lyzr-agents 0.1.0

A framework for building AI agents powered by Lyzr

Lyzr Agents

A Python framework for building AI agents powered by Lyzr.

Installation

# Basic installation
pip install lyzr-agents

# With STORM agent support
pip install lyzr-agents[storm]

Quick Start

Basic Agent Execution

from lyzr_agents import LyzrAgent

# Initialize the client
client = LyzrAgent(
    api_key="your-lyzr-api-key",
    user_id="your-user-id"
)

# Execute an agent
response = client.execute(
    agent_id="your-agent-id",
    message="Hello, what can you help me with?"
)

print(response.response)

STORM Agent - Long-form Article Generation

The STORM agent implements Stanford's STORM algorithm for generating comprehensive, research-backed articles.

from lyzr_agents.storm import StormAgent

# Initialize with default Lyzr agents
agent = StormAgent(
    lyzr_api_key="your-api-key",
    user_id="your-user-id",
    no_of_personas=3,    # Number of expert perspectives
    no_of_questions=3,   # Questions per persona
    no_of_sections=5,    # Article sections
)

# Generate an article (sync)
result = agent.write("quantum computing")
result.toFile("quantum_computing.md")

# Or use async for parallel execution (faster)
import asyncio

async def main():
    result = await agent.write_async("quantum computing")
    result.print()

asyncio.run(main())

Custom Agent Configuration

from lyzr_agents.storm import StormAgent, StormAgentConfig

config = StormAgentConfig(
    persona_generator_agent_id="your-persona-agent",
    question_generator_agent_id="your-question-agent",
    research_agent_id="your-research-agent",
    outline_generator_agent_id="your-outline-agent",
    section_generator_agent_id="your-section-agent",
)

agent = StormAgent(
    lyzr_api_key="your-api-key",
    user_id="your-user-id",
    config=config,
)

Event Callbacks for Visualization

Track the STORM process in real-time for building UIs:

from lyzr_agents.storm import StormAgent

def on_event(event):
    print(f"[{event.status}] {event.step_name}: {event.event_type.value}")

agent = StormAgent(
    lyzr_api_key="your-api-key",
    user_id="your-user-id",
    on_event=on_event,
)

result = agent.write("artificial intelligence")

# Get React Flow compatible graph data
graph_data = result.get_graph_data()
print(graph_data)  # {"nodes": [...], "edges": [...]}

Live Terminal Visualization

Watch STORM execution in real-time with a beautiful tree display:

from lyzr_agents.storm import StormAgent, StormVisualizer

agent = StormAgent(
    lyzr_api_key="your-api-key",
    user_id="your-user-id",
)

# Sync with live visualization
with StormVisualizer() as viz:
    result = agent.write("quantum computing", on_event=viz.on_event)

# Async with live visualization
async with StormVisualizer() as viz:
    result = await agent.write_async("quantum computing", on_event=viz.on_event)

Terminal Output:

STORM: quantum computing [OK]
├── Generate Personas [OK] (3 items)
│   ├── Persona 1 [OK]
│   ├── Persona 2 [OK]
│   └── Persona 3 [OK]
├── Expert 1 Session [OK]
│   ├── Q1 [OK]
│   ├── Research Q1 [OK] (445 chars)
│   └── Q2 [OK]
├── Generate Outline [OK]
│   ├── Introduction [OK]
│   └── Core Concepts [OK]
├── Write: Introduction... [OK] (559 chars)
└── Assemble Article [OK]

Test Mode

Run without making API calls:

from lyzr_agents.storm import StormAgent, test_config

agent = StormAgent(
    lyzr_api_key="test",
    user_id="test",
    config=test_config(),
)

# Returns mock data for testing
result = agent.write("test topic")
print(result.article)

STORM Algorithm

The STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) algorithm works in 6 steps:

1. Persona Discovery - Generate diverse expert personas
2. Question Generation - Each persona asks insightful questions
3. Research - Answer questions using RAG-powered research
4. Outline Generation - Create article structure from research
5. Section Writing - Write each section with research context
6. Assembly - Combine into final article

The async write_async() method runs persona conversations in parallel for faster execution.

API Reference

StormResult

The result object supports fluent method chaining:

result = agent.write("topic")

# Chain methods
result.toFile("output.md").print()

# Access data
print(result.article)      # Final article text
print(result.personas)     # List of generated personas
print(result.questions)    # Dict of persona -> questions
print(result.research)     # Dict of question -> answer
print(result.outline)      # List of section titles
print(result.sections)     # Dict of title -> content
print(result.events)       # List of StormEvent objects

StormVisualizer

Context manager for live terminal visualization:

from lyzr_agents.storm import StormVisualizer

# Customize refresh rate (default: 4 times/second)
with StormVisualizer(refresh_rate=10) as viz:
    result = agent.write("topic", on_event=viz.on_event)

# Hide details like character counts
with StormVisualizer(show_details=False) as viz:
    result = agent.write("topic", on_event=viz.on_event)

Event Types

from lyzr_agents.storm import StormEventType

# Lifecycle
StormEventType.STORM_STARTED
StormEventType.STORM_COMPLETED
StormEventType.STORM_FAILED

# Per-step events
StormEventType.PERSONA_GENERATION_STARTED
StormEventType.PERSONA_CREATED
StormEventType.QUESTION_GENERATION_STARTED
StormEventType.RESEARCH_STARTED
StormEventType.SECTION_WRITING_STARTED
# ... and more

License

MIT License

Links

• Lyzr Platform
• Documentation
• GitHub Repository