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

# Create Cultural Knowledge

> Create cultural knowledge to use with your Agents.

```python create_cultural_knowledge.py theme={null}
"""
01 Create Cultural Knowledge
=============================

Create cultural knowledge to use with your Agents.
"""

from agno.culture.manager import CultureManager
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from rich.pretty import pprint

# ---------------------------------------------------------------------------
# Step 1. Initialize the database used for storing cultural knowledge
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/demo.db")

# ---------------------------------------------------------------------------
# Step 2. Create the Culture Manager
# ---------------------------------------------------------------------------
# The CultureManager distills reusable insights into the shared cultural layer
# that your Agents can access for consistent reasoning and behavior.
culture_manager = CultureManager(
    db=db,
    model=OpenAIResponses(id="gpt-5.2"),
)

# ---------------------------------------------------------------------------
# Step 3. Create cultural knowledge from a message
# ---------------------------------------------------------------------------
# You can feed in any insight, principle, or lesson you’d like the system to remember.
# The model will generalize it into structured cultural knowledge entries.
#
# For example:
# - Communication best practices
# - Decision-making patterns
# - Design or engineering principles
#
# Try to phrase inputs as *reusable truths* or *guiding principles*,
# not one-off observations.
message = (
    "All technical guidance should follow the 'Operational Thinking' principle:\n"
    "\n"
    "1. **State the Objective** — What outcome are we trying to achieve and why.\n"
    "2. **Show the Procedure** — List clear, reproducible steps (prefer commands or configs).\n"
    "3. **Surface Pitfalls** — Mention what usually fails and how to detect it early.\n"
    "4. **Define Validation** — How to confirm it’s working (logs, tests, metrics).\n"
    "5. **Close the Loop** — Suggest next iterations or improvements.\n"
    "\n"
    "Keep answers short, structured, and directly actionable. Avoid general theory unless "
    "it informs an operational decision."
)

culture_manager.create_cultural_knowledge(message=message)

# ---------------------------------------------------------------------------
# Step 4. Retrieve and inspect the stored cultural knowledge
# ---------------------------------------------------------------------------
cultural_knowledge = culture_manager.get_all_knowledge()

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    print("\n=== Cultural Knowledge Entries ===")
    pprint(cultural_knowledge)
```

## 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 `create_cultural_knowledge.py`, then run:

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

Full source: [cookbook/02\_agents/14\_advanced/01\_create\_cultural\_knowledge.py](https://github.com/agno-agi/agno/blob/main/cookbook/02_agents/14_advanced/01_create_cultural_knowledge.py)