Documentation Index
Fetch the complete documentation index at: https://docs.orkestration.com/llms.txt
Use this file to discover all available pages before exploring further.
Agents are LLM-powered workers with a name, description, and bound LLM client.
Parameters
- name: Human-friendly label.
- description: What the agent does.
- model_provider: LLM vendor (e.g.,
openai).
- model_name: Model ID (e.g.,
gpt-4o, gpt-3.5-turbo).
- api_secret: Provider API key (distinct from Orkestra
api_key).
Attributes
- config.name: Agent name
- config.description: Agent description
- llm: Underlying LLM abstraction
Create an agent
from orkestra import Orkestra, LLMProvider
client = Orkestra()
agent = client.Agent(
name="Summarizer",
description="Summarizes text",
model_provider="openai",
model_name="gpt-3.5-turbo",
api_secret="YOUR_OPENAI_API_KEY",
)
Generate: plain text
response = agent.generate("Write a bedtime story about a friendly unicorn.")
print(response)
Generate: structured output (Pydantic)
from pydantic import BaseModel
class CalendarEvent(BaseModel):
name: str
date: str
participants: list[str]
event = agent.generate(
"Alice and Bob are going to a science fair on Friday.",
response_model=CalendarEvent,
)
print(event)
