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WorkflowAI Python SDK is a library that allows you to programmatically create and run agents in Python, while being able to use the full power of the WorkflowAI platform.

workflowai requires Python >= 3.9.

Get your API key from your WorkflowAI Cloud dashboard or from your self-hosted WorkflowAI dashboard.

Set the WORKFLOWAI_API_KEY environment variable.

import os
import workflowai

workflowai.init( # This initialization is optional when using default settings
    api_key=os.environ.get("WORKFLOWAI_API_KEY"),  # This is the default and can be omitted
    url="https://run.workflowai.com",  # This is the default and can be omitted
)

You can also set the WORKFLOWAI_API_URL environment variable to point to your self-hosted WorkflowAI.

An agent is in essence an async function with the added constraints that:

it has a single argument that is a Pydantic model, which is the input to the agent
it has a single return value that is a Pydantic model, which is the output of the agent
it is decorated with the @workflowai.agent() decorator

The following agent, given a city, returns the country, capital, and a fun fact about the city.

You have created your first agent! Congratulations.

Agents created by the SDK are also available in the Playground.

Runs are automatically logged as well from the Runs section.

Let's go through in more detail how to setup an agent.

Last updated 11 months ago

import workflowai
from pydantic import BaseModel
from workflowai import Model

class CityInput(BaseModel):
    city: str

class CapitalOutput(BaseModel):
    country: str
    capital: str 
    fun_fact: str

@workflowai.agent()
async def get_capital_info(city_input: CityInput) -> CapitalOutput:
    ...

output = await get_capital_info.run(
    CityInput(city="New York"), 
    model=Model.GPT_4_LATEST
)
print(output)

# {
#   "country": "United States",
#   "capital": "Washington, D.C.",
#   "fun_fact": "New York City is known as 'The Big Apple' and is famous for its cultural diversity and iconic landmarks like Times Square and Central Park."
# }
# ==================================================
# Cost: $ 0.00091
# Latency: 1.65s

output = await get_capital_info.run(
    CityInput(city="New York"),
    model=Model.CLAUDE_3_5_SONNET_LATEST
)
print(output)

# Output:
# ==================================================
# {
#   "country": "United States",
#   "capital": "Washington, D.C.",
#   "fun_fact": "New York City's Federal Reserve Bank has the largest gold storage in the world, containing approximately 7,000 tons of gold bullion stored 80 feet below street level."
# }
# ==================================================
# Cost: $ 0.001755
# Latency: 2.43s

output = await get_capital_info.run(
    CityInput(city="New York"),
    model=Model.GEMINI_2_0_FLASH_LATEST
)
print(output)

# Output:
# ==================================================
# {
#   "country": "United States of America",
#   "capital": "Washington, D.C.",
#   "fun_fact": "New York City is home to over 8 million people and over 800 languages are spoken in New York City, making it the most linguistically diverse city in the world."
# }
# ==================================================
# Cost: $ 0.00005
# Latency: 1.26s

# use `.stream()` to stream the output
async for chunk in get_capital_info.stream(
    CityInput(city="New York"),
    model=Model.GPT_4O_MINI_LATEST
):
    print(chunk)

# {
#   "country": "United"
# }
# {
#   "country": "United States"
# }
# {
#   "country": "United States"
#   "capital": "Washington"
# }
# streaming continues...
# ...