harshalmore31
3 weeks ago
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dd2b0c2a3c
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"""
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AgentLoader Example: Research Team Collaboration
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===============================================
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This example demonstrates using the AgentLoader to create a research team
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from markdown files and orchestrate them in a sequential workflow.
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"""
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import os
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import sys
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import tempfile
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from pathlib import Path
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# Add local swarms to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from swarms.structs.agent import Agent
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from swarms.structs.sequential_workflow import SequentialWorkflow
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from swarms.utils.agent_loader import AgentLoader
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def create_research_agents():
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"""Create markdown files for research team agents"""
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market_researcher = """| name | description | model |
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|------|-------------|-------|
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| market-researcher | Expert in market analysis and competitive intelligence | gpt-4 |
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## Focus Areas
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- Market size and growth analysis
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- Competitive landscape assessment
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- Consumer behavior patterns
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- Industry trend identification
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## Approach
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1. Gather comprehensive market data
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2. Analyze quantitative and qualitative indicators
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3. Identify key market drivers and barriers
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4. Evaluate competitive positioning
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5. Assess market opportunities and threats
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## Output
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- Market analysis reports with key metrics
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- Competitive intelligence briefings
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- Market opportunity assessments
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- Consumer behavior insights
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"""
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financial_analyst = """| name | description | model |
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|------|-------------|-------|
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| financial-analyst | Specialist in financial modeling and investment analysis | gpt-4 |
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## Focus Areas
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- Financial statement analysis
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- Valuation modeling techniques
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- Investment risk assessment
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- Cash flow projections
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## Approach
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1. Conduct thorough financial analysis
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2. Build comprehensive financial models
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3. Perform multiple valuation methods
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4. Assess financial risks and sensitivities
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5. Provide investment recommendations
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## Output
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- Financial analysis reports
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- Valuation models with scenarios
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- Investment recommendation memos
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- Risk assessment matrices
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"""
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industry_expert = """| name | description | model |
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|------|-------------|-------|
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| industry-expert | Domain specialist with deep industry knowledge | gpt-4 |
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## Focus Areas
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- Industry structure and dynamics
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- Regulatory environment analysis
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- Technology trends and disruptions
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- Supply chain analysis
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## Approach
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1. Map industry structure and stakeholders
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2. Analyze regulatory framework
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3. Identify technology trends
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4. Evaluate supply chain dynamics
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5. Assess competitive positioning
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## Output
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- Industry landscape reports
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- Regulatory compliance assessments
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- Technology trend analysis
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- Strategic positioning recommendations
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"""
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return {
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"market_researcher.md": market_researcher,
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"financial_analyst.md": financial_analyst,
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"industry_expert.md": industry_expert
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}
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def main():
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"""Main execution function"""
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temp_dir = tempfile.mkdtemp()
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try:
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# Create markdown files
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agent_definitions = create_research_agents()
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file_paths = []
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for filename, content in agent_definitions.items():
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file_path = os.path.join(temp_dir, filename)
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with open(file_path, 'w', encoding='utf-8') as f:
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f.write(content)
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file_paths.append(file_path)
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# Load agents using AgentLoader
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loader = AgentLoader()
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agents = loader.load_multiple_agents(
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file_paths,
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max_loops=1,
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verbose=False
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)
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print(f"Loaded {len(agents)} agents")
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for i, agent in enumerate(agents):
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print(f"Agent {i}: {agent.agent_name} - LLM: {hasattr(agent, 'llm')}")
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# Create sequential workflow
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research_workflow = SequentialWorkflow(
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agents=agents,
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max_loops=1,
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)
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# Define research task
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task = """
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Analyze the AI-powered healthcare diagnostics market for a potential $50M investment.
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Focus on:
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1. Market size, growth, and key drivers
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2. Competitive landscape and major players
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3. Financial viability and investment metrics
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4. Industry dynamics and regulatory factors
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Provide strategic recommendations for market entry.
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"""
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# Execute workflow
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result = research_workflow.run(task)
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return result
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finally:
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# Cleanup
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for file_path in file_paths:
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if os.path.exists(file_path):
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os.remove(file_path)
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os.rmdir(temp_dir)
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if __name__ == "__main__":
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result = main()
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print("Research Analysis Complete:")
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print("-" * 50)
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print(result)
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@ -1,244 +0,0 @@
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"""
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Test script for the AgentLoader functionality.
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This tests the core functionality without requiring external models.
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"""
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import os
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import tempfile
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from pathlib import Path
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import sys
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# Add swarms to path for local testing
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sys.path.insert(0, os.path.join(os.path.dirname(__file__)))
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from swarms.utils.agent_loader import AgentLoader, MarkdownAgentConfig
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def test_markdown_parsing():
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"""Test markdown parsing functionality."""
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print("Testing markdown parsing...")
