Skyvern MCP server lets AI agents control a real browser to navigate websites, fill forms, authenticate, and extract structured data. Supports multi-step automa
Skyvern MCP server lets AI agents control a real browser to navigate websites, fill forms, authenticate, and extract structured data. Supports multi-step automation workflows via natural language.
README
🐉 Automate Browser-based workflows using LLMs and Computer Vision 🐉
Skyvern automates browser-based workflows using LLMs and computer vision. It provides a Playwright-compatible SDK that adds AI functionality on top of playwright, as well as a no-code workflow builder to help both technical and non-technical users automate manual workflows on any website, 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 learn and interact with the websites.
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:
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
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
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
A detailed technical report can be found here.
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.
Run Locally (UI + Server)
Choose your preferred setup method:
Option A: pip install (Recommended)
Dependencies needed:
Python 3.11.x, works with 3.12, not ready yet for 3.13
Skyvern is a Playwright extension that adds AI-powered browser automation. It gives you the full power of Playwright with additional AI capabilities—use natural language prompts to interact with elements, extract data, and automate complex multi-step workflows.
Installation:
Python: pip install skyvern then run skyvern quickstart for local setup
TypeScript: npm install @skyvern/client
AI-Powered Page Commands
Skyvern adds four core AI commands directly on the page object:
Command
Description
page.act(prompt)
Perform actions using natural language (e.g., "Click the login button")
page.extract(prompt, schema)
Extract structured data from the page with optional JSON schema
page.validate(prompt)
Validate page state, returns bool (e.g., "Check if user is logged in")
page.prompt(prompt, schema)
Send arbitrary prompts to the LLM with optional response schema
# 1. Traditional Playwright - CSS/XPath selectors
await page.click("#submit-button")
# 2. AI-powered - natural language
await page.click(prompt="Click the green Submit button")
# 3. AI fallback - tries selector first, falls back to AI if it fails
await page.click("#submit-btn", prompt="Click the Submit button")
Core AI Commands - Examples
# act - Perform actions using natural language
await page.act("Click the login button and wait for the dashboard to load")
# extract - Extract structured data with optional JSON schema
result = await page.extract("Get the product name and price")
result = await page.extract(
prompt="Extract order details",
schema={"order_id": "string", "total": "number", "items": "array"}
)
# validate - Check page state (returns bool)
is_logged_in = await page.validate("Check if the user is logged in")
# prompt - Send arbitrary prompts to the LLM
summary = await page.prompt("Summarize what's on this page")
from skyvern import Skyvern
# Local mode
skyvern = Skyvern.local()
# Or connect to Skyvern Cloud
skyvern = Skyvern(api_key="your-api-key")
# Launch browser and get page
browser = await skyvern.launch_cloud_browser()
page = await browser.get_working_page()
# Mix Playwright with AI-powered actions
await page.goto("https://example.com")
await page.click("#login-button") # Traditional Playwright
await page.agent.login(credential_type="skyvern", credential_id="cred_123") # AI login
await page.click(prompt="Add first item to cart") # AI-augmented click
await page.agent.run_task("Complete checkout with: John Snow, 12345") # AI task
TypeScript SDK:
import { Skyvern } from "@skyvern/client";
const skyvern = new Skyvern({ apiKey: "your-api-key" });
const browser = await skyvern.launchCloudBrowser();
const page = await browser.getWorkingPage();
// Mix Playwright with AI-powered actions
await page.goto("https://example.com");
await page.click("#login-button"); // Traditional Playwright
await page.agent.login("skyvern", { credentialId: "cred_123" }); // AI login
await page.click({ prompt: "Add first item to cart" }); // AI-augmented click
await page.agent.runTask("Complete checkout with: John Snow, 12345"); // AI task
await browser.close();
Simple task execution:
from skyvern import Skyvern
skyvern = Skyvern()
task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task)
Advanced Usage
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.
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",
)
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
Connect Skyvern Cloud to your local browser
Let Skyvern Cloud control a Chrome browser running on your machine — with all your existing cookies, logins, and extensions. Useful for automating sites where you're already logged in or behind a VPN.
# One command to start Chrome + create a tunnel to Skyvern Cloud
skyvern browser serve --tunnel
Then use the tunnel URL in your task:
from skyvern import Skyvern
skyvern = Skyvern(api_key="your-api-key")
task = await skyvern.run_task(
prompt="Download the latest invoice from my account",
browser_address="https://abc123.ngrok-free.dev",
)
[!WARNING]
Always use --api-key when exposing your browser via a tunnel. Without it, anyone with the URL has full control of your browser. See the security docs.
See the full documentation for all options, manual tunnel setup, and troubleshooting.
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
Performance & Evaluation
Skyvern has SOTA performance on the WebBench benchmark with a 64.4% accuracy. The technical report + evaluation can be found here
Performance on WRITE tasks (eg filling out forms, logging in, downloading files, etc)
Skyvern is the best performing agent on WRITE tasks (eg filling out forms, logging in, downloading files, etc), which is primarily used for RPA (Robotic Process Automation) adjacent tasks.
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:
Browser Task
Browser Action
Data Extraction
Validation
For Loops
File parsing
Sending emails
Text Prompts
HTTP Request Block
Custom Code Block
Uploading files to block storage
(Coming soon) Conditionals
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.
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!
Run this to create your virtual environment (.venv)
uv sync --group dev
Perform initial server configuration
uv run skyvern quickstart
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
GPT-5, GPT-5.2, GPT-4.1, o3, o4-mini
Anthropic
Claude 4 (Sonnet, Opus), Claude 4.5 (Haiku, Sonnet, Opus)
Azure OpenAI
Any GPT models. Better performance with a multimodal llm (azure/gpt4-o)
AWS Bedrock
Claude 3.5, Claude 3.7, Claude 4 (Sonnet, Opus), Claude 4.5 (Sonnet, Opus)
Note: Set OLLAMA_SUPPORTS_VISION=true for vision models like qwen3-vl, llava, etc.
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
Recommended 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.
Open Source - Open Source Skyvern's core codebase
Workflow support - Allow support to chain multiple Skyvern calls together
Improved context - Improve Skyvern's ability to understand content around interactable elements by introducing feeding relevant label context through the text prompt
Cost Savings - Improve Skyvern's stability and reduce the cost of running Skyvern by optimizing the context tree passed into Skyvern
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
Workflow UI Builder - Introduce a UI to allow users to build and analyze workflows visually
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)
Past Runs UI - Deprecate the Streamlit UI in favour of a React-based UI that allows you to visualize past runs and their results
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".
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.
