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OpenClaw can investigate a failed test or error report, modify code, run your test suite, and open a GitHub pull request—but it does not provide one official “automatic bug fixing” switch. A reliable setup combines three parts: a trigger that supplies a reproducible bug, a GitHub connection and terminal capability for the agent, and OpenClaw health checks plus automations for repeatable runs.
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Use the workflow below to keep changes review-gated. OpenClaw should create a branch and a pull request; a developer should inspect the diagnosis, diff, test output, and security impact before merging. Command names, skill packages, and Doctor behavior can change between OpenClaw releases, so verify them against the current OpenClaw documentation installed with your version.
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What “automatic bug fixing” means in OpenClaw
In this context, automatic bug fixing is an unattended pipeline, not unrestricted permission to edit production. OpenClaw receives a concrete failure, reasons about a likely cause, edits a checkout, runs the project’s tests, and proposes the result as a pull request. Your normal code-review and deployment controls remain in place.
The practical design has four stages:
- Detection: an error log, failed test result, or chat report containing a stack trace enters the workflow.
- Diagnosis: the agent classifies the failure, locates relevant code, and records a structured explanation and proposed remedy.
- Verification: a terminal-capable skill runs the repository’s tests and other checks against the branch.
- Delivery: a GitHub-capable skill commits the change and opens a pull request for a person to review.
There is no published OpenClaw success rate or defect-reduction statistic to rely on. Treat the system as an assistant that can prepare changes quickly, not as evidence that defects will be fixed correctly without oversight.
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Before you automate: prerequisites and boundaries
Prepare a safe repository
- Use a sandbox or a non-production repository for the first runs.
- Make sure the default branch is protected and requires pull-request review.
- Keep a working test command documented in the repository, such as
npm test,python -m pytest, orcargo test. - Provide a reproducible issue: the failing test name, stack trace, expected result, observed result, and the commit or build where it appeared.
- Exclude secrets, production credentials, customer data, and unrelated repositories from the agent’s workspace.
Decide what the agent may do
Write the boundary down before connecting any tools. A conservative first policy allows OpenClaw to read source and issues, create a short-lived branch, run tests, commit to that branch, and open a pull request. It does not allow direct pushes to the default branch, merging, releasing, changing repository settings, or rotating credentials.
Expect version drift
OpenClaw’s command names, skill names, release notes, and Doctor behavior are volatile. The procedure below uses commands documented in the available material, but an option or plugin installation command may differ in your release. If a command is not recognized, consult the current OpenClaw documentation rather than copying an install command from an old tutorial.
Step 1: run OpenClaw Doctor before connecting automation
Doctor is OpenClaw’s diagnostic and repair entry point. It checks configuration and runtime health, and it can migrate or repair state. Run it before adding a GitHub skill or scheduling jobs so that a pre-existing problem is not mistaken for an automation failure.
Choose a read-only check first
Use a read-only mode while you are inspecting a new installation:
openclaw doctor --jsonproduces a machine-readable report.openclaw doctor --lintis suitable for a CI-style configuration gate.openclaw doctorruns the normal diagnostic flow.
Use openclaw doctor --fix only when an operator explicitly wants Doctor to edit configuration or state. A repair run can write a backup, remove unknown configuration keys, and attempt common fixes; review the resulting changes before proceeding.
Check the findings that affect bug fixing
Pay particular attention to configuration syntax, model authentication, Gateway connectivity, and Node.js compatibility. Doctor can also warn when a skill allowed for the default agent is unavailable because a binary, environment variable, configuration value, or operating-system requirement is missing.
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Record the clean report, then run Doctor again after every OpenClaw update and after every plugin or skill installation. If Doctor reports a failed job marked state.autoDisabled, repair the cause before assuming the scheduler will resume it.
