Show HN: Patchwork – Open-source framework to automate development gruntwork

The linked article is about a project called "Patchwork" which is a collection of utilities for patching and modifying various software components. It includes tools for patching binaries, managing patches, and automating the patching process. The project aims to provide a comprehensive solution for developers and system administrators who need to apply custom patches to their software applications.

Hi HN! We’re Asankhaya and Rohan and we are building Patchwork.

Patchwork tackles development gruntwork—like reviews, docs, linting, and security fixes—through customizable, code-first 'patchflows' using LLMs and modular code management steps, all in Python. Here's a quick overview video: https://youtu.be/MLyn6B3bFMU

From our time building DevSecOps tools, we experienced first-hand the frustrations our users faced as they built complex delivery pipelines. Almost a third of developer time is spent on code management tasks[1], yet backlogs remain.

Patchwork lets you combine well-defined prompts with effective workflow orchestration to automate as much as 80% of these gruntwork tasks using LLMs[2]. For instance, the AutoFix patchflow can resolve 82% of issues flagged by semgrep using gpt-4 (or 68% with llama-3.1-8B) without fine-tuning or providing specialized context [3]. Success rates are higher for text-based patchflows like PR Review and Generate Docstring, but lower for more complex tasks like Dependency Upgrades.

We are not a coding assistant or a black-box GitHub bot. Our automation workflows run outside your IDE via the CLI or CI scripts without your active involvement.

We are also not an ‘AI agent’ framework. In our experience, LLM agents struggle with planning and rarely identify the right execution path. Instead, Patchwork requires explicitly defined workflows that provide greater success and full control.

Patchwork is open-source so you can build your own patchflows, integrate your preferred LLM endpoints, and fully self-host, ensuring privacy and compliance for large teams.

As devs, we prefer to build our own ‘AI-enabled automation’ given how easy it is to consume LLM APIs. If you do, try patchwork via a simple 'pip install patchwork-cli' or find us on Github[4].

Sources:

[1] https://blog.tidelift.com/developers-spend-30-of-their-time-...

[2] https://www.patched.codes/blog/patched-rtc-evaluating-llms-f...

[3] https://www.patched.codes/blog/how-good-are-llms

[4] https://github.com/patched-codes/patchwork

[Sample PRs] https://github.com/patched-demo/sample-injection/pulls