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Quickstart Guide

This guide will help you get started with OmniMind quickly. We’ll cover installation, basic configuration, and running your first agent.

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Installation

You can install OmniMind using pip:

pip install omnimind

Or install from source:

git clone https://github.com/Techiral/OmniMind.git
cd OmniMind
pip install -e .

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Installing LangChain Providers

OmniMind works with various LLM providers through LangChain. You’ll need to install the appropriate LangChain provider package for your chosen LLM. For example:

# For OpenAI
pip install langchain-openai

# For Anthropic
pip install langchain-anthropic

# For other providers, check the [LangChain chat models documentation](https://python.langchain.com/docs/integrations/chat/)

Important: Only models with tool calling capabilities can be used with OmniMind. Make sure your chosen model supports function calling or tool use.

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Environment Setup

Set up your environment variables in a .env file:

OPENAI_API_KEY=your_api_key_here
ANTHROPIC_API_KEY=your_api_key_here

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Your First Agent

Here’s a simple example to get you started:

# Add your own server to OmniMind
from omnimind import OmniMind

agent = OmniMind()
agent.add_server("my_server", command="python", args=["my_server.py"])
agent.run()  # Your server’s live!

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Configuration Options

You can also add the servers configuration from a config file:

# Initialize OmniMind with the multi-server config
agent = OmniMind(config_path="browser_mcp.json", api_key=api_key)

Example configuration file (browser_mcp.json):

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"],
      "env": {
        "DISPLAY": ":1"
      }
    }
  }
}

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Using Multiple Servers

The MCPClient can be configured with multiple MCP servers, allowing your agent to access tools from different sources. This capability enables complex workflows spanning various domains (e.g., web browsing and API interaction).

Configuration:

Define multiple servers in your configuration file (multi_server_config.json):

{
    "mcpServers": {
        "fetch": {
            "command": "uvx",
            "args": ["mcp-server-fetch"]
        },
        "memory": {
            "command": "C:\\Program Files\\nodejs\\npx.cmd",
            "args": [
                "-y",
                "@modelcontextprotocol/server-memory"
            ],
            "env": {
                "MEMORY_FILE_PATH": "C:\\Users\\Lenovo\\OneDrive\\Desktop\\final JARVIS\\mcp\\workspace\\memory.json"
            }
        },
        "filesystem": {
            "command": "C:\\Program Files\\nodejs\\npx.cmd",
            "args": [
                "-y",
                "@modelcontextprotocol/server-filesystem",
                "C:\\Users\\Lenovo\\OneDrive\\Desktop",
                "C:\\Users\\Lenovo\\OneDrive\\Desktop\\final JARVIS\\mcp\\workspace"
            ]
        }
    }
}

Usage:

When working with an OmniMind agent is configured for multiple servers, the agent can access tools available on all the connected servers. However, for tasks targeting a specific server, you may need to explicitly specify which server to use. This is done using the server_name parameter in the agent.run() method, as demonstrated in the following code snippet:

# Initialize OmniMind with the multi-server config
agent = OmniMind(config_path="multi_server_config.json", api_key=api_key)
try:
    # Connect to all servers
    await agent._connect_servers()
    print("Tools loaded:", [tool.name for tool in agent.tools])

    # Step 1: Fetch web content
    fetch_query = "Fetch the content of 'https://marvel.fandom.com/wiki/Stark_Industries_(Earth-616)' and summarize it"
    fetch_response = await agent.invoke(fetch_query)
    summary = fetch_response["messages"][-1].content  # Extract the summary from the last message
    print("\nStep 1 - Web Summary:", summary)
except Exception as e:
    print(f"Error during execution: {e}")
finally:
     await agent.close()

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Available MCP Servers

OmniMind supports any MCP server, allowing you to connect to a wide range of server implementations. For a comprehensive list of available servers, check out the awesome-mcp-servers repository.

Each server requires its own configuration. Check the Configuration Guide for details.

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Next Steps