> ## 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.

# Composition: LearningMachine + FileSystem, One Deliberate Order

> The point of the manual door: LearningMachine, FileSystem and your own system prompt compose in one order you can read off the page.

The point of the manual door: LearningMachine, FileSystem and your own system prompt compose in one order you can read off the page. Nothing is attached behind your back.

```python with_filesystem.py theme={null}
"""
Composition: LearningMachine + FileSystem, One Deliberate Order
===============================================================
The point of the manual door: LearningMachine, FileSystem and your own
system prompt compose in one order you can read off the page. Nothing is
attached behind your back.

Run:
    .venvs/demo/bin/python cookbook/08_learning/11_composition/with_filesystem.py
"""

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.fs import FileSystem
from agno.learn import LearningMachine, LearningMode, UserMemoryConfig
from agno.models.openai import OpenAIResponses

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

learning = LearningMachine(
    db=db,
    model=OpenAIResponses(id="gpt-5.5"),  # the manual door injects nothing
    user_memory=UserMemoryConfig(mode=LearningMode.AGENTIC),
)
fs = FileSystem(db, namespace="composition-notes")

USER_ID = "composer@example.com"

agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    db=db,
    tools=[*learning.get_tools(user_id=USER_ID), fs.tools()],
    instructions=[
        "You are a research assistant. Keep running notes on topics you research.",
        learning.instructions(),
        fs.instructions(),
    ],
    user_id=USER_ID,
    markdown=True,
)

if __name__ == "__main__":
    agent.print_response(
        "Note down: the vector-db comparison is due Friday. And remember that "
        "I want conclusions first in every summary.",
        stream=True,
    )
    print("\n--- files ---")
    for f in fs.list():
        print(f.path)
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" 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>

  <Snippet file="run-pgvector-step.mdx" />

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

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

Full source: [cookbook/08\_learning/11\_composition/with\_filesystem.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/08_learning/11_composition/with_filesystem.py)