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

# LLMs.txt Tools - Agentic Documentation Discovery

> The llms.txt format (https://llmstxt.org) is a standardized way for websites to provide LLM-friendly documentation indexes.

```python llms_txt_tools.py theme={null}
"""
LLMs.txt Tools - Agentic Documentation Discovery
=============================

Demonstrates how to use LLMsTxtTools in agentic mode where the agent:
1. Reads the llms.txt index to discover available documentation pages
2. Decides which pages are relevant to the user's question
3. Fetches only the specific pages it needs

The llms.txt format (https://llmstxt.org) is a standardized way for websites
to provide LLM-friendly documentation indexes.
"""

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.llms_txt import LLMsTxtTools

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------

agent = Agent(
    model=OpenAIResponses(id="gpt-5.4"),
    tools=[LLMsTxtTools()],
    instructions=[
        "You can read llms.txt files to discover documentation for any project.",
        "First use get_llms_txt_index to see what pages are available.",
        "Then use read_llms_txt_url to fetch only the pages relevant to the user's question.",
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response(
        "Using the llms.txt at https://docs.agno.com/llms.txt, "
        "find and read the documentation about how to create an agent with tools",
        markdown=True,
        stream=True,
    )
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run the example">
    Save the code above as `llms_txt_tools.py`, then run:

    ```bash theme={null}
    python llms_txt_tools.py
    ```
  </Step>
</Steps>

Full source: [cookbook/91\_tools/llms\_txt\_tools.py](https://github.com/agno-agi/agno/blob/main/cookbook/91_tools/llms_txt_tools.py)