Skyvern/integrations/langchain

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• Skyvern Langchain
  • Installation
  • Usage
    • Run a task(sync) with skyvern agent (calling skyvern agent function directly in the tool)
    • Run a task(async) with skyvern agent (calling skyvern agent function directly in the tool)
    • Run a task(sync) with skyvern client (calling skyvern OpenAPI in the tool)
    • Run a task(async) with skyvern client (calling skyvern OpenAPI in the tool)

Skyvern Langchain

This is a langchain integration for Skyvern.

Installation

pip install skyvern-langchain

Usage

Run a task(sync) with skyvern agent (calling skyvern agent function directly in the tool)

sync task won't return until the task is finished.

⚠️ ⚠️ if you want to run this code block, you need to run skyvern init --openai-api-key <your_openai_api_key> command in your terminal to set up skyvern first.

import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from skyvern_langchain.agent import run_task_v2

load_dotenv()

llm = ChatOpenAI(model="gpt-4o", temperature=0)

agent = initialize_agent(
    llm=llm,
    tools=[run_task_v2],
    verbose=True,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)


async def main():
    # to run skyvern agent locally, must run `skyvern init` first
    print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'"))


if __name__ == "__main__":
    asyncio.run(main())

Run a task(async) with skyvern agent (calling skyvern agent function directly in the tool)

async task will return immediately and the task will be running in the background. You can use get_task_v2 tool to poll the task information until the task is finished.

⚠️ ⚠️ if you want to run this code block, you need to run skyvern init --openai-api-key <your_openai_api_key> command in your terminal to set up skyvern first.

import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from skyvern_langchain.agent import queue_task_v2, get_task_v2

from langchain_community.tools.sleep.tool import SleepTool

load_dotenv()

llm = ChatOpenAI(model="gpt-4o", temperature=0)

agent = initialize_agent(
    llm=llm,
    tools=[
        queue_task_v2,
        get_task_v2,
        SleepTool(),
    ],
    verbose=True,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)


async def main():
    # use sleep tool to set up the polling logic until the task is completed, if you only want to queue a task, you can remove the sleep tool
    print(await agent.ainvoke("Queue a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))


if __name__ == "__main__":
    asyncio.run(main())

Run a task(sync) with skyvern client (calling skyvern OpenAPI in the tool)

sync task won't return until the task is finished.

no need to run skyvern init command in your terminal to set up skyvern before using this integration.

import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from skyvern_langchain.client import RunSkyvernClientTaskV2Tool

load_dotenv()

llm = ChatOpenAI(model="gpt-4o", temperature=0)

run_task_v2 = RunSkyvernClientTaskV2Tool(
    credential="<your_organization_api_key>",
)

agent = initialize_agent(
    llm=llm,
    tools=[run_task_v2],
    verbose=True,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)

async def main():
    print(await agent.ainvoke("Run a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.'"))


if __name__ == "__main__":
    asyncio.run(main())

Run a task(async) with skyvern client (calling skyvern OpenAPI in the tool)

async task will return immediately and the task will be running in the background. You can use GetSkyvernClientTaskV2Tool tool to poll the task information until the task is finished.

no need to run skyvern init command in your terminal to set up skyvern before using this integration.

import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from skyvern_langchain.client import (
    QueueSkyvernClientTaskV2Tool,
    GetSkyvernClientTaskV2Tool,
)

from langchain_community.tools.sleep.tool import SleepTool

load_dotenv()

llm = ChatOpenAI(model="gpt-4o", temperature=0)

queue_task_v2 = QueueSkyvernClientTaskV2Tool(
    credential="<your_organization_api_key>",
)

get_task_v2 = GetSkyvernClientTaskV2Tool(
    credential="<your_organization_api_key>",
)

agent = initialize_agent(
    llm=llm,
    tools=[
        queue_task_v2,
        get_task_v2,
        SleepTool(),
    ],
    verbose=True,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
)


async def main():
    # use sleep tool to set up the polling logic until the task is completed, if you only want to queue a task, you can remove the sleep tool
    print(await agent.ainvoke("Queue a task with Skyvern. The task is about 'Navigate to the Hacker News homepage and get the top 3 posts.' Then, get this task information until it's completed. The task information re-get interval should be 60s."))


if __name__ == "__main__":
    asyncio.run(main())