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Step 2: choose and normalize the bug trigger
An agent cannot fix an underspecified complaint such as “the game is broken.” Select one trigger type and convert it into a consistent input record.
| Trigger | Useful input | Typical advantage | Common weakness |
|---|---|---|---|
| Failed test | Test command, test name, output, commit SHA | Highly reproducible and easy to verify | Only covers behavior represented by tests |
| Error log | Timestamp, stack trace, build or version, reproduction steps | Captures failures that tests missed | Logs may omit the state needed to reproduce the bug |
| Chat report | Reporter, expected and actual behavior, stack trace or screenshot, environment | Convenient intake for a team | Reports often need clarification before coding |
Create a structured issue payload
Have the trigger produce a short, deterministic payload. Include the repository and branch, the failing command, the complete error output, the expected behavior, the observed behavior, reproduction steps, and any constraints such as “do not change save-file format.” Strip access tokens and personal data before the payload reaches OpenClaw.
Ask the agent to return a diagnosis with these fields: suspected root cause, files and symbols involved, proposed change, tests to run, unresolved assumptions, and a confidence statement. Requiring this structure makes a vague or speculative response visible before code is changed.
Step 3: connect GitHub with least privilege
The GitHub skill is the bridge between OpenClaw and the repository. The documented workflow expects it to read code, issues, and comments; create a branch; commit changes; and open a pull request. A dedicated official GitHub-skill page was not identified, so confirm the skill name and installation procedure in the documentation for your release.
Create a fine-grained token
Use a fine-grained personal access token scoped to only the repository or repositories the agent needs. A commonly recommended minimum is:
| Capability | Suggested level | Why it is needed |
|---|---|---|
| Repository contents | Read and write | Read source and write commits on an agent branch |
| Pull requests | Read and write | Create and update the proposed change |
| Issues | Read-only | Read bug reports and discussion without editing triage data |
| Repository administration | None by default | Branch protection and settings should remain operator-controlled |
Store the token in the runtime’s secret mechanism or an environment variable that the skill expects; never paste it into a prompt or commit it. Rotate it about every 90 days, or immediately when an operator, machine, or log that could contain it is exposed. Polling is usually simpler than webhooks and works behind firewalls, but it introduces a small delay and consumes GitHub API rate-limit quota. Webhooks can reduce latency, but they require an externally reachable endpoint and additional signature validation.
Limit the branch and pull-request behavior
Configure a predictable branch prefix such as openclaw/bugfix/, require the agent to use a fresh branch for each issue, and prevent force-pushes unless your review policy explicitly allows them. The pull-request body should include the original failure, diagnosis, files changed, commands run, test results, and any remaining uncertainty.
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Step 4: give the agent a terminal skill without giving it production access
The terminal capability is what lets OpenClaw run the project’s checks instead of merely suggesting a patch. Grant it access to a disposable checkout with the project’s development dependencies installed.
Define an allowlist of commands
Start with read-only inspection and the project’s normal test command. Examples include:
npm testfor a JavaScript or TypeScript project that defines that script.python -m pytestfor a Python project using pytest.cargo testfor a Rust project.
Add formatters, linters, or targeted test commands only after you know their side effects. Do not allow shell access to deployment tools, cloud consoles, production databases, package publishing, or unrestricted network destinations. If integration tests need services, provide local containers or a test environment with throwaway credentials.
Make the test result unambiguous
Tell the agent which exit code means success, where test artifacts are written, and how long a normal run takes. A timeout should be reported as “not verified,” not as a passing result. Require the agent to include the exact command and its exit status in the pull request so a reviewer can reproduce it.
Step 5: assemble the OpenClaw workflow
Once Doctor is clean, the trigger, GitHub skill, and terminal skill can be connected into one flow. Keep each action explicit rather than asking for an open-ended “fix everything.”
- Receive: accept one issue, failed test, or log event and assign it an identifier.
- Validate: reject inputs without a repository, commit, reproduction, or complete error output. Redact secrets before continuing.
- Inspect: check the current branch, read contribution instructions, locate the failing test, and search related issues and recent commits.
