This article explores the concept of an "agent harness," the essential software infrastructure that wraps around a Large Language Model (LLM) to enable autonomous, goal-directed behavior. While foundation models provide the core reasoning capabilities, the harness manages the orchestration loop, tool integration, memory, context management, state persistence, and error handling. The author breaks down the eleven critical components of a production-grade harness, drawing insights from industry leaders such as Anthropic, OpenAI, and LangChain. By comparing the harness to an operating system and the LLM to a CPU, the piece provides a technical framework for understanding how to move from simple demos to robust, production-ready AI agents.
Salute is a JavaScript library designed for controlling Large Language Models (LLMs) with a React-like, declarative approach. It emphasizes composability, minimal abstraction, and transparency – ensuring you see exactly what prompts are being sent to the LLM. Salute offers low-level control and supports features like type-checking, linting, and auto-completion for a smoother development experience. The library's design allows for easy creation of chat sequences, nesting of components, and dynamic prompt generation. It's compatible with OpenAI models but is intended to support any LLM in the future.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
sudo for AI agents - allow, deny, or ask before any tool runs. AI agents run tools autonomously, but some calls are too risky to run unchecked. agentpriv gives you a permission layer to control what goes through.
Scaling a simple RAG pipeline from simple notes to full books. This post elaborates on how to utilize larger files with your RAG pipeline by adding an extra step to the process — chunking.
This repository contains the source code for the summarize-and-chat project. This project provides a unified document summarization and chat framework with LLMs, aiming to address the challenges of building a scalable solution for document summarization while facilitating natural language interactions through chat interfaces.
Mariya Mansurova explores using CrewAI's multi-agent framework to create a solution for writing documentation based on tables and answering related questions.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
Learn how to summarize large documents using LangChain and OpenAI, addressing contextual limits and cost effectively. This tutorial covers text preprocessing, semantic chunking, K-means clustering, and document summarization.
A personal productivity assistant that utilizes Retrieval-Augmented Generation (RAG). Allows users to chat with their documents and apps using various AI models. A local and private alternative to OpenAI GPTs and ChatGPT.
