Skyvern

Skyvern-AI

13083

Automate browser-based workflows with LLMs and Computer Vision

#api #automation #browser #computer #gpt #llm #playwright #python #rpa #vision #workflow #browser-automation

Overview

What is Skyvern

Skyvern is a platform that automates browser-based workflows using Large Language Models (LLMs) and Computer Vision, providing a simple API to streamline manual processes across various websites.

How to Use

To use Skyvern, developers can integrate its API into their applications, allowing them to automate tasks on websites without the need for complex scripting or maintenance.

Key Features

Key features of Skyvern include automation of manual workflows, a user-friendly API, and the ability to handle a wide range of websites without relying on fragile scripts.

Where to Use

Skyvern can be used in various fields such as web scraping, data entry automation, testing web applications, and any scenario where browser interactions are required.

Use Cases

Use cases for Skyvern include automating form submissions, extracting data from web pages, performing repetitive tasks on e-commerce sites, and integrating with other software tools for enhanced productivity.

Usage

Installation

Generate your exclusive SSE URL:

MCP Server Configuration (for all clients):

Installation in Cursor

Click on the Cursor settings icon in the top right, find the MCP option, then click the + button in the top right of that page and add the configuration in the config panel.

Note: Do not delete previously added configuration items

After completion, restart Cursor, chat with Cursor, and you can use MCP functions

Installation in Claude

Find this json file at the following path: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json If this file doesn't exist, create one and paste the content above into the file.

Note: Do not delete previously added configuration items.

After completion, restart Claude, and you can use MCP functions through the Claude dialog

SDK

Installation in Python

Install composio-toolset by running the following commands in your terminal:

pip install composio_openai

Python example code:

from composio_openai import ComposioToolSet, App
from openai import OpenAI

openai_client = OpenAI()
composio_toolset = ComposioToolSet(entity_id="default")

tools = composio_toolset.get_tools(apps=[App.GITHUB])

Installation in TypeScript

Install composio-core by running the following commands in your terminal:

npm install -g composio_core

TypeScript example code:

import { OpenAI } from "openai";
import { OpenAIToolSet } from "composio-core";

const openaiClient = new OpenAI();
const composioToolset = new OpenAIToolSet();

const tools = await composioToolset.getTools({
  apps: ["github"],
});

