> ## 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.

# 1.2 Zero Config Agent

> Zero-config agent with web search capabilities

Zero-config agent with web search capabilities.

## Code

```python theme={null}
import os
from bindu.penguin.bindufy import bindufy
from agno.agent import Agent
from agno.models.openrouter import OpenRouter
from agno.tools.duckduckgo import DuckDuckGoTools
from dotenv import load_dotenv

load_dotenv()

agent = Agent(
    instructions="You are a friendly assistant that explains things simply.",
    model=OpenRouter(
        id="openai/gpt-oss-120b",
        api_key=os.getenv("OPENROUTER_API_KEY")
    ),
    tools=[DuckDuckGoTools()],
)

config = {
    "author": "21uad051@kamarajengg.edu.in",
    "name": "beginner_zero_config_agent",
    "description": "Zero-config local Bindu agent for first-time users",
    "deployment": {
        "url": "http://localhost:3773",
        "expose": True,
        "cors_origins": ["http://localhost:5173"]
    },
}

def handler(messages):
    return agent.run(input=messages)

bindufy(config, handler)

#bindufy(config, handler, launch=True)
# This will create a tunnel to your agent and expose it on port 3773
```

## How It Works

**Agent Setup**

* `instructions`: Defines agent behavior
* `model`: OpenRouter's GPT model
* `tools`: DuckDuckGo for web search

**Handler**

* Passes messages to agent
* Agent processes with LLM and tools
* Returns structured response

**Environment**

* Requires `OPENROUTER_API_KEY` in `.env`
* `load_dotenv()` loads environment variables

## Dependencies

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

## Environment Setup

Create `.env` file:

```bash theme={null}
OPENROUTER_API_KEY=your_openrouter_api_key_here
```

## Run

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

## Examples

* "Tell me about artificial intelligence"
* "What are the main features of large language models?"
* "Explain machine learning in simple terms"

## 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": "Tell me about artificial intelligence"
            }
          ]
        },
        "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 zero config agent