!pip install "camel-ai[all]==0.2.58"

import os
from getpass import getpass

# Prompt for the API key securely
openai_api_keys = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_keys

from camel.models import ModelFactory
from camel.agents import ChatAgent
from camel.types import ModelPlatformType, ModelType
from camel.configs import ChatGPTConfig

from camel.toolkits import FunctionTool

def my_weird_add(a: int, b: int) -> int:
    r"""Adds two numbers and includes a constant offset.
    Args:
        a (int): The first number to be added.
        b (int): The second number to be added.
    Returns:
        integer: The sum of the two numbers plus 7.
    """
    return a + b + 7

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O,
    api_key = openai_api_keys
)

agent = ChatAgent(
    model=model,
    tools=[FunctionTool(my_weird_add)]
)

response = agent.step("What is 15+15")
print(response.msgs[0].content)

{
  "mcpServers": {
    "time": {
      "command": "uvx",
      "args": ["mcp-server-time", "--local-timezone=Asia/Riyadh"]
    }
  }
}

import asyncio
from camel.toolkits.mcp_toolkit import MCPToolkit

async def run_time_example():
    # Initialize the MCPToolkit with your configuration file
    mcp_toolkit = MCPToolkit(config_path="config/time.json")
    # Connect to all configured MCP servers
    await mcp_toolkit.connect()
    camel_agent = ChatAgent(
        model=model,
        tools=[*mcp_toolkit.get_tools()],
    )
    response = await camel_agent.astep("What time is it now?")
    print(response.msgs[0].content)
    print(response.info['tool_calls'])
    # Disconnect from all servers
    await mcp_toolkit.disconnect()

toolkit = ArxivToolkit(timeout=args.timeout) # existing toolkit
    # Run the toolkit as an MCP server
    toolkit.mcp.run(args.mode)

search_toolkit = PulseMCPSearchToolkit()
search_toolkit.search_mcp_servers(
    query="Slack",
    package_registry="npm",  # Only search for servers registered in npm
    top_k=1,
)

{
  "name": "Slack",
  "url": "https://www.pulsemcp.com/servers/slack",
  "external_url": null,
  "short_description": "Send messages, manage channels, and access workspace history.",
  "source_code_url": "https://github.com/modelcontextprotocol/servers/tree/HEAD/src/slack",
  "github_stars": 41847,
  "package_registry": "npm",
  "package_name": "@modelcontextprotocol/server-slack",
  "package_download_count": 188989,
  "EXPERIMENTAL_ai_generated_description": "This Slack MCP Server, developed by the Anthropic team, provides a robust interface for language models to interact with Slack workspaces. It enables AI agents to perform a wide range of Slack-specific tasks including listing channels, posting messages, replying to threads, adding reactions, retrieving channel history, and accessing user information. The implementation distinguishes itself by offering comprehensive Slack API integration, making it ideal for AI-driven workplace communication and automation. By leveraging Slack's Bot User OAuth Tokens, it ensures secure and authorized access to workspace data. This tool is particularly powerful for AI assistants designed to enhance team collaboration, automate routine communication tasks, and provide intelligent insights from Slack conversations."
}

from camel.agents import MCPAgent, MCPRegistryConfig, MCPRegistryType
smithery_config = MCPRegistryConfig(
  type=MCPRegistryType.SMITHERY,
  api_key=os.getenv("SMITHERY_API_KEY")
)

# Create MCPAgent with registry configurations
agent = MCPAgent(
  model=model,
  registry_configs=[smithery_config]
)

async with agent:
    message = "Use Brave API to search info about CAMEL-AI.org"
    response = await agent.astep(message)
    print(f"\nResponse from {message}:")
    print(response.msgs[0].content)

Response from Use Brave MCP search tools to search info about CAMEL-AI.org.:
 # CAMEL-AI.org: Information and Purpose
 Based on my search results, here's what I found about CAMEL-AI.org:
 ## Organization Overview
 CAMEL-AI.org is the first LLM (Large Language Model) multi-agent framework and
 an open-source community. The name CAMEL stands for "Communicative Agents for
 Mind Exploration of Large Language Model Society."
 ## Core Purpose
 The organization is dedicated to "Finding the Scaling Law of Agents" - this
 appears to be their primary research mission, focusing on understanding how
 agent-based AI systems scale and develop.
 ## Research Focus
 CAMEL-AI is a research-driven organization that explores:
 - Scalable techniques for autonomous cooperation among communicative agents
 - Multi-agent frameworks for AI systems
 - Data generation for AI training
 - AI society simulations
 ## Community and Collaboration
 - They maintain an active open-source community
 - They invite contributors and collaborators through platforms like Slack and
 Discord
 - The organization has a research collaboration questionnaire for those
 interested in building or researching environments for LLM-based agents
 ## Technical Resources
 - Their code is available on GitHub (github.com/camel-ai) with 18 repositories
 - They provide documentation for developers and researchers at
 docs.camel-ai.org
 - They offer tools and cookbooks for working with their agent framework
 ## Website and Online Presence
 - Main website: https://www.camel-ai.org/
 - GitHub: https://github.com/camel-ai
 - Documentation: https://docs.camel-ai.org/
 The organization appears to be at the forefront of research on multi-agent AI
 systems, focusing on how these systems can cooperate autonomously and scale
 effectively.

# In services/agent_config.py
# Create a default chat agent - customize as needed
chat_agent = ChatAgent()
chat_agent_description = "A general-purpose assistant that can answer questions and help with various tasks."

reasoning_agent = ChatAgent(
    model=ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI,
        model_type="gpt-4o-mini",
    )
)
reasoning_agent_description = "A specialized assistant focused on logical reasoning and problem-solving."

# Create another agent for searching the web
from camel.toolkits import SearchToolkit
search_agent = ChatAgent(
    model=ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI,
        model_type="gpt-4o",
    ),
    tools=[FunctionTool(SearchToolkit().search_brave)],
)
search_agent_description = "A research assistant capable of retrieving information from the web."

# Provide a list of agents with names
agents_dict = {
    "general": chat_agent,
    "search": search_agent,
    "reasoning": reasoning_agent,
}

# Provide descriptions for each agent
description_dict = {
    "general": chat_agent_description,
    "search": search_agent_description,
    "reasoning": reasoning_agent_description,
}

"camel-chat-agent": {
  "command": "/Users/jinx0a/micromamba/bin/python",
  "args": [
    "/Users/jinx0a/Repo/camel/services/agent_mcp_server.py"
  ],
  "env": {
    "OPENAI_API_KEY": "...",
    "OPENROUTER_API_KEY": "...",
    "BRAVE_API_KEY": "..."
  }
}