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AI contributions policy

PostHog is built with plenty of AI assistance. This policy exists because of a growing volume of low-quality, AI-generated contributions that waste maintainer time.

The standard

You own what you submit. Understand your code, test it, and be ready to explain why it's correct and how it interacts with the rest of the system (without re-prompting an LLM). This is no different from what we'd expect of any contribution; AI makes it easier to skip the work – please don't skip the work.

Prove it works. Before submitting, please verify the change actually works end-to-end — don't rely on "it compiles" or "tests pass" alone.

  • Frontend changes: include a short demo (screenshot, screen recording, or GIF) of the feature working in the PR description. Ideally you demo more than just the happy path.
  • Backend changes: add tests for new behavior, and describe your test strategy in the PR description: what you tested, how you tested it, and what edge cases you considered.

PRs that clearly weren't run or tested will be closed under this policy.

Disclose AI usage. Our PR template includes an Agent context section — please use it (most agents will pick it up automatically). If an agent co-authored or authored your PR, say so and leave context about the tools and session. This helps reviewers calibrate.

Prefer PRs over AI-generated issues. If AI helped you find a bug, fix it and open a pull request — don't paste the AI's output into an issue. Unreviewed, AI-generated bug reports and security disclosures will be closed without response.

Don't submit unsolicited AI-generated PR reviews. If you didn't write the code and aren't a maintainer, don't point an LLM at someone else's PR and leave its output as a review comment. This is generally never helpful.

What happens when contributions don't meet this bar

  • First time: We'll close the PR/issue with a link to this policy and a brief explanation.
  • Two or more closures: We'll block the account.

Why we're not anti-AI

We think the best contributions in 2026 often involve AI. A contributor who uses an LLM to help them understand unfamiliar code, draft a first pass, or catch edge cases they'd miss is probably more productive than someone doing everything by hand. The key difference is that they're driving the AI, not the other way around.

If you're new to open-source contributing and want to learn, we're genuinely happy to help — open an issue, ask questions, submit small PRs.