OpenCode can help with code debugging by acting as a repo-aware assistant that can read and search your codebase, run commands, apply targeted edits, and roll back changes as you iterate. The simplest way to think about it is: you bring it a reproducible failure (a stack trace, a failing test, a broken build command), and it helps you close the loop faster by moving between “inspect code → form hypothesis → test → patch → retest” without you manually stitching context together. OpenCode supports dedicated debugging-oriented behavior via agents/modes (for example, a debug-focused setup that enables read + bash while staying conservative about edits), and it supports workflow commands like /undo that can revert the last interaction and its associated file changes (as long as your project is a Git repo). That makes debugging less risky because you can experiment and then cleanly reset.
A concrete debugging loop looks like this. First, you run the failing command yourself (for example pnpm test, go test ./..., or pytest -q) and paste the exact output into the OpenCode session, including error messages and a note about what you expected to happen. Then you ask OpenCode to localize the bug: “find where this function is defined and who calls it,” “search for this error string,” or “trace the code path that triggers this exception.” OpenCode can use file-reading and search tools to pinpoint relevant code, then use the bash tool (if permitted) to rerun tests, print environment variables, or execute small repro scripts. When it proposes a fix, you can have it apply a patch and immediately rerun the same command to confirm the result. If the patch makes things worse, /undo will remove the last user message, subsequent responses, and any file changes tied to that step—effectively giving you a “safe checkpoint” mechanism during a multi-step investigation.
This gets especially useful in debugging problems that span multiple layers: config + code + runtime behavior. For example, if you’re debugging a semantic search pipeline backed by a vector database such as Milvus or Zilliz Cloud, failures often show up as “retrieval quality is worse,” “latency spiked,” or “index build fails in CI,” not as a single obvious exception. OpenCode can help you systematically check each layer: confirm how embeddings are generated and normalized; verify the schema and index parameters you’re using; run quick scripts to compare top-k results before/after a change; and trace which configuration file is actually taking effect in a given environment. Because OpenCode can keep multiple sessions, you can split the work: one session focuses on reproducing and measuring (commands, logs, timings), another session focuses on code changes (refactors, fixes, tests), and a third focuses on documentation or postmortem notes. The net benefit is not magic; it’s reduced context-switching and a more repeatable, tool-assisted workflow for the boring but essential mechanics of debugging.