CLI Configuration

This guide covers how to configure agents and swarms using YAML configuration files and environment variables.

YAML Configuration

The Swarms CLI supports loading agent configurations from YAML files, allowing you to define complex agent setups and swarm architectures declaratively.

Basic YAML Structure

A Swarms YAML configuration file has two main sections:

agents - List of agent configurations
swarm_architecture (optional) - Swarm configuration

Agent Configuration

Single Agent Example

agents:
  - agent_name: "Research-Agent"
    model:
      model_name: "gpt-4"
      temperature: 0.1
      max_tokens: 2000
    system_prompt: "You are an expert research analyst specializing in technology trends."
    max_loops: 1
    autosave: true
    dashboard: false
    verbose: true
    dynamic_temperature_enabled: true
    saved_state_path: "research_agent.json"
    user_name: "researcher"
    retry_attempts: 3
    context_length: 4000
    return_step_meta: false
    output_type: "str"
    task: "Analyze the latest developments in quantum computing"

Multiple Agents Example

agents:
  - agent_name: "Financial-Analysis-Agent"
    model:
      model_name: "gpt-4"
      temperature: 0.1
      max_tokens: 2000
    system_prompt: "You are a financial analyst expert."
    max_loops: 1
    autosave: true
    dashboard: false
    verbose: true
    dynamic_temperature_enabled: true
    saved_state_path: "finance_agent.json"
    user_name: "swarms_corp"
    retry_attempts: 1
    context_length: 4000
    return_step_meta: false
    output_type: "str"
    task: "How can I establish a ROTH IRA to buy stocks and get a tax break?"

  - agent_name: "Stock-Analysis-Agent"
    model:
      model_name: "gpt-4"
      temperature: 0.2
      max_tokens: 1500
    system_prompt: "You are a stock market analysis expert."
    max_loops: 2
    autosave: true
    dashboard: false
    verbose: true
    dynamic_temperature_enabled: false
    saved_state_path: "stock_agent.json"
    user_name: "stock_user"
    retry_attempts: 3
    context_length: 4000
    return_step_meta: true
    output_type: "json"
    task: "What is the best strategy for long-term stock investment?"

Swarm Architecture Configuration

You can define how agents work together using the swarm_architecture section:

agents:
  - agent_name: "Financial-Analysis-Agent"
    # ... agent config ...

  - agent_name: "Stock-Analysis-Agent"
    # ... agent config ...

swarm_architecture:
  name: "Financial-Advisory-Swarm"
  description: "A swarm of agents working together to provide comprehensive financial advice"
  swarm_type: "SequentialWorkflow"
  max_loops: 2
  task: "Analyze ROTH IRA setup requirements and provide a comprehensive long-term investment strategy"
  autosave: true
  return_json: false
  rules: |
    1. Financial-Analysis-Agent first explains ROTH IRA setup process and requirements
    2. Stock-Analysis-Agent then provides specific investment strategies suitable for ROTH IRA
    3. Both agents should ensure advice is tax-aware and compliant with retirement account regulations
    4. Focus on practical, actionable steps the user can take

Agent Configuration Parameters

Parameter	Type	Required	Default	Description
`agent_name`	string	Yes	-	Unique name for the agent
`system_prompt`	string	Yes	-	System prompt defining agent behavior
`model_name`	string	No	”gpt-4”	LLM model to use
`max_loops`	integer	No	1	Maximum number of execution loops
`autosave`	boolean	No	true	Enable automatic state saving
`dashboard`	boolean	No	false	Enable agent dashboard
`verbose`	boolean	No	false	Enable verbose logging
`dynamic_temperature_enabled`	boolean	No	false	Enable dynamic temperature adjustment
`saved_state_path`	string	No	null	Path to save agent state
`user_name`	string	No	”default_user”	Username associated with agent
`retry_attempts`	integer	No	3	Number of retry attempts on failure
`context_length`	integer	No	100000	Maximum context length
`return_step_meta`	boolean	No	false	Return metadata for each step
`output_type`	string	No	”str”	Output format (“str” or “json”)
`task`	string	No	null	Task for the agent to execute
`auto_generate_prompt`	boolean	No	false	Auto-generate system prompts
`artifacts_on`	boolean	No	false	Enable artifact generation
`artifacts_file_extension`	string	No	”.md”	File extension for artifacts
`artifacts_output_path`	string	No	""	Output path for artifacts

Model Configuration

Within each agent, you can specify model parameters:

model:
  model_name: "gpt-4"           # Model identifier
  temperature: 0.1               # Randomness (0.0-2.0)
  max_tokens: 2000              # Maximum tokens per response

Swarm Architecture Parameters

Parameter	Type	Required	Description
`name`	string	Yes	Name of the swarm
`description`	string	Yes	Description of swarm purpose
`swarm_type`	string	Yes	Type of swarm (e.g., “SequentialWorkflow”)
`max_loops`	integer	No	Maximum loops for swarm execution
`task`	string	No	Overall task for the swarm
`flow`	object	No	Flow configuration for agent routing
`autosave`	boolean	No	Enable swarm state autosave
`return_json`	boolean	No	Return results in JSON format
`rules`	string	No	Rules governing swarm behavior

Running YAML Configurations

To execute agents from a YAML file:

swarms run-agents --yaml-file agents.yaml

With a custom file path:

swarms run-agents --yaml-file /path/to/my-config.yaml

Environment Variables

The Swarms CLI uses environment variables for API keys and configuration.

