Summary

GTPlanner automates the generation of structured technical documentation (Agent PRDs/SOPs) from natural language. It targets developers building AI agents, simplifying workflow creation by ensuring deterministic, composable, and platform-flexible agent execution.

How It Works

GTPlanner transforms natural language into clear SOPs, eliminating AI ambiguity for predictable execution. It emphasizes Determinism, Composability (via reusable "Prefabs"), and Freedom (minimal frameworks, native Python). SOPs are broken into Prefabs and task components, enabling composable workflows without platform lock-in.

Quick Start & Requirements

Local setup requires Python ≥ 3.10 (3.11+ recommended) and an LLM API Key. Installation involves cloning the repo and running uv sync. Configuration is managed via .env (e.g., LLM_API_KEY). Usage is supported via CLI (python gtplanner.py) or FastAPI service. GTPlanner also functions as a Claude Code plugin (clone to ~/.claude/plugins, init with /gtplanner-init). A live Web UI demo is available. Key documentation links include Quick Start, Configuration, and Prefab Guides.

Highlighted Details

Features include a recommended Web UI for streamlined AI planning, instant documentation, and output optimized for Vibe Coding (Cursor, GitHub Copilot). It integrates as a Claude Code Skill for direct PRD generation. GTPlanner supports the Model Context Protocol (MCP) for AI programming tools. A key differentiator is its Prefab ecosystem: standardized, reusable AI functional components that extend capabilities, are discoverable, deployable, and version-managed, fostering community contributions.

Maintenance & Community

Community contributions, especially Prefabs, are encouraged to extend capabilities and integrate into the recommendation system. Detailed guides exist for Prefab and core code contributions. The project leverages community wisdom for growth.

Licensing & Compatibility

This project is licensed under the MIT License, which permits broad use, including commercial applications and integration into closed-source projects, with minimal restrictions.

Limitations & Caveats

The research tool requires a JINA_API_KEY. The project's focus on community development suggests ongoing evolution.