Popular GitHub Repositories for Memory Engineering

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

? Guides, papers, lecture, notebooks and resources for prompt engineering

A high-throughput and memory-efficient inference and serving engine for LLMs

? Toolkit to help you get started with Spec-Driven Development

Finetune Llama 3.3, DeepSeek-R1 & Reasoning LLMs 2x faster with 70% less memory! ?

Learn how to design, develop, deploy and iterate on production-grade ML applications.

The Memory layer for AI Agents

Agno is a lightweight library for building Multimodal Agents. It exposes LLMs as a unified API and gives them superpowers like memory, knowledge, tools and reasoning.

? Papers & tech blogs by companies sharing their work on data science & machine learning in production.

This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.

Letta (formerly MemGPT) is the stateful agents framework with memory, reasoning, and context management.

? Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. ?YC W23

Turns Data and AI algorithms into production-ready web applications in no time.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

Machine Learning Engineering Open Book

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.

A curated list of references for MLOps