> ## Documentation Index > Fetch the complete documentation index at: https://docs.swarms.world/llms.txt > Use this file to discover all available pages before exploring further. # CLI Commands > Complete reference for all Swarms CLI commands with examples and parameters # CLI Commands Reference This page provides detailed documentation for all Swarms CLI commands, including their parameters, usage examples, and common use cases. ## Setup & Configuration Commands ### onboarding Run a comprehensive environment setup check to verify your Swarms installation. **Usage:** ```bash theme={null} swarms onboarding [--verbose] ``` **Parameters:** * `--verbose` (optional) - Show detailed diagnostics and version detection steps **Example:** ```bash theme={null} swarms onboarding --verbose ``` **Checks performed:** * Python version (requires 3.10+) * Swarms version * API key configuration * Required dependencies (torch, transformers, litellm, rich) * Environment file (.env) * Workspace directory (WORKSPACE\_DIR) *** ### setup-check Identical to `onboarding`. Runs comprehensive environment setup checks. **Usage:** ```bash theme={null} swarms setup-check [--verbose] ``` **Parameters:** * `--verbose` (optional) - Enable detailed output **Example:** ```bash theme={null} swarms setup-check --verbose ``` *** ### get-api-key Open your browser to retrieve API keys from the Swarms platform. **Usage:** ```bash theme={null} swarms get-api-key ``` **Parameters:** None **Example:** ```bash theme={null} swarms get-api-key ``` This opens `https://swarms.world/platform/api-keys` in your default browser. *** ### check-login Verify authentication status and initialize the authentication cache. **Usage:** ```bash theme={null} swarms check-login ``` **Parameters:** None **Example:** ```bash theme={null} swarms check-login ``` *** ## Agent Creation & Execution Commands ### agent Create and run a custom agent with specified parameters. The task parameter is optional - if not provided, the agent runs in interactive mode by default. **Usage:** ```bash theme={null} swarms agent \ --name \ --description \ --system-prompt \ [--task ] \ [OPTIONS] ``` **Required Parameters:** * `--name` - Name of the agent * `--description` - Description of the agent's purpose * `--system-prompt` - System prompt defining agent behavior (can use `--marketplace-prompt-id` instead) **Optional Parameters:** * `--task` - Task to execute (if omitted, runs in interactive mode) * `--model-name` - LLM model to use (default: "gpt-4") * `--temperature` - Temperature setting (0.0-2.0) * `--max-loops` - Maximum loops (integer or "auto" for autonomous) * `--interactive` - Enable interactive mode (default: True) * `--no-interactive` - Disable interactive mode * `--verbose` - Enable verbose output * `--streaming-on` - Enable streaming mode * `--context-length` - Context window size * `--retry-attempts` - Number of retry attempts * `--return-step-meta` - Return step metadata * `--dashboard` - Enable dashboard * `--autosave` - Enable autosave * `--saved-state-path` - Path for saving agent state * `--user-name` - Username for the agent * `--mcp-url` - MCP URL for the agent * `--marketplace-prompt-id` - Fetch system prompt from marketplace * `--auto-generate-prompt` - Enable auto-generation of prompts * `--dynamic-temperature-enabled` - Enable dynamic temperature adjustment * `--dynamic-context-window` - Enable dynamic context window * `--output-type` - Output type (e.g., "str", "json") **Examples:** Create an agent with a task: ```bash theme={null} swarms agent \ --name "Trading Agent" \ --description "Advanced trading analysis agent" \ --system-prompt "You are an expert trader with deep knowledge of financial markets" \ --task "Analyze the current market trends for tech stocks" \ --model-name "gpt-4" \ --temperature 0.1 ``` Create an agent in interactive mode (no task): ```bash theme={null} swarms agent \ --name "Assistant" \ --description "General purpose assistant" \ --system-prompt "You are a helpful assistant" ``` With autonomous loops: ```bash theme={null} swarms agent \ --name "Research Agent" \ --description "Autonomous research agent" \ --system-prompt "You are a research expert" \ --task "Research the latest AI developments" \ --max-loops "auto" \ --verbose ``` *** ### chat Start an interactive chat agent with optimized defaults for conversation. Uses autonomous loops (`max_loops="auto"`) by default. **Usage:** ```bash theme={null} swarms chat [OPTIONS] ``` **Optional Parameters:** * `--name` - Agent name (default: "Swarms Agent") * `--description` - Agent description (default: "A Swarms agent that can chat with the user") * `--system-prompt` - Custom system prompt * `--task` - Initial task/message to start the conversation **Examples:** Start a basic chat: ```bash theme={null} swarms chat ``` With custom