Required Environment Variables

At least one API key is required:

# OpenAI (most common)
export OPENAI_API_KEY="sk-..."

# Anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

# Google
export GOOGLE_API_KEY="AIza..."

# Cohere
export COHERE_API_KEY="..."

Optional Environment Variables

# Workspace directory for agent outputs
export WORKSPACE_DIR="./agent_workspace"

# Wandb (for tracking)
export WANDB_API_KEY="..."
export WANDB_SILENT="true"

# TensorFlow logging level
export TF_CPP_MIN_LOG_LEVEL="3"

Using .env Files

Create a .env file in your project root:

# .env file
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
WORKSPACE_DIR=./agent_workspace
WANDB_SILENT=true

The Swarms CLI automatically loads .env files from the current directory.

Environment Variable Priority

The CLI checks for API keys in this order:

Environment variables set in the current shell
Variables from .env file in current directory
System-wide environment variables

Workspace Directory

The WORKSPACE_DIR environment variable determines where agents store their outputs:

export WORKSPACE_DIR="/path/to/workspace"

Default behavior:

If not set, some swarm operations will create a default workspace
Agents will autosave state files to this directory
Artifacts and outputs are stored here

Directory structure example:

agent_workspace/
├── finance_agent.json
├── stock_agent.json
├── logs/
└── artifacts/

Markdown Agent Configuration

You can also define agents using Markdown files with YAML frontmatter.

Markdown Format

---
name: Research Agent
description: Expert research analyst
model_name: gpt-4
temperature: 0.1
max_loops: 1
autosave: true
verbose: true
---

You are an expert research analyst with deep knowledge of:
- Scientific literature review
- Data analysis and interpretation
- Trend identification
- Report generation

Provide thorough, well-researched responses backed by evidence.

Loading Markdown Agents

Load a single agent:

swarms load-markdown --markdown-path ./agent.md

Load all agents from a directory:

swarms load-markdown --markdown-path ./agents/

With concurrent processing:

swarms load-markdown --markdown-path ./agents/ --concurrent

Configuration Best Practices

1. API Key Management

Never commit API keys to version control
Use .env files (add to .gitignore)
Rotate keys regularly
Use different keys for development and production

2. Agent Configuration

Start with low temperature (0.1-0.3) for consistent outputs
Use appropriate context lengths based on your use case
Enable autosave for long-running agents
Set retry attempts for production reliability

3. Swarm Configuration

Define clear rules for agent collaboration
Use descriptive names for agents and swarms
Test with max_loops=1 before increasing
Enable verbose mode during development

4. File Organization

project/
├── .env                    # API keys (gitignored)
├── .gitignore             # Ignore .env and workspace
├── agents/
│   ├── research.yaml      # Agent configs
│   └── analysis.yaml
├── swarms/
│   └── financial.yaml     # Swarm configs
└── agent_workspace/       # Outputs (gitignored)

Validation

Check Your Configuration

Verify your environment setup:

swarms setup-check --verbose

This checks:

✓ Python version (3.10+)
✓ Swarms installation
✓ API keys
✓ Dependencies
✓ .env file
✓ WORKSPACE_DIR

Test Agent Configuration

Test a YAML configuration:

swarms run-agents --yaml-file test-config.yaml

Example Configurations

Minimal Agent Config

agents:
  - agent_name: "Simple-Agent"
    system_prompt: "You are a helpful assistant"
    task: "Hello, world!"

Production Agent Config

agents:
  - agent_name: "Production-Agent"
    model:
      model_name: "gpt-4"
      temperature: 0.2
      max_tokens: 4000
    system_prompt: "You are a production-ready agent with error handling."
    max_loops: 3
    autosave: true
    dashboard: true
    verbose: true
    retry_attempts: 5
    context_length: 8000
    saved_state_path: "prod_agent.json"
    user_name: "production"
    output_type: "json"
    task: "Process production workload"

Multi-Agent Swarm

agents:
  - agent_name: "Researcher"
    system_prompt: "You gather and analyze information."
    task: "Research the topic"
    
  - agent_name: "Writer"
    system_prompt: "You write clear, engaging content."
    task: "Write a report"
    
  - agent_name: "Editor"
    system_prompt: "You review and improve content."
    task: "Edit the final report"

swarm_architecture:
  name: "Content-Creation-Swarm"
  description: "Research, write, and edit content"
  swarm_type: "SequentialWorkflow"
  max_loops: 1
  rules: |
    1. Researcher gathers information
    2. Writer creates content from research
    3. Editor polishes the final output

Troubleshooting

Common Issues

Issue: “No API keys found”

# Solution: Set API key in .env
echo "OPENAI_API_KEY=sk-..." > .env
swarms setup-check

Issue: “YAML file not found”

# Solution: Check file path
ls agents.yaml
swarms run-agents --yaml-file ./path/to/agents.yaml

Issue: “WORKSPACE_DIR not set”

# Solution: Set workspace directory
export WORKSPACE_DIR="./agent_workspace"
mkdir -p $WORKSPACE_DIR

Issue: “Invalid YAML format”

# Solution: Validate YAML syntax
python -c "import yaml; yaml.safe_load(open('agents.yaml'))"

​CLI Configuration

​YAML Configuration

​Basic YAML Structure

​Agent Configuration

​Single Agent Example

​Multiple Agents Example

​Swarm Architecture Configuration

​Agent Configuration Parameters

​Model Configuration

​Swarm Architecture Parameters

​Running YAML Configurations

​Environment Variables

​Required Environment Variables

​Optional Environment Variables

​Using .env Files

​Environment Variable Priority

​Workspace Directory

​Markdown Agent Configuration

​Markdown Format

​Loading Markdown Agents

​Configuration Best Practices

​1. API Key Management

​2. Agent Configuration

​3. Swarm Configuration

​4. File Organization

​Validation

​Check Your Configuration

​Test Agent Configuration

​Example Configurations

​Minimal Agent Config

​Production Agent Config

​Multi-Agent Swarm

​Troubleshooting

​Common Issues

​Next Steps

CLI Overview

Commands Reference

​Resources