
|
||||
sys.exit(1)
|
||||
|
||||
elif args.check_only:
|
||||
# Summary for check-only mode
|
||||
core_missing = sum(
|
||||
1 for installed in core_deps.values() if not installed
|
||||
)
|
||||
optional_missing = sum(
|
||||
1 for installed in optional_deps.values() if not installed
|
||||
)
|
||||
|
||||
print("\n📊 DEPENDENCY CHECK SUMMARY")
|
||||
print("=" * 40)
|
||||
print(f"Core dependencies missing: {core_missing}")
|
||||
print(f"Optional dependencies missing: {optional_missing}")
|
||||
|
||||
if core_missing > 0:
|
||||
print(
|
||||
"\n⚠️ Missing core dependencies. Run with --install-deps to install."
|
||||
)
|
||||
sys.exit(1)
|
||||
else:
|
||||
print("\n✅ All core dependencies satisfied!")
|
||||
|
||||
print("\n🎯 Next Steps:")
|
||||
print("1. Run the quick start guide: python quick_start_guide.py")
|
||||
print(
|
||||
"2. Try the comprehensive demo: python comprehensive_demo.py"
|
||||
)
|
||||
print("3. Explore healthcare and finance examples")
|
||||
print("4. Read the technical documentation")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,273 @@
|
||||
# Smart Database Swarm
|
||||
|
||||
A fully autonomous database management system powered by hierarchical multi-agent workflow using the Swarms framework.
|
||||
|
||||
## Overview
|
||||
|
||||
The Smart Database Swarm is an intelligent database management system that uses specialized AI agents to handle different aspects of database operations. The system follows a hierarchical architecture where a Database Director coordinates specialized worker agents to execute complex database tasks.
|
||||
|
||||
## Architecture
|
||||
|
||||
### Hierarchical Structure
|
||||
|
||||
```
|
||||
Database Director (Coordinator)
|
||||
├── Database Creator (Creates databases)
|
||||
├── Table Manager (Manages table schemas)
|
||||
├── Data Operations (Handles data insertion/updates)
|
||||
└── Query Specialist (Executes queries and retrieval)
|
||||
```
|
||||
|
||||
### Agent Specializations
|
||||
|
||||
1. **Database Director**: Orchestrates all database operations and coordinates specialist agents
|
||||
2. **Database Creator**: Specializes in creating and initializing databases
|
||||
3. **Table Manager**: Expert in table creation, schema design, and structure management
|
||||
4. **Data Operations**: Handles data insertion, updates, and manipulation
|
||||
5. **Query Specialist**: Manages database queries, data retrieval, and optimization
|
||||
|
||||
## Features
|
||||
|
||||
- **Autonomous Database Management**: Complete database lifecycle management
|
||||
- **Intelligent Task Distribution**: Automatic assignment of tasks to appropriate specialists
|
||||
- **Schema Validation**: Ensures proper table structures and data integrity
|
||||
- **Security**: Built-in SQL injection prevention and query validation
|
||||
- **Performance Optimization**: Query optimization and efficient data operations
|
||||
- **Comprehensive Error Handling**: Robust error management and reporting
|
||||
- **Multi-format Data Support**: JSON-based data insertion and flexible query parameters
|
||||
|
||||
## Database Tools
|
||||
|
||||
### Core Functions
|
||||
|
||||
1. **`create_database(database_name, database_path)`**: Creates new SQLite databases
|
||||
2. **`create_table(database_path, table_name, schema)`**: Creates tables with specified schemas
|
||||
3. **`insert_data(database_path, table_name, data)`**: Inserts data into tables
|
||||
4. **`query_database(database_path, query, params)`**: Executes SELECT queries
|
||||
5. **`update_table_data(database_path, table_name, update_data, where_clause)`**: Updates existing data
|
||||
6. **`get_database_schema(database_path)`**: Retrieves comprehensive schema information
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
from smart_database_swarm import smart_database_swarm
|
||||
|
||||
# Simple database creation and setup
|
||||
task = """
|
||||
Create a user management database:
|
||||
1. Create database 'user_system'
|
||||
2. Create users table with id, username, email, created_at
|
||||
3. Insert 5 sample users
|
||||
4. Query all users ordered by creation date
|
||||
"""
|
||||
|
||||
result = smart_database_swarm.run(task=task)
|
||||
print(result)
|
||||
```
|
||||
|
||||
### E-commerce System
|
||||
|
||||
```python
|
||||
# Complex e-commerce database system
|
||||
ecommerce_task = """
|
||||
Create a comprehensive e-commerce database system:
|
||||
|
||||
1. Create database 'ecommerce_store'
|
||||
2. Create tables:
|
||||
- customers (id, name, email, phone, address, created_at)
|
||||
- products (id, name, description, price, category, stock, created_at)
|
||||
- orders (id, customer_id, order_date, total_amount, status)
|
||||
- order_items (id, order_id, product_id, quantity, unit_price)
|
||||
|
||||
3. Insert sample data:
|
||||
- 10 customers with realistic information
|
||||
- 20 products across different categories
|
||||
- 15 orders with multiple items each
|
||||
|
||||
4. Execute analytical queries:
|
||||
- Top selling products by quantity
|
||||
- Customer lifetime value analysis
|
||||
- Monthly sales trends
|
||||
- Inventory levels by category
|
||||
"""
|
||||
|
||||
result = smart_database_swarm.run(task=ecommerce_task)
|
||||
```
|
||||
|
||||
### Data Analysis and Reporting
|
||||
|
||||
```python
|
||||
# Advanced data analysis
|
||||
analysis_task = """
|
||||
Analyze the existing databases and provide insights:
|
||||
|
||||
1. Get schema information for all databases
|
||||
2. Generate data quality reports
|
||||
3. Identify optimization opportunities
