Persistent Memory

import json

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.agent.sqlite import SqliteAgentStorage
from rich.console import Console
from rich.json import JSON
from rich.panel import Panel

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    # Store agent sessions in a database
    storage=SqliteAgentStorage(
        table_name="agent_sessions", db_file="tmp/agent_storage.db"
    ),
    # Set add_history_to_messages=true to add the previous chat history to the messages sent to the Model.
    add_history_to_messages=True,
    # Number of historical responses to add to the messages.
    num_history_responses=3,
    # The session_id is used to identify the session in the database
    # You can resume any session by providing a session_id
    # session_id="xxxx-xxxx-xxxx-xxxx",
    # Description creates a system prompt for the agent
    description="You are a helpful assistant that always responds in a polite, upbeat and positive manner.",
)

console = Console()


def print_chat_history(agent):
    # -*- Print history
    console.print(
        Panel(
            JSON(
                json.dumps(
                    [
                        m.model_dump(include={"role", "content"})
                        for m in agent.memory.messages
                    ]
                ),
                indent=4,
            ),
            title=f"Chat History for session_id: {agent.session_id}",
            expand=True,
        )
    )


# -*- Create a run
agent.print_response("Share a 2 sentence horror story", stream=True)
# -*- Print the chat history
print_chat_history(agent)

# -*- Ask a follow up question that continues the conversation
agent.print_response("What was my first message?", stream=True)
# -*- Print the chat history
print_chat_history(agent)