Jazzberry
Jazzberry · Coding
Jazzberry is a GitHub bot that finds real bugs by running your code in a secure sandbox every time a pull request opens, then posts the results as a comment.
About Jazzberry
What Is Jazzberry
Jazzberry is an AI bug finder that plugs into GitHub. When someone opens a pull request, the service clones the repository into a secure microVM, hands the diff to an AI agent, and lets that agent run the code. The agent can read files, search, install packages, run interpreters, and execute test snippets, then watch what happens and narrow in on a fault. Instead of guessing from code patterns, it confirms a bug by making it happen.
Mateo Perez and Marco Dewey built the tool and took it through Y Combinator in 2025. Perez holds a PhD in reinforcement learning and formal methods, and Dewey studied LLMs for automated test generation. The team later renamed the company Cartpole and moved the old site over, so the original jazzberry.ai address now points elsewhere.
The limits are worth knowing up front. Jazzberry is not a general code review tool, so it won't comment on style or naming. It only inspects the changes in a pull request, gets strongest signal when code can run in isolation, and sends your repository to a third-party service for testing.
Getting Started
- Open the Jazzberry page on GitHub and install the app on the account or organization you want covered.
- Pick the repositories you want the bot to watch.
- Open a pull request as usual, and wait for the agent to clone the repo and run its tests.
- Read the bug report posted as a pull request comment, then fix or dismiss each finding.
Product Information
A quick look at Jazzberry's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Backend and API developers
- Security-minded teams
- Solo builders and small teams
- QA and release leads
Tasks
- Testing a pull request automatically
- Catching security bugs early
- Reproducing hard-to-spot bugs
- Triaging findings by severity
Scenarios
- Merging a large AI-authored pull request
- Running a lean review process
- Vetting a new dependency or integration
- Onboarding to an unfamiliar codebase
Key features
Automated bug detection through real code execution
Jazzberry's core idea is that running code beats reading it. On each pull request, an AI agent works inside the cloned repository, writing and executing snippets to confirm a fault exists. Because the agent observes actual behavior, this automated bug detection can flag logic errors and runtime problems that a static scanner would miss. The team frames this as providing concrete evidence of what's broken and how, not a list of maybe-issues.
A secure microVM for every review
Each review runs in its own locked-down microVM. The repository is cloned into that isolated machine, the agent does its work there, and the environment is destroyed afterward. This keeps one project's run separate from others and limits what any single job can touch. The tradeoff is that your code does leave your infrastructure for the duration of the check.
Automatic pull request integration
There's no separate dashboard to babysit. Once the GitHub app is installed, Jazzberry listens for pull requests and starts on its own. Findings arrive as a comment on the pull request itself, right where a reviewer is already reading. This fits teams that live in GitHub and don't want another tab open.
Markdown bug reports with severity and reproduction
The output is a markdown table posted to the pull request. Each row names the bug, tags a severity from Critical down to Low, shows a sample test case, and explains what goes wrong. That structure lets a reviewer scan the table and decide what to act on, rather than digging through logs. Early users reported catches like a SQL injection flaw and an API key validation error.
Language-agnostic testing
Jazzberry isn't tied to one stack. Because the agent installs packages and runs interpreters inside the sandbox, it can work across mainstream languages such as Python, JavaScript, TypeScript, Go, Java, and Ruby. Teams with mixed codebases don't need a separate tool per language, and very unusual build setups may just need extra configuration.
Pros and cons
Pros
- Confirms bugs by running code, so findings carry evidence instead of guesses.
- Posts results straight into the pull request, with no extra dashboard to check.
- Isolated microVM runs keep each review self-contained and disposable.
- Works across common languages, so a mixed repo needs only one tool.
- Severity labels help a team triage the worst bugs first.
Cons
- Your repository is sent to a third-party service for testing, which some teams can't allow.
- It only looks at changes in a pull request, so it's not a full codebase audit.
- Complex or integration-heavy systems can be hard to run in an isolated sandbox, which weakens results.
- Findings still need a human to judge, since dynamic tests can miss context.
Frequently asked questions
It's an AI bug finder for GitHub. When a pull request opens, Jazzberry clones the repository into a secure sandbox, runs the changed code with an AI agent, and posts a bug report as a comment on the pull request.
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