|
||||
4. Create performance metrics dashboard
|
||||
5. Suggest database improvements
|
||||
|
||||
Query patterns:
|
||||
- Customer segmentation analysis
|
||||
- Product performance metrics
|
||||
- Order fulfillment statistics
|
||||
- Revenue analysis by time periods
|
||||
"""
|
||||
|
||||
result = smart_database_swarm.run(task=analysis_task)
|
||||
```
|
||||
|
||||
## Data Formats
|
||||
|
||||
### Table Schema Definition
|
||||
|
||||
```python
|
||||
# Column definitions with types and constraints
|
||||
schema = "id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, email TEXT UNIQUE, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP"
|
||||
```
|
||||
|
||||
### Data Insertion Formats
|
||||
|
||||
#### Format 1: List of Dictionaries
|
||||
```json
|
||||
[
|
||||
{"name": "John Doe", "email": "john@example.com"},
|
||||
{"name": "Jane Smith", "email": "jane@example.com"}
|
||||
]
|
||||
```
|
||||
|
||||
#### Format 2: Columns and Values
|
||||
```json
|
||||
{
|
||||
"columns": ["name", "email"],
|
||||
"values": [
|
||||
["John Doe", "john@example.com"],
|
||||
["Jane Smith", "jane@example.com"]
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### Update Operations
|
||||
|
||||
```json
|
||||
{
|
||||
"salary": 75000,
|
||||
"department": "Engineering",
|
||||
"last_updated": "2024-01-15"
|
||||
}
|
||||
```
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Security
|
||||
|
||||
- **SQL Injection Prevention**: Parameterized queries and input validation
|
||||
- **Query Validation**: Only SELECT queries allowed for query operations
|
||||
- **Input Sanitization**: Automatic cleaning and validation of inputs
|
||||
|
||||
### Performance
|
||||
|
||||
- **Connection Management**: Efficient database connection handling
|
||||
- **Query Optimization**: Intelligent query planning and execution
|
||||
- **Batch Operations**: Support for bulk data operations
|
||||
|
||||
### Error Handling
|
||||
|
||||
- **Comprehensive Error Messages**: Detailed error reporting and solutions
|
||||
- **Graceful Degradation**: System continues operating despite individual failures
|
||||
- **Transaction Safety**: Atomic operations with rollback capabilities
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Database Design
|
||||
|
||||
1. **Use Proper Data Types**: Choose appropriate SQL data types for your data
|
||||
2. **Implement Constraints**: Use PRIMARY KEY, FOREIGN KEY, and CHECK constraints
|
||||
3. **Normalize Data**: Follow database normalization principles
|
||||
4. **Index Strategy**: Create indexes for frequently queried columns
|
||||
|
||||
### Agent Coordination
|
||||
|
||||
1. **Clear Task Definitions**: Provide specific, actionable task descriptions
|
||||
2. **Sequential Operations**: Allow agents to complete dependencies before next steps
|
||||
3. **Comprehensive Requirements**: Include all necessary details in task descriptions
|
||||
4. **Result Validation**: Review agent outputs for completeness and accuracy
|
||||
|
||||
### Data Operations
|
||||
|
||||
1. **Backup Before Updates**: Always backup data before major modifications
|
||||
2. **Test Queries**: Validate queries on sample data before production execution
|
||||
3. **Monitor Performance**: Track query execution times and optimize as needed
|
||||
4. **Validate Data**: Ensure data integrity through proper validation
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
examples/guides/smart_database/
|
||||
├── smart_database_swarm.py # Main implementation
|
||||
├── README.md # This documentation
|
||||
└── databases/ # Generated databases (auto-created)
|
||||
```
|
||||
|
||||
## Dependencies
|
||||
|
||||
- `swarms`: Core framework for multi-agent systems
|
||||
- `sqlite3`: Database operations (built-in Python)
|
||||
- `json`: Data serialization (built-in Python)
|
||||
- `pathlib`: File path operations (built-in Python)
|
||||
- `loguru`: Minimal logging functionality
|
||||
|
||||
## Running the System
|
||||
|
||||
```bash
|
||||
# Navigate to the smart_database directory
|
||||
cd examples/guides/smart_database
|
||||
|
||||
# Run the demonstration
|
||||
python smart_database_swarm.py
|
||||
|
||||
# The system will create databases in ./databases/ directory
|
||||
# Check the generated databases and results
|
||||
```
|
||||
|
||||
## Expected Output
|
||||
|
||||
The system will create:
|
||||
|
||||
1. **Databases**: SQLite database files in `./databases/` directory
|
||||
2. **Detailed Results**: JSON-formatted operation results
|
||||
3. **Agent Coordination**: Logs showing how tasks are distributed
|
||||
4. **Performance Metrics**: Execution times and success statistics
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Database Not Found**: Ensure database path is correct and accessible
|
||||
2. **Schema Errors**: Verify SQL syntax in table creation statements
|
||||
3. **Data Format Issues**: Check JSON formatting for data insertion
|
||||
4. **Permission Errors**: Ensure write permissions for database directory
|
||||
|
||||
### Debug Mode
|
||||
|
||||
Enable verbose logging to see detailed agent interactions:
|
||||
|
||||
```python
|
||||
smart_database_swarm.verbose = True
|
||||
result = smart_database_swarm.run(task=your_task)
|
||||
```
|
||||
|
||||
## Contributing
|
||||
|
||||
To extend the Smart Database Swarm:
|
||||
|
||||
1. **Add New Tools**: Create additional database operation functions
|
||||
2. **Enhance Agents**: Improve agent prompts and capabilities
|
||||
3. **Add Database Types**: Support for PostgreSQL, MySQL, etc.
