add langchain support (#1805)

integrations/langchain/README.md

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+<!-- START doctoc generated TOC please keep comment here to allow auto update -->
+<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
+**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*
+
+- [Skyvern Langchain](#skyvern-langchain)
+  - [Installation](#installation)
+  - [Usage](#usage)
+    - [Run a task(sync) with skyvern agent (calling skyvern agent function directly in the tool)](#run-a-tasksync-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-taskasync-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-tasksync-with-skyvern-client-calling-skyvern-openapi-in-the-tool)
+    - [Run a task(async) with skyvern client (calling skyvern OpenAPI in the tool)](#run-a-taskasync-with-skyvern-client-calling-skyvern-openapi-in-the-tool)
+
+<!-- END doctoc generated TOC please keep comment here to allow auto update -->
+
+# Skyvern Langchain
+
+This is a langchain integration for Skyvern.
+
+## Installation
+
+```bash
+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.
+
+:warning: :warning: 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.
+
+
+```python
+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.
+
+:warning: :warning: 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.
+
+```python
+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.
+
+```python
+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.
+
+```python
+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())
+```

integrations/langchain/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "skyvern-langchain"
+version = "0.1.1"
+description = ""
+authors = ["lawyzheng <lawy@skyvern.com>"]
+packages = [{ include = "skyvern_langchain" }]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11,<3.12"
+skyvern = "^0.1.56"
+langchain = "^0.3.19"
+
+
+[tool.poetry.group.dev.dependencies]
+twine = "^6.1.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

integrations/langchain/skyvern_langchain/agent.py

@@ -0,0 +1,48 @@
+from typing import Any, Dict
+
+from langchain.tools import tool
+from skyvern_langchain.schema import GetTaskInput, TaskV1Request, TaskV2Request
+
+from skyvern.agent import Agent
+from skyvern.forge.sdk.schemas.observers import ObserverTask
+from skyvern.forge.sdk.schemas.tasks import CreateTaskResponse, TaskResponse
+
+
+@tool("run-skyvern-agent-task-v1", args_schema=TaskV1Request)
+async def run_task_v1(**kwargs: Dict[str, Any]) -> TaskResponse:
+    """Use Skyvern agent to run a v1 task. It is usually used for the simple tasks. This function won't return until the task is finished."""
+    task_request = TaskV1Request(**kwargs)
+    return await Agent().run_task(task_request=task_request, timeout_seconds=task_request.timeout_seconds)
+
+
+@tool("queue-skyvern-agent-task-v1", args_schema=TaskV1Request)
+async def queue_task_v1(**kwargs: Dict[str, Any]) -> CreateTaskResponse:
+    """Use Skyvern agent to queue a v1 task. It is usually used for the simple tasks. This function will return immediately and the task will be running in the background."""
+    task_request = TaskV1Request(**kwargs)
+    return await Agent().create_task(task_request=task_request)
+
+
+@tool("get-skyvern-agent-task-v1", args_schema=GetTaskInput)
+async def get_task_v1(task_id: str) -> TaskResponse | None:
+    """Use Skyvern agent to get a v1 task. v1 tasks are usually simple tasks."""
+    return await Agent().get_task(task_id=task_id)
+
+
+@tool("run-skyvern-agent-task-v2", args_schema=TaskV2Request)
+async def run_task_v2(**kwargs: Dict[str, Any]) -> ObserverTask:
+    """Use Skyvern agent to run a v2 task. It is usually used for the complicated tasks. This function won't return until the task is finished."""
+    task_request = TaskV2Request(**kwargs)
+    return await Agent().run_observer_task_v_2(task_request=task_request, timeout_seconds=task_request.timeout_seconds)
+
+
+@tool("queue-skyvern-agent-task-v2", args_schema=TaskV2Request)
+async def queue_task_v2(**kwargs: Dict[str, Any]) -> ObserverTask:
+    """Use Skyvern agent to queue a v2 task. It is usually used for the complicated tasks. This function will return immediately and the task will be running in the background."""
+    task_request = TaskV2Request(**kwargs)
+    return await Agent().observer_task_v_2(task_request=task_request)
+
+
+@tool("get-skyvern-agent-task-v2", args_schema=GetTaskInput)
+async def get_task_v2(task_id: str) -> ObserverTask | None:
+    """Use Skyvern agent to get a v2 task. v2 tasks are usually complicated tasks."""
+    return await Agent().get_observer_task_v_2(task_id=task_id)

