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Stores and enforces project-level metadata such as goals, tech stack, and style guides as managed artifacts that influence all subsequent work.
Distinct from Project Context Managers: Distinct from Project Context Managers: focuses on persisting and enforcing project metadata as artifacts, not on hierarchical configuration for agent scope.
Explore 4 awesome GitHub repositories matching software engineering & architecture · Persistent Project Artifacts. Refine with filters or upvote what's useful.
AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel
Persists architectural decisions and naming conventions as managed artifacts to maintain project context across sessions.
This project is an autonomous AI software development framework designed to plan, code, test, and commit software milestones without human intervention. It functions as a state-machine-driven agent loop that orchestrates development through a recurring cycle of research, execution, and verification. The system distinguishes itself through a git-isolated task runner that executes milestones in separate worktrees and branches, ensuring changes are squash-merged into a linear commit history. It features a multi-model routing gateway that assigns different LLM providers to specific workflow phase
Maintains project requirements and learned patterns through persistent markdown artifacts and a database.
This project is a spec-driven development framework and workflow automation system for Claude Code. It provides a structured pipeline that converts product requirements and technical designs into atomic implementation tasks for AI coding agents. The system features a context management system that organizes steering documents and technical specifications to optimize token usage and maintain LLM coherence. It also includes a bug resolution pipeline that manages software defects through a systematic five-stage process of reporting, analysis, fixing, and verification. Progress is tracked via a
Maintains project-level metadata and steering documents as managed artifacts that guide all workflow commands.
Conductor is an agentic coding tool that plans, generates, and manages software features through structured tracks and human-reviewed plans. It operates as a plan-driven code generator, reading structured plan files to determine the sequence of tasks and their dependencies before executing any code generation or modification. The system also functions as a feature specification manager, defining features in formal specification files that capture goals, requirements, and implementation steps as machine-readable documents. The tool distinguishes itself through a git-history-based undo system t
Saves and enforces project-level details such as goals, tech stack, style guides, and workflow preferences as managed artifacts.