Patchwork – Open-source framework to automate development gruntwork
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…
In plain words
Patchwork is an open-source Python framework that automates routine development tasks such as code reviews, documentation, linting, and security fixes using large language models. Developers define customizable workflows called "patchflows" that combine LLM prompts with modular code management steps. The tool is designed for development teams looking to reduce time spent on code management gruntwork by orchestrating automated fixes and improvements across their codebase.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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
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