integrations/langchain/skyvern_langchain/client.py

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+from typing import Any, Dict, Type
+
+from httpx import AsyncClient
+from langchain.tools import BaseTool
+from pydantic import BaseModel
+from skyvern_langchain.schema import GetTaskInput, TaskV1Request, TaskV2Request
+
+from skyvern.client import AsyncSkyvern
+from skyvern.forge.sdk.schemas.tasks import CreateTaskResponse, TaskResponse
+
+
+class SkyvernClientBaseTool(BaseTool):
+    credential: str = ""
+    base_url: str = "https://api.skyvern.com"
+
+    def get_client(self) -> AsyncSkyvern:
+        httpx_client = AsyncClient(
+            headers={
+                "Content-Type": "application/json",
+                "x-api-key": self.credential,
+            },
+        )
+        return AsyncSkyvern(base_url=self.base_url, httpx_client=httpx_client)
+
+    def _run(self) -> None:
+        raise NotImplementedError("skyvern client tool does not support sync")
+
+
+class RunSkyvernClientTaskV1Tool(SkyvernClientBaseTool):
+    name: str = "run-skyvern-client-task-v1"
+    description: str = """Use Skyvern client to run a v1 task. It is usually used for the simple tasks. This function won't return until the task is finished."""
+    args_schema: Type[BaseModel] = TaskV1Request
+
+    async def _arun(self, **kwargs: Dict[str, Any]) -> TaskResponse:
+        task_request = TaskV1Request(**kwargs)
+        return await self.get_client().agent.run_task(
+            max_steps_override=task_request.max_steps,
+            timeout_seconds=task_request.timeout_seconds,
+            url=task_request.url,
+            title=task_request.title,
+            webhook_callback_url=task_request.webhook_callback_url,
+            totp_verification_url=task_request.totp_verification_url,
+            totp_identifier=task_request.totp_identifier,
+            navigation_goal=task_request.navigation_goal,
+            data_extraction_goal=task_request.data_extraction_goal,
+            navigation_payload=task_request.navigation_goal,
+            error_code_mapping=task_request.error_code_mapping,
+            proxy_location=task_request.proxy_location,
+            extracted_information_schema=task_request.extracted_information_schema,
+            complete_criterion=task_request.complete_criterion,
+            terminate_criterion=task_request.terminate_criterion,
+            browser_session_id=task_request.browser_session_id,
+        )
+
+
+class QueueSkyvernClientTaskV1Tool(SkyvernClientBaseTool):
+    name: str = "queue-skyvern-client-task-v1"
+    description: str = """Use Skyvern client to queue a v1 task. It is usually used for the simple tasks. This function will return immediately and the task will be running in the background."""
+    args_schema: Type[BaseModel] = TaskV1Request
+
+    async def _arun(self, **kwargs: Dict[str, Any]) -> CreateTaskResponse:
+        task_request = TaskV1Request(**kwargs)
+        return await self.get_client().agent.create_task(
+            max_steps_override=task_request.max_steps,
+            url=task_request.url,
+            title=task_request.title,
+            webhook_callback_url=task_request.webhook_callback_url,
+            totp_verification_url=task_request.totp_verification_url,
+            totp_identifier=task_request.totp_identifier,
+            navigation_goal=task_request.navigation_goal,
+            data_extraction_goal=task_request.data_extraction_goal,
+            navigation_payload=task_request.navigation_goal,
+            error_code_mapping=task_request.error_code_mapping,
+            proxy_location=task_request.proxy_location,
+            extracted_information_schema=task_request.extracted_information_schema,
+            complete_criterion=task_request.complete_criterion,
