Command Line Interface

Use PraisonAI directly from your terminal with simple commands

from praisonaiagents import Agent

agent = Agent(name="cli-assistant", instructions="Help users with CLI commands.")
agent.start("List all available praisonai CLI commands.")

The user runs a praisonai command in the terminal; the CLI loads config and drives the agent to completion.

How It Works

PraisonAI CLI provides a simple way to interact with AI agents directly from your terminal. You can run quick commands, specify LLM options, or use YAML configuration files for more complex scenarios.

Quick Start

1. Install Package

   Install the PraisonAI package:

   pip install praisonai
   
2. Set API Key

   Set your OpenAI API key as an environment variable:

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
   

Usage Examples

Simple Command

Run a simple command directly:

praisonai "write a movie script in 3 lines"

With LLM Option

Specify a different LLM model:

praisonai "write a movie script in 3 lines" --llm gpt-4o-mini

Using YAML Config

Run agents defined in a YAML file:

praisonai agents.yaml

Configuration

Create Config

Initialize a new agents.yaml file for your project:

praisonai --init "Create a movie script about AI"

This will create an agents.yaml file with predefined configuration for your task.

If no LLM provider is configured, --init prints provider-setup guidance (supporting OpenAI, Anthropic, Google/Gemini, Groq, Cohere, Ollama, OpenRouter and 100+ models via LiteLLM) and exits. Run praisonai setup first to configure a provider.

Auto Mode

Let PraisonAI automatically create and configure agents:

praisonai --auto "Create a movie script about AI"

This mode will analyze your task and set up appropriate agents automatically.

Features

Simple Commands

Run AI tasks directly from your terminal with simple commands.

LLM Options

Choose from different LLM models for your specific needs.

YAML Support

Use YAML files for complex agent configurations and workflows.

Auto Configuration

Automatic agent setup based on task requirements.

Model Catalogue

Discover, describe, and validate LLM models from the CLI.

Validate CLI

Validate YAML configuration files before running:

# Validate a single file
praisonai validate agents.yaml

# Validate all YAML files in a directory
praisonai validate check . --strict

# Print the full configuration schema
praisonai validate schema

# JSON output for CI
praisonai validate agents.yaml --json

Subcommand	Purpose
validate <file>	Validate a single YAML config
validate check [dir]	Scan a directory for YAML errors
validate schema	Print schema fields, types, and required markers

See the Validate CLI reference for all flags and output formats.

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Discovering Models

Browse the full model catalogue, inspect capabilities and costs, or validate a model ID before running an agent:

# List all available models
praisonai models list

# Filter by provider
praisonai models list --provider openai

# Inspect a specific model
praisonai models describe gpt-4o

# Validate a model ID (shows suggestions on typos)
praisonai models validate gpt-4o-mni

Model Catalogue

Full reference for praisonai models list, describe, and validate

Workflow CLI

Manage and execute YAML workflows directly from the command line:

# Run a YAML workflow
praisonai workflow run research.yaml

# Run with variables
praisonai workflow run research.yaml --var topic="AI trends"

# Validate a workflow
praisonai workflow validate research.yaml

# Create from template (simple, routing, parallel, loop)
praisonai workflow template routing --output my_workflow.yaml

# Auto-generate a workflow from topic
praisonai workflow auto "Research AI trends" --pattern parallel

# List available workflows
praisonai workflow list

# Show help
praisonai workflow help

Workflow CLI Options

Flag	Description
--var key=value	Set variable for YAML workflows
--pattern <pattern>	Pattern for auto-generation (sequential, parallel, routing, loop)
--output <file>	Output file for templates/auto-generation
--planning	Enable planning mode
--reasoning	Enable reasoning mode
--verbose	Enable verbose output
--save	Save output to file

Next Steps

YAML Workflows

Learn about YAML workflow configuration and patterns

Workflow Patterns

Explore routing, parallel, loop, and repeat patterns

API Reference

View the complete API documentation

Best Practices

Validate YAML before running

Catch schema errors early with praisonai validate agents.yaml (or praisonai validate check . --strict for a whole directory) before executing a workflow. In CI, add --json for machine-readable output so a broken config fails the pipeline instead of the agent run.

Pin the model per command

A bare praisonai "..." uses the default provider. Pass --llm to make each invocation reproducible and cost-predictable:

praisonai "write a movie script in 3 lines" --llm gpt-4o-mini

Confirm an ID exists first with praisonai models validate gpt-4o-mini to avoid silent typos.

Start with --init, graduate to YAML

Use praisonai --init "task" to scaffold an agents.yaml, then edit and re-run with praisonai agents.yaml. This keeps one-off prompts fast while giving repeatable, version-controlled configs for anything you run more than once.

Use --var to keep workflows reusable

Parameterise workflow YAML instead of hard-coding values so one file serves many runs:

praisonai workflow run research.yaml --var topic="AI trends"

Related

Structure agents and tasks in a reusable YAML config file.

Discover, describe, and validate LLM models from the terminal.