> ## 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.

# Agentic RAG: Tool-Based Search

> The agent gets a search_knowledge_base tool and decides when to query the knowledge base.

The agent gets a search\_knowledge\_base tool and decides when to query the knowledge base. This is more flexible than basic RAG - the agent can choose to search multiple times, refine queries, or skip searching entirely.

```python agentic_rag.py theme={null}
"""
Agentic RAG: Tool-Based Search
================================
The agent gets a search_knowledge_base tool and decides when to query the
knowledge base. This is more flexible than basic RAG - the agent can choose
to search multiple times, refine queries, or skip searching entirely.

This is the default behavior when you set knowledge on an Agent.

Steps:
1. Create a Knowledge base with a vector database
2. Load a document
3. Create an Agent with search_knowledge=True (the default)
4. Ask questions - agent decides when to search

See also: 01_basic_rag.py for automatic context injection.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

qdrant_url = "http://localhost:6333"

knowledge = Knowledge(
    vector_db=Qdrant(
        collection="agentic_rag",
        url=qdrant_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

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

# Agentic RAG: the agent gets a search tool and decides when to use it.
# This is the default when knowledge is provided to an Agent.
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        await knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
        )

        print("\n" + "=" * 60)
        print("Agentic RAG: Agent decides when to search")
        print("=" * 60 + "\n")

        agent.print_response(
            "How do I make chicken and galangal in coconut milk soup",
            stream=True,
        )

        print("\n" + "=" * 60)
        print("Multi-part question: agent may search multiple times")
        print("=" * 60 + "\n")

        agent.print_response(
            "I want to make a 3 course Thai meal. Can you recommend a soup, "
            "a curry for the main course, and a dessert?",
            stream=True,
        )

    asyncio.run(main())
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno fastembed openai qdrant-client
    ```
  </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 Qdrant">
    ```bash theme={null}
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

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

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

Full source: [cookbook/07\_knowledge/01\_getting\_started/02\_agentic\_rag.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/01_getting_started/02_agentic_rag.py)