Plumb vs mshumer/gpt-prompt-engineer - GitHub

Plumb lets AI consultants build, deploy, and sell agentic workflows from a visual node editor without writing code. Each flow gets its own front end, runs on demand or on autopilot, and accepts text, audio, images, and video inputs. Access runs through a private beta request at signup.useplumb.com.

Where generic workflow tools stop at internal tasks, Plumb targets people who sell AI work. You can publish flows, push updates without cloning copies, set paywalls with one-time fees, subscriptions, or per-run charges, and share promo pages. That consultant-first monetization layer is the main split from broad no-code automation suites.

Freelance AI consultants, agency teams, and in-house champions who prototype multimodal model pipelines fit here best. Flows branch on conditions and route outputs to email, SMS, or Slack.

mshumer/gpt-prompt-engineer is an open source prompt engineering toolkit that generates, tests, and ranks candidate prompts for a task you define. You describe the use case, supply test cases, and the notebooks create multiple prompt variants, run them against every test case, and sort results with an ELO rating system starting at 1200. It ships as Jupyter notebooks you can run in Google Colab or locally.

Most prompt tools help you write one prompt at a time. gpt-prompt-engineer treats prompt selection like a tournament: dozens of candidates compete on your test cases, and the highest ELO scores surface the winners. Separate notebooks cover classification tasks, Claude 3 Opus with auto-generated test cases, and Opus-to-Haiku conversion for cheaper inference. Optional Weights & Biases and Portkey logging trace each run.

ML engineers, prompt engineers, and AI developers use it when they need reproducible prompt tuning instead of manual trial and error. The repo has 9.7k GitHub stars and runs on your own OpenAI or Anthropic API keys. It is free under the MIT license.