feat: Add WhatsApp MCP integration use case for OWL

community_usecase/Whatsapp-MCP/README.md

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+# OWL WhatsApp MCP Integration
+
+This project demonstrates a use case connecting **OWL** (from [CAMEL-AI.org](https://camel-ai.org)) with the **WhatsApp MCP Server**. It enables fully automated, agentic replies within messaging apps—without needing predefined workflows or manual tool selection. Simply send a plain message and let the system handle the rest.
+
+---
+
+## Overview
+
+### What It Does
+
+- **Automated, Agentic Messaging:**  
+  OWL interacts with the WhatsApp MCP server in real time. Roleplay agents infer the intent behind your messages, automatically selecting the correct toolkit to generate and send responses.
+
+- **Seamless Communication Integration:**  
+  This integration demonstrates how intelligent agents can operate across different communication platforms with zero manual orchestration.
+
+### Key Features
+
+- **No Predefined Workflow:**  
+  No need for rigid, pre-set procedures. The system dynamically handles each conversation.
+
+- **Zero Tool Selection Required:**  
+  Simply send a message; the right toolkit is autonomously selected based on the agent’s inference.
+
+- **Real-Time Interaction:**  
+  OWL continuously communicates with the WhatsApp MCP server to access message histories, send replies, and manage media files.
+
+---
+
+## Technology Stack
+
+- **OWL Framework:**  
+  An open-source multi-agent collaboration framework from [CAMEL-AI](https://github.com/camel-ai/owl).  
+- **WhatsApp MCP Server:**  
+  An MCP (Model Context Protocol) server that connects directly to WhatsApp via the WhatsApp Web multi-device API. Explore the project on [GitHub](https://github.com/lharries/whatsapp-mcp).
+
+---
+
+## How It Works
+
+1. **Message Processing:**  
+   - A plain text message is sent by a user via a messaging app.
+   - The OWL framework receives the message and activates roleplay agents.
+
+2. **Agent Collaboration:**  
+   - Agents, acting in different roles, infer the intent and formulate a response.
+   - The appropriate toolkit is dynamically selected.
+
+3. **Automated Reply:**  
+   - The system communicates in real time with the WhatsApp MCP server.
+   - The response is generated and sent back to the messaging app autonomously.
+
+---
+
+## Getting Started
+
+### Prerequisites
+
+- **Go:** Required for the WhatsApp Bridge component.
+- **Python 3.6+:** For running the MCP server and OWL integration.
+- **UV Package Manager:** Install with:
+  ```sh
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  ```
+- **FFmpeg (Optional):** Needed for converting and sending audio messages in the correct format (e.g., .ogg Opus).
+
+### Installation Steps
+
+1. **Clone Repositories:**
+   - OWL Framework:  
+     ```sh
+     git clone https://github.com/camel-ai/owl.git
+     ```
+   - WhatsApp MCP Server:  
+     ```sh
+     git clone https://github.com/lharries/whatsapp-mcp.git
+     ```
+
+2. **Set Up the WhatsApp Bridge:**
+   - Navigate to the WhatsApp bridge directory:
+     ```sh
+     cd whatsapp-mcp/whatsapp-bridge
+     go run main.go
+     ```
+   - **Authentication:**  
+     On first run, scan the provided QR code using your WhatsApp app. (Re-authentication may be required after ~20 days.)
+
+3. **Configure MCP Server Integration:**
+   - Create a JSON configuration file (e.g., `claude_desktop_config.json` or `mcp.json`) with the following content (ensure you replace the placeholder paths):
+     ```json
+     {
+       "mcpServers": {
+         "whatsapp": {
+           "command": "<PATH_TO_UV>",
+           "args": [
+             "--directory",
+             "<PATH_TO_REPO>/whatsapp-mcp/whatsapp-mcp-server",
+             "run",
+             "main.py"
+           ]
+         }
+       }
+     }
+     ```
+   - Place this file in your Claude Desktop configuration directory or your Cursor configuration directory as appropriate.
+
+4. **Running the OWL MCP Integration:**
+   - With the WhatsApp Bridge running and MCP configuration set up, start your OWL-powered agent system:
+     ```sh
+     python owl/community_usecase/Whatsapp-MCP/app.py
+     ```
+   - The OWL framework will connect to the MCP server and be ready to process incoming messages.
