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Add Claude 4.5 Opus support and improve SDK documentation (#4633)

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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Suchintan
2026-02-08 00:46:19 -05:00
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README.md
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@@ -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> <a href="https://www.linkedin.com/company/95726232"><img src="https://img.shields.io/badge/Follow%20 on%20LinkedIn-8A2BE2?logo=linkedin"/></a>
</p> </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 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.
<p align="center"> <p align="center">
<img src="fern/images/geico_shu_recording_cropped.gif"/> <img src="fern/images/geico_shu_recording_cropped.gif"/>
@@ -48,32 +48,12 @@ 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 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 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 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://www.skyvern.com/blog/skyvern-2-0-state-of-the-art-web-navigation-with-85-8-on-webvoyager-eval/). A detailed technical report can be found [here](https://www.skyvern.com/blog/skyvern-2-0-state-of-the-art-web-navigation-with-85-8-on-webvoyager-eval/).
# Demo # Demo
<!-- Redo demo --> <!-- Redo demo -->
https://github.com/user-attachments/assets/5cab4668-e8e2-4982-8551-aab05ff73a7f 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://www.skyvern.com/blog/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
## Skyvern Cloud ## Skyvern Cloud
@@ -81,7 +61,11 @@ Skyvern is the best performing agent on WRITE tasks (eg filling out forms, loggi
If you'd like to try it out, navigate to [app.skyvern.com](https://app.skyvern.com) and create an account. If you'd like to try it out, navigate to [app.skyvern.com](https://app.skyvern.com) and create an account.
## Install & Run ## Run Locally (UI + Server)
Choose your preferred setup method:
### Option A: pip install (Recommended)
Dependencies needed: Dependencies needed:
- [Python 3.11.x](https://www.python.org/downloads/), works with 3.12, not ready yet for 3.13 - [Python 3.11.x](https://www.python.org/downloads/), works with 3.12, not ready yet for 3.13
@@ -91,14 +75,13 @@ Additionally, for Windows:
- [Rust](https://rustup.rs/) - [Rust](https://rustup.rs/)
- VS Code with C++ dev tools and Windows SDK - VS Code with C++ dev tools and Windows SDK
### 1. Install Skyvern #### 1. Install Skyvern
```bash ```bash
pip install skyvern pip install skyvern
``` ```
### 2. Run Skyvern #### 2. Run Skyvern
This is most helpful for first time run (db setup, db migrations etc).
```bash ```bash
skyvern quickstart skyvern quickstart
@@ -111,20 +94,140 @@ local Docker PostgreSQL setup:
skyvern quickstart --database-string "postgresql+psycopg://user:password@localhost:5432/skyvern" skyvern quickstart --database-string "postgresql+psycopg://user:password@localhost:5432/skyvern"
``` ```
### 3. Run task ### Option B: Docker Compose
#### UI (Recommended) 1. Install [Docker Desktop](https://www.docker.com/products/docker-desktop/)
2. Clone the repository:
```bash
git clone https://github.com/skyvern-ai/skyvern.git && cd skyvern
```
3. Run quickstart with Docker Compose:
```bash
pip install skyvern && skyvern quickstart
```
When prompted, choose "Docker Compose" for the full containerized setup.
4. Navigate to http://localhost:8080
Start the Skyvern service and UI (when DB is up and running) ## SDK
**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 |
Additionally, `page.agent` provides higher-level workflow commands:
| Command | Description |
|---------|-------------|
| `page.agent.run_task(prompt)` | Execute complex multi-step tasks |
| `page.agent.login(credential_type, credential_id)` | Authenticate with stored credentials (Skyvern, Bitwarden, 1Password) |
| `page.agent.download_files(prompt)` | Navigate and download files |
| `page.agent.run_workflow(workflow_id)` | Execute pre-built workflows |
### AI-Augmented Playwright Actions
All standard Playwright actions support an optional `prompt` parameter for AI-powered element location:
| Action | Playwright | AI-Augmented |
|--------|------------|--------------|
| Click | `page.click("#btn")` | `page.click(prompt="Click login button")` |
| Fill | `page.fill("#email", "a@b.com")` | `page.fill(prompt="Email field", value="a@b.com")` |
| Select | `page.select_option("#country", "US")` | `page.select_option(prompt="Country dropdown", value="US")` |
| Upload | `page.upload_file("#file", "doc.pdf")` | `page.upload_file(prompt="Upload area", files="doc.pdf")` |
**Three interaction modes:**
```python
# 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
```python
# 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")
```
### Quick Start Examples
**Run via UI:**
```bash ```bash
skyvern run all skyvern run all
``` ```
Navigate to http://localhost:8080 to run tasks through the web interface.
