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Run LLM Council and Heavy Swarm directly from the command line for seamless DevOps integration. Execute sophisticated multi-agent workflows without writing Python code.

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Overview

FeatureDescription
LLM Council CLIRun collaborative decision-making from terminal
Heavy Swarm CLIExecute comprehensive research swarms
DevOps ReadyIntegrate into CI/CD pipelines and scripts
ConfigurableFull parameter control from command line

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Step 1: Install and Verify

Ensure Swarms is installed and verify CLI access: 
# Install swarms
pip install swarms

# Verify CLI is available
swarms --help
You should see the Swarms CLI banner and available commands.

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Step 2: Set Environment Variables

Configure your API keys: 
# Set your OpenAI API key (or other provider)
export OPENAI_API_KEY="your-openai-api-key"

# Optional: Set workspace directory
export WORKSPACE_DIR="./agent_workspace"
Or add to your .env file: 
OPENAI_API_KEY=your-openai-api-key
WORKSPACE_DIR=./agent_workspace

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Step 3: Run Multi-Agent Commands

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LLM Council

Run a collaborative council of AI agents: 
# Basic usage
swarms llm-council --task "What is the best approach to implement microservices architecture?"

# With verbose output
swarms llm-council --task "Evaluate investment opportunities in AI startups" --verbose

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Heavy Swarm

Run comprehensive research and analysis: 
# Basic usage
swarms heavy-swarm --task "Analyze the current state of quantum computing"

# With configuration options
swarms heavy-swarm \
    --task "Research renewable energy market trends" \
    --loops-per-agent 2 \
    --question-agent-model-name gpt-4o-mini \
    --worker-model-name gpt-4o-mini \
    --verbose

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Complete CLI Reference

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LLM Council Command

swarms llm-council --task "<your query>" [options]
OptionDescription
--taskRequired. The query or question for the council
--verboseEnable detailed output logging
Examples: 
# Strategic decision
swarms llm-council --task "Should our startup pivot from B2B to B2C?"

# Technical evaluation
swarms llm-council --task "Compare React vs Vue for enterprise applications"

# Business analysis
swarms llm-council --task "What are the risks of expanding to European markets?"

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Heavy Swarm Command

swarms heavy-swarm --task "<your task>" [options]
OptionDefaultDescription
--task-Required. The research task
--loops-per-agent1Number of loops per agent
--question-agent-model-namegpt-4o-miniModel for question agent
--worker-model-namegpt-4o-miniModel for worker agents
--random-loops-per-agentFalseRandomize loops per agent
--verboseFalseEnable detailed output
Examples: 
# Comprehensive research
swarms heavy-swarm --task "Research the impact of AI on healthcare diagnostics" --verbose

# With custom models
swarms heavy-swarm \
    --task "Analyze cryptocurrency regulation trends globally" \
    --question-agent-model-name gpt-4 \
    --worker-model-name gpt-4 \
    --loops-per-agent 3

# Quick analysis
swarms heavy-swarm --task "Summarize recent advances in battery technology"

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Other Useful CLI Commands

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Setup Check

Verify your environment is properly configured: 
swarms setup-check --verbose

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Run Single Agent

Execute a single agent task: 
swarms agent \
    --name "Research-Agent" \
    --task "Summarize recent AI developments" \
    --model "gpt-5.4" \
    --max-loops 1

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Auto Swarm

Automatically generate and run a swarm configuration: 
swarms autoswarm --task "Build a content analysis pipeline" --model gpt-4

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Show All Commands

Display all available CLI features: 
swarms show-all

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Troubleshooting

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Common Issues

IssueSolution
”Command not found”Ensure pip install swarms completed successfully
”API key not set”Export OPENAI_API_KEY environment variable
”Task cannot be empty”Always provide --task argument
Timeout errorsCheck network connectivity and API rate limits

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Debug Mode

Run with verbose output for debugging: 
swarms llm-council --task "Your query" --verbose 2>&1 | tee debug.log

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Next Steps