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

# Use Cultural Knowledge in Agent

> Use cultural knowledge with your Agents.

```python use_cultural_knowledge_in_agent.py theme={null}
"""
02 Use Cultural Knowledge In Agent
=============================

Use cultural knowledge with your Agents.
"""

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses

# ---------------------------------------------------------------------------
# Step 1. Initialize the database (same one used in 01_create_cultural_knowledge.py)
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/demo.db")

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# The Agent will automatically load shared cultural knowledge (e.g., how to
# format responses, how to write tutorials, or tone/style preferences).
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    # This flag will add the cultural knowledge to the agent's context:
    add_culture_to_context=True,
    # This flag will update cultural knowledge after every run:
    # update_cultural_knowledge=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # (Optional) Quick A/B switch to show the difference without culture:
    # agent_no_culture = Agent(model=OpenAIResponses(id="gpt-5.2"))

    # ---------------------------------------------------------------------------
    # Step 3. Ask the Agent to generate a response that benefits from culture
    # ---------------------------------------------------------------------------
    # If `01_create_cultural_knowledge.py` added principles like:
    #   "Start technical explanations with code examples and then reasoning"
    # The Agent will apply that here, starting with a concrete FastAPI example.
    print("\n=== With Culture ===\n")
    agent.print_response(
        "How do I set up a FastAPI service using Docker? ",
        stream=True,
        markdown=True,
    )

    # (Optional) Run without culture for contrast:
    # print("\n=== Without Culture ===\n")
    # agent_no_culture.print_response("How do I set up a FastAPI service using Docker?", stream=True, markdown=True)
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai sqlalchemy
    ```
  </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 `use_cultural_knowledge_in_agent.py`, then run:

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

Full source: [cookbook/02\_agents/14\_advanced/02\_use\_cultural\_knowledge\_in\_agent.py](https://github.com/agno-agi/agno/blob/main/cookbook/02_agents/14_advanced/02_use_cultural_knowledge_in_agent.py)