skyvern

Automate browser-based workflows with LLMs and Computer Vision

Skyvern-AI
13529
1095
Python


🐉 Automate Browser-based workflows using LLMs and Computer Vision 🐉

Skyvern 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.

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 Vision LLMs to interact with the websites.

Want to see examples of Skyvern in action? Jump to #real-world-examples-of-skyvern

Quickstart

Skyvern Cloud

Skyvern Cloud is a managed cloud version of Skyvern that allows you to run Skyvern without worrying about 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, navigate to app.skyvern.com and create an account.

Install & Run

⚠️ Supported Python Versions: Python 3.11, 3.12, 3.13 ⚠️

1. Install Skyvern

pip install skyvern

2. Run Skyvern

skyvern quickstart

3. Run task

from skyvern import Skyvern

skyvern = Skyvern()
task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task)

Skyvern starts running the task in a browser that pops up and closes it when the task is done. You will be able to review the task from http://localhost:8080/history

You can also run a task on Skyvern Cloud:

from skyvern import Skyvern

skyvern = Skyvern(api_key="SKYVERN API KEY")
task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task)

Or your local Skyvern service from step 2:

# Find your API KEY in .env
skyvern = Skyvern(base_url="http://localhost:8000", api_key="LOCAL SKYVERN API KEY")
task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task)

Check out more features to use for Skyvern task in our official doc. Here are a couple of interesting examples:

Control your own browser (Chrome)

⚠️ WARNING: Since Chrome 136, Chrome refuses any CDP connect to the browser using the default user_data_dir. In order to use your browser data, Skyvern copies your default user_data_dir to ./tmp/user_data_dir the first time connecting to your local browser. ⚠️

  1. Just With Python Code
from skyvern import Skyvern

# The path to your Chrome browser. This example path is for Mac.
browser_path = "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"
skyvern = Skyvern(
    base_url="http://localhost:8000",
    api_key="YOUR_API_KEY",
    browser_path=browser_path,
)
task = await skyvern.run_task(
    prompt="Find the top post on hackernews today",
)
  1. With Skyvern Service

Add two variables to your .env file:

# The path to your Chrome browser. This example path is for Mac.
CHROME_EXECUTABLE_PATH="/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"
BROWSER_TYPE=cdp-connect

Restart Skyvern service skyvern run all and run the task through UI or code:

from skyvern import Skyvern

skyvern = Skyvern(
    base_url="http://localhost:8000",
    api_key="YOUR_API_KEY",
)
task = await skyvern.run_task(
    prompt="Find the top post on hackernews today",
)

Run Skyvern with any remote browser

Grab the cdp connection url and pass it to Skyvern

from skyvern import Skyvern

skyvern = Skyvern(cdp_url="your cdp connection url")
task = await skyvern.run_task(
    prompt="Find the top post on hackernews today",
)

Get consistent output schema from your run

You can do this by adding the data_extraction_schema parameter:

from skyvern import Skyvern

skyvern = Skyvern()
task = await skyvern.run_task(
    prompt="Find the top post on hackernews today",
    data_extraction_schema={
        "type": "object",
        "properties": {
            "title": {
                "type": "string",
                "description": "The title of the top post"
            },
            "url": {
                "type": "string",
                "description": "The URL of the top post"
            },
            "points": {
                "type": "integer",
                "description": "Number of points the post has received"
            }
        }
    }
)

Helpful commands to debug issues

Launch the Skyvern Server Separately

skyvern run server

Launch the Skyvern UI

skyvern run ui

Check status of the Skyvern service

skyvern status

Stop the Skyvern service

skyvern stop all

Stop the Skyvern UI

skyvern stop ui

Stop the Skyvern Server Separately

skyvern stop server

Docker Compose setup

  1. Make sure you have Docker Desktop installed and running on your machine
  2. Make sure you don’t have postgres running locally (Run docker ps to check)
  3. Clone the repository and navigate to the root directory
  4. Run skyvern init llm to generate a .env file. This will be copied into the Docker image.
  5. Fill in the LLM provider key on the 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.
  6. Run the following command via the commandline:
     docker compose up -d
  7. Navigate to http://localhost:8080 in your browser to start using the UI

Important: Only one Postgres container can run on port 5432 at a time. If you switch from the CLI-managed Postgres to Docker Compose, you must first remove the original container:

docker rm -f postgresql-container

If you encounter any database related errors while using Docker to run Skyvern, check which Postgres container is running with docker ps.

How it works

Skyvern was inspired by the Task-Driven autonomous agent design popularized by BabyAGI and AutoGPT – with one major bonus: we give Skyvern the ability to interact with websites using browser automation libraries like Playwright.

Skyvern uses a swarm of agents to comprehend a website, and plan and execute its actions:

This approach has 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
  2. 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
  3. 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
  4. 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
    2. 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!)

Demo

https://github.com/user-attachments/assets/5cab4668-e8e2-4982-8551-aab05ff73a7f

Skyvern Features

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).

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
  2. Action
  3. Data Extraction
  4. Loops
  5. File parsing
  6. Uploading files to block storage
  7. Sending emails
  8. Text Prompts
  9. Tasks (general)
  10. (Coming soon) Conditionals
  11. (Coming soon) Custom Code Block

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

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 or discord.

🔐 2FA Support (TOTP)

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)
  2. Email based 2FA
  3. SMS based 2FA

🔐 Learn more about 2FA support here.

Password Manager Integrations

Skyvern currently supports the following password manager integrations:

  • [x] Bitwarden
  • [ ] 1Password
  • [ ] LastPass

Model Context Protocol (MCP)

Skyvern supports the Model Context Protocol (MCP) to allow you to use any LLM that supports MCP.

