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CLI YAML Configuration Guide: Run Agents from Config Files
Run multiple agents from YAML configuration files for reproducible, version-controlled agent deployments.
Basic YAML Configuration
Step 1: Create YAML Config File
Create a file named agents.yaml:
agents:
- name: "Research-Agent"
description: "AI research specialist"
model_name: "gpt-4o-mini"
system_prompt: |
You are an expert researcher.
Provide comprehensive, well-structured research summaries.
Include key insights and data points.
temperature: 0.3
max_loops: 2
task: "Research current trends in renewable energy"
- name: "Analysis-Agent"
description: "Data analysis specialist"
model_name: "gpt-4o-mini"
system_prompt: |
You are a data analyst.
Provide detailed statistical analysis and insights.
Use data-driven reasoning.
temperature: 0.2
max_loops: 3
task: "Analyze market opportunities in the EV sector"
Step 2: Run Agents from YAML
swarms run-agents --yaml-file agents.yaml
Step 3: View Results
Results are displayed in the terminal with formatted output for each agent.
Complete YAML Schema
Agent Configuration Options
agents:
- name: "Agent-Name" # Required: Agent identifier
description: "Agent description" # Required: What the agent does
model_name: "gpt-4o-mini" # Model to use
system_prompt: "Your instructions" # Agent's system prompt
temperature: 0.5 # Creativity (0.0-2.0)
max_loops: 3 # Maximum execution loops
task: "Task to execute" # Task for this agent
# Optional settings
context_length: 8192 # Context window size
streaming_on: true # Enable streaming
verbose: true # Verbose output
autosave: true # Auto-save state
saved_state_path: "./states/agent.json" # State file path
output_type: "json" # Output format
retry_attempts: 3 # Retries on failure
Use Case Examples
Multi-Agent Research Pipeline
# research_pipeline.yaml
agents:
- name: "Data-Collector"
description: "Collects and organizes research data"
model_name: "gpt-4o-mini"
system_prompt: |
You are a research data collector.
Gather comprehensive information on the given topic.
Organize findings into structured categories.
temperature: 0.3
max_loops: 2
task: "Collect data on AI applications in healthcare"
- name: "Trend-Analyst"
description: "Analyzes trends and patterns"
model_name: "gpt-4o-mini"
system_prompt: |
You are a trend analyst.
Identify emerging patterns and trends from data.
Provide statistical insights and projections.
temperature: 0.2
max_loops: 2
task: "Analyze AI healthcare adoption trends from 2020-2024"
- name: "Report-Writer"
description: "Creates comprehensive reports"
model_name: "gpt-4"
system_prompt: |
You are a professional report writer.
Create comprehensive, well-structured reports.
Include executive summaries and key recommendations.
temperature: 0.4
max_loops: 1
task: "Write an executive summary on AI in healthcare"
Run:
swarms run-agents --yaml-file research_pipeline.yaml
Financial Analysis Team
# financial_team.yaml
agents:
- name: "Market-Analyst"
description: "Analyzes market conditions"
model_name: "gpt-4"
system_prompt: |
You are a CFA-certified market analyst.
Provide detailed market analysis with technical indicators.
Include risk assessments and market outlook.
temperature: 0.2
max_loops: 3
task: "Analyze current S&P 500 market conditions"
- name: "Risk-Assessor"
description: "Evaluates investment risks"
model_name: "gpt-4"
system_prompt: |
You are a risk management specialist.
Evaluate investment risks and provide mitigation strategies.
Use quantitative risk metrics.
temperature: 0.1
max_loops: 2
task: "Assess risks in current tech sector investments"
- name: "Portfolio-Advisor"
description: "Provides portfolio recommendations"
model_name: "gpt-4"
system_prompt: |
You are a portfolio advisor.
Provide asset allocation recommendations.
Consider risk tolerance and market conditions.
temperature: 0.3
max_loops: 2
task: "Recommend portfolio adjustments for Q4 2024"
Content Creation Pipeline
# content_pipeline.yaml
agents:
- name: "Topic-Researcher"
description: "Researches content topics"
model_name: "gpt-4o-mini"
system_prompt: |
You are a content researcher.
Research topics thoroughly and identify key angles.
Find unique perspectives and data points.
temperature: 0.4
max_loops: 2
task: "Research content angles for 'Future of Remote Work'"
- name: "Content-Writer"
description: "Writes engaging content"
model_name: "gpt-4"
system_prompt: |
You are a professional content writer.
Write engaging, SEO-friendly content.
Use clear structure with headers and bullet points.
temperature: 0.7
max_loops: 2
task: "Write a blog post about remote work trends"
- name: "Editor"
description: "Edits and polishes content"
model_name: "gpt-4o-mini"
system_prompt: |
You are a professional editor.
Review content for clarity, grammar, and style.
Suggest improvements and optimize for readability.
temperature: 0.2
max_loops: 1
task: "Edit and polish the blog post for publication"
Advanced Configuration
Environment Variables in YAML
You can reference environment variables:
agents:
- name: "API-Agent"
description: "Agent with API access"
model_name: "${MODEL_NAME:-gpt-4o-mini}" # Default if not set
system_prompt: "You are an API integration specialist."
task: "Test API integration"
Multiple Config Files
Organize agents by purpose:
# Run different configurations
swarms run-agents --yaml-file research_agents.yaml
swarms run-agents --yaml-file analysis_agents.yaml
swarms run-agents --yaml-file reporting_agents.yaml
Pipeline Script
#!/bin/bash
# run_pipeline.sh
echo "Starting research pipeline..."
swarms run-agents --yaml-file configs/research.yaml
echo "Starting analysis pipeline..."
swarms run-agents --yaml-file configs/analysis.yaml
echo "Starting reporting pipeline..."
swarms run-agents --yaml-file configs/reporting.yaml
echo "Pipeline complete!"
Markdown Configuration
Alternative: Load from Markdown
Create agents using markdown with YAML frontmatter:
---
name: Research Agent
description: AI research specialist
model_name: gpt-4o-mini
temperature: 0.3
max_loops: 2
---
You are an expert researcher specializing in technology trends.
Provide comprehensive research summaries with:
- Key findings and insights
- Data points and statistics
- Recommendations and implications
Always cite sources when available and maintain objectivity.
Load from markdown:
# Load single file
swarms load-markdown --markdown-path ./agents/research_agent.md
# Load directory (concurrent processing)
swarms load-markdown --markdown-path ./agents/ --concurrent
Best Practices
!!! tip "Configuration Management" - Version control your YAML files - Use descriptive agent names - Document purpose in descriptions
!!! tip "Template Organization"
configs/ ├── research/ │ ├── tech_research.yaml │ └── market_research.yaml ├── analysis/ │ ├── financial_analysis.yaml │ └── data_analysis.yaml └── production/ └── prod_agents.yaml
!!! tip "Testing Configurations"
- Test with --verbose flag first
- Use lower max_loops for testing
- Start with gpt-4o-mini for cost efficiency
!!! warning "Common Pitfalls"
- Ensure proper YAML indentation (2 spaces)
- Quote strings with special characters
- Use | for multi-line prompts
CI/CD Integration
GitHub Actions
# .github/workflows/run-agents.yml
name: Run Agent Pipeline
on:
schedule:
- cron: '0 9 * * 1' # Every Monday at 9 AM
jobs:
run-agents:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install Swarms
run: pip install swarms
- name: Run Agents
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: swarms run-agents --yaml-file agents.yaml
Next Steps
- CLI Agent Guide - Create agents from command line
- CLI Multi-Agent Guide - LLM Council and Heavy Swarm
- CLI Reference - Complete command documentation