Tags: #agent

Memori provides SQL-native, LLM-agnostic memory infrastructure for AI agents, transforming execution and conversation into structured, persistent state.

LangChain is an agent engineering platform and framework for building LLM-powered applications by chaining interoperable components and integrating diverse data sources.

The ultimate guide to AI Agent Skills, providing quick starts, curated resources, and practical tools to enhance AI's specialized capabilities and streamline task automation.

An open-source AI agent harness designed to orchestrate multiple large language models for efficient code generation and complex task automation, avoiding vendor lock-in.

A Golang framework for building LLM applications, offering reusable components, an Agent Development Kit, and flexible composition for complex AI workflows.

A provider-agnostic TypeScript toolkit designed to simplify the development of AI-powered applications and agents across various UI frameworks.

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.

An open LLM devops platform for building, deploying, and managing next-generation enterprise AI applications with comprehensive features like GenAI workflow, RAG, and Agent capabilities.

An AI coding companion for Neovim, integrating various LLMs and coding agents to enhance developer workflow.

DeepChat is an open-source desktop AI agent platform that unifies various large language models, tools, and agents for a seamless and private AI interaction experience.

An open-source memory layer for AI coding agents, enhancing context, collaboration, and seamless integration across various IDEs and LLMs.

An open-source, offline-deployable RAG and Agent application built with Langchain, supporting various local and online Large Language Models for knowledge-based Q&A.

ByteRover CLI provides persistent, structured memory and context management for autonomous AI coding agents through an interactive REPL interface.

A comprehensive and continuously updated collection of the world's best resources for Large Language Models (LLMs), covering various aspects from data to advanced applications.

Qiushi-Skill arms AI agents with a core principle and nine methodological tools derived from classical dialectical materialism and practical philosophy to enhance their problem-solving capabilities.

A minimalist, 100-line LLM framework designed for building AI agents and workflows with zero bloat and maximum expressiveness.