Multi-Agent CLI Quickstart

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-5.4-mini \
    --worker-model-name gpt-5.4-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-5.4	Model for question agent
`--worker-model-name`	gpt-5.4	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. --description and --system-prompt are required by the agent command, and models are set with --model-name (--model only applies to autoswarm):

swarms agent \
    --name "Research-Agent" \
    --description "Summarizes recent developments in a field" \
    --system-prompt "You are a research analyst who summarizes recent developments." \
    --task "Summarize recent AI developments" \
    --model-name "gpt-5.4" \
    --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 commands and flags:

swarms --help

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

Explore CLI Reference Documentation for all commands
See CLI Examples for more use cases
Learn about LLM Council Python API
Try Heavy Swarm Documentation for advanced configuration

Index

Basic Examples

Model Providers

Single Agent

Multi-Agent Examples

Auto Agent Builder

Scheduled Agents

Applications

Research

Technical Analysis

Use Cases

Finance

Voice Agents

Integrations

MCP Tool Integrations

Deployment

CLI

Multi-Agent CLI Quickstart

Overview

Step 1: Install and Verify

Step 2: Set Environment Variables

Step 3: Run Multi-Agent Commands

LLM Council

Heavy Swarm

Complete CLI Reference

LLM Council Command

Heavy Swarm Command

Other Useful CLI Commands

Setup Check

Run Single Agent

Auto Swarm

Show All Commands

Troubleshooting

Common Issues

Debug Mode

Next Steps

​Overview

​Step 1: Install and Verify

​Step 2: Set Environment Variables

​Step 3: Run Multi-Agent Commands

​LLM Council

​Heavy Swarm

​Complete CLI Reference

​LLM Council Command

​Heavy Swarm Command

​Other Useful CLI Commands

​Setup Check

​Run Single Agent

​Auto Swarm

​Show All Commands

​Troubleshooting

​Common Issues

​Debug Mode

​Next Steps

Overview

Step 1: Install and Verify

Step 2: Set Environment Variables

Step 3: Run Multi-Agent Commands

LLM Council

Heavy Swarm

Complete CLI Reference

LLM Council Command

Heavy Swarm Command

Other Useful CLI Commands

Setup Check

Run Single Agent

Auto Swarm

Show All Commands

Troubleshooting

Common Issues

Debug Mode

Next Steps