|
||||
4. **Performance Optimization**: Implement caching and connection pooling
|
||||
|
||||
## License
|
||||
|
||||
This project is part of the Swarms framework and follows the same licensing terms.
|
||||
File diff suppressed because it is too large
Load Diff
|
Before Width: | Height: | Size: 11 KiB After Width: | Height: | Size: 11 KiB |
|
Before Width: | Height: | Size: 11 KiB After Width: | Height: | Size: 11 KiB |
|
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
@ -0,0 +1,20 @@
|
||||
from swarms import Agent
|
||||
|
||||
# Initialize the agent
|
||||
agent = Agent(
|
||||
agent_name="Quantitative-Trading-Agent",
|
||||
agent_description="Quantitative trading and analysis agent",
|
||||
system_prompt="You are an expert quantitative trading agent. Answer concisely and accurately using your knowledge of trading strategies, risk management, and financial markets.",
|
||||
model_name="mistral/mistral-tiny",
|
||||
dynamic_temperature_enabled=True,
|
||||
output_type="str-all-except-first",
|
||||
max_loops="auto",
|
||||
interactive=True,
|
||||
no_reasoning_prompt=True,
|
||||
streaming_on=True,
|
||||
)
|
||||
|
||||
out = agent.run(
|
||||
task="What are the best top 3 etfs for gold coverage?"
|
||||
)
|
||||
print(out)
|
||||
@ -0,0 +1,225 @@
|
||||
# Swarms Docker Image
|
||||
|
||||
This repository includes a Docker image for running Swarms, an AI agent framework. The image is automatically built and published to DockerHub on every push to the main branch and on version tags.
|
||||
|
||||
## 🐳 Quick Start
|
||||
|
||||
### Pull and Run
|
||||
|
||||
```bash
|
||||
# Pull the latest image
|
||||
docker pull kyegomez/swarms:latest
|
||||
|
||||
# Run a simple test
|
||||
docker run --rm kyegomez/swarms:latest python test_docker.py
|
||||
|
||||
# Run with interactive shell
|
||||
docker run -it --rm kyegomez/swarms:latest bash
|
||||
```
|
||||
|
||||
### Using Specific Versions
|
||||
|
||||
```bash
|
||||
# Pull a specific version
|
||||
docker pull kyegomez/swarms:v8.0.4
|
||||
|
||||
# Run with specific version
|
||||
docker run --rm kyegomez/swarms:v8.0.4 python -c "import swarms; print(swarms.__version__)"
|
||||
```
|
||||
|
||||
## 🏗️ Building Locally
|
||||
|
||||
### Prerequisites
|
||||
|
||||
- Docker installed on your system
|
||||
- Git to clone the repository
|
||||
|
||||
### Build Steps
|
||||
|
||||
```bash
|
||||
# Clone the repository
|
||||
git clone https://github.com/kyegomez/swarms.git
|
||||
cd swarms
|
||||
|
||||
# Build the image
|
||||
docker build -t swarms:latest .
|
||||
|
||||
# Test the image
|
||||
docker run --rm swarms:latest python test_docker.py
|
||||
```
|
||||
|
||||
## 🚀 Usage Examples
|
||||
|
||||
### Basic Agent Example
|
||||
|
||||
```bash
|
||||
# Create a Python script (agent_example.py)
|
||||
cat > agent_example.py << 'EOF'
|
||||
from swarms import Agent
|
||||
|
||||
# Create an agent
|
||||
agent = Agent(
|
||||
agent_name="test_agent",
|
||||
system_prompt="You are a helpful AI assistant."
|
||||
)
|
||||
|
||||
# Run the agent
|
||||
result = agent.run("Hello! How are you today?")