See the MCP documentation here

Zapier / Make.com / N8N Integration

Skyvern supports Zapier, Make.com, and N8N to allow you to connect your Skyvern workflows to other apps.

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

Automate the job application process

💡 See it in action

Automate materials procurement for a manufacturing company

💡 See it in action

Navigating to government websites to register accounts or fill out forms

💡 See it in action

Filling out random contact us forms

💡 See it in action

Retrieving insurance quotes from insurance providers in any language

💡 See it in action

💡 See it in action

Contributor Setup

For a complete local environment CLI Installation

pip install -e .

The following command sets up your development environment to use pre-commit (our commit hook handler)

skyvern quickstart contributors
  1. Navigate to http://localhost:8080 in your browser to start using the UI
    The Skyvern CLI supports Windows, WSL, macOS, and Linux environments.

Documentation

More extensive documentation can be found on our 📕 docs page. Please let us know if something is unclear or missing by opening an issue or reaching out to us via email or discord.

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 Run any locally hosted model via Ollama
OpenRouter Access models through OpenRouter
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)
OpenAI-compatible Any custom API endpoint that follows OpenAI’s API format (via liteLLM)

Environment Variables

OpenAI
Variable Description Type Sample Value
ENABLE_OPENAI Register OpenAI models Boolean true, false
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

Supported LLM Keys: OPENAI_GPT4_TURBO, OPENAI_GPT4V, OPENAI_GPT4O, OPENAI_GPT4O_MINI

Anthropic
Variable Description Type Sample Value
ENABLE_ANTHROPIC Register Anthropic models Boolean true, false
ANTHROPIC_API_KEY Anthropic API key String sk-1234567890

Supported LLM Keys: ANTHROPIC_CLAUDE3, ANTHROPIC_CLAUDE3_OPUS, ANTHROPIC_CLAUDE3_SONNET, ANTHROPIC_CLAUDE3_HAIKU, ANTHROPIC_CLAUDE3.5_SONNET

Azure OpenAI
Variable Description Type Sample Value
ENABLE_AZURE Register Azure OpenAI models Boolean true, false
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

Supported LLM Key: AZURE_OPENAI

AWS Bedrock
Variable Description Type Sample Value
ENABLE_BEDROCK Register AWS Bedrock models. To use AWS Bedrock, you need to make sure your AWS configurations are set up correctly first. Boolean true, false

Supported LLM Keys: BEDROCK_ANTHROPIC_CLAUDE3_OPUS, BEDROCK_ANTHROPIC_CLAUDE3_SONNET, BEDROCK_ANTHROPIC_CLAUDE3_HAIKU, BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET, BEDROCK_AMAZON_NOVA_PRO, BEDROCK_AMAZON_NOVA_LITE

Gemini
Variable Description Type Sample Value
ENABLE_GEMINI Register Gemini models Boolean true, false
GEMINI_API_KEY Gemini API Key String your_google_gemini_api_key

Supported LLM Keys: GEMINI_PRO, GEMINI_FLASH

Novita AI
Variable Description Type Sample Value
ENABLE_NOVITA Register Novita AI models Boolean true, false
NOVITA_API_KEY Novita AI API Key String your_novita_api_key

Supported LLM Keys: 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

Ollama
Variable Description Type Sample Value
ENABLE_OLLAMA Register local models via Ollama Boolean true, false
OLLAMA_SERVER_URL URL for your Ollama server String http://host.docker.internal:11434
OLLAMA_MODEL Ollama model name to load String qwen2.5:7b-instruct

Supported LLM Key: OLLAMA

OpenRouter
Variable Description Type Sample Value
ENABLE_OPENROUTER Register OpenRouter models Boolean true, false
OPENROUTER_API_KEY OpenRouter API key String sk-1234567890
OPENROUTER_MODEL OpenRouter model name String mistralai/mistral-small-3.1-24b-instruct
OPENROUTER_API_BASE OpenRouter API base URL String https://api.openrouter.ai/v1

Supported LLM Key: OPENROUTER

OpenAI-Compatible
Variable Description Type Sample Value
ENABLE_OPENAI_COMPATIBLE Register a custom OpenAI-compatible API endpoint Boolean true, false
OPENAI_COMPATIBLE_MODEL_NAME Model name for OpenAI-compatible endpoint String yi-34b, gpt-3.5-turbo, mistral-large, etc.
OPENAI_COMPATIBLE_API_KEY API key for OpenAI-compatible endpoint String sk-1234567890
OPENAI_COMPATIBLE_API_BASE Base URL for OpenAI-compatible endpoint String https://api.together.xyz/v1, http://localhost:8000/v1, etc.
OPENAI_COMPATIBLE_API_VERSION API version for OpenAI-compatible endpoint, optional String 2023-05-15
OPENAI_COMPATIBLE_MAX_TOKENS Maximum tokens for completion, optional Integer 4096, 8192, etc.
OPENAI_COMPATIBLE_TEMPERATURE Temperature setting, optional Float 0.0, 0.5, 0.7, etc.
OPENAI_COMPATIBLE_SUPPORTS_VISION Whether model supports vision, optional Boolean true, false

Supported LLM Key: OPENAI_COMPATIBLE

General LLM Configuration
Variable Description Type Sample Value
LLM_KEY The name of the model you want to use String See supported LLM keys above
SECONDARY_LLM_KEY The name of the model for mini agents skyvern runs with String See supported LLM keys above
LLM_CONFIG_MAX_TOKENS Override the max tokens used by the LLM Integer 128000

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 or discord.

  • [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 or discord.
Please have a look at our contribution guide and
“Help Wanted” issues 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.

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, 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 and we would be happy to help.

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