> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Agentic RAG With Reasoning

> Demonstrates agentic RAG with reranking and explicit reasoning tools.

```python agentic_rag_with_reasoning.py theme={null}
"""
Agentic Rag With Reasoning
=============================

Demonstrates agentic RAG with reranking and explicit reasoning tools.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.cohere import CohereEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.openai import OpenAIResponses
from agno.tools.reasoning import ReasoningTools
from agno.vectordb.lancedb import LanceDb, SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
knowledge = Knowledge(
    # Use LanceDB as the vector database, store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=CohereEmbedder(id="embed-v4.0"),
        reranker=CohereReranker(model="rerank-v3.5"),
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    # Agentic RAG is enabled by default when `knowledge` is provided to the Agent.
    knowledge=knowledge,
    # search_knowledge=True gives the Agent the ability to search on demand
    # search_knowledge is True by default
    search_knowledge=True,
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Include sources in your response.",
        "Always search your knowledge before answering the question.",
    ],
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    asyncio.run(
        knowledge.ainsert_many(urls=["https://docs.agno.com/agents/overview.md"])
    )
    agent.print_response(
        "What are Agents?",
        stream=True,
        show_full_reasoning=True,
    )
```

## Run the Example

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

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

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

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

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

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

Full source: [cookbook/02\_agents/07\_knowledge/agentic\_rag\_with\_reasoning.py](https://github.com/agno-agi/agno/blob/main/cookbook/02_agents/07_knowledge/agentic_rag_with_reasoning.py)