Quick Start¶
Get up and running with LionAGI in minutes. This guide shows you the essential concepts and gets you building multi-agent workflows immediately.
What You'll Learn¶
In this quickstart, you'll discover how to:
- Create specialized AI agents with distinct roles and capabilities
- Coordinate multiple agents working in parallel
- Build workflows that leverage different AI perspectives
Getting Started¶
- Installation - Install and configure LionAGI
- Your First Flow - Build your first multi-agent workflow
- Claude Code Integration - Use with Anthropic's Claude Code
Core Concept: Multiple Perspectives¶
The power of LionAGI lies in orchestrating multiple AI agents with different perspectives. Instead of relying on a single AI response, you can gather diverse viewpoints and synthesize them into more comprehensive insights.
import asyncio
from lionagi import Branch, iModel
# Create agents with distinct roles
analyst = Branch(chat_model=iModel(provider="openai", model="gpt-4o-mini"))
critic = Branch(chat_model=iModel(provider="openai", model="gpt-4o-mini"))
# Run them together in parallel
async def analyze(topic):
analysis, critique = await asyncio.gather(
analyst.chat(f"Analyze: {topic}"),
critic.chat(f"Find issues with: {topic}")
)
return {"analysis": analysis, "critique": critique}
result = asyncio.run(analyze("AI safety"))
This example demonstrates the core LionAGI pattern: create specialized agents, run them in parallel using asyncio.gather()
, and combine their outputs. The analyst provides comprehensive analysis while the critic identifies potential problems - giving you both perspectives simultaneously.
Why This Approach Works¶
- Parallel Processing: Both agents work simultaneously, reducing total execution time
- Specialized Perspectives: Each agent focuses on their specific role and expertise
- Comprehensive Coverage: Multiple viewpoints provide more thorough analysis than any single agent
- Minimal Complexity: Clean, readable code that's easy to understand and modify
This foundation scales from simple two-agent patterns to complex multi-agent orchestration workflows with dozens of specialized agents working together.