Webagent

🚀 Call web agent through APIs!

A very simple web agent server using excellent Browser-use tool.

• 🔗 Integrate into any app with our API
• 🌐 Multiple LLM compatibility
• 🔒 Host on your infrastructure
• 💽 Store and retrieve results in database
• 🪝 Get notified of results with webhooks

Installation

Option 1: Local Installation

1. Make sure you have Python 3.12+ installed.
2. Install the dependencies:

pip install -r requirements.txt

3. Configure your .env file with the necessary environment variables (API keys, etc.) Add your API keys for the provider you want to use to your .env file.

OPENAI_API_KEY=
ANTHROPIC_API_KEY=
AZURE_ENDPOINT=
AZURE_OPENAI_API_KEY=
GEMINI_API_KEY=
DEEPSEEK_API_KEY=
GROK_API_KEY=
NOVITA_API_KEY=

Option 2: Docker Installation

Using Docker

1. Build the Docker image:

docker build -t webagent .

2. Run the container:

docker run -p 8080:8080 --env-file .env webagent

The Docker image includes all necessary dependencies, including Playwright for browser automation.

Using Docker Compose

1. Configure your environment variables in the docker-compose.yml file or in a .env file.

2. Start the service:

docker-compose up

Starting the server

Local Start

python server.py

Docker Start

# Using Docker
docker run -p 8080:8080 --env-file .env webagent

# Using Docker Compose
docker-compose up

The server will start at http://localhost:8080

API Documentation

Once the server is started, you can access the interactive API documentation at:

• http://localhost:8080/docs (Swagger UI)
• http://localhost:8080/redoc (ReDoc)

Using the API

Execute a task with the agent

Endpoint: POST /run

Request body:

{
  "task": "go to this site https://example.com and retrieve the page title",
  "provider": "openai",
  "model": "gpt-4o"
}

Response:

{
  "history": [
    {
      "is_done": true,
      "success": true,
      "extracted_content": "Example Domain",
      "error": null,
      "include_in_memory": false
    }
  ],
  "result": "Example Domain",
  "is_done": true,
  "is_successful": true,
  "status": "success",
  "screenshots": [
    "data:image/png;base64,..."
  ]
}

Example usage with curl

# Execute a task
curl -X 'POST' \
  'http://localhost:8080/run' \
  -H 'Content-Type: application/json' \
  -d '{
  "task": "go to this site https://example.com and retrieve the page title",
  "model": "gpt-4o",
  "provider": "openai"
}'

Example usage with Python

import requests

# Base URL of the API
base_url = "http://localhost:8080"

# Execute a task
response = requests.post(
    f"{base_url}/run",
    json={
        "task": "go to this site https://example.com and retrieve the page title",
        "model": "gpt-4o",
        "provider": "openai"
    }
)

# Print the response
print(f"Response: {response.json()}")