Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Agents CLI
The PraisonAI TypeScript CLI provides commands for running multi-agent orchestration directly from the command line.
Commands Overview
# Run multiple agents
praisonai-ts agents run --config agents.yaml
# Run with inline definitions
praisonai-ts agents run --agents "researcher,writer" --task "Research and write about AI"
agents run
Run multiple agents in sequence or parallel.
Basic Usage
praisonai-ts agents run --config agents.yaml
With Inline Agents
praisonai-ts agents run \
--agents "Researcher:Research the topic,Writer:Write based on research" \
--task "AI trends in 2024"
Options
| Option | Description | Default |
|---|
--config, -c | Path to agents YAML config | - |
--agents, -a | Inline agent definitions (name:instructions) | - |
--task, -t | Task to execute | - |
--process, -p | Process mode: sequential, parallel | sequential |
--model, -m | LLM model for all agents | gpt-4o-mini |
--verbose, -v | Enable verbose output | true |
--json | Output as JSON | false |
Examples
# Sequential pipeline
praisonai-ts agents run \
--agents "Researcher:Research AI,Analyst:Analyze findings,Writer:Write report" \
--task "AI market analysis" \
--process sequential
# Parallel execution
praisonai-ts agents run \
--agents "Analyst1:Analyze market A,Analyst2:Analyze market B" \
--task "Compare markets" \
--process parallel
# With config file
praisonai-ts agents run --config ./my-agents.yaml
# JSON output
praisonai-ts agents run \
--agents "Agent1:Task 1,Agent2:Task 2" \
--task "Complete workflow" \
--json
agents.yaml
agents:
- name: Researcher
instructions: |
You are a research specialist.
Find accurate information on the given topic.
model: gpt-4o-mini
- name: Writer
instructions: |
You are a content writer.
Write engaging content based on research.
model: gpt-4o-mini
process: sequential
verbose: true
With Tasks
agents:
- name: Researcher
instructions: Research the topic
- name: Writer
instructions: Write based on research
tasks:
- Research AI developments in 2024
- Write a 500-word summary
process: sequential
agents:
- name: WebResearcher
instructions: Search the web for information
tools:
- web_search
- read_url
- name: DataAnalyst
instructions: Analyze the collected data
tools:
- analyze_data
{
"success": true,
"data": {
"results": [
{
"agent": "Researcher",
"output": "Research findings..."
},
{
"agent": "Writer",
"output": "Written article..."
}
],
"process": "sequential",
"agentCount": 2
},
"meta": {
"duration_ms": 5432,
"model": "gpt-4o-mini"
}
}
Process Modes
Sequential
Agents run one after another. Each agent receives the previous agent’s output:
praisonai-ts agents run \
--agents "Step1:First task,Step2:Second task,Step3:Third task" \
--process sequential
Output flow:
Step1 → output → Step2 → output → Step3 → final output
Parallel
All agents run simultaneously:
praisonai-ts agents run \
--agents "Worker1:Task A,Worker2:Task B,Worker3:Task C" \
--process parallel
Output flow:
Worker1 → output1
Worker2 → output2 (all run at same time)
Worker3 → output3
Environment Variables
# API Keys
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
# Default model
export PRAISONAI_MODEL=gpt-4o-mini
# Behavior
export PRAISON_VERBOSE=true
Scripting Examples
Bash Pipeline
#!/bin/bash
# Run agents and process results
RESULT=$(praisonai-ts agents run \
--agents "Researcher:Find data,Analyst:Analyze data" \
--task "Market analysis" \
--json)
# Extract specific agent output
RESEARCH=$(echo $RESULT | jq -r '.data.results[0].output')
ANALYSIS=$(echo $RESULT | jq -r '.data.results[1].output')
echo "Research: $RESEARCH"
echo "Analysis: $ANALYSIS"
Error Handling
#!/bin/bash
if ! praisonai-ts agents run --config agents.yaml; then
echo "Agent execution failed"
exit 1
fi
Common Patterns
Research → Write Pipeline
praisonai-ts agents run \
--agents "Researcher:Research thoroughly,Writer:Write engaging content" \
--task "Write about quantum computing"
Multi-Perspective Analysis
praisonai-ts agents run \
--agents "Optimist:Find positives,Pessimist:Find risks,Analyst:Balance views" \
--task "Analyze the new product launch" \
--process sequential
Parallel Data Collection
praisonai-ts agents run \
--agents "NewsAgent:Get news,SocialAgent:Get social trends,DataAgent:Get statistics" \
--task "Gather information about AI" \
--process parallel
See Also