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# Create a sample markdown content
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sample_content = """| name | description | model |
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|------|-------------|-------|
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| test-agent | A test agent for validation | gpt-4 |
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## Focus Areas
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- Testing functionality
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- Validating implementation
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- Ensuring compatibility
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## Approach
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1. Parse markdown structure
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2. Extract configuration data
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3. Validate parsed results
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4. Create agent instance
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## Output
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- Test results
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- Validation reports
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- Configuration summary
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"""
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# Test parsing functionality
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loader = AgentLoader()
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# Test table parsing
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table_data = loader.parse_markdown_table(sample_content)
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assert table_data['name'] == 'test-agent'
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assert table_data['description'] == 'A test agent for validation'
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assert table_data['model_name'] == 'gpt-4'
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print("[OK] Table parsing successful")
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# Test section extraction
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sections = loader.extract_sections(sample_content)
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assert 'focus areas' in sections
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assert len(sections['focus areas']) == 3
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assert 'approach' in sections
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assert len(sections['approach']) == 4
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print("[OK] Section extraction successful")
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# Test system prompt building
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config_data = {
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'description': table_data['description'],
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'focus_areas': sections['focus areas'],
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'approach': sections['approach'],
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'output': sections.get('output', [])
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}
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system_prompt = loader.build_system_prompt(config_data)
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assert 'Role:' in system_prompt
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assert 'Focus Areas:' in system_prompt
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assert 'Approach:' in system_prompt
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print("[OK] System prompt building successful")
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print("Markdown parsing tests passed!")
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return True
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def test_file_operations():
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"""Test file loading operations."""
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print("\\nTesting file operations...")
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# Create temporary markdown file
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sample_content = """| name | description | model |
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|------|-------------|-------|
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| file-test-agent | Agent created from file | gpt-4 |
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## Focus Areas
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- File processing
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- Configuration validation
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## Approach
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1. Load from file
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2. Parse content
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3. Create configuration
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## Output
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- Loaded agent
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- Configuration object
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"""
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with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False) as f:
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f.write(sample_content)
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temp_file = f.name
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try:
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loader = AgentLoader()
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# Test file parsing
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config = loader.parse_markdown_file(temp_file)
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assert isinstance(config, MarkdownAgentConfig)
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assert config.name == 'file-test-agent'
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assert config.description == 'Agent created from file'
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print("[OK] File parsing successful")
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# Test configuration validation
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assert len(config.focus_areas) == 2
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assert len(config.approach) == 3
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assert config.system_prompt is not None
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print("[OK] Configuration validation successful")
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finally:
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# Cleanup
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if os.path.exists(temp_file):
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os.remove(temp_file)
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print("File operations tests passed!")
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return True
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def test_multiple_files():
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"""Test loading multiple files."""
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print("\\nTesting multiple file loading...")
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# Create multiple temporary files
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files = []
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for i in range(3):
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content = f"""| name | description | model |
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|------|-------------|-------|
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| agent-{i} | Test agent number {i} | gpt-4 |
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## Focus Areas
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- Multi-agent testing
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- Batch processing
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## Approach
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1. Process multiple files
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2. Create agent configurations
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3. Return agent list
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## Output
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- Multiple agents
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- Batch results
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"""
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temp_file = tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False)
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temp_file.write(content)
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temp_file.close()
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files.append(temp_file.name)
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try:
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loader = AgentLoader()
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# Test parsing multiple files
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configs = []
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for file_path in files:
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config = loader.parse_markdown_file(file_path)
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configs.append(config)
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assert len(configs) == 3
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for i, config in enumerate(configs):
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assert config.name == f'agent-{i}'
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print("[OK] Multiple file parsing successful")
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finally:
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# Cleanup
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for file_path in files:
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if os.path.exists(file_path):
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os.remove(file_path)
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print("Multiple file tests passed!")
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return True
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def test_error_handling():
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"""Test error handling scenarios."""
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print("\\nTesting error handling...")
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loader = AgentLoader()
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# Test non-existent file
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try:
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loader.parse_markdown_file("nonexistent.md")
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assert False, "Should have raised FileNotFoundError"
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except FileNotFoundError:
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print("[OK] FileNotFoundError handling successful")
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# Test invalid markdown
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with tempfile.NamedTemporaryFile(mode='w', suffix='.md', delete=False) as f:
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f.write("Invalid markdown content without proper structure")
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invalid_file = f.name
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try:
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# This should not raise an error, but should handle gracefully
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config = loader.parse_markdown_file(invalid_file)
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# Should have defaults
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assert config.name is not None
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print("[OK] Invalid markdown handling successful")
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finally:
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if os.path.exists(invalid_file):
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os.remove(invalid_file)
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print("Error handling tests passed!")
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return True
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def main():
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"""Run all tests."""
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print("=== AgentLoader Test Suite ===")
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tests = [
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test_markdown_parsing,
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test_file_operations,
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test_multiple_files,
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test_error_handling
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]
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passed = 0
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total = len(tests)
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for test in tests:
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try:
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if test():
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passed += 1
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except Exception as e:
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print(f"[FAIL] Test {test.__name__} failed: {e}")
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print(f"\\n=== Results: {passed}/{total} tests passed ===")
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if passed == total:
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print("All tests passed!")
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return True
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else:
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print("Some tests failed!")
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return False
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if __name__ == "__main__":
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success = main()
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exit(0 if success else 1)
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Loading…
Reference in new issue