letta-memory 0.1.0

An experimental SDK for using Letta subagents for pluggable memory management

Memory SDK

An experimental SDK for using Letta agents for long-term memory and learning in a pluggable way. "Subconsious" Letta agents learn from data like conversational interactions, files, and other text content to generate learned context blocks that you can plug into your agent's system prompt - a form of "system prompt learning".

+========================================+
|         SYSTEM PROMPT                  |
+========================================+
|    LEARNED CONTEXT (USER)              | <- Subconscious Agent (learning from message history)
+========================================+
|    LEARNED CONTEXT (FILES)             | <- Subconscious Agent (learning from files) 
+========================================+
|           MESSAGES                     |
|  * User -> Assistant                   |
|  * User -> Assistant                   |
|  * User -> Assistant                   |
|  * ...                                 |
+========================================+

Quickstart

1. Create an API Key
2. Install: pip install letta-memory

Usage: Conversational Memory

You can save conversation histories using the Memory SDK, and later retrieve the learned context block to place into your system prompt. This allows your agents to have an evolving understand of the user. Example: Create a basic OpenAI gpt-4o-mini chat agent with memory

from openai import OpenAI
from memory import Memory

openai_client = OpenAI()
memory = Memory()

def chat_with_memories(message: str, user_id: str = "default_user") -> str:

    # get the user memory 
    user_memory = memory.get_user_memory(user_id)
    if not user_memory:
        memory.initialize_user_memory(user_id, reset=True)
        user_memory = memory.get_user_memory(user_id)
    
    # format the user memory 
    user_memory_prompt= memory.get_user_memory(user_id, prompt_formatted=True)

    # generate the assistant response
    system_prompt = f"<system>You are a helpful AI assistant</system>"
    system_prompt += f"\n{user_memory_prompt}"
    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
    response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
    assistant_response = response.choices[0].message.content

    # Create new memories from the conversation
    messages.append({"role": "assistant", "content": assistant_response})
    memory.add_messages(user_id, messages)

    return assistant_response

def main():
    print("Chat with AI (type 'exit' to quit)")
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() == 'exit':
            print("Goodbye!")
            break
        print(f"AI: {chat_with_memories(user_input)}")

if __name__ == "__main__":
    main()

The memory will have a summary and user memory block that you can place into your system prompt.

<conversation_summary>
Sarah introduced herself and asked the assistant to tell about itself. The assistant provided a brief self-description and offered further help.
</conversation_summary>

<human description="Details about the human user you are speaking to.">
Name: Sarah
Interests: Likes cats (2025-09-03)
</human>

You can customize the prompt format by getting the raw summary or user block string with prompt_formatted=False.

Roadmap

• Learning from files
• Query historical messages
• Save messages as archival memories
• Query archival memory
• Add "sleep" (offline collective revisioning of all data)