4 个仓库
Systems that dynamically provide relevant information and tools to enhance the operational context of AI agents.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Context Engineering. Refine with filters or upvote what's useful.
LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing
Augments operational context by dynamically injecting relevant data and tool access into agent prompts.
This project is an autonomous software development assistant and project management tool that utilizes a multi-agent orchestrator to automate complex workflows. It functions as an agentic framework designed to research, plan, execute, and verify software development tasks by coordinating specialized agents that manage context windows and system performance. The system distinguishes itself through a structured, interview-based requirement engineering phase that clarifies project objectives before initiating automated work. It employs atomic task decomposition to break goals into independent un
Maintains project-specific documentation and state files to provide high-quality context for automated operations.
Agent-skills is a collection of structured instructions and behavioral personas designed to standardize how AI coding agents perform engineering tasks. It functions as a workflow orchestrator that maps natural language intent to repeatable technical sequences and verification checklists. The project distinguishes itself through the use of specialized markdown-defined roles, such as security auditors or test engineers, to apply targeted domain expertise. It employs an evidence-based verification model that requires runtime data or passing tests as mandatory exit criteria to ensure AI-generated
Organizes project information into a hierarchy of global rules, architecture specs, and transient outputs to optimize agent context.
本项目是一套标准化的知识库与能力模型集合,旨在为产品经理及人工智能代理定义专业方法论。它提供了一个结构化的专业技能与知识框架,确保在产品发现、战略规划及利益相关者对齐等环节中,产出质量的一致性。 该仓库专注于大语言模型产品管理的专业框架,包括人工智能就绪度评估、上下文工程以及多代理工作流编排的指南。它利用基于 Markdown 的知识结构化方式,引导 AI 代理生成专业的交付物与战略分析,而非通用的输出内容。 项目涵盖了广泛的产品管理能力,包括用于运营健康度分析的业务指标评估、客户发现与假设验证,以及使用优先级模型进行的战略路线图规划。此外,它还包含撰写产品需求文档(PRD)与用户故事、利益相关者影响力映射以及针对领导力转型的行政教练框架。
Implements systems for organizing domain knowledge and operational constraints into prompts to guide AI agent orchestration.