vodorod/Dorod-Sky
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Improve TOTP docs & README & Make CLI actually support typer + py3.11 (#2791)

Browse Source 
Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
This commit is contained in:
Suchintan
2025-06-25 12:59:56 -04:00
committed by GitHub
parent 60dcd6bcb1
commit 9c9760d6ca
9 changed files with 307 additions and 179 deletions
Download Patch File Download Diff File Expand all files Collapse all files

198
README.md
View File

@@ -23,7 +23,7 @@
<a href="https://www.linkedin.com/company/95726232"><img src="https://img.shields.io/badge/Follow%20 on%20LinkedIn-8A2BE2?logo=linkedin"/></a>
</p>
[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.
[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.
<p align="center">
<img src="fern/images/geico_shu_recording_cropped.gif"/>
@@ -31,11 +31,10 @@
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.
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)
# Quickstart
## Skyvern Cloud
@@ -44,7 +43,6 @@ Want to see examples of Skyvern in action? Jump to [#real-world-examples-of-skyv
If you'd like to try it out, navigate to [app.skyvern.com](https://app.skyvern.com) and create an account.
## Install & Run
> ⚠️ **Supported Python Versions**: Python 3.11, 3.12, 3.13 ⚠️
### 1. Install Skyvern
@@ -60,6 +58,18 @@ skyvern quickstart
### 3. Run task
#### UI (Recommended)
Start the Skyvern service and UI
```bash
skyvern run all
```
Go to http://localhost:8080 and use the UI to run a task
#### Code
```python
from skyvern import Skyvern
@@ -67,27 +77,66 @@ 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
Skyvern starts running the task in a browser that pops up and closes it when the task is done. You will be able to view the task from http://localhost:8080/history
You can also run a task on Skyvern Cloud:
You can also run a task on different targets:
```python
from skyvern import Skyvern
# Run on Skyvern Cloud
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:
```python
# Find your API KEY in .env
# Local Skyvern service
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](https://docs.skyvern.com/running-tasks/run-tasks). Here are a couple of interesting examples:
#### Control your own browser (Chrome)
# 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
### Control your own browser (Chrome)
> ⚠️ WARNING: Since [Chrome 136](https://developer.chrome.com/blog/remote-debugging-port), 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
@@ -115,20 +164,9 @@ CHROME_EXECUTABLE_PATH="/Applications/Google Chrome.app/Contents/MacOS/Google Ch
BROWSER_TYPE=cdp-connect
```
Restart Skyvern service `skyvern run all` and run the task through UI or code:
```python
from skyvern import Skyvern
Restart Skyvern service `skyvern run all` and run the task through UI or code
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
### Run Skyvern with any remote browser
Grab the cdp connection url and pass it to Skyvern
```python
@@ -140,7 +178,7 @@ task = await skyvern.run_task(
)
```
#### Get consistent output schema from your run
### Get consistent output schema from your run
You can do this by adding the `data_extraction_schema` parameter:
```python
from skyvern import Skyvern
@@ -170,37 +208,24 @@ task = await skyvern.run_task(
### Helpful commands to debug issues
**Launch the Skyvern Server Separately**
```bash
# Launch the Skyvern Server Separately*
skyvern run server
```
**Launch the Skyvern UI**
```bash
# Launch the Skyvern UI
skyvern run ui
```
**Check status of the Skyvern service**
```bash
# Check status of the Skyvern service
skyvern status
```
**Stop the Skyvern service**
```bash
# Stop the Skyvern service
skyvern stop all
```
**Stop the Skyvern UI**
```bash
# Stop the Skyvern UI
skyvern stop ui
```
**Stop the Skyvern Server Separately**
```bash
# Stop the Skyvern Server Separately
skyvern stop server
```
@@ -225,36 +250,13 @@ skyvern stop server
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](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!)
# Demo
<!-- Redo 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 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).
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).
<p align="center">
<img src="fern/images/skyvern_2_0_screenshot.png"/>
@@ -262,7 +264,7 @@ Tasks require you to specify a `url`, `prompt`, and can optionally include a `da
## Skyvern Workflows
Workflows are a way to chain multiple tasks together to form a cohesive unit of work.
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.
@@ -308,10 +310,10 @@ Skyvern supports a number of different authentication methods to make it easier
### 🔐 2FA Support (TOTP)
Skyvern supports a number of different 2FA methods to allow you to automate workflows that require 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. QR-based 2FA (e.g. Google Authenticator, Authy)
