# PromethAI-Memory

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Open-source framework that manages memory for AI Agents and LLM apps

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![Infographic Image](https://github.com/topoteretes/PromethAI-Memory/blob/main/infographic_final.png) ## The Motivation Browsing the database of theresanaiforthat.com, we can observe around [7000 new, mostly semi-finished projects](https://theresanaiforthat.com/) in the field of applied AI, whose development is fueled by new improvements in foundation models and open-source community contributions. It seems it has never been easier to create a startup, build an app, and go to market… and fail. AI apps currently being pushed out still mostly feel and perform like demos. To address this issue, [dlthub](https://dlthub.com/) and [prometh.ai](http://prometh.ai/) will collaborate on productionizing a common use-case, progressing step by step. We will utilize the LLMs, frameworks, and services, refining the code until we attain a clearer understanding of what a modern LLM architecture stack might entail. We go on a journey and propose a new way to reason about Language Architecture for Agents based on cognitive sciences. #### Read more on our blog post [prometh.ai](http://prometh.ai/promethai-memory-blog-post-one) #### Or check this [Princeton paper](https://arxiv.org/abs/2309.02427) released after our demo ## PromethAI-Memory Repo Structure The repository contains a set of folders that represent the steps in the evolution of the modern data stack from POC to production #### Level 1 - CMD script to process PDFs We introduce the following concepts: 1. Structured output with Pydantic 2. CMD script to process custom PDFs #### Level 2 - Memory Manager implemented in Python We introduce the following concepts: 1. Long Term Memory 2. Short Term Memory 3. Episodic Buffer 4. Attention Modulators The code at this level contains: 1. Simple PDF ingestion 2. FastAPI 3. Docker Image 4. Memory manager 5. Langchain-based Agent Simulator 6. Data schema ## How to use Each of the folders contains a README to get started.