|
||||
print(result)
|
||||
EOF
|
||||
|
||||
# Run in Docker
|
||||
docker run --rm -v $(pwd):/app swarms:latest python /app/agent_example.py
|
||||
```
|
||||
|
||||
### Interactive Development
|
||||
|
||||
```bash
|
||||
# Run with volume mount for development
|
||||
docker run -it --rm \
|
||||
-v $(pwd):/app \
|
||||
-w /app \
|
||||
swarms:latest bash
|
||||
|
||||
# Inside the container, you can now run Python scripts
|
||||
python your_script.py
|
||||
```
|
||||
|
||||
### Using Environment Variables
|
||||
|
||||
```bash
|
||||
# Run with environment variables
|
||||
docker run --rm \
|
||||
-e OPENAI_API_KEY=your_api_key_here \
|
||||
-e ANTHROPIC_API_KEY=your_anthropic_key_here \
|
||||
swarms:latest python your_script.py
|
||||
```
|
||||
|
||||
## 🔧 Configuration
|
||||
|
||||
### Environment Variables
|
||||
|
||||
The Docker image supports the following environment variables:
|
||||
|
||||
- `OPENAI_API_KEY`: Your OpenAI API key
|
||||
- `ANTHROPIC_API_KEY`: Your Anthropic API key
|
||||
- `GOOGLE_API_KEY`: Your Google API key
|
||||
- `PYTHONPATH`: Additional Python path entries
|
||||
- `PYTHONUNBUFFERED`: Set to 1 for unbuffered output
|
||||
|
||||
### Volume Mounts
|
||||
|
||||
Common volume mount patterns:
|
||||
|
||||
```bash
|
||||
# Mount current directory for development
|
||||
-v $(pwd):/app
|
||||
|
||||
# Mount specific directories
|
||||
-v $(pwd)/data:/app/data
|
||||
-v $(pwd)/models:/app/models
|
||||
|
||||
# Mount configuration files
|
||||
-v $(pwd)/config:/app/config
|
||||
```
|
||||
|
||||
## 🐛 Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Permission Denied**
|
||||
```bash
|
||||
# Fix permission issues
|
||||
docker run --rm -v $(pwd):/app:rw swarms:latest python your_script.py
|
||||
```
|
||||
|
||||
2. **Memory Issues**
|
||||
```bash
|
||||
# Increase memory limit
|
||||
docker run --rm --memory=4g swarms:latest python your_script.py
|
||||
```
|
||||
|
||||
3. **Network Issues**
|
||||
```bash
|
||||
# Use host network
|
||||
docker run --rm --network=host swarms:latest python your_script.py
|
||||
```
|
||||
|
||||
### Debug Mode
|
||||
|
||||
```bash
|
||||
# Run with debug output
|
||||
docker run --rm -e PYTHONUNBUFFERED=1 swarms:latest python -u your_script.py
|
||||
|
||||
# Run with interactive debugging
|
||||
docker run -it --rm swarms:latest python -m pdb your_script.py
|
||||
```
|
||||
|
||||
## 🔄 CI/CD Integration
|
||||
|
||||
The Docker image is automatically built and published via GitHub Actions:
|
||||
|
||||
- **Triggers**: Push to main branch, version tags (v*.*.*)
|
||||
- **Platforms**: linux/amd64, linux/arm64
|
||||
- **Registry**: DockerHub (kyegomez/swarms)
|
||||
|
||||
### GitHub Actions Secrets Required
|
||||
|
||||
- `DOCKERHUB_USERNAME`: Your DockerHub username
|
||||
- `DOCKERHUB_TOKEN`: Your DockerHub access token
|
||||
|
||||
## 📊 Image Details
|
||||
|
||||
### Base Image
|
||||
- Python 3.11-slim-bullseye
|
||||
- Multi-stage build for optimization
|
||||
- UV package manager for faster installations
|
||||
|
||||
### Image Size
|
||||
- Optimized for minimal size
|
||||
- Multi-stage build reduces final image size
|
||||
- Only necessary dependencies included
|
||||
|
||||
### Security
|
||||
- Non-root user execution
|
||||
- Minimal system dependencies
|
||||
- Regular security updates
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
To contribute to the Docker setup:
|
||||
|
||||
1. Fork the repository
|
||||
2. Make your changes to the Dockerfile
|
||||
3. Test locally: `docker build -t swarms:test .`
|
||||
4. Submit a pull request
|
||||
|
||||
### Testing Changes
|
||||
|
||||
```bash
|
||||
# Build test image
|
||||
docker build -t swarms:test .
|
||||
|
||||
# Run tests
|
||||
docker run --rm swarms:test python test_docker.py
|
||||
|
||||
# Test with your code
|
||||
docker run --rm -v $(pwd):/app swarms:test python your_test_script.py
|
||||
```
|
||||
|
||||
## 📝 License
|
||||
|
||||
This Docker setup is part of the Swarms project and follows the same MIT license.
|
||||
|
||||
## 🆘 Support
|
||||
|
||||
For issues with the Docker image:
|
||||
|
||||
1. Check the troubleshooting section above
|
||||
2. Review the GitHub Actions logs for build issues
|
||||
3. Open an issue on GitHub with detailed error information
|
||||
4. Include your Docker version and system information
|
||||
|
||||
---
|
||||
|
||||
**Note**: This Docker image is automatically updated with each release. For production use, consider pinning to specific version tags for stability.