Go to http://localhost:8080 and use the UI to run a task **Python SDK:**
```python
from skyvern import Skyvern
#### Code # 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:**
```typescript
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:**
```python ```python
from skyvern import Skyvern from skyvern import Skyvern
@@ -132,88 +235,6 @@ skyvern = Skyvern()
task = await skyvern.run_task(prompt="Find the top post on hackernews today") task = await skyvern.run_task(prompt="Find the top post on hackernews today")
print(task) 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 view the task from http://localhost:8080/history
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")
# 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)
```
## SDK
**Installation:**
- Python: `pip install skyvern` then run `skyvern quickstart` for local setup
- TypeScript: `npm install @skyvern/client`
Skyvern provides SDKs for both Python and TypeScript to integrate browser automation into your applications.
### Python SDK
```python
from skyvern import Skyvern
# Connect to Skyvern Cloud
skyvern = Skyvern(api_key="your-api-key")
# Or run locally
skyvern = Skyvern.local()
# Launch a cloud browser
browser = await skyvern.launch_cloud_browser()
page = await browser.get_working_page()
# Use AI-powered actions for complex workflows
await page.agent.run_task("Navigate to the most recent invoice and download it")
# Or mix with Playwright actions
await page.goto("https://example.com")
await page.click("#button")
```
### TypeScript SDK
```typescript
import { Skyvern } from "@skyvern/client";
// Connect to Skyvern Cloud
const skyvern = new Skyvern({ apiKey: "your-api-key" });
// Launch a cloud browser
const browser = await skyvern.launchCloudBrowser();
const page = await browser.getWorkingPage();
// Use AI-powered actions for complex workflows
await page.agent.runTask("Navigate to the most recent invoice and download it");
// Or mix with Playwright actions
await page.goto("https://example.com");
await page.click("#button");
```
Skyvern enhances Playwright methods with AI capabilities. Use regular Playwright syntax with a `prompt` parameter to make any action AI-powered:
```python
# Traditional Playwright - uses selectors
await page.click("#submit-button")
# AI-augmented Playwright - uses natural language
await page.click(prompt="Click on the Submit button")
await page.fill(prompt="Enter email address", value="user@example.com")
# Mix both approaches in the same workflow
await page.goto("https://example.com/dashboard")
await page.click(prompt="Click on the most recent unpaid invoice")
await page.click("#download-button")
```
## Advanced Usage ## Advanced Usage
@@ -311,28 +332,21 @@ skyvern stop ui
skyvern stop server skyvern stop server
``` ```
## Docker Compose setup # Performance & Evaluation
1. Make sure you have [Docker Desktop](https://www.docker.com/products/docker-desktop/) installed and running on your machine Skyvern has SOTA performance on the [WebBench benchmark](webbench.ai) with a 64.4% accuracy. The technical report + evaluation can be found [here](https://www.skyvern.com/blog/web-bench-a-new-way-to-compare-ai-browser-agents/)
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. Run `skyvern init llm` to generate a `.env` file. This will be copied into the Docker image.