+
+---
+
+## Deep Dive: MCP & OWL Integration
+
+### MCP Overview
+- **What is MCP?**  
+  MCP (Model Context Protocol) standardizes communication between AI models and external tools and data sources. Learn more at the [MCP Documentation](https://modelcontextprotocol.io/introduction).
+
+- **Basic Architecture:**  
+  MCP uses a client-server model, where a host application communicates with multiple MCP servers to seamlessly access local and remote data sources.
+
+### OWL Framework
+- **Role-Playing Agents:**  
+  OWL leverages roleplay agents that deconstruct tasks and work collaboratively, ensuring robust automation.
+
+- **Real-Time Decision-Making:**  
+  Using methods such as POMDP (Partially Observable Markov Decision Processes), OWL optimizes decisions dynamically.
+
+- **Multi-Modal Tool Integration:**  
+  From web data scraping to code execution and document processing, OWL integrates various toolkits to empower agent collaboration.
+
+### Code Example Snippet
+
+Below is a simplified version of how to initialize and connect to the MCP toolkit:
+
+```python
+from pathlib import Path
+from mcp_toolkit import MCPToolkit
+
+# Load configuration and initialize MCP toolkit
+config_path = Path(__file__).parent / "mcp_servers_config.json"
+mcp_toolkit = MCPToolkit(config_path=str(config_path))
+
+# Establish asynchronous connection
+await mcp_toolkit.connect()
+```
+
+Then, construct the agent environment and run the task:
+
+```python
+question = (
+    "I'd like an academic report about Andrew Ng, including his research direction, "
+    "published papers, and affiliated institutions. Organize the report in Markdown format and save it to my desktop."
+)
+tools = list(mcp_toolkit.get_tools())
+society = await construct_society(question, tools)
+answer, chat_history, token_count = await run_society(society)
+print(f"Answer: {answer}")
+```
+
+For a complete guide on using MCP with the OWL framework, please refer to [the detailed guide](https://www.camel-ai.org/blogs/owl-mcp-toolkit-practice).
+
+---
+
+## WhatsApp MCP Server Details
+
+- **Capabilities:**
+  - **Message Management:** Search, read, and send messages including media (images, videos, documents, audio).
+  - **Media Handling:** Supports conversion (with FFmpeg) and downloading of media files.
+
+- **Architecture:**
+  - **Go WhatsApp Bridge:** Connects to WhatsApp’s Web API, handles QR code authentication, and stores messages locally using SQLite.
+  - **Python MCP Server:** Implements the MCP protocol to relay communications between OWL agents and WhatsApp.
+
+- **Installation & Running:**
+  - Follow the instructions under the **Installation Steps** section above.
+  - Additional details and troubleshooting tips are available in the [WhatsApp MCP GitHub repository](https://github.com/lharries/whatsapp-mcp).
+
+---
+
+## Troubleshooting & FAQs
+
+- **QR Code Not Displaying:**  
+  Ensure your terminal supports displaying QR codes, or try restarting the authentication script.
+
+- **Device Limit Issues:**  
+  If you reach the maximum number of devices on WhatsApp, remove an existing device from your phone’s linked devices settings.
+
+- **Running on Windows:**  
+  For Windows setups, ensure that CGO is enabled and a C compiler (e.g., through MSYS2) is installed. See the [Windows Compatibility Guide](#) for step-by-step instructions.
+
+---
+
+## References & Further Reading
+
+- **MCP Documentation:** [modelcontextprotocol.io](https://modelcontextprotocol.io/introduction)
+- **CAMEL-AI MCP:** [camel-ai.org/mcp](https://www.camel-ai.org/mcp)
+- **WhatsApp MCP Server Repository:** [GitHub](https://github.com/lharries/whatsapp-mcp)
+- **OWL Framework Repository:** [GitHub](https://github.com/camel-ai/owl)
+
+---
+
+
+

community_usecase/Whatsapp-MCP/app.py

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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+"""MCP Multi-Agent System Example
+
+This example demonstrates how to use MCP (Model Context Protocol) with CAMEL agents
+for advanced information retrieval and processing tasks.
