Awesome GitHub RepositoriesDocumentation-Based Context Persistence

ANUS is an automated coding agent and development framework that uses a large language model to execute technical tasks and modify project files. It functions as a tool-integrated platform that combines a sandboxed shell executor with a system for maintaining persistent project goals. The framework is distinguished by its context-aware development model, which uses local markdown files to track instructions and maintain state across different sessions. It employs a loop-based autonomous development cycle to plan, code, and verify changes, while utilizing a standardized protocol to integrate e

This project is a multi-agent development framework and orchestrator designed to coordinate autonomous AI agents for software engineering tasks. It functions as an engine that plans, implements, and reviews complex code changes across multiple files and isolated worktrees from a command line interface. The system distinguishes itself through a multi-agent coordination layer that decomposes tasks into sequential phases and applies changes across isolated worktrees to validate solutions. It maintains project-specific knowledge and constraints across sessions via context persistence using dedica

This project provides a framework for AI agent orchestration and context management, enabling the deployment of specialized AI personas and subagents to solve multi-step technical goals. It centers on managing specialized agents with isolated contexts and role-based prompts to handle domain-specific tasks. The system differentiates itself through a hierarchical project memory using markdown files to maintain coding standards and a secure execution model that utilizes sandboxed environments and git worktree isolation. It also features a Model Context Protocol integration for external tool conn

This project is an AI development workflow orchestrator and context management framework. It provides a context-aware project knowledge base and a structured prompting system designed to guide large language models through the planning, implementation, and verification phases of software development. The system optimizes AI coding contexts by using a collection of markdown files to track project state and architectural memory. It employs mode-based rule isolation and just-in-time context loading to reduce noise and ensure that only relevant rules and documentation are active for a given task.