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
> [!Important] <p align="center">
> 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: <img src="fern/images/performance/webbench_overall.png"/>
> ```bash </p>
> 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`. ## 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>
# Skyvern Features # Skyvern Features
@@ -494,11 +508,11 @@ More extensive documentation can be found on our [📕 docs page](https://www.sk
# Supported LLMs # Supported LLMs
| Provider | Supported Models | | Provider | Supported Models |
| -------- | ------- | | -------- | ------- |
| OpenAI | gpt4-turbo, gpt-4o, gpt-4o-mini | | OpenAI | GPT-5, GPT-5.2, GPT-4.1, o3, o4-mini |
| Anthropic | Claude 3 (Haiku, Sonnet, Opus), Claude 3.5 (Sonnet) | | 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) | | 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) | | AWS Bedrock | Claude 3.5, Claude 3.7, Claude 4 (Sonnet, Opus), Claude 4.5 (Sonnet, Opus) |
| Gemini | Gemini 2.5 Pro and flash, Gemini 2.0 | | Gemini | Gemini 3 Pro/Flash, Gemini 2.5 Pro/Flash |
| Ollama | Run any locally hosted model via [Ollama](https://github.com/ollama/ollama) | | Ollama | Run any locally hosted model via [Ollama](https://github.com/ollama/ollama) |
| OpenRouter | Access models through [OpenRouter](https://openrouter.ai) | | OpenRouter | Access models through [OpenRouter](https://openrouter.ai) |
| OpenAI-compatible | Any custom API endpoint that follows OpenAI's API format (via [liteLLM](https://docs.litellm.ai/docs/providers/openai_compatible)) | | OpenAI-compatible | Any custom API endpoint that follows OpenAI's API format (via [liteLLM](https://docs.litellm.ai/docs/providers/openai_compatible)) |
@@ -513,7 +527,7 @@ More extensive documentation can be found on our [📕 docs page](https://www.sk
| `OPENAI_API_BASE` | OpenAI API Base, optional | String | `https://openai.api.base` | | `OPENAI_API_BASE` | OpenAI API Base, optional | String | `https://openai.api.base` |
| `OPENAI_ORGANIZATION` | OpenAI Organization ID, optional | String | `your-org-id` | | `OPENAI_ORGANIZATION` | OpenAI Organization ID, optional | String | `your-org-id` |
Recommended `LLM_KEY`: `OPENAI_GPT4O`, `OPENAI_GPT4O_MINI`, `OPENAI_GPT4_1`, `OPENAI_O4_MINI`, `OPENAI_O3` Recommended `LLM_KEY`: `OPENAI_GPT5`, `OPENAI_GPT5_2`, `OPENAI_GPT4_1`, `OPENAI_O3`, `OPENAI_O4_MINI`
##### Anthropic ##### Anthropic
| Variable | Description| Type | Sample Value| | Variable | Description| Type | Sample Value|
@@ -521,7 +535,7 @@ Recommended `LLM_KEY`: `OPENAI_GPT4O`, `OPENAI_GPT4O_MINI`, `OPENAI_GPT4_1`, `OP
| `ENABLE_ANTHROPIC` | Register Anthropic models| Boolean | `true`, `false` | | `ENABLE_ANTHROPIC` | Register Anthropic models| Boolean | `true`, `false` |
| `ANTHROPIC_API_KEY` | Anthropic API key| String | `sk-1234567890` | | `ANTHROPIC_API_KEY` | Anthropic API key| String | `sk-1234567890` |
Recommended`LLM_KEY`: `ANTHROPIC_CLAUDE3.5_SONNET`, `ANTHROPIC_CLAUDE3.7_SONNET`, `ANTHROPIC_CLAUDE4_OPUS`, `ANTHROPIC_CLAUDE4_SONNET` Recommended `LLM_KEY`: `ANTHROPIC_CLAUDE4.5_OPUS`, `ANTHROPIC_CLAUDE4.5_SONNET`, `ANTHROPIC_CLAUDE4_OPUS`, `ANTHROPIC_CLAUDE4_SONNET`
##### Azure OpenAI ##### Azure OpenAI
| Variable | Description| Type | Sample Value| | Variable | Description| Type | Sample Value|
@@ -539,7 +553,7 @@ Recommended `LLM_KEY`: `AZURE_OPENAI`
| -------- | ------- | ------- | ------- | | -------- | ------- | ------- | ------- |