Content

<h1 align="center"> <a href="https://www.skyvern.com"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/images/skyvern_logo.png"/> <img height="120" src="docs/images/skyvern_logo_blackbg.png"/> </picture> </a> </h1> 🐉 Automate Browser-based workflows using LLMs and Computer Vision 🐉 <a href="https://www.skyvern.com/"><img src="https://img.shields.io/badge/Website-blue?logo=googlechrome&logoColor=black"/></a> <a href="https://docs.skyvern.com/"><img src="https://img.shields.io/badge/Docs-yellow?logo=gitbook&logoColor=black"/></a> <a href="https://discord.gg/fG2XXEuQX3"><img src="https://img.shields.io/discord/1212486326352617534?logo=discord&label=discord"/></a>  <a href="https://github.com/skyvern-ai/skyvern"><img src="https://img.shields.io/github/stars/skyvern-ai/skyvern" /></a> <a href="https://github.com/Skyvern-AI/skyvern/blob/main/LICENSE"><img src="https://img.shields.io/github/license/skyvern-ai/skyvern"/></a> <a href="https://twitter.com/skyvernai"><img src="https://img.shields.io/twitter/follow/skyvernai?style=social"/></a> <a href="https://www.linkedin.com/company/95726232"><img src="https://img.shields.io/badge/Follow%20 on%20LinkedIn-8A2BE2?logo=linkedin"/></a> [Skyvern](https://www.skyvern.com) automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows on a large number of websites, replacing brittle or unreliable automation solutions. <img src="docs/images/geico_shu_recording_cropped.gif"/> Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern relies on prompts in addition to computer vision and LLMs to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 1. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 1. Skyvern is able to take a single workflow and apply it to a large number of websites, as it’s able to reason through the interactions necessary to complete the workflow 1. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 1. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to [#real-world-examples-of-skyvern](#real-world-examples-of-skyvern) # How it works Skyvern was inspired by the Task-Driven autonomous agent design popularized by [BabyAGI](https://github.com/yoheinakajima/babyagi) and [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) -- with one major bonus: we give Skyvern the ability to interact with websites using browser automation libraries like [Playwright](https://playwright.dev/). Skyvern uses a swarm of agents to comprehend a website, and plan and execute its actions: 1. **Interactable Element Agent**: This agent is responsible for parsing the HTML of a website and extracting the interactable elements. 2. **Navigation Agent**: This agent is responsible for planning the navigation to complete a task. Examples include clicking buttons, inserting text, selecting options, etc. 3. **Data Extraction Agent**: This agent is responsible for extracting data from a website. It's capable of reading the tables and text on the page, and extracting the output in a user-defined structured format 4. **Password Agent**: This agent is responsible for filling out password forms on a website. It's capable of reading the username and password from a password manager, and filling out the form while preserving the privacy of the user-defined secrets. 5. **2FA Agent**: This agent is responsible for filling out 2FA forms on a website. It's capable of intercepting website requests for 2FAs, and either requesting user-defined APIs for 2FA codes or waiting for users to feed 2FA codes into it, and then completing the login process. 6. **Dynamic Auto-complete Agent**: This agent is responsible for filling out dynamic auto-complete forms on a website. It's capable of reading the options presented to it, selecting the appropriate option based on the user's input, and adjusting its inputs based on the feedback from inside the form. Popular examples include: Address forms, university dropdowns, and more. <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/images/skyvern_2_0_system_diagram.png" /> <img src="docs/images/skyvern_2_0_system_diagram.png" /> </picture> # Demo  https://github.com/user-attachments/assets/5cab4668-e8e2-4982-8551-aab05ff73a7f # Skyvern Cloud We offer a managed cloud version of Skyvern that allows you to run Skyvern without having to manage the infrastructure. It allows you