🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents.

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

Semi-automatic feature engineering process using Language Models and your dataset descriptions. Based on the paper "LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering" by Hollmann, Müller, and Hutter (2023).

A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

🧠 Context Engineering is an open-source toolkit to visualize, test, and optimize how LLMs process context windows. Includes a Streamlit app, RAG simulation, educational docs, and Docker support—perfect for prompt engineers, researchers, and AI developers.

An LLM-powered platform for software development, featuring precise context engineering, traceable code generation, and AI-assisted search and data analysis.

Generate Markdown representations of your codebase to provide context for Large Language Models (LLMs). Improve LLM interactions (like ChatGPT) by providing accurate project context. Enhance code analysis, prompt engineering, and AI-driven development. For developers using LLMs in their workflow.

Context Engineering Framework for LLM-Assisted Development

CrewBot: An AI-powered chatbot for project management. Create, edit, and run crews within a chat interface using OpenAI, LM Studio, and Groq LLMs. Supports context-aware conversations, pre-prompt engineering, and PDF export of chat summaries.

LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering

Proof-of-concept implementation of the Agentic Context Engineering (ACE) framework — demonstrating Generator-Reflector-Curator interactions for self-improving LLMs on the HotpotQA dataset.

An open-source LLM context engineering toolkit for analyzing, compressing, and visualizing prompt contexts.

Context Engineering - The art of providing all the context for the task to be plausibly solvable by the LLM.

Prompt & Context Engineering — Practical tutorials, templates, and best practices for crafting reliable LLM prompts and grounding them with contextual data. 📘⚙️

🐍 Companion piece to Building Context-aware Question-Answering Systems with LLMs, a step-by-step guide to using embeddings, vector search, and prompt engineering for building context-aware question-answering system

Web2LLM.txt – A fast, open-source website-to-LLM context file generator. Paste any https:// URL and instantly get a clean llm.txt file with token & cost estimation—ideal for RAG, prompt engineering, and AI training workflows.

Empirical proof of SOTA LLM (GPT-5/Gemini-Pro/Claude-Pro) context saturation in complex engineering. Contains the "Misuraca Protocol" for deterministic logical segmentation to prevent entropy drift.

A Rust-based Model Context Protocol (MCP) server for AI code generation. Solves the context engineering problem by organizing and serving structured context from a local SQLite database to LLMs and AI tools. Fast, standards-based, and easy to run locally.

🎭 MoE Agent: Replace Multi-Agent with single-prompt AI architecture | RAG + SQL + Tool calling without routing | Zero context loss, 10x cost reduction | LLM Prompt engineering for AGI | Production-ready template | Solve ambiguous queries & cross-domain reasoning

MCP server for AI-assisted JavaScript reverse engineering. Beautifies minified/obfuscated code and truncates long strings to prevent LLM context overflow.

This project focuses on text-generation LLMs, offering a deep dive into building and fine-tuning large language models for generating human-like text. It covers key techniques in training, prompt engineering, and model optimization, enabling the creation of powerful, context-aware text generation applications for diverse use cases.

A Model Context Protocol (MCP) server that provides a prompt optimization service for Large Language Models (LLMs) using Google Gemini, with advanced prompt engineering thanks to 22365_3_Prompt_Engineering_v7.pdf authored by Lee Boonstra

What happens when you build software with AI as a true collaborator? Exploring intelligent Engineering (iE) through a credit card lending platform — TDD, context docs for LLMs, and automated workflows.

Context Engineering library for LLM pipelines. Compression, token budgeting, context modes, and provider-agnostic execution.

MCP Code Mode: Context Engineering for Efficient Tool Execution in LLM Agents

Context engineering for AI Agents. Manage your LLM's context window with caching, compaction, summarization, and graceful degradation.

🧠 The Complete Guide to Context Engineering — Research Papers, Blogs, YouTube Videos, Tools & Best Practices for LLM Context Window Optimization

Research artifacts exploring Search-based vs. Agentic RAG strategies for AI Code Review. A deep dive into solving the "Context Instability" problem in LLM-based software engineering, analyzing trade-offs in recall, latency, and cost.

Step by step guidelines for trying out AI, LLMs, Agents, Context Engineering, Optimizations, etc

A practical framework extending Agentic Context Engineering (ACE) for LLM services