|
||||
@ -1,25 +1,37 @@
|
||||
# Use a lightweight Python image
|
||||
# Multi-stage build for optimized Docker image
|
||||
FROM python:3.11-slim-bullseye as builder
|
||||
|
||||
# Install system dependencies for building
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
build-essential gcc curl \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install UV for faster package management
|
||||
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
ENV PATH="/root/.cargo/bin:${PATH}"
|
||||
|
||||
# Create a virtual environment and install dependencies
|
||||
RUN uv venv /opt/venv
|
||||
ENV PATH="/opt/venv/bin:$PATH"
|
||||
|
||||
# Install the swarms package using UV
|
||||
RUN uv pip install --system -U swarms
|
||||
|
||||
# Final stage
|
||||
FROM python:3.11-slim-bullseye
|
||||
|
||||
# Environment config for speed and safety
|
||||
ENV PYTHONDONTWRITEBYTECODE=1 \
|
||||
PYTHONUNBUFFERED=1 \
|
||||
PIP_NO_CACHE_DIR=1 \
|
||||
PIP_DISABLE_PIP_VERSION_CHECK=1 \
|
||||
PATH="/app:${PATH}" \
|
||||
PATH="/opt/venv/bin:${PATH}" \
|
||||
PYTHONPATH="/app:${PYTHONPATH}" \
|
||||
USER=swarms
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# System dependencies (minimal)
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
build-essential gcc \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install the swarms package
|
||||
RUN pip install --upgrade pip && pip install -U swarms
|
||||
# Copy virtual environment from builder stage
|
||||
COPY --from=builder /opt/venv /opt/venv
|
||||
|
||||
# Add non-root user
|
||||
RUN useradd -m -s /bin/bash -U $USER && \
|
||||
@ -0,0 +1,71 @@
|
||||
version: '3.8'
|
||||
|
||||
services:
|
||||
swarms:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: swarms:latest
|
||||
container_name: swarms-container
|
||||
environment:
|
||||
- PYTHONUNBUFFERED=1
|
||||
- PYTHONPATH=/app
|
||||
# Add your API keys here or use .env file
|
||||
# - OPENAI_API_KEY=${OPENAI_API_KEY}
|
||||
# - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
|
||||
# - GOOGLE_API_KEY=${GOOGLE_API_KEY}
|
||||
volumes:
|
||||
- .:/app
|
||||
- ./data:/app/data
|
||||
- ./models:/app/models
|
||||
working_dir: /app
|
||||
command: python test_docker.py
|
||||
restart: unless-stopped
|
||||
healthcheck:
|
||||
test: ["CMD", "python", "-c", "import swarms; print('Health check passed')"]
|
||||
interval: 30s
|
||||
timeout: 10s
|
||||
retries: 3
|
||||
start_period: 40s
|
||||
|
||||
swarms-dev:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: swarms:dev
|
||||
container_name: swarms-dev-container
|
||||
environment:
|
||||
- PYTHONUNBUFFERED=1
|
||||
- PYTHONPATH=/app
|
||||
volumes:
|
||||
- .:/app
|
||||
- ./data:/app/data
|
||||
- ./models:/app/models
|
||||
working_dir: /app
|
||||
command: bash
|
||||
stdin_open: true
|
||||
tty: true
|
||||
restart: unless-stopped
|
||||
|
||||
swarms-api:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: swarms:api
|
||||
container_name: swarms-api-container
|
||||
environment:
|
||||
- PYTHONUNBUFFERED=1
|
||||
- PYTHONPATH=/app
|
||||
volumes:
|
||||
- .:/app
|
||||
working_dir: /app
|
||||
ports:
|
||||
- "8000:8000"
|
||||
command: python -m uvicorn main:app --host 0.0.0.0 --port 8000 --reload
|
||||
restart: unless-stopped
|
||||
depends_on:
|
||||
- swarms
|
||||
|
||||
networks:
|
||||
default:
|
||||
name: swarms-network
|
||||
@ -0,0 +1,58 @@
|
||||
name: Docker Test Build
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
branches: [ "master" ]
|
||||
workflow_dispatch:
|
||||
|
||||
env:
|
||||
REGISTRY: docker.io
|
||||
IMAGE_NAME: ${{ github.repository }}
|
||||
|
||||
jobs:
|
||||
test-build:
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
# Setup QEMU for multi-platform builds
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
|
||||
# Setup Docker BuildX
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
# Build Docker image (without pushing)
|
||||
- name: Build Docker image
|
||||
id: build
|
||||
uses: docker/build-push-action@v6
|
||||
with:
|
||||
context: .
|
||||
push: false
|
||||
tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:test
|
||||
platforms: linux/amd64
|
||||
cache-from: type=gha
|
||||
cache-to: type=gha,mode=max
|
||||
build-args: |
|
||||
BUILDKIT_INLINE_CACHE=1
|
||||
|
||||
# Test the built image
|
||||
- name: Test Docker image
|
||||
run: |
|
||||
docker run --rm ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:test python test_docker.py
|
||||
|
||||
# Show image size
|
||||
- name: Show image size
|
||||
run: |
|
||||
docker images ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:test --format "table {{.Repository}}\t{{.Tag}}\t{{.Size}}"
|
||||
|
||||
# Clean up test image
|
||||
- name: Clean up test image
|
||||
if: always()
|
||||
run: |
|
||||
docker rmi ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:test || true
|
||||
@ -0,0 +1,139 @@
|
||||
# Docker utilities for Swarms project (PowerShell version)
|
||||
# Usage: .\scripts\docker-utils.ps1 [command]
|
||||
|
||||
param(
|
||||
[Parameter(Position=0)]
|
||||
[string]$Command = "help"
|
||||
)
|
||||
|
||||
# Configuration
|
||||
$ImageName = "swarms"
|
||||
$Registry = "kyegomez"
|
||||
$FullImageName = "$Registry/$ImageName"
|
||||
|
||||
# Functions
|
||||
function Write-Usage {
|
||||
Write-Host "Docker Utilities for Swarms" -ForegroundColor Blue
|
||||
Write-Host ""
|
||||
Write-Host "Usage: .\scripts\docker-utils.ps1 [command]"
|
||||
Write-Host ""
|
||||
Write-Host "Commands:"
|
||||
Write-Host " build Build the Docker image locally"
|
||||
Write-Host " test Test the Docker image"
|
||||
Write-Host " run Run the Docker image interactively"
|
||||
Write-Host " push Push to DockerHub (requires login)"
|
||||
Write-Host " clean Clean up Docker images and containers"
|
||||
Write-Host " logs Show logs from running containers"
|
||||
Write-Host " shell Open shell in running container"
|
||||
Write-Host " compose-up Start services with docker-compose"
|
||||
Write-Host " compose-down Stop services with docker-compose"
|
||||
Write-Host " help Show this help message"
|
||||
Write-Host ""
|
||||
}
|
||||
|
||||
function Build-Image {
|
||||
Write-Host "Building Docker image..." -ForegroundColor Green
|
||||
docker build -t "$ImageName`:latest" .
