> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getbindu.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 1.11 MiniMax Research Agent

> A Bindu agent powered by MiniMax's M2.7 model via OpenAI-compatible API

A Bindu agent powered by MiniMax's M2.7 model via OpenAI-compatible API.

## Code

Create `minimax_example.py` with the code below, or save it directly from your editor.

```python theme={null}
"""MiniMax AI Research Agent

A Bindu agent powered by MiniMax's M2.7 model via OpenAI-compatible API.
MiniMax offers high-performance models with up to 1M context window.

Features:
- MiniMax M2.7 model (1M context)
- Web search integration via DuckDuckGo
- Research and summarization capabilities

Usage:
    python minimax_example.py

Environment:
    Requires MINIMAX_API_KEY in .env file
    Get your API key at https://platform.minimaxi.com
"""

import os
from bindu.penguin.bindufy import bindufy
from agno.agent import Agent
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.models.openai import OpenAILike

from dotenv import load_dotenv

load_dotenv()

# MiniMax API configuration
MINIMAX_API_KEY = os.getenv("MINIMAX_API_KEY")
MINIMAX_BASE_URL = "https://api.minimax.io/v1"

# Define your agent with MiniMax M2.7
agent = Agent(
    instructions="You are a research assistant that finds and summarizes information.",
    model=OpenAILike(
        id="MiniMax-M2.7",
        api_key=MINIMAX_API_KEY,
        base_url=MINIMAX_BASE_URL,
    ),
    tools=[DuckDuckGoTools()],
)


# Configuration
config = {
    "author": "your.email@example.com",
    "name": "minimax_research_agent",
    "description": "A research assistant agent powered by MiniMax AI",
    "deployment": {
        "url": os.getenv("BINDU_DEPLOYMENT_URL", "http://localhost:3773"),
        "expose": True,
        "cors_origins": ["http://localhost:5173"]
    },
}


# Handler function
def handler(messages: list[dict[str, str]]):
    """Process messages and return agent response.

    Args:
        messages: List of message dictionaries containing conversation history

    Returns:
        Agent response result
    """
    result = agent.run(input=messages)
    return result


# Bindu-fy it
if __name__ == "__main__":
    bindufy(config, handler)
```

## How It Works

**MiniMax Integration**

* Uses MiniMax M2.7 model with 1M context window
* OpenAI-compatible API via `OpenAILike` model wrapper
* High-performance research and analysis capabilities

**Web Search**

* `DuckDuckGoTools()`: Enables web search for current information
* Research assistant can find and summarize information from the web
* Combines web search with MiniMax's powerful reasoning

**Configuration**

* Environment variable for deployment URL flexibility
* Standard Bindu deployment on localhost:3773
* CORS enabled for frontend integration

## Dependencies

```bash theme={null}
uv init
uv add bindu agno python-dotenv
```

## Environment Setup

Create `.env` file:

```bash theme={null}
MINIMAX_API_KEY=your_minimax_api_key_here
BINDU_DEPLOYMENT_URL=http://localhost:3773
```

Get your MiniMax API key at [https://platform.minimaxi.com](https://platform.minimaxi.com)

## Run

```bash theme={null}
uv run minimax_example.py
```

**Examples:**

* "Research the latest developments in quantum computing"
* "Find information about climate change and its effects"
* "Summarize current trends in artificial intelligence"

## Example API Calls

<AccordionGroup>
  <Accordion title="Message Send Request">
    ```json theme={null}
    {
      "jsonrpc": "2.0",
      "method": "message/send",
      "params": {
        "message": {
          "role": "user",
          "kind": "message",
          "messageId": "9f11c870-5616-49ad-b187-d93cbb100001",
          "contextId": "9f11c870-5616-49ad-b187-d93cbb100002",
          "taskId": "9f11c870-5616-49ad-b187-d93cbb100003",
          "parts": [
            {
              "kind": "text",
              "text": "Research the latest developments in quantum computing"
            }
          ]
        },
        "configuration": {
          "acceptedOutputModes": ["application/json"]
        }
      },
      "id": "9f11c870-5616-49ad-b187-d93cbb100003"
    }
    ```
  </Accordion>

  <Accordion title="Task get Request">
    ```json theme={null}
    {
      "jsonrpc": "2.0",
      "method": "tasks/get",
      "params": {
        "taskId": "9f11c870-5616-49ad-b187-d93cbb100003"
      },
      "id": "9f11c870-5616-49ad-b187-d93cbb100004"
    }
    ```
  </Accordion>
</AccordionGroup>

## Frontend Setup

```bash theme={null}
# Clone the Bindu repository
git clone https://github.com/GetBindu/Bindu

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start frontend development server
npm run dev
```

Open [http://localhost:5173](http://localhost:5173) and try to chat with the MiniMax research agent