Prompt Manager

Prompt Manager – A Centralized AI Prompt Lifecycle Tool

Prompt Manager addresses the common pain point of scattered, hard‑to‑reuse prompts that developers and data scientists often encounter when building AI applications. By offering a single, web‑based interface coupled with a RESTful API and WebSocket support, it lets teams create, version, and compose prompts in a structured way. The system’s focus on keyboard shortcuts and real‑time editing reduces context switching, allowing users to iterate quickly without leaving the terminal or code editor.

At its core, Prompt Manager stores prompts as plain text files on disk, organized into directories that mirror project structure. Prompts can be simple or composite: a composite prompt references other prompts via inclusion syntax, enabling modularity and reuse across different models or tasks. The server automatically expands these inclusions when requested, delivering a fully rendered prompt ready for the model. This templating capability saves developers from duplicating boilerplate and ensures consistency across experiments.

The API exposes a full CRUD surface for prompt objects, as well as endpoints for directory management and bulk reloading. A dedicated WebSocket route () allows collaborative, live editing with instant push notifications. These features make Prompt Manager suitable for both single‑user workflows and team environments where multiple assistants or scripts need to fetch the latest prompt versions on demand.

Prompt Manager’s integration with the Model Context Protocol (MCP) is a standout feature. By exposing prompts as MCP resources, AI assistants like Claude can request prompt expansions or directory listings directly through the protocol, enabling sophisticated prompting strategies (e.g., dynamic prompt selection based on context or user intent). This tight coupling removes the need for custom adapters and keeps prompt logic in a single, version‑controlled location.

Real‑world use cases include rapid prototyping of conversational agents, A/B testing of prompt variations for language models, and continuous integration pipelines that deploy updated prompts to production systems. Teams can maintain a central prompt library, enforce naming conventions, and audit changes via standard version control, all while keeping the workflow lightweight through keyboard navigation and real‑time feedback.