| `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_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` |
Recommended `LLM_KEY`: `BEDROCK_ANTHROPIC_CLAUDE3.7_SONNET_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4_OPUS_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4_SONNET_INFERENCE_PROFILE` Recommended `LLM_KEY`: `BEDROCK_ANTHROPIC_CLAUDE4.5_OPUS_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4.5_SONNET_INFERENCE_PROFILE`, `BEDROCK_ANTHROPIC_CLAUDE4_OPUS_INFERENCE_PROFILE`
##### Gemini ##### Gemini
| Variable | Description| Type | Sample Value| | Variable | Description| Type | Sample Value|
@@ -547,7 +561,7 @@ Recommended `LLM_KEY`: `BEDROCK_ANTHROPIC_CLAUDE3.7_SONNET_INFERENCE_PROFILE`, `
| `ENABLE_GEMINI` | Register Gemini models| Boolean | `true`, `false` | | `ENABLE_GEMINI` | Register Gemini models| Boolean | `true`, `false` |
| `GEMINI_API_KEY` | Gemini API Key| String | `your_google_gemini_api_key`| | `GEMINI_API_KEY` | Gemini API Key| String | `your_google_gemini_api_key`|
Recommended `LLM_KEY`: `GEMINI_2.5_PRO_PREVIEW`, `GEMINI_2.5_FLASH_PREVIEW` Recommended `LLM_KEY`: `GEMINI_2.5_PRO`, `GEMINI_2.5_FLASH`, `GEMINI_2.5_PRO_PREVIEW`, `GEMINI_2.5_FLASH_PREVIEW`
##### Ollama ##### Ollama
| Variable | Description| Type | Sample Value| | Variable | Description| Type | Sample Value|

14
skyvern/cli/llm_setup.py
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@@ -74,12 +74,12 @@ def setup_llm_providers() -> None:
update_or_add_env_var("ENABLE_OPENAI", "true") update_or_add_env_var("ENABLE_OPENAI", "true")
model_options.extend( model_options.extend(
[ [
"OPENAI_GPT5",
"OPENAI_GPT5_2",
"OPENAI_GPT4_1", "OPENAI_GPT4_1",
"OPENAI_GPT4_1_MINI",
"OPENAI_GPT4_1_NANO",
"OPENAI_GPT4O",
"OPENAI_O4_MINI",
"OPENAI_O3", "OPENAI_O3",
"OPENAI_O4_MINI",
"OPENAI_GPT4O",
] ]
) )
else: else:
@@ -97,13 +97,11 @@ def setup_llm_providers() -> None:
update_or_add_env_var("ENABLE_ANTHROPIC", "true") update_or_add_env_var("ENABLE_ANTHROPIC", "true")
model_options.extend( model_options.extend(
[ [
"ANTHROPIC_CLAUDE3.5_SONNET", "ANTHROPIC_CLAUDE4.5_OPUS",
"ANTHROPIC_CLAUDE3.7_SONNET", "ANTHROPIC_CLAUDE4.5_SONNET",
"ANTHROPIC_CLAUDE3.5_HAIKU",
"ANTHROPIC_CLAUDE4_OPUS", "ANTHROPIC_CLAUDE4_OPUS",
"ANTHROPIC_CLAUDE4_SONNET", "ANTHROPIC_CLAUDE4_SONNET",
"ANTHROPIC_CLAUDE4.5_HAIKU", "ANTHROPIC_CLAUDE4.5_HAIKU",
"ANTHROPIC_CLAUDE4.5_SONNET",
] ]
) )
else: else:

123
skyvern/cli/quickstart.py
View File

@@ -2,14 +2,17 @@
import asyncio import asyncio
import subprocess import subprocess
from pathlib import Path
import typer import typer
from rich.panel import Panel from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TextColumn from rich.progress import Progress, SpinnerColumn, TextColumn
from rich.prompt import Confirm
# Import console after skyvern.cli to ensure proper initialization # Import console after skyvern.cli to ensure proper initialization
from skyvern.cli.console import console from skyvern.cli.console import console
from skyvern.cli.init_command import init_env # init is used directly from skyvern.cli.init_command import init_env # init is used directly
from skyvern.cli.llm_setup import setup_llm_providers
from skyvern.cli.utils import start_services from skyvern.cli.utils import start_services
quickstart_app = typer.Typer(help="Quickstart command to set up and run Skyvern with one command.") quickstart_app = typer.Typer(help="Quickstart command to set up and run Skyvern with one command.")