|
||||
Write-Host " Image built successfully!" -ForegroundColor Green
|
||||
}
|
||||
|
||||
function Test-Image {
|
||||
Write-Host "Testing Docker image..." -ForegroundColor Green
|
||||
docker run --rm "$ImageName`:latest" python test_docker.py
|
||||
Write-Host " Image test completed!" -ForegroundColor Green
|
||||
}
|
||||
|
||||
function Run-Interactive {
|
||||
Write-Host "Running Docker image interactively..." -ForegroundColor Green
|
||||
docker run -it --rm -v "${PWD}:/app" -w /app "$ImageName`:latest" bash
|
||||
}
|
||||
|
||||
function Push-ToDockerHub {
|
||||
Write-Host "⚠ Make sure you're logged into DockerHub first!" -ForegroundColor Yellow
|
||||
Write-Host "Pushing to DockerHub..." -ForegroundColor Green
|
||||
|
||||
# Tag the image
|
||||
docker tag "$ImageName`:latest" "$FullImageName`:latest"
|
||||
|
||||
# Push to DockerHub
|
||||
docker push "$FullImageName`:latest"
|
||||
|
||||
Write-Host " Image pushed to DockerHub!" -ForegroundColor Green
|
||||
}
|
||||
|
||||
function Clean-Docker {
|
||||
Write-Host "Cleaning up Docker resources..." -ForegroundColor Yellow
|
||||
|
||||
# Stop and remove containers
|
||||
docker ps -aq | ForEach-Object { docker rm -f $_ }
|
||||
|
||||
# Remove images
|
||||
docker images "$ImageName" -q | ForEach-Object { docker rmi -f $_ }
|
||||
|
||||
# Remove dangling images
|
||||
docker image prune -f
|
||||
|
||||
Write-Host " Docker cleanup completed!" -ForegroundColor Green
|
||||
}
|
||||
|
||||
function Show-Logs {
|
||||
Write-Host "Showing logs from running containers..." -ForegroundColor Green
|
||||
docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
|
||||
Write-Host ""
|
||||
|
||||
# Show logs for swarms containers
|
||||
$containers = docker ps --filter "name=swarms" --format "{{.Names}}"
|
||||
foreach ($container in $containers) {
|
||||
Write-Host "Logs for $container:" -ForegroundColor Blue
|
||||
docker logs $container --tail 20
|
||||
Write-Host ""
|
||||
}
|
||||
}
|
||||
|
||||
function Open-Shell {
|
||||
Write-Host "Opening shell in running container..." -ForegroundColor Green
|
||||
|
||||
# Find running swarms container
|
||||
$container = docker ps --filter "name=swarms" --format "{{.Names}}" | Select-Object -First 1
|
||||
|
||||
if (-not $container) {
|
||||
Write-Host " No running swarms container found!" -ForegroundColor Red
|
||||
Write-Host "Start a container first with: .\scripts\docker-utils.ps1 run"
|
||||
exit 1
|
||||
}
|
||||
|
||||
Write-Host "Opening shell in $container..." -ForegroundColor Blue
|
||||
docker exec -it $container bash
|
||||
}
|
||||
|
||||
function Compose-Up {
|
||||
Write-Host "Starting services with docker-compose..." -ForegroundColor Green
|
||||
docker-compose up -d
|
||||
Write-Host " Services started!" -ForegroundColor Green
|
||||
Write-Host "Use 'docker-compose logs -f' to view logs"
|
||||
}
|
||||
|
||||
function Compose-Down {
|
||||
Write-Host "Stopping services with docker-compose..." -ForegroundColor Yellow
|
||||
docker-compose down
|
||||
Write-Host " Services stopped!" -ForegroundColor Green
|
||||
}
|
||||
|
||||
# Main script logic
|
||||
switch ($Command.ToLower()) {
|
||||
"build" { Build-Image }
|
||||
"test" { Test-Image }
|
||||
"run" { Run-Interactive }
|
||||
"push" { Push-ToDockerHub }
|
||||
"clean" { Clean-Docker }
|
||||
"logs" { Show-Logs }
|
||||
"shell" { Open-Shell }
|
||||
"compose-up" { Compose-Up }
|
||||
"compose-down" { Compose-Down }
|
||||
"help" { Write-Usage }
|
||||
default {
|
||||
Write-Host " Unknown command: $Command" -ForegroundColor Red
|
||||
Write-Usage
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,167 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Docker utilities for Swarms project
|
||||
# Usage: ./scripts/docker-utils.sh [command]
|
||||
|
||||
set -e
|
||||
|
||||
# Colors for output
|
||||
RED='\033[0;31m'
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
BLUE='\033[0;34m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
# Configuration
|
||||
IMAGE_NAME="swarms"
|
||||
REGISTRY="kyegomez"
|
||||
FULL_IMAGE_NAME="${REGISTRY}/${IMAGE_NAME}"
|
||||
|
||||
# Functions
|
||||
print_usage() {
|
||||
echo -e "${BLUE}Docker Utilities for Swarms${NC}"
|
||||
echo ""
|
||||
echo "Usage: $0 [command]"
|
||||
echo ""
|
||||
echo "Commands:"
|
||||
echo " build Build the Docker image locally"
|
||||
echo " test Test the Docker image"
|
||||
echo " run Run the Docker image interactively"
|
||||
echo " push Push to DockerHub (requires login)"
|
||||
echo " clean Clean up Docker images and containers"
|
||||
echo " logs Show logs from running containers"
|
||||
echo " shell Open shell in running container"
|
||||
echo " compose-up Start services with docker-compose"
|
||||
echo " compose-down Stop services with docker-compose"
|
||||
echo " help Show this help message"
|
||||
echo ""
|
||||
}
|
||||
|
||||
build_image() {
|
||||
echo -e "${GREEN}Building Docker image...${NC}"
|
||||
docker build -t "${IMAGE_NAME}:latest" .
