aworld-mcp-servers

The accompanying repository for the AWorld project.

• 🦩 [2025/06/19] AWorld has achieved 72.43 on the GAIA test. The #1 open-source project—and the only one in GAIA's top 10. 🐦 tweets

API Integration and Testing

This repository contains examples of API requests and testing for various services, including health checks, web scraping with Google API and BeautifulSoup, and DeepResearcher search workflow re-implementation.

Quick Setup

Prerequisites

• Python 3.12 or higher
• uv package manager

Installation

1. Install uv (if not already installed):

   # macOS/Linux
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Windows
   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
   
   # Or via pip
   pip install uv

2. Clone the repository:

   git clone https://github.com/your-username/aworld-mcp-servers.git
   cd aworld-mcp-servers

3. Install dependencies:

   uv sync

4. Activate the virtual environment (optional, uv handles this automatically):

   source .venv/bin/activate  # macOS/Linux
   # or
   .venv\Scripts\activate     # Windows

Running the Application

• Main Flask server:

  uv run aworld-server

• FastAPI server:

  uv run aworld-fastapi

• GAIA runner:

  uv run gaia-runner

• Run as module (alternative):

  uv run python -m src.main

Development Setup

1. Install development dependencies:

   uv sync --dev

2. Install pre-commit hooks:

   uv run pre-commit install

3. Run tests:

   uv run pytest

4. Code formatting and linting:

   uv run black .
   uv run ruff check .
   uv run mypy src/

Table of Contents

• Health Check
• Google API + BeautifulSoup
• Deep Researcher
• OpenRouter API
• Browser Use API

Health Check

This section demonstrates how to perform a health check on a specific service endpoint.

curl -X GET http://DEPLOYED_HOST:PORT/health

Notes

• The health check endpoint is used to verify the availability and connectivity of the service.
• If the request fails, it may indicate that the service is down or there is a network issue.

Google API + BeautifulSoup

This section shows how to use the Google API and BeautifulSoup to scrape web pages.

curl -X POST http://DEPLOYED_HOST:PORT/search \
     -H "Content-Type: application/json" \
     -d '{
             "api_key": "YOUR_GOOGLE_API_KEY",
             "cse_id": "YOUR_GOOGLE_CSE_ID",
             "queries": ["machine learning"],
             "num_results": 5,
             "fetch_content": true,
             "language": "en",
             "country": "US",
             "safe_search": true,
             "max_len": 8192 # optional, max length of the content to fetch, only works when fetch_content is true
         }'

Notes

• Replace YOUR_GOOGLE_API_KEY and YOUR_GOOGLE_CSE_ID with your actual Google API key and Custom Search Engine (CSE) ID.
• This request searches for web pages related to "machine learning" and fetches the content of the top 5 results.

Deep Researcher

This section demonstrates how to perform a deep research query using Serper API.

curl -X POST http://DEPLOYED_HOST:PORT/search/agentic \
  -H "Content-Type: application/json" \
  -d '{
    "question": "machine learning",
    "search_queries": ["machine learning"],
    "base_url": "YOUR_LLM_ENDPOINT",
    "api_key": "YOUR_API_KEY",
    "llm_model_name": "qwen/qwen-plus",
    "serper_api_key": "YOUR_SERPER_API_KEY",
    "topk": 5
  }'

Notes

• Replace YOUR_LLM_ENDPOINT, YOUR_API_KEY and YOUR_SERPER_API_KEY with your actual API keys.
• The base_url is the endpoint for the deep research service.
• This request searches for information related to "machine learning" and returns the top 5 results.

OpenRouter API

This section demonstrates how to use the OpenRouter API for LLM chat completions and model listing.

Chat Completions

curl -X POST http://DEPLOYED_HOST:PORT/openrouter/completions \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "YOUR_OPENROUTER_API_KEY",
    "model": "google/gemini-2.5-pro",
    "messages": [
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    "site_url": "https://your-site.com",
    "site_name": "Your Site Name"
  }'

List Available Models

curl -X GET http://DEPLOYED_HOST:PORT/openrouter/models

Notes

• Replace YOUR_OPENROUTER_API_KEY with your actual OpenRouter API key.
• The model parameter supports various models available through OpenRouter (e.g., "google/gemini-2.5-pro", "anthropic/claude-opus-4", "openai/gpt-4").
• site_url and site_name are optional parameters for tracking and attribution.

Browser Use API

This section demonstrates how to use the Browser Use API for automated web browsing tasks.

curl -X POST http://DEPLOYED_HOST:PORT/browser_use \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Go to google.com and search for machine learning",
    "base_url": "YOUR_LLM_ENDPOINT",
    "api_key": "YOUR_API_KEY",
    "model_name": "gpt-4o",
    "temperature": 0.3,
    "enable_memory": false,
    "browser_port": "9111",
    "user_data_dir": "/tmp/chrome-debug/0000",
    "headless": true,
    "extract_base_url": "YOUR_LLM_ENDPOINT",
    "extract_api_key": "YOUR_API_KEY",
    "extract_model_name": "gpt-4o",
    "extract_temperature": 0.3,
    "return_trace": false
  }'

Notes

• Replace YOUR_LLM_ENDPOINT and YOUR_API_KEY with your actual LLM service endpoint and API key.
• The question parameter should contain natural language instructions for the browser automation task.
• model_name supports various models (e.g., "gpt-4o", "claude-3-opus-20240229", "gemini-pro").
• Set headless to false if you want to see the browser window during automation.
• enable_memory allows the agent to remember previous interactions.
• return_trace includes detailed execution trace in the response.

License

This repository is licensed under the MIT License. See the LICENSE file for details.