@@ -29,6 +32,78 @@ def check_docker() -> bool:
return False return False
def check_docker_compose_file() -> bool:
"""Check if docker-compose.yml exists in the current directory."""
return Path("docker-compose.yml").exists() or Path("docker-compose.yaml").exists()
def check_postgres_container_conflict() -> bool:
"""Check if postgresql-container exists and is using port 5432."""
try:
result = subprocess.run(
["docker", "ps", "-a", "--filter", "name=postgresql-container", "--format", "{{.Names}}"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
)
return "postgresql-container" in result.stdout
except (FileNotFoundError, subprocess.SubprocessError):
return False
def run_docker_compose_setup() -> None:
"""Run the Docker Compose setup for Skyvern."""
console.print("\n[bold blue]Setting up Skyvern with Docker Compose...[/bold blue]")
# Check for postgres container conflict
if check_postgres_container_conflict():
console.print(
Panel(
"[bold yellow]Warning: Existing PostgreSQL container detected![/bold yellow]\n\n"
"A container named 'postgresql-container' already exists, which may conflict\n"
"with the PostgreSQL service in Docker Compose (both use port 5432).\n\n"
"To avoid conflicts, remove the existing container first:\n"
"[cyan]docker rm -f postgresql-container[/cyan]",
border_style="yellow",
)
)
proceed = Confirm.ask("Do you want to continue anyway?", default=False)
if not proceed:
console.print("[yellow]Aborting Docker Compose setup. Please remove the container and try again.[/yellow]")
raise typer.Exit(0)
# Configure LLM provider
console.print("\n[bold blue]Step 1: Configure LLM Provider[/bold blue]")
setup_llm_providers()
# Run docker compose up
console.print("\n[bold blue]Step 2: Starting Docker Compose...[/bold blue]")
with Progress(
SpinnerColumn(), TextColumn("[progress.description]{task.description}"), transient=True, console=console
) as progress:
progress.add_task("[bold blue]Starting Docker containers...", total=None)
try:
subprocess.run(
["docker", "compose", "up", "-d"],
check=True,
capture_output=True,
text=True,
)
console.print("✅ [green]Docker Compose started successfully.[/green]")
except subprocess.CalledProcessError as e:
console.print(f"[bold red]Error starting Docker Compose: {e.stderr}[/bold red]")
raise typer.Exit(1)
console.print(
Panel(
"[bold green]Skyvern is now running![/bold green]\n\n"
"Navigate to [link]http://localhost:8080[/link] to start using the UI.\n\n"
"To stop Skyvern, run: [cyan]docker compose down[/cyan]",
border_style="green",
)
)
@quickstart_app.callback(invoke_without_command=True) @quickstart_app.callback(invoke_without_command=True)
def quickstart( def quickstart(
ctx: typer.Context, ctx: typer.Context,
@@ -42,12 +117,16 @@ def quickstart(
False, "--skip-browser-install", help="Skip Chromium browser installation" False, "--skip-browser-install", help="Skip Chromium browser installation"
), ),
server_only: bool = typer.Option(False, "--server-only", help="Only start the server, not the UI"), server_only: bool = typer.Option(False, "--server-only", help="Only start the server, not the UI"),
docker_compose: bool = typer.Option(False, "--docker-compose", help="Use Docker Compose for full setup"),
) -> None: ) -> None:
"""Quickstart command to set up and run Skyvern with one command.""" """Quickstart command to set up and run Skyvern with one command."""
