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README Updates (#3442)

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@@ -33,7 +33,46 @@ Traditional approaches to browser automations required writing custom scripts fo
Instead of only relying on code-defined XPath interactions, Skyvern relies on Vision LLMs to learn and interact with the websites. Instead of only relying on code-defined XPath interactions, Skyvern relies on Vision LLMs to learn and interact with the websites.
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:
<picture>
<source media="(prefers-color-scheme: dark)" srcset="fern/images/skyvern_2_0_system_diagram.png" />
<img src="fern/images/skyvern_2_0_system_diagram.png" />
</picture>
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
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!)
A detailed technical report can be found [here](https://blog.skyvern.com/skyvern-2-0-state-of-the-art-web-navigation-with-85-8-on-webvoyager-eval/).
# Demo
<!-- Redo demo -->
https://github.com/user-attachments/assets/5cab4668-e8e2-4982-8551-aab05ff73a7f
# Performance & Evaluation
Skyvern has SOTA performance on the [WebBench benchmark](webbench.ai) with a 64.4% accuracy. The technical report + evaluation can be found [here](https://blog.skyvern.com/web-bench-a-new-way-to-compare-ai-browser-agents/)
<p align="center">
<img src="fern/images/performance/webbench_overall.png"/>
</p>
## 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.
<p align="center">
<img src="fern/images/performance/webbench_write.png"/>
</p>
# Quickstart # Quickstart
@@ -102,47 +141,6 @@ task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task) print(task)
``` ```
# 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:
<picture>
<source media="(prefers-color-scheme: dark)" srcset="fern/images/skyvern_2_0_system_diagram.png" />
<img src="fern/images/skyvern_2_0_system_diagram.png" />
</picture>
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
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!)
A detailed technical report can be found [here](https://blog.skyvern.com/skyvern-2-0-state-of-the-art-web-navigation-with-85-8-on-webvoyager-eval/).
# Demo
<!-- Redo demo -->
https://github.com/user-attachments/assets/5cab4668-e8e2-4982-8551-aab05ff73a7f
# Performance & Evaluation
Skyvern has SOTA performance on the [WebBench benchmark](webbench.ai) with a 64.4% accuracy. The technical report + evaluation can be found [here](https://blog.skyvern.com/web-bench-a-new-way-to-compare-ai-browser-agents/)
<p align="center">
<img src="fern/images/performance/webbench_overall.png"/>
</p>
## 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.
<p align="center">
<img src="fern/images/performance/webbench_write.png"/>
</p>
## Advanced Usage ## Advanced Usage
### Control your own browser (Chrome) ### Control your own browser (Chrome)
@@ -280,20 +278,21 @@ For example, if you wanted to download all invoices newer than January 1st, you
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. 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: Supported workflow features include:
1. Navigation 1. Browser Task
1. Action 1. Browser Action
1. Data Extraction 1. Data Extraction
1. Loops 1. Validation
1. For Loops
1. File parsing 1. File parsing
1. Uploading files to block storage
1. Sending emails 1. Sending emails
1. Text Prompts 1. Text Prompts
1. Tasks (general) 1. HTTP Request Block
1. Custom Code Block
1. Uploading files to block storage
1. (Coming soon) Conditionals 1. (Coming soon) Conditionals
1. (Coming soon) Custom Code Block
<p align="center"> <p align="center">
<img src="fern/images/invoice_downloading_workflow_example.png"/> <img src="fern/images/block_example_v2.png"/>
</p> </p>
## Livestreaming ## Livestreaming

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fern/integrations/n8n.mdx
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@@ -8,23 +8,28 @@ slug: integrations/n8n
1. Get a [Skyvern](https://app.skyvern.com/) account, navigate to settings and copy your API Key, you will need to link this in the n8n. 1. Get a [Skyvern](https://app.skyvern.com/) account, navigate to settings and copy your API Key, you will need to link this in the n8n.
2. Install Skyvern as a community node. Following the [n8n docs](https://docs.n8n.io/integrations/community-nodes/installation/gui-install/) to install on GUI. 1. Install [Skyvern node](https://n8n.io/integrations/skyvern/Skyvern) in n8n. If you prefer using the Community Node, follow the [Community Node Setup](#community-node-setup) section.
3. Search for `n8n-nodes-skyvern` and install.
![image](../images/n8n/ui_install.png)
4. Wait until it's successfully installed. ## Running Skyvern
![image](../images/n8n/ui_install_success.png) 1. Create a new workflow. Search for the `Skyvern` node.
## Running a Skyvern Task
5. Create a new workflow. Search for the `Skyvern` node.
![image](../images/n8n/add_node.png) ![image](../images/n8n/add_node.png)
6. Create a new credential. 1. Create a new credential.
![image](../images/n8n/add_credential.png) ![image](../images/n8n/add_credential.png)
7. Store the API Key from step 1 in the `API Key` field and `Save` 1. Store the API Key from step 1 in the `API Key` field and `Save`
![image](../images/n8n/save_credential.png) ![image](../images/n8n/save_credential.png)
8. Dispatch a task! 1. Dispatch a task or workflow
![image](../images/n8n/dispatch_task.png) ![image](../images/n8n/dispatch_task.png)
## Community Node Setup
1. Install Skyvern as a community node. Following the [n8n docs](https://docs.n8n.io/integrations/community-nodes/installation/gui-install/) to install on GUI.
1. Search for `n8n-nodes-skyvern` and install.
![image](../images/n8n/ui_install.png)
1. Wait until it's successfully installed.
![image](../images/n8n/ui_install_success.png)