- Plan: write the structured diagnosis and a minimal change plan before editing.
- Branch: create a new branch from the current protected default branch or the commit named by the report.
- Edit: make the smallest change that addresses the identified cause. Avoid drive-by refactors.
- Verify: run the allowlisted tests, formatter, and linter. Capture output and exit codes.
- Deliver: commit with a descriptive message and open a pull request containing the diagnosis, diff summary, verification output, and known limitations.
- Stop: if tests fail, the cause is ambiguous, required dependencies are unavailable, or the requested change exceeds scope, leave the branch and report the blocker instead of guessing.
Test this flow with several known failures in a sandbox repository before connecting live issue intake. Include at least one bug with a straightforward fix, one that should be declined because it lacks reproduction details, and one where the tests continue to fail after a plausible patch.
Step 6: schedule runs with OpenClaw automations
OpenClaw calls its scheduled jobs “automations.” Jobs and run history are stored in a shared SQLite state database. You can inspect configured jobs with:
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Use the automation’s run-history views or queries to filter by status and delivery state. The exact query syntax can vary by release, so use the command help and current documentation rather than embedding an unverified example in production scripts.
Choose polling frequency deliberately
A short polling interval reduces the time between an issue and a proposed fix but consumes more API quota and creates more concurrent work. A longer interval is gentler on rate limits and easier to supervise. Start with one repository and a conservative interval, then adjust after observing queue size, test duration, and GitHub API usage.
Check the switches that can disable scheduling
Two configuration flags can silently stop scheduling. If automations do not run, inspect both disable settings, clear them according to your version’s configuration syntax, and restart the Gateway. Failed jobs may also be marked state.autoDisabled; Doctor reports this state.
Headless runs—such as cron or messaging-triggered executions without a terminal—cannot answer interactive prompts. Put approvals, scope checks, and stop conditions in the workflow itself. Do not depend on a person being available to answer “continue?” at runtime.
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Doctor checks cron-related state, including jobs left marked in-flight, and can migrate historical cron stores into SQLite. Run it after upgrades and inspect the automation list and recent run history before enabling new jobs. Release notes for version 2026.9.7 state that scripts creating system-event jobs should remove the --timeout-seconds flag from those commands; verify that note against the version you are running before changing scripts.
Step 7: review every generated pull request
Human review is the safety boundary for this workflow. A reviewer should compare the patch with the original failure, inspect every changed file, and run the relevant checks independently when practical.
Use a repeatable review checklist
- Does the pull request reproduce the reported bug, or is the diagnosis based on an assumption?
- Is the change limited to the stated scope, with no unrelated formatting or refactoring?
- Do tests cover the regression and the neighboring behavior that could be affected?
- Are error handling, input validation, permissions, and data migration paths safe?
- Did the agent alter dependencies, lockfiles, build scripts, or generated files unexpectedly?
- Does the test output correspond to the submitted commit rather than an earlier workspace state?
- Are secrets, personal data, and production endpoints absent from the diff and logs?
Only merge after the reviewer is satisfied with both the code and the evidence. Keep deployment, release, and repository-administration actions outside the agent’s token scope.
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Security and operational safeguards
Protect credentials and data
Use separate credentials for each repository or environment, prefer short-lived credentials where supported, and mask secrets in logs. A token that can write code should not also administer repositories or access production services. Make the workspace disposable so a malicious fixture or test cannot persist beyond the run.
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Issue text, log lines, and stack traces are untrusted input. An attacker could place instructions in a comment that attempt to override your workflow. Treat the repository’s contribution rules and your system policy as higher priority than issue prose. Never allow an issue to expand the agent’s permissions, reveal secrets, or change its own review requirements.
Control cost and concurrency
Limit the number of simultaneous runs per repository, cancel duplicate jobs for the same failure, and impose a maximum execution time. Store artifacts such as logs and diffs with the issue identifier so a reviewer