LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Context Engineering Guide By DAIR.AI Academy Table of Contents What is Context Engineering? Context Engineering is Action System Prompt Instructions User Input Structured Inputs and Outputs Tool Calling RAG & Memory State & Historical Context Advanced Context Engineering Resources What is Co...

Fall 2024

Context Engineering is the new skill in AI. It is about providing the right information and tools, in the right format, at the right time.

TL;DR Agents need context to perform tasks. Context engineering is the art and science of filling the context window with just the right information at each step of an agent’s trajectory. In this post, we break down some common strategies — write, select, compress, and isolate — for context engineering

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This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design...

In-depth tutorials on LLMs, RAGs and real-world AI agent applications. - patchy631/ai-engineering-hub

The crucial layers of infrastructure that make up a production grade Agentic AI system.

I rewrote my "tech writer" agent in 7 frameworks: Agno, Autogen, Google ADK, Atomic Agents, DSPy, Langgraph, and Pydantic AI. You'll NEVER guess the winners.

A complete walkthrough for building a code-generating AI agent that recommends grocery items by querying SurrealDB's HNSW vector index.

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for re...

Frameworks for LLM Agents have been surprisingly disappointing. I want to offer some principles for building agents based on our own trial & error, and explain why some tempting ideas are actually quite bad in practice.

Discover 8 powerful tools transforming prompt engineering from trial-and-error into scalable systems—featuring visual workflows, auto-tuned prompts, and memory-

My step-by-step approach for building and evolving Agentic Systems that work.

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? - humanlayer/12-factor-agents

An example showing how A2A and MCP can be used together - ishanExtreme/a2a_mcp-example

Learn about Google's Agent2Agent (A2A), an open protocol for AI agent collaboration across different systems.

Weights & Biases, developer tools for machine learning

DeerFlow is a community-driven framework for deep research, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source communit...

A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs - business-science/awesome-generative-...

Explaining the Model Context Protocol and everything that might go wrong.

Learn common agentic design patterns and workflows for building robust, scalable AI applications, understanding when to use each.

Learn about how Azure API Center can be a centralized registry for MCP servers in your organization. Developers and other stakeholders can use the API Center portal to discover MCP servers.