Open Source Contribution Guide 2026

Open Source Contribution Guide 2026 | Generated by AI

Home 2026.02

Question: How to participate in open source projects like vLLM, PyTorch, llama.cpp, Maven, etc.?

Answer:

Participating in open source projects (contributing code, docs, issues, reviews, etc.) follows a similar general process across most projects, but each has its own specifics.

General Step-by-Step Process to Start Contributing

Find a project that interests you
Browse GitHub repositories (stars, recent activity, open issues labeled “good first issue”).
Read the documentation first
- README.md
- CONTRIBUTING.md (most important)
- CODE_OF_CONDUCT.md
- Project wiki or docs site (especially for large projects)
Set up the development environment
- Fork the repository (on GitHub)
- Clone your fork locally
- Install dependencies (Python, C++, Java, build tools, etc.)
- Build the project locally and run tests
Find something to work on
Look for these labels (priority order for beginners):
- good first issue / beginner-friendly
- help wanted
- documentation
- bug (small & clear reproduction)
  Comment on the issue → ask if it’s still open / if you can work on it.
Make changes
- Create a new branch (git checkout -b fix-something)
- Follow the project’s coding style (black, clang-format, checkstyle, etc.)
- Write / update tests
- Keep commits small and logical
Submit a Pull Request (PR)
- Push branch to your fork
- Open PR against the original repo (usually main or develop branch)
- Write a clear PR title & description
- Link the issue it solves (Fixes #123)
- Pass CI checks (lint, build, tests)
Respond to feedback
Reviewers will comment → push new commits to the same branch → they auto-update the PR.
After merge
Celebrate! → continue with more issues → eventually you may get triage / maintainer rights.

Project-Specific Notes (2026 status)

vLLM (high-throughput LLM inference engine)
→ Very active, welcomes all kinds of contributions
→ Good entry points: documentation, new model support, bug reports, small features
→ Sign-off commits required (git commit -s)
→ Main guide: https://docs.vllm.ai/en/latest/contributing.html
→ GitHub: https://github.com/vllm-project/vllm
PyTorch (very large & mature project)
→ Steeper learning curve, but excellent for learning serious open source
→ Read The Ultimate Guide to PyTorch Contributions first
→ Must sign PyTorch Contributor License Agreement (CLA)
→ Many areas: Python/C++/build/documentation
→ Start with docs, small bug fixes, operator improvements
→ Main guide: https://github.com/pytorch/pytorch/wiki/The-Ultimate-Guide-to-PyTorch-Contributions
llama.cpp (ggml-org/llama.cpp — efficient C/C++ LLM inference)
→ Very active community, lots of hardware backends
→ Prefers minimal dependencies, clean & portable code
→ Good first issues are clearly labeled
→ Strong rules about AI-generated code/PR descriptions (many restrictions in 2025–2026)
→ Main guide: https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md
→ Look for “good first issue” label
Maven (Apache Maven — Java build tool)
→ Classic Apache project → very formal process
→ You need to sign ICLA (Individual Contributor License Agreement)
→ Start by reporting bugs / suggesting improvements → later code
→ Mailing list + Jira for issues
→ Guide: https://maven.apache.org/guides/development/
→ Repo: https://github.com/apache/maven (but decisions mostly on Apache infrastructure)

Quick Tips for Success

Start small (typo fix → one bug → small feature)
Communicate early (comment on issue before spending 20+ hours)
Be patient — reviews can take days/weeks in big projects
Learn git rebase & force-push (used very often when updating PRs)
Join community channels (Discord/Slack for vLLM & llama.cpp, discuss.pytorch.org for PyTorch)

Most important sentence: Read CONTRIBUTING.md of the project before writing any code.

Good luck and welcome to open source!

References:

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