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Memory Management Strategies

Configure memory behavior for optimal performance and cost.

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Limiting Message History

import asyncio
from upsonic import Agent, Chat


async def main():
    agent = Agent("openai/gpt-4o")

    chat = Chat(
        session_id="session1",
        user_id="user1",
        agent=agent,
        num_last_messages=20  # Only keep last 20 messages in context
    )

    # Invoke chat
    response = await chat.invoke("What is Machine Learning?")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

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Memory with Summarization

import asyncio
from upsonic import Agent, Chat


async def main():
    agent = Agent("openai/gpt-4o")

    chat = Chat(
        session_id="session1",
        user_id="user1",
        agent=agent,
        full_session_memory=True,
        summary_memory=True  # Automatically summarize long conversations
    )

    # Invoke chat
    response = await chat.invoke("Explain Deep Learning")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

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Tool Call Results in Memory

import asyncio
from upsonic import Agent, Chat


async def main():
    agent = Agent("openai/gpt-4o")

    chat = Chat(
        session_id="session1",
        user_id="user1",
        agent=agent,
        feed_tool_call_results=True  # Include tool execution results in memory
    )

    # Invoke chat
    response = await chat.invoke("What is NLP?")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

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User Profile Management

import asyncio
from upsonic import Agent, Chat


async def main():
    agent = Agent("openai/gpt-4o")

    chat = Chat(
        session_id="session1",
        user_id="user1",
        agent=agent,
        user_analysis_memory=True,
        user_memory_mode="update"  # Incrementally update profiles
    )

    # Invoke chat
    response = await chat.invoke("Describe Computer Vision")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

​
Dynamic Profile Schema

import asyncio
from upsonic import Agent, Chat


async def main():
    agent = Agent("openai/gpt-4o")

    chat = Chat(
        session_id="session1",
        user_id="user1",
        agent=agent,
        dynamic_user_profile=True  # Generate profile schema automatically
    )

    # Invoke chat
    response = await chat.invoke("What is Reinforcement Learning?")
    print(response)


if __name__ == "__main__":
    asyncio.run(main())

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Memory Optimization Tips

  1. Use num_last_messages to limit context size
  2. Enable summary_memory for long conversations
  3. Use user_memory_mode="update" for incremental profiles
  4. Disable feed_tool_call_results if tool outputs are large