TL;DR

• We're combining a custom LangChain Chain as our AI Companion agent, and OpenAI Functions Agent as Entity Extractor for entity memory

Details

• User sends a message to the AI Companion
• Agent 1 receives the message and respond back with a LangChain LLMChain
• Agent 2 grabs the message and figures out whether or not it warrants a user profile update
• User receives a respond from Agent 1. Agent 2 updates the user's data at the same time.

How memory works

• Messages are kept in a messages_history array in memory.py
• Old messages are passed into the Agent's prompt but only the last K messages
• Messages older than K are summarized and stored in summaries, also in memory.py and passed into the prompt
• User's data (entities memory) is kept in entities dictionary, in memory.py, to provide persistent information about the user. This is what the Agent 2 will update.

python -m venv agent-with-memory (Mac)

py -m venv agent-with-memory (Windows 11)

.\agent-with-memory\Scripts\activate (Windows)

source agent-with-memory/bin/activate (Mac)

poetry install --sync or poetry install

OPENAI_API_KEY=XXXXXX

py main.py (Windows 11)

python main.py (Mac)