+
+Environment Setup:
+1. Configure the required dependencies of owl library
+   Refer to: https://github.com/camel-ai/owl for installation guide
+
+2. MCP Server Setup:
+
+
+   2.1 MCP Playwright Service:
+   ```bash
+   # Install MCP service
+   npm install -g @executeautomation/playwright-mcp-server
+   npx playwright install-deps
+
+   # Configure in mcp_servers_config.json:
+   {
+     "mcpServers": {
+       "playwright": {
+         "command": "npx",
+         "args": ["-y", "@executeautomation/playwright-mcp-server"]
+       }
+     }
+   }
+   ```
+
+   2.2 MCP Fetch Service (Optional - for better retrieval):
+   ```bash
+   # Install MCP service
+   pip install mcp-server-fetch
+
+   # Configure in mcp_servers_config.json:
+   {
+     "mcpServers": {
+       "fetch": {
+         "command": "python",
+         "args": ["-m", "mcp_server_fetch"]
+       }
+     }
+   }
+   ```
+
+Usage:
+1. Ensure all MCP servers are properly configured in mcp_servers_config.json
+2. Run this script to create a multi-agent system that can:
+   - Access and manipulate files through MCP Desktop Commander
+   - Perform web automation tasks using Playwright
+   - Process and generate information using GPT-4o
+   - Fetch web content (if fetch service is configured)
+3. The system will execute the specified task while maintaining security through
+   controlled access
+
+Note:
+- All file operations are restricted to configured directories
+- System uses GPT-4o for both user and assistant roles
+- Supports asynchronous operations for efficient processing
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+from typing import List
+
+from dotenv import load_dotenv
+
+from camel.models import ModelFactory
+from camel.toolkits import FunctionTool
+from camel.types import ModelPlatformType, ModelType
+from camel.logger import set_log_level
+from camel.toolkits import MCPToolkit,SearchToolkit
+
+from owl.utils.enhanced_role_playing import OwlRolePlaying, arun_society
+
+load_dotenv()
+
+set_log_level(level="DEBUG")
+
+
+async def construct_society(
+    question: str,
+    tools: List[FunctionTool],
+) -> OwlRolePlaying:
+    r"""build a multi-agent OwlRolePlaying instance.
+
+    Args:
+        question (str): The question to ask.
+        tools (List[FunctionTool]): The MCP tools to use.
+    """
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=ModelType.GPT_4O,
+            model_config_dict={"temperature": 0},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=ModelType.GPT_4O,
+            model_config_dict={"temperature": 0},
+        ),
+    }
+
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {
+        "model": models["assistant"],
+        "tools": tools,
+    }
+
+    task_kwargs = {
+        "task_prompt": question,
+        "with_task_specify": False,
+    }
+
+    society = OwlRolePlaying(
+        **task_kwargs,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+    return society
+
+
+async def main():
+    config_path = Path(__file__).parent / "mcp_servers_config.json"
+    mcp_toolkit = MCPToolkit(config_path=str(config_path))
+
+    try:
+        print("Attempting to connect to MCP servers...")
+        await mcp_toolkit.connect()
+
+        # Default task
+        default_task = (
+            "Read the unread messages from {contact name} on whatsapp and reply to his query"
+        )
+
+        # Override default task if command line argument is provided
+        task = sys.argv[1] if len(sys.argv) > 1 else default_task
+        # Connect to all MCP toolkits
+        tools = [*mcp_toolkit.get_tools(),SearchToolkit().search_duckduckgo,]
+        society = await construct_society(task, tools)
+        answer, chat_history, token_count = await arun_society(society)
+        print(f"\033[94mAnswer: {answer}\033[0m")
+
+    except Exception as e:
+        print(f"An error occurred during connection: {e}")
+
+    finally:
+        # Make sure to disconnect safely after all operations are completed.
+        try:
+            await mcp_toolkit.disconnect()
+        except Exception:
+            print("Disconnect failed")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

community_usecase/Whatsapp-MCP/mcp_servers_config.json

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+{
+  "mcpServers": {
+    "whatsapp": {
+      "command": "{{PATH_TO_UV}}", 
+      "args": [
+        "--directory",
+        "{{PATH_TO_SRC}}/whatsapp-mcp/whatsapp-mcp-server",
+        "run",
+        "main.py"
+      ]
+    }
+  }
+}