to run multiple Skyvern instances in parallel and comes bundled with anti-bot detection mechanisms, proxy network, and CAPTCHA solvers. If you'd like to try it out, 1. Navigate to [app.skyvern.com](https://app.skyvern.com) 1. Create an account & Get $5 of credits on us 1. Kick off your first task and see Skyvern in action! # Quickstart This quickstart guide will walk you through getting Skyvern up and running on your local machine. ## Local > ⚠️ **REQUIREMENT**: This project requires Python 3.11 ⚠️ 1. **Install Skyvern** ```bash pip install skyvern ``` 2. **Configure Skyvern** Run the setup wizard which will guide you through the configuration process, including Skyvern [MCP](https://github.com/Skyvern-AI/skyvern/blob/main/integrations/mcp/README.md) integration. This will generate a `.env` as the configuration settings file. ```bash skyvern init ``` 3. **Launch the Skyvern Server** ```bash skyvern run server ``` 4. **Launch the Skyvern UI** ```bash skyvern run ui ``` ## Docker Compose setup 1. Make sure you have [Docker Desktop](https://www.docker.com/products/docker-desktop/) installed and running on your machine 1. Make sure you don't have postgres running locally (Run `docker ps` to check) 1. Clone the repository and navigate to the root directory 1. Fill in the LLM provider key on the [docker-compose.yml](./docker-compose.yml). *If you want to run Skyvern on a remote server, make sure you set the correct server ip for the UI container in [docker-compose.yml](./docker-compose.yml).* 2. Run the following command via the commandline: ```bash docker compose up -d ``` 3. Navigate to `http://localhost:8080` in your browser to start using the UI ## Model Context Protocol (MCP) See the MCP documentation [here](https://github.com/Skyvern-AI/skyvern/blob/main/integrations/mcp/README.md) ## Prompting Tips Here are some tips that may help you on your adventure: 1. Skyvern is really good at carrying out a single goal. If you give it too many instructions to do, it has a high likelihood of hallucinating along the way. 2. Being really explicit about goals is very important. For example, if you're generating an insurance quote, let it know very clearly how it can identify it has accomplished its goals. Use words like "COMPLETE" or "TERMINATE" to indicate success and failure modes, respectively. 3. Workflows can be used if you'd like to do more advanced things such as chaining multiple instructions together, or securely logging in. If you need any help with this, please feel free to book some time with us! We're always happy to help # Supported Functionality ## Skyvern 2.0 Skyvern 2.0 is a major overhaul of Skyvern that includes a multi-agent architecture with a planner + validator agent, allowing Skyvern to complete more complex tasks with a zero-shot prompt. ## Skyvern Tasks Tasks are the fundamental building block inside Skyvern. Each task is a single request to Skyvern, instructing it to navigate through a website and accomplish a specific goal. Tasks require you to specify a `url`, `prompt`, and can optionally include a `data schema` (if you want the output to conform to a specific schema) and `error codes` (if you want Skyvern to stop running in specific situations). <img src="docs/images/skyvern_2_0_screenshot.png"/> ## Skyvern Workflows Workflows are a way to chain multiple tasks together to form a cohesive unit of work. For example, if you wanted to download all invoices newer than January 1st, you could create a workflow that first navigated to the invoices page, then filtered down to only show invoices newer than January 1st, extracted a list of all eligible invoices, and iterated through each invoice to download it. Another example is if you wanted to automate purchasing products from an e-commerce store, you could create a workflow that first navigated to the desired product, then added it to a cart. Second, it would navigate to the cart and validate the cart state. Finally, it would go through the checkout process to purchase the items. Supported workflow features include: 1. Navigation 1. Action 1. Data Extraction 1. Loops 1. File parsing 1. Uploading files to block storage 1. Sending emails 1. Text Prompts 1. Tasks (general) 1. (Coming soon) Conditionals 1. (Coming soon) Custom Code Block <img