+            terminate_criterion=task_request.terminate_criterion,
+            browser_session_id=task_request.browser_session_id,
+        )
+
+
+class GetSkyvernClientTaskV1Tool(SkyvernClientBaseTool):
+    name: str = "get-skyvern-client-task-v1"
+    description: str = """Use Skyvern client to get a v1 task. v1 tasks are usually simple tasks."""
+    args_schema: Type[BaseModel] = GetTaskInput
+
+    async def _arun(self, task_id: str) -> TaskResponse:
+        return await self.get_client().agent.get_task(task_id=task_id)
+
+
+class RunSkyvernClientTaskV2Tool(SkyvernClientBaseTool):
+    name: str = "run-skyvern-client-task-v2"
+    description: str = """Use Skyvern client to run a v2 task. It is usually used for the complicated tasks. This function won't return until the task is finished."""
+    args_schema: Type[BaseModel] = TaskV2Request
+
+    async def _arun(self, **kwargs: Dict[str, Any]) -> Dict[str, Any | None]:
+        task_request = TaskV2Request(**kwargs)
+        return await self.get_client().agent.run_observer_task_v_2(
+            max_iterations_override=task_request.max_iterations,
+            timeout_seconds=task_request.timeout_seconds,
+            user_prompt=task_request.user_prompt,
+            url=task_request.url,
+            browser_session_id=task_request.browser_session_id,
+            webhook_callback_url=task_request.webhook_callback_url,
+            totp_verification_url=task_request.totp_verification_url,
+            totp_identifier=task_request.totp_identifier,
+            proxy_location=task_request.proxy_location,
+        )
+
+
+class QueueSkyvernClientTaskV2Tool(SkyvernClientBaseTool):
+    name: str = "queue-skyvern-client-task-v2"
+    description: str = """Use Skyvern client to queue a v2 task. It is usually used for the complicated tasks. This function will return immediately and the task will be running in the background."""
+    args_schema: Type[BaseModel] = TaskV2Request
+
+    async def _arun(self, **kwargs: Dict[str, Any]) -> Dict[str, Any | None]:
+        task_request = TaskV2Request(**kwargs)
+        return await self.get_client().agent.observer_task_v_2(
+            max_iterations_override=task_request.max_iterations,
+            user_prompt=task_request.user_prompt,
+            url=task_request.url,
+            browser_session_id=task_request.browser_session_id,
+            webhook_callback_url=task_request.webhook_callback_url,
+            totp_verification_url=task_request.totp_verification_url,
+            totp_identifier=task_request.totp_identifier,
+            proxy_location=task_request.proxy_location,
+        )
+
+
+class GetSkyvernClientTaskV2Tool(SkyvernClientBaseTool):
+    name: str = "get-skyvern-client-task-v2"
+    description: str = """Use Skyvern client to get a v2 task. It is usually used for the complicated tasks."""
+    args_schema: Type[BaseModel] = GetTaskInput
+
+    async def _arun(self, task_id: str) -> Dict[str, Any | None]:
+        return await self.get_client().agent.get_observer_task_v_2(task_id=task_id)

integrations/langchain/skyvern_langchain/schema.py

@@ -0,0 +1,18 @@
+from pydantic import BaseModel
+
+from skyvern.forge.sdk.schemas.observers import ObserverTaskRequest
+from skyvern.forge.sdk.schemas.tasks import TaskRequest
+
+
+class TaskV1Request(TaskRequest):
+    max_steps: int = 10
+    timeout_seconds: int = 60 * 60
+
+
+class TaskV2Request(ObserverTaskRequest):
+    max_iterations: int = 10
+    timeout_seconds: int = 60 * 60
+
+
+class GetTaskInput(BaseModel):
+    task_id: str

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "skyvern"
-version = "0.1.55"
+version = "0.1.56"
 description = ""
 authors = ["Skyvern AI <info@skyvern.com>"]
 readme = "README.md"