configuration: ```bash theme={null} swarms chat \ --name "ChatBot" \ --system-prompt "You are a friendly and helpful assistant" \ --task "Hello, I need help with Python programming" ``` *** ### run-agents Execute agents from a YAML configuration file. **Usage:** ```bash theme={null} swarms run-agents [--yaml-file ] ``` **Parameters:** * `--yaml-file` - Path to YAML configuration file (default: "agents.yaml") **Example:** ```bash theme={null} swarms run-agents --yaml-file my_agents.yaml ``` See the [Configuration](/cli/configuration) page for YAML file format. *** ### load-markdown Load agents from markdown files with YAML frontmatter. **Usage:** ```bash theme={null} swarms load-markdown --markdown-path [--concurrent] ``` **Required Parameters:** * `--markdown-path` - Path to markdown file or directory **Optional Parameters:** * `--concurrent` - Enable concurrent processing (default: True) **Examples:** Load from a single file: ```bash theme={null} swarms load-markdown --markdown-path ./agent.md ``` Load from a directory: ```bash theme={null} swarms load-markdown --markdown-path ./agents/ ``` **Markdown Format:** ```markdown theme={null} --- name: Agent Name description: Agent Description model_name: gpt-4 temperature: 0.1 --- Your system prompt content here... ``` *** ## Swarm Operations Commands ### autoswarm Generate and execute an autonomous swarm configuration based on a task. **Usage:** ```bash theme={null} swarms autoswarm --task --model ``` **Required Parameters:** * `--task` - Task description for the swarm * `--model` - Model name for swarm generation (e.g., "gpt-4") **Example:** ```bash theme={null} swarms autoswarm \ --task "Analyze customer feedback and generate insights" \ --model "gpt-4" ``` *** ### heavy-swarm Run HeavySwarm with specialized agents for complex task analysis. HeavySwarm breaks down tasks into questions and uses worker agents to process them. **Usage:** ```bash theme={null} swarms heavy-swarm --task [OPTIONS] ``` **Required Parameters:** * `--task` - Task for HeavySwarm to process **Optional Parameters:** * `--loops-per-agent` - Number of execution loops per agent (default: 1) * `--question-agent-model-name` - Model for question generation (default: "gpt-5.4") * `--worker-model-name` - Model for worker agents (default: "gpt-5.4") * `--random-loops-per-agent` - Enable random loops (1-10 range) * `--verbose` - Enable verbose output **Examples:** Basic usage: ```bash theme={null} swarms heavy-swarm \ --task "Analyze the current market trends in renewable energy" ``` With custom configuration: ```bash theme={null} swarms heavy-swarm \ --task "Analyze market trends" \ --loops-per-agent 3 \ --question-agent-model-name "gpt-4" \ --worker-model-name "gpt-4" \ --verbose ``` *** ### llm-council Run the LLM Council where multiple agents collaborate on a task, providing different perspectives and evaluating responses. **Usage:** ```bash theme={null} swarms llm-council --task [--verbose] ``` **Required Parameters:** * `--task` - Task or question for the council to process **Optional Parameters:** * `--verbose` - Show verbose output (default: True) **Examples:** Basic usage: ```bash theme={null} swarms llm-council \ --task "What is the best approach to implementing a microservices architecture?" ``` With verbose output: ```bash theme={null} swarms llm-council \ --task "Analyze the pros and cons of different database solutions" \ --verbose ``` *** ## Utility Commands ### help Display comprehensive help message with all commands and parameters. **Usage:** ```bash theme={null} swarms help ``` **Parameters:** None **Example:** ```bash theme={null} swarms help ``` *** ### features Display all available features and actions in a comprehensive table. **Usage:** ```bash theme={null} swarms features ``` **Parameters:** None **Example:** ```bash theme={null} swarms features ``` *** ### upgrade Update Swarms to the latest version. **Usage:** ```bash theme={null} swarms upgrade ``` **Parameters:** None **Example:** ```bash theme={null} swarms upgrade ``` This executes: `pip install --upgrade swarms` *** ## Command Categories | Category | Commands | | ---------------- | --------------------------------------------------------- | | Setup | `onboarding`, `setup-check`, `get-api-key`, `check-login` | | Agent Operations | `agent`, `chat`, `run-agents`, `load-markdown` | | Swarm Operations | `autoswarm`, `heavy-swarm`, `llm-council` | | Utilities | `help`, `features`, `upgrade` | ## Common Flags Many commands support these common flags: * `--verbose` - Enable detailed output * `--task` - Specify a task to execute * `--model-name` - Specify the LLM model * `--temperature` - Control randomness (0.0-2.0) * `--max-loops` - Set iteration limits ## Next Steps Learn how to configure agents using YAML files Return to CLI overview and quick start guide