src="docs/images/invoice_downloading_workflow_example.png"/> ## Livestreaming Skyvern allows you to livestream the viewport of the browser to your local machine so that you can see exactly what Skyvern is doing on the web. This is useful for debugging and understanding how Skyvern is interacting with a website, and intervening when necessary ## Form Filling Skyvern is natively capable of filling out form inputs on websites. Passing in information via the `navigation_goal` will allow Skyvern to comprehend the information and fill out the form accordingly. ## Data Extraction Skyvern is also capable of extracting data from a website. You can also specify a `data_extraction_schema` directly within the main prompt to tell Skyvern exactly what data you'd like to extract from the website, in jsonc format. Skyvern's output will be structured in accordance to the supplied schema. ## File Downloading Skyvern is also capable of downloading files from a website. All downloaded files are automatically uploaded to block storage (if configured), and you can access them via the UI. ## Authentication (Beta) Skyvern supports a number of different authentication methods to make it easier to automate tasks behind a login. If you'd like to try it out, please reach out to us [via email](mailto:founders@skyvern.com) or [discord](https://discord.gg/fG2XXEuQX3). ### Password Manager Integrations Skyvern currently supports the following password manager integrations: - [x] Bitwarden - [ ] 1Password - [ ] LastPass <img src="docs/images/secure_password_task_example.png"/> ### 2FA Skyvern supports a number of different 2FA methods to allow you to automate workflows that require 2FA. Examples include: 1. QR-based 2FA (e.g. Google Authenticator, Authy) 1. Email based 2FA 1. SMS based 2FA # Real-world examples of Skyvern We love to see how Skyvern is being used in the wild. Here are some examples of how Skyvern is being used to automate workflows in the real world. Please open PRs to add your own examples! ## Invoice Downloading on many different websites [Book a demo to see it live](https://meetings.hubspot.com/skyvern/demo) <img src="docs/images/invoice_downloading.gif"/> ## Automate the job application process [💡 See it in action](https://app.skyvern.com/tasks/create/job_application) <img src="docs/images/job_application_demo.gif"/> ## Automate materials procurement for a manufacturing company [💡 See it in action](https://app.skyvern.com/tasks/create/finditparts) <img src="docs/images/finditparts_recording_crop.gif"/> ## Navigating to government websites to register accounts or fill out forms [💡 See it in action](https://app.skyvern.com/tasks/create/california_edd) <img src="docs/images/edd_services.gif"/>  ## Filling out random contact us forms [💡 See it in action](https://app.skyvern.com/tasks/create/contact_us_forms) <img src="docs/images/contact_forms.gif"/> ## Retrieving insurance quotes from insurance providers in any language [💡 See it in action](https://app.skyvern.com/tasks/create/bci_seguros) <img src="docs/images/bci_seguros_recording.gif"/> [💡 See it in action](https://app.skyvern.com/tasks/create/geico) <img src="docs/images/geico_shu_recording_cropped.gif"/> # Contributor Setup ### Prerequisites > :warning: :warning: MAKE SURE YOU ARE USING PYTHON 3.11 :warning: :warning: :warning: :warning: Only well-tested on MacOS :warning: :warning: Before you begin, make sure you have the following installed: - [Brew (if you're on a Mac)](https://brew.sh/) - [Poetry](https://python-poetry.org/docs/#installation) - `brew install poetry` - [node](https://nodejs.org/en/download/) - [Docker](https://docs.docker.com/engine/install/) Note: Our setup script does these two for you, but they are here for reference. - [Python 3.11](https://www.python.org/downloads/) - `poetry env use 3.11` - [PostgreSQL 14](https://www.postgresql.org/download/) (if you're on a Mac, setup script will install it for you if you have homebrew installed) - `brew install postgresql` ## Setup (Contributors) 1. Clone the repository and navigate to the root directory 1. Open Docker Desktop (Works for Windows, macOS, and Linux) or run Docker Daemon 1. Run the setup script to install the necessary dependencies and setup your environment ```bash skyvern/scripts/setup.sh ``` 1. Start the server ```bash ./run_skyvern.sh ``` 