1. Email based 2FA
1. SMS based 2FA
@@ -319,7 +321,7 @@ Examples include:
### Password Manager Integrations
Skyvern currently supports the following password manager integrations:
- [x] Bitwarden
- [x] Bitwarden
- [ ] 1Password
- [ ] LastPass
@@ -336,6 +338,8 @@ Skyvern supports Zapier, Make.com, and N8N to allow you to connect your Skyvern
* [Make.com](https://docs.skyvern.com/integrations/make.com)
* [N8N](https://docs.skyvern.com/integrations/n8n)
🔐 Learn more about 2FA support [here](https://docs.skyvern.com/credentials/totp).
# 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!
@@ -359,7 +363,7 @@ We love to see how Skyvern is being used in the wild. Here are some examples of
<img src="fern/images/finditparts_recording_crop.gif"/>
</p>
## Navigating to government websites to register accounts or fill out forms
## Navigating to government websites to register accounts or fill out forms
[💡 See it in action](https://app.skyvern.com/tasks/create/california_edd)
<p align="center">
<img src="fern/images/edd_services.gif"/>
@@ -410,11 +414,9 @@ More extensive documentation can be found on our [📕 docs page](https://docs.s
| 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) |
| Gemini | Gemini 2.5 Pro and flash, Gemini 2.0 |
| Ollama | Run any locally hosted model via [Ollama](https://github.com/ollama/ollama) |
| OpenRouter | Access models through [OpenRouter](https://openrouter.ai) |
| 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](https://docs.litellm.ai/docs/providers/openai_compatible)) |
#### Environment Variables
@@ -427,7 +429,7 @@ More extensive documentation can be found on our [📕 docs page](https://docs.s
| `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`
Recommended `LLM_KEY`: `OPENAI_GPT4O`, `OPENAI_GPT4O_MINI`, `OPENAI_GPT4_1`, `OPENAI_O4_MINI`, `OPENAI_O3`
##### Anthropic
| Variable | Description| Type | Sample Value|
@@ -435,7 +437,7 @@ Supported LLM Keys: `OPENAI_GPT4_TURBO`, `OPENAI_GPT4V`, `OPENAI_GPT4O`, `OPENAI
| `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`
Recommended`LLM_KEY`: `ANTHROPIC_CLAUDE3.5_SONNET`, `ANTHROPIC_CLAUDE3.7_SONNET`, `ANTHROPIC_CLAUDE4_OPUS`, `ANTHROPIC_CLAUDE4_SONNET`
##### Azure OpenAI
| Variable | Description| Type | Sample Value|
@@ -446,14 +448,14 @@ Supported LLM Keys: `ANTHROPIC_CLAUDE3`, `ANTHROPIC_CLAUDE3_OPUS`, `ANTHROPIC_CL
| `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`
Recommended `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](https://github.com/boto/boto3?tab=readme-ov-file#using-boto3) 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`
Recommended `LLM_KEY`: `BEDROCK_ANTHROPIC_CLAUDE3.7_SONNET_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4_OPUS_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4_SONNET_INFERENCE_PROFILE`
##### Gemini
| Variable | Description| Type | Sample Value|
@@ -461,15 +463,7 @@ Supported LLM Keys: `BEDROCK_ANTHROPIC_CLAUDE3_OPUS`, `BEDROCK_ANTHROPIC_CLAUDE3
| `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`
Recommended `LLM_KEY`: `GEMINI_2.5_PRO_PREVIEW`, `GEMINI_2.5_FLASH_PREVIEW`
##### Ollama
| Variable | Description| Type | Sample Value|
@@ -478,7 +472,9 @@ Supported LLM Keys: `NOVITA_DEEPSEEK_R1`, `NOVITA_DEEPSEEK_V3`, `NOVITA_LLAMA_3_
| `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`
Recommended `LLM_KEY`: `OLLAMA`
Note: Ollama does not support vision yet.
##### OpenRouter
| Variable | Description| Type | Sample Value|
@@ -488,7 +484,7 @@ Supported LLM Key: `OLLAMA`
| `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`
Recommended `LLM_KEY`: `OPENROUTER`
##### OpenAI-Compatible
| Variable | Description| Type | Sample Value|
@@ -515,7 +511,7 @@ Supported LLM Key: `OPENAI_COMPATIBLE`
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] **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
@@ -523,14 +519,14 @@ This is our planned roadmap for the next few months. If you have any suggestions
- [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
- [x] **Prompt Caching** - Introduce a caching layer to the LLM calls to dramatically reduce the cost of running Skyvern (memorize past actions and repeat them!)
- [x] **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".
- [x] **Langchain Integration** - Create langchain integration in langchain_community to use Skyvern as a "tool".
# Contributing
@@ -545,9 +541,9 @@ If you want to chat with the skyvern repository to get a high level overview of
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.
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.
If you have any questions or concerns around licensing, please [contact us](mailto:support@skyvern.com) and we would be happy to help.
# Star History