|
||||
echo -e "${GREEN} Image built successfully!${NC}"
|
||||
}
|
||||
|
||||
test_image() {
|
||||
echo -e "${GREEN}Testing Docker image...${NC}"
|
||||
docker run --rm "${IMAGE_NAME}:latest" python test_docker.py
|
||||
echo -e "${GREEN} Image test completed!${NC}"
|
||||
}
|
||||
|
||||
run_interactive() {
|
||||
echo -e "${GREEN}Running Docker image interactively...${NC}"
|
||||
docker run -it --rm \
|
||||
-v "$(pwd):/app" \
|
||||
-w /app \
|
||||
"${IMAGE_NAME}:latest" bash
|
||||
}
|
||||
|
||||
push_to_dockerhub() {
|
||||
echo -e "${YELLOW}⚠ Make sure you're logged into DockerHub first!${NC}"
|
||||
echo -e "${GREEN}Pushing to DockerHub...${NC}"
|
||||
|
||||
# Tag the image
|
||||
docker tag "${IMAGE_NAME}:latest" "${FULL_IMAGE_NAME}:latest"
|
||||
|
||||
# Push to DockerHub
|
||||
docker push "${FULL_IMAGE_NAME}:latest"
|
||||
|
||||
echo -e "${GREEN} Image pushed to DockerHub!${NC}"
|
||||
}
|
||||
|
||||
clean_docker() {
|
||||
echo -e "${YELLOW}Cleaning up Docker resources...${NC}"
|
||||
|
||||
# Stop and remove containers
|
||||
docker ps -aq | xargs -r docker rm -f
|
||||
|
||||
# Remove images
|
||||
docker images "${IMAGE_NAME}" -q | xargs -r docker rmi -f
|
||||
|
||||
# Remove dangling images
|
||||
docker image prune -f
|
||||
|
||||
echo -e "${GREEN} Docker cleanup completed!${NC}"
|
||||
}
|
||||
|
||||
show_logs() {
|
||||
echo -e "${GREEN}Showing logs from running containers...${NC}"
|
||||
docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
|
||||
echo ""
|
||||
|
||||
# Show logs for swarms containers
|
||||
for container in $(docker ps --filter "name=swarms" --format "{{.Names}}"); do
|
||||
echo -e "${BLUE}Logs for $container:${NC}"
|
||||
docker logs "$container" --tail 20
|
||||
echo ""
|
||||
done
|
||||
}
|
||||
|
||||
open_shell() {
|
||||
echo -e "${GREEN}Opening shell in running container...${NC}"
|
||||
|
||||
# Find running swarms container
|
||||
container=$(docker ps --filter "name=swarms" --format "{{.Names}}" | head -1)
|
||||
|
||||
if [ -z "$container" ]; then
|
||||
echo -e "${RED} No running swarms container found!${NC}"
|
||||
echo "Start a container first with: $0 run"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo -e "${BLUE}Opening shell in $container...${NC}"
|
||||
docker exec -it "$container" bash
|
||||
}
|
||||
|
||||
compose_up() {
|
||||
echo -e "${GREEN}Starting services with docker-compose...${NC}"
|
||||
docker-compose up -d
|
||||
echo -e "${GREEN} Services started!${NC}"
|
||||
echo "Use 'docker-compose logs -f' to view logs"
|
||||
}
|
||||
|
||||
compose_down() {
|
||||
echo -e "${YELLOW}Stopping services with docker-compose...${NC}"
|
||||
docker-compose down
|
||||
echo -e "${GREEN} Services stopped!${NC}"
|
||||
}
|
||||
|
||||
# Main script logic
|
||||
case "${1:-help}" in
|
||||
build)
|
||||
build_image
|
||||
;;
|
||||
test)
|
||||
test_image
|
||||
;;
|
||||
run)
|
||||
run_interactive
|
||||
;;
|
||||
push)
|
||||
push_to_dockerhub
|
||||
;;
|
||||
clean)
|
||||
clean_docker
|
||||
;;
|
||||
logs)
|
||||
show_logs
|
||||
;;
|
||||
shell)
|
||||
open_shell
|
||||
;;
|
||||
compose-up)
|
||||
compose_up
|
||||
;;
|
||||
compose-down)
|
||||
compose_down
|
||||
;;
|
||||
help|--help|-h)
|
||||
print_usage
|
||||
;;
|
||||
*)
|
||||
echo -e "${RED} Unknown command: $1${NC}"
|
||||
print_usage
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
@ -0,0 +1,113 @@
|
||||
# Setting up DockerHub Secrets for GitHub Actions
|
||||
|
||||
This guide will help you set up the required secrets for the Docker workflow to automatically build and push images to DockerHub.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. A DockerHub account
