gsd-build/gsd-2

Features

• Autonomous Agent Loops - Orchestrates autonomous software development through a recurring state-machine loop of research, planning, execution, and verification.
• Autonomous Agent Frameworks - Provides a complete autonomous agent framework for planning, coding, and verifying software through a state-machine loop.
• Autonomous Software Engineering - Provides a full framework for building complete software projects autonomously through AI-driven planning, coding, and testing.
• Worktree Isolation - Uses isolated git worktrees to maintain separate file states for different autonomous development milestones.
• Autonomous Agent Orchestration - Provides a framework for managing the lifecycle and persistent memory of autonomous agents over long-running development sessions.
• Context Compression - Reduces token consumption and manages memory by compressing context windows through a preference-driven pipeline.
• Context Memory Management - Implements a pipeline for long-term project memory using context compression and database-backed state management.
• Model Routing Gateways - Assigns different LLM providers or models to each development phase with automatic fallback and budget-aware cost control.
• Multi-step Goal Execution - Uses LLMs to independently plan and execute multi-step sequences of research, coding, and verification.
• Provider Response Routing - Implements logic to route requests to specific LLM providers based on the requirements of different development phases.
• Subagent Orchestrations - Orchestrates specialized helper agents with isolated contexts to perform research and codebase reconnaissance.
• Agentic Workflow Isolation - Isolates AI-generated changes within dedicated git worktrees and branches to maintain a clean commit history.
• Construction - Builds complete software projects autonomously by cycling through research, planning, and coding.
• Git Worktree Management - Manages multiple concurrent git worktrees to provide isolated environments for developing different software milestones.
• Git Workspace Isolations - Executes development milestones in isolated git worktrees to maintain a clean and linear squash-merged commit history.
• Development Cycle Orchestration - Orchestrates AI-driven development cycles involving research, coding, and verification phases.
• Branch-Based Isolation - Uses separate git branches to isolate AI-generated changes before squash-merging them into the main history.
• Agent State Persistence - Persists session state and milestone progress in a database to enable crash recovery and project inspection.
• Persistent Project Artifacts - Maintains project requirements and learned patterns through persistent markdown artifacts and a database.
• Task Outcome Enforcement - Ensures autonomous workflows conclude by running static checks and behavioral tests to verify requirements.
• Behavior Guidance - Loads coding standards and architectural rules from markdown files to guide the agent's implementation behavior.
• Human-in-the-Loop Steering - Enables humans to interact with and redirect active autonomous agent flows and milestones in real-time.
• MCP Server Integrations - Integrates with Model Context Protocol servers to provide agents with specialized tools and external context.
• Autonomous Agent Orchestrators - Coordinates autonomous agents by integrating Model Context Protocol servers and external tools for complex task execution.
• External Tool Integrations - Connects AI agents to external utilities and servers to expand their operational capabilities beyond simple text generation.
• MCP Protocol Integrations - Provides a standardized way for agents to interact with external tools and context servers via the Model Context Protocol.
• Model Gateways - Provides a centralized gateway to manage communication and routing across multiple AI model providers.
• Per-Component LLM Routings - Implements routing logic to assign different LLM models to specific workflow phases based on cost and quality requirements.
• Step-by-Step Task Plans - Implements multi-step task plans that are submitted for manual approval before agent execution.
• Phase-Specific Model Overrides - The project assigns specific model providers to different project phases and sets automatic fallbacks for failed requests.
• LLM Session Recovery - Provides the ability to restore the state and progress of AI agent sessions after a system failure.
• Spec-Driven Generation - Guides a structured discussion to capture project vision and then generates the initial codebase and roadmap.
• Spec-Driven Project Initialization - Captures project vision and constraints through a guided discussion to create the initial structure and roadmap.
• Agent Interaction Monitors - Provides tools for observing and interacting with live AI agent execution streams from a separate terminal.
• Agent Steering Interfaces - Synchronizes state between background autonomous processes and foreground terminals for live steering and monitoring.
• Research Progress Dashboards - Displays a real-time dashboard showing task progress, elapsed time, and cost projections for autonomous runs.
• Token Cost Optimizations - Optimizes AI operational costs via context compression and the selection of cheaper model tiers.

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