# Check Docker only if not using custom database # Check Docker
if not database_string: with console.status("Checking Docker installation...") as status:
with console.status("Checking Docker installation...") as status: docker_available = check_docker()
if not check_docker(): if docker_available:
status.update("✅ Docker is installed and running")
else:
if not database_string:
console.print( console.print(
Panel( Panel(
"[bold red]Docker is not installed or not running.[/bold red]\n" "[bold red]Docker is not installed or not running.[/bold red]\n"
@@ -57,13 +136,45 @@ def quickstart(
) )
) )
raise typer.Exit(1) raise typer.Exit(1)
status.update("✅ Docker is installed and running")
# Run initialization # Run initialization
console.print(Panel("[bold green]🚀 Starting Skyvern Quickstart[/bold green]", border_style="green")) console.print(Panel("[bold green]🚀 Starting Skyvern Quickstart[/bold green]", border_style="green"))
# Check if Docker Compose option was explicitly requested or offer choice
docker_compose_available = check_docker_compose_file()
if docker_compose:
if not docker_compose_available:
console.print(
Panel(
"[bold red]docker-compose.yml not found in current directory.[/bold red]\n"
"Please clone the Skyvern repository first:\n"
"[cyan]git clone https://github.com/skyvern-ai/skyvern.git && cd skyvern[/cyan]",
border_style="red",
)
)
raise typer.Exit(1)
run_docker_compose_setup()
return
# If Docker Compose file exists, offer the choice
if docker_compose_available and docker_available and not database_string:
console.print("\n[bold blue]Setup Method[/bold blue]")
console.print("Docker Compose file detected. Choose your setup method:\n")
console.print(" [cyan]1.[/cyan] [green]Docker Compose (Recommended)[/green] - Full containerized setup")
console.print(" [cyan]2.[/cyan] pip install - Local Python setup with Docker for PostgreSQL only\n")
use_docker_compose = Confirm.ask(
"Would you like to use Docker Compose for the full setup?",
default=True,
)
if use_docker_compose:
run_docker_compose_setup()
return
try: try:
# Initialize Skyvern # Initialize Skyvern (pip install path)
console.print("\n[bold blue]Initializing Skyvern...[/bold blue]") console.print("\n[bold blue]Initializing Skyvern...[/bold blue]")
run_local = init_env(no_postgres=no_postgres, database_string=database_string) run_local = init_env(no_postgres=no_postgres, database_string=database_string)

30
skyvern/forge/sdk/api/llm/config_registry.py
View File

@@ -377,6 +377,16 @@ if settings.ENABLE_ANTHROPIC:
max_completion_tokens=64000, max_completion_tokens=64000,
), ),
) )
LLMConfigRegistry.register_config(
"ANTHROPIC_CLAUDE4.5_OPUS",
LLMConfig(
"anthropic/claude-opus-4-5-20251101",
["ANTHROPIC_API_KEY"],
supports_vision=True,
add_assistant_prefix=True,
max_completion_tokens=64000,
),
)
if settings.ENABLE_BEDROCK: if settings.ENABLE_BEDROCK:
# Supported through AWS IAM authentication # Supported through AWS IAM authentication
@@ -494,6 +504,26 @@ if settings.ENABLE_BEDROCK:
max_completion_tokens=32000, max_completion_tokens=32000,
), ),
) )
LLMConfigRegistry.register_config(
"BEDROCK_ANTHROPIC_CLAUDE4.5_SONNET_INFERENCE_PROFILE",
LLMConfig(
"bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0",
["AWS_REGION"],
supports_vision=True,
add_assistant_prefix=True,
max_completion_tokens=64000,
),
)
LLMConfigRegistry.register_config(
"BEDROCK_ANTHROPIC_CLAUDE4.5_OPUS_INFERENCE_PROFILE",
LLMConfig(
"bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0",
["AWS_REGION"],
supports_vision=True,
add_assistant_prefix=True,
max_completion_tokens=64000,
),
)
if settings.ENABLE_AZURE: if settings.ENABLE_AZURE: