Skip to content

CLI Multi-Agent Features: 3-Step Quickstart Guide

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

Overview

Feature Description
LLM Council CLI Run collaborative decision-making from terminal
Heavy Swarm CLI Execute comprehensive research swarms
DevOps Ready Integrate into CI/CD pipelines and scripts
Configurable Full parameter control from command line

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.

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

Step 3: Run Multi-Agent Commands

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

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

Complete CLI Reference

LLM Council Command

swarms llm-council --task "<your query>" [options]
Option Description
--task Required. The query or question for the council
--verbose Enable 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?"

Heavy Swarm Command

swarms heavy-swarm --task "<your task>" [options]
Option Default Description
--task - Required. The research task
--loops-per-agent 1 Number of loops per agent
--question-agent-model-name gpt-4o-mini Model for question agent
--worker-model-name gpt-4o-mini Model for worker agents
--random-loops-per-agent False Randomize loops per agent
--verbose False Enable 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"

Other Useful CLI Commands

Setup Check

Verify your environment is properly configured:

swarms setup-check --verbose

Run Single Agent

Execute a single agent task:

swarms agent \
    --name "Research-Agent" \
    --task "Summarize recent AI developments" \
    --model "gpt-4o-mini" \
    --max-loops 1

Auto Swarm

Automatically generate and run a swarm configuration:

swarms autoswarm --task "Build a content analysis pipeline" --model gpt-4

Show All Commands

Display all available CLI features:

swarms show-all

Troubleshooting

Common Issues

Issue Solution
"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 errors Check network connectivity and API rate limits

Debug Mode

Run with verbose output for debugging:

swarms llm-council --task "Your query" --verbose 2>&1 | tee debug.log

Next Steps