|
||||
2. Admin access to the GitHub repository
|
||||
3. DockerHub access token
|
||||
|
||||
## Step 1: Create a DockerHub Access Token
|
||||
|
||||
1. Log in to [DockerHub](https://hub.docker.com/)
|
||||
2. Go to your account settings
|
||||
3. Navigate to "Security" → "Access Tokens"
|
||||
4. Click "New Access Token"
|
||||
5. Give it a name (e.g., "GitHub Actions")
|
||||
6. Set the permissions to "Read & Write"
|
||||
7. Copy the generated token (you won't be able to see it again!)
|
||||
|
||||
## Step 2: Add Secrets to GitHub Repository
|
||||
|
||||
1. Go to your GitHub repository
|
||||
2. Navigate to "Settings" → "Secrets and variables" → "Actions"
|
||||
3. Click "New repository secret"
|
||||
4. Add the following secrets:
|
||||
|
||||
### Required Secrets
|
||||
|
||||
| Secret Name | Value | Description |
|
||||
|-------------|-------|-------------|
|
||||
| `DOCKERHUB_USERNAME` | Your DockerHub username | Your DockerHub username (e.g., `kyegomez`) |
|
||||
| `DOCKERHUB_TOKEN` | Your DockerHub access token | The access token you created in Step 1 |
|
||||
|
||||
## Step 3: Verify Setup
|
||||
|
||||
1. Push a commit to the `main` branch
|
||||
2. Go to the "Actions" tab in your GitHub repository
|
||||
3. You should see the "Docker Build and Publish" workflow running
|
||||
4. Check that it completes successfully
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Authentication Failed**
|
||||
- Double-check your DockerHub username and token
|
||||
- Ensure the token has "Read & Write" permissions
|
||||
- Make sure the token hasn't expired
|
||||
|
||||
2. **Permission Denied**
|
||||
- Verify you have admin access to the repository
|
||||
- Check that the secrets are named exactly as shown above
|
||||
|
||||
3. **Workflow Not Triggering**
|
||||
- Ensure you're pushing to the `main` branch
|
||||
- Check that the workflow file is in `.github/workflows/`
|
||||
- Verify the workflow file has the correct triggers
|
||||
|
||||
### Testing Locally
|
||||
|
||||
You can test the Docker build locally before pushing:
|
||||
|
||||
```bash
|
||||
# Build the image locally
|
||||
docker build -t swarms:test .
|
||||
|
||||
# Test the image
|
||||
docker run --rm swarms:test python test_docker.py
|
||||
|
||||
# If everything works, push to GitHub
|
||||
git add .
|
||||
git commit -m "Add Docker support"
|
||||
git push origin main
|
||||
```
|
||||
|
||||
## Security Notes
|
||||
|
||||
- Never commit secrets directly to your repository
|
||||
- Use repository secrets for sensitive information
|
||||
- Regularly rotate your DockerHub access tokens
|
||||
- Consider using organization-level secrets for team repositories
|
||||
|
||||
## Additional Configuration
|
||||
|
||||
### Custom Registry
|
||||
|
||||
If you want to use a different registry (not DockerHub), update the workflow file:
|
||||
|
||||
```yaml
|
||||
env:
|
||||
REGISTRY: your-registry.com
|
||||
IMAGE_NAME: your-org/your-repo
|
||||
```
|
||||
|
||||
### Multiple Tags
|
||||
|
||||
The workflow automatically creates tags based on:
|
||||
- Git branch name
|
||||
- Git commit SHA
|
||||
- Version tags (v*.*.*)
|
||||
- Latest tag for main branch
|
||||
|
||||
You can customize this in the workflow file under the "Extract Docker metadata" step.
|
||||
|
||||
## Support
|
||||
|
||||
If you encounter issues:
|
||||
|
||||
1. Check the GitHub Actions logs for detailed error messages
|
||||
2. Verify your DockerHub credentials
|
||||
3. Ensure the workflow file is properly configured
|
||||
4. Open an issue in the repository with the error details
|
||||
@ -0,0 +1,66 @@
|
||||
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to verify Swarms installation in Docker container.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from typing import Dict, Any
|
||||
|
||||
def test_swarms_import() -> Dict[str, Any]:
|
||||
"""
|
||||
Test that swarms can be imported and basic functionality works.
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Test results
|
||||
"""
|
||||
try:
|
||||
import swarms
|
||||
print(f" Swarms imported successfully. Version: {swarms.__version__}")
|
||||
|
||||
# Test basic functionality
|
||||
from swarms import Agent
|
||||
print(" Agent class imported successfully")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"version": swarms.__version__,
|
||||
"message": "Swarms package is working correctly"
|
||||
}
|
||||
|
||||
except ImportError as e:
|
||||
print(f" Failed to import swarms: {e}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e),
|
||||
"message": "Swarms package import failed"
|
||||
}
|
||||
except Exception as e:
|
||||
print(f" Unexpected error: {e}")
|
||||
return {
|
||||
"status": "error",
|
||||
"error": str(e),
|
||||
"message": "Unexpected error occurred"
|
||||
}
|
||||
|
||||
def main() -> None:
|
||||
"""Main function to run tests."""
|
||||
print(" Testing Swarms Docker Image...")
|
||||
print("=" * 50)
|
||||
|
||||
# Test Python version
|
||||
print(f"Python version: {sys.version}")
|
||||
|
||||
# Test swarms import
|
||||
result = test_swarms_import()
|
||||
|
||||
print("=" * 50)
|
||||
if result["status"] == "success":
|
||||
print(" All tests passed! Docker image is working correctly.")
|
||||
sys.exit(0)
|
||||
else:
|
||||
print(" Tests failed! Please check the Docker image.")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue