Osloq
Osloq is an AI-powered issue investigation tool that integrates with GitHub to automate bug reproduction. Developers connect their repository, select an open issue, and Osloq attempts to reproduce the problem in an isolated sandbox. It traces through code, commits, and runtime context, capturing logs, screenshots, and the exact code path to provide evidence-backed findings. The tool currently supports JavaScript, TypeScript, Python, and Go, with plans to expand to more languages. After reproduction, Osloq generates a clear report summarizing the verdict, linking all evidence, and even suggesting a fix or providing a draft issue comment. This allows developers to quickly verify bugs and move toward resolution without manual debugging. Osloq runs in ephemeral sandboxes that are destroyed after each investigation, ensuring that source code is never stored or used for training. Users can optionally provide environment secrets for reproduction, but the tool can bypass dependencies when necessary. Findings are stored in the user's account for reference. Osloq offers a free tier for personal projects and open source, with paid plans for professionals and teams that increase monthly investigation quotas and add features like concurrent investigations and role-based access control.
- Category
- AI Developer Tools
- Pricing
- Unknown
Problem solved
Manually reproducing and verifying reported bugs in code is time-consuming and often lacks concrete evidence. Osloq automates this process by receiving a GitHub issue, analyzing the codebase, and running the relevant flow in a sandbox to reproduce the bug, then returning a verdict backed by logs, screenshots, and the exact code path. This eliminates guesswork and accelerates root-cause analysis.
Use cases
- Automatically reproducing bugs reported in GitHub issues
- Generating evidence-backed reports for faster debugging
- Verifying fixes by re-running issue reproductions
- Onboarding developers by investigating historical issues
- Providing draft fix suggestions based on reproduced failure
- Collaborating on complex issues with shared evidence trails
Audience
- Developers
- Enterprise
- Startups
- Hobbyists
Pros
- Saves time by automating issue reproduction
- Provides concrete evidence (logs, screenshots, code paths)
- Supports multiple languages and frameworks
- Isolated sandbox ensures code safety and privacy
- Free tier available for open-source projects
Cons
- Limited monthly investigations on free plan
- Currently limited to JavaScript, TypeScript, Python, and Go
- Requires GitHub integration; no support for other issue trackers
- AI reproduction may not cover all edge cases
- No self-hosted option for on-premise security needs
Official website: https://osloq.com/
Canonical LambdaBase page: https://www.lambdabase.com/tools/osloq