1. You can start sending requests to the server, but we built a simple UI to help you get started. To start the UI, run the following command: ```bash ./run_ui.sh ``` 1. Navigate to `http://localhost:8080` in your browser to start using the UI ## Additional Setup for Contributors If you're looking to contribute to Skyvern, you'll need to install the pre-commit hooks to ensure code quality and consistency. You can do this by running the following command: ```bash pre-commit install ``` # Documentation More extensive documentation can be found on our [docs page](https://docs.skyvern.com). Please let us know if something is unclear or missing by opening an issue or reaching out to us [via email](mailto:founders@skyvern.com) or [discord](https://discord.gg/fG2XXEuQX3). # Supported LLMs | Provider | Supported Models | | -------- | ------- | | OpenAI | gpt4-turbo, gpt-4o, gpt-4o-mini | | Anthropic | Claude 3 (Haiku, Sonnet, Opus), Claude 3.5 (Sonnet) | | Azure OpenAI | Any GPT models. Better performance with a multimodal llm (azure/gpt4-o) | | AWS Bedrock | Anthropic Claude 3 (Haiku, Sonnet, Opus), Claude 3.5 (Sonnet) | | Ollama | Coming soon (contributions welcome) | | Gemini | Coming soon (contributions welcome) | | Llama 3.2 | Coming soon (contributions welcome) | | Novita AI | Llama 3.1 (8B, 70B), Llama 3.2 (1B, 3B, 11B Vision) | #### Environment Variables | Variable | Description| Type | Sample Value| | -------- | ------- | ------- | ------- | | `ENABLE_OPENAI`| Register OpenAI models | Boolean | `true`, `false` | | `ENABLE_ANTHROPIC` | Register Anthropic models| Boolean | `true`, `false` | | `ENABLE_AZURE` | Register Azure OpenAI models | Boolean | `true`, `false` | | `ENABLE_BEDROCK` | Register AWS Bedrock models. To use AWS Bedrock, you need to make sure your [AWS configurations](https://github.com/boto/boto3?tab=readme-ov-file#using-boto3) are set up correctly first. | Boolean | `true`, `false` | | `ENABLE_GEMINI` | Register Gemini models| Boolean | `true`, `false` | | `ENABLE_NOVITA`| Register Novita AI models | Boolean | `true`, `false` | | `LLM_KEY` | The name of the model you want to use | String | Currently supported llm keys: `OPENAI_GPT4_TURBO`, `OPENAI_GPT4V`, `OPENAI_GPT4O`, `OPENAI_GPT4O_MINI`, `ANTHROPIC_CLAUDE3`, `ANTHROPIC_CLAUDE3_OPUS`, `ANTHROPIC_CLAUDE3_SONNET`, `ANTHROPIC_CLAUDE3_HAIKU`, `ANTHROPIC_CLAUDE3.5_SONNET`, `BEDROCK_ANTHROPIC_CLAUDE3_OPUS`, `BEDROCK_ANTHROPIC_CLAUDE3_SONNET`, `BEDROCK_ANTHROPIC_CLAUDE3_HAIKU`, `BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET`, `AZURE_OPENAI`, `GEMINI_PRO`, `GEMINI_FLASH`, `BEDROCK_AMAZON_NOVA_PRO`, `BEDROCK_AMAZON_NOVA_LITE`| | `SECONDARY_LLM_KEY` | The name of the model for mini agents skyvern runs with | String | Currently supported llm keys: `OPENAI_GPT4_TURBO`, `OPENAI_GPT4V`, `OPENAI_GPT4O`, `OPENAI_GPT4O_MINI`, `ANTHROPIC_CLAUDE3`, `ANTHROPIC_CLAUDE3_OPUS`, `ANTHROPIC_CLAUDE3_SONNET`, `ANTHROPIC_CLAUDE3_HAIKU`, `ANTHROPIC_CLAUDE3.5_SONNET`, `BEDROCK_ANTHROPIC_CLAUDE3_OPUS`, `BEDROCK_ANTHROPIC_CLAUDE3_SONNET`, `BEDROCK_ANTHROPIC_CLAUDE3_HAIKU`, `BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET`, `AZURE_OPENAI`, `GEMINI_PRO`, `GEMINI_FLASH`, `NOVITA_DEEPSEEK_R1`, `NOVITA_DEEPSEEK_V3`, `NOVITA_LLAMA_3_3_70B`, `NOVITA_LLAMA_3_2_1B`, `NOVITA_LLAMA_3_2_3B`, `NOVITA_LLAMA_3_2_11B_VISION`, `NOVITA_LLAMA_3_1_8B`, `NOVITA_LLAMA_3_1_70B`, `NOVITA_LLAMA_3_1_405B`, `NOVITA_LLAMA_3_8B`, `NOVITA_LLAMA_3_70B`| | `OPENAI_API_KEY` | OpenAI API Key | String | `sk-1234567890` | | `OPENAI_API_BASE` | OpenAI API Base, optional | String | `https://openai.api.base` | | `OPENAI_ORGANIZATION` | OpenAI Organization ID, optional | String | `your-org-id` | | `ANTHROPIC_API_KEY` | Anthropic API key| String | `sk-1234567890` | | `AZURE_API_KEY` | Azure deployment API key | String | `sk-1234567890` | | `AZURE_DEPLOYMENT` | Azure OpenAI Deployment Name | String | `skyvern-deployment`| | `AZURE_API_BASE` | Azure deployment api base url| String | `https://skyvern-deployment.openai.azure.com/`| | `AZURE_API_VERSION` | Azure API Version| String | `2024-02-01`| | `GEMINI_API_KEY` | Gemini API Key| String | `your_google_gemini_api_key`| # Feature Roadmap This is our planned roadmap for the next few months. If you have any suggestions or would like to see a feature added, please don't hesitate to reach out to us [via email](mailto:founders@skyvern.com) or [discord](https://discord.gg/fG2XXEuQX3). - [x] **Open Source** - Open Source Skyvern's core codebase - [x] **[BETA] Workflow support** - Allow support to chain multiple Skyvern calls together - [x] **Improved context** - Improve Skyvern's ability to understand content around interactable elements by introducing feeding relevant label context through the text prompt - [x] **Cost Savings** - Improve Skyvern's stability and reduce the cost of running Skyvern by optimizing the context tree passed into Skyvern - [x] **Self-serve UI** - Deprecate the Streamlit UI in favour of a React-based UI component that allows users to kick off new jobs in Skyvern - [x] **Workflow UI Builder** - Introduce a UI to allow users to build and analyze workflows visually - [x] **Chrome Viewport streaming** - Introduce a way to live-stream the Chrome viewport to the user's browser (as a part of the self-serve UI) - [x] **Past Runs UI** - Deprecate the Streamlit UI in favour of a React-based UI that allows you to visualize past runs and their results - [X] **Auto workflow builder ("Observer") mode** - Allow Skyvern to auto-generate workflows as it's navigating the web to make it easier to build new workflows - [ ] **Prompt Caching** - Introduce a caching layer to the LLM calls to dramatically reduce the cost of running Skyvern (memorize past actions and repeat them!) - [ ] **Web Evaluation Dataset** - Integrate Skyvern with public benchmark tests to track the quality of our models over time - [ ] **Improved Debug mode** - Allow Skyvern to plan its actions and get "approval" before running them, allowing you to debug what it's doing and more easily iterate on the prompt - [ ] **Chrome Extension** - Allow users to interact with Skyvern through a Chrome extension (incl voice mode, saving tasks, etc.) - [ ] **Skyvern Action Recorder** - Allow Skyvern to watch a user complete a task and then automatically generate a workflow for it - [ ] **Interactable Livestream** - Allow users to interact with the livestream in real-time to intervene when necessary (such as manually submitting sensitive forms) - [ ] **Integrate LLM Observability tools** - Integrate LLM Observability tools to allow back-testing prompt changes with specific data sets + visualize the performance of Skyvern over time - [ ] **Langchain Integration** - Create langchain integration in langchain_community to use Skyvern as a "tool". # Contributing We welcome PRs and suggestions! Don't hesitate to open a PR/issue or to reach out to us [via email](mailto:founders@skyvern.com) or [discord](https://discord.gg/fG2XXEuQX3). Please have a look at our [contribution guide](CONTRIBUTING.md) and ["Help Wanted" issues](https://github.com/skyvern-ai/skyvern/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) to get started! If you want to chat with the skyvern repository to get a high level overview of how it is structured, how to build off it, and how to resolve usage questions, check out [Code Sage](https://sage.storia.ai?utm_source=github&utm_medium=referral&utm_campaign=skyvern-readme). # Telemetry By Default, Skyvern collects basic usage statistics to help us understand how Skyvern is being used. If you would like to opt-out of telemetry, please set the `SKYVERN_TELEMETRY` environment variable to `false`. # License Skyvern's open source repository is supported via a managed cloud. All of the core logic powering Skyvern is available in this open source repository licensed under the [AGPL-3.0 License](LICENSE), with the exception of anti-bot measures available in our managed cloud offering. If you have any questions or concerns around licensing, please [contact us](mailto:founders@skyvern.com) and we would be happy to help. # Star History [![Star History Chart](https://api.star-history.com/svg?repos=Skyvern-AI/skyvern&type=Date)](https://star-history.com/#Skyvern-AI/skyvern&Date)

Skyvern

微信扫一扫分享

Overview

What is Skyvern

How to Use

Key Features

Where to Use

Use Cases

Usage

Installation

Installation in Cursor

Installation in Claude

SDK

Installation in Python

Installation in TypeScript

Content

You Might Also Like

MarkItDown

Github MCP

Blender

Playwright

Cloudfare

Browserbase

servers

semantic-kernel

apisix

repomix