# PromethAI-Memory
Open-source framework that manages memory for AI Agents and LLM apps
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## 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.