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

# Storage

**Agents use storage to persist sessions by storing them in a database.**

Agents come with built-in memory, but it only lasts while the session is active. To continue conversations across sessions, we store agent sessions in a database like PostgreSQL.

The general syntax for adding storage to an Agent looks like:

```python theme={null}
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.duckduckgo import DuckDuckGo
from phi.storage.agent.postgres import PgAgentStorage

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    storage=PgAgentStorage(table_name="agent_sessions", db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
    tools=[DuckDuckGo()],
    show_tool_calls=True,
    add_history_to_messages=True,
)
agent.print_response("How many people live in Canada?")
agent.print_response("What is their national anthem called?")
agent.print_response("Which country are we speaking about?")
```

## Example

<Steps>
  <Step title="Run Postgres">
    Install [docker desktop](https://docs.docker.com/desktop/install/mac-install/) and run **Postgres** on port **5532** using:

    ```bash theme={null}
    docker run -d \
      -e POSTGRES_DB=ai \
      -e POSTGRES_USER=ai \
      -e POSTGRES_PASSWORD=ai \
      -e PGDATA=/var/lib/postgresql/data/pgdata \
      -v pgvolume:/var/lib/postgresql/data \
      -p 5532:5432 \
      --name pgvector \
      phidata/pgvector:16
    ```
  </Step>

  <Step title="Create an Agent with Storage">
    Create a file `agent_with_storage.py` with the following contents

    ```python theme={null}
    import typer
    from typing import Optional, List
    from phi.agent import Agent
    from phi.storage.agent.postgres import PgAgentStorage
    from phi.knowledge.pdf import PDFUrlKnowledgeBase
    from phi.vectordb.pgvector import PgVector, SearchType

    db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
    knowledge_base = PDFUrlKnowledgeBase(
        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid),
    )
    # Load the knowledge base: Comment after first run
    knowledge_base.load(upsert=True)
    storage = PgAgentStorage(table_name="pdf_agent", db_url=db_url)

    def pdf_agent(new: bool = False, user: str = "user"):
        session_id: Optional[str] = None

        if not new:
            existing_sessions: List[str] = storage.get_all_session_ids(user)
            if len(existing_sessions) > 0:
                session_id = existing_sessions[0]

        agent = Agent(
            session_id=session_id,
            user_id=user,
            knowledge=knowledge_base,
            storage=storage,
            # Show tool calls in the response
            show_tool_calls=True,
            # Enable the agent to read the chat history
            read_chat_history=True,
            # We can also automatically add the chat history to the messages sent to the model
            # But giving the model the chat history is not always useful, so we give it a tool instead
            # to only use when needed.
            # add_history_to_messages=True,
            # Number of historical responses to add to the messages.
            # num_history_responses=3,
        )
        if session_id is None:
            session_id = agent.session_id
            print(f"Started Session: {session_id}\n")
        else:
            print(f"Continuing Session: {session_id}\n")

        # Runs the agent as a cli app
        agent.cli_app(markdown=True)


    if __name__ == "__main__":
        typer.run(pdf_agent)
    ```
  </Step>

  <Step title="Run the agent">
    Install libraries

    <CodeGroup>
      ```bash Mac theme={null}
      pip install -U phidata openai pgvector pypdf "psycopg[binary]" sqlalchemy
      ```

      ```bash Windows theme={null}
      pip install -U phidata openai pgvector pypdf "psycopg[binary]" sqlalchemy
      ```
    </CodeGroup>

    Run the agent

    ```bash theme={null}
    python agent_with_storage.py
    ```

    Now the agent continues across sessions. Ask a question:

    ```
    How do I make pad thai?
    ```

    Then message `bye` to exit, start the app again and ask:

    ```
    What was my last message?
    ```
  </Step>

  <Step title="Start a new run">
    Run the `agent_with_storage.py` file with the `--new` flag to start a new run.

    ```bash theme={null}
    python agent_with_storage.py --new
    ```
  </Step>
</Steps>

## Params

| Parameter | Type                     | Default | Description                                     |
| --------- | ------------------------ | ------- | ----------------------------------------------- |
| `storage` | `Optional[AgentStorage]` | `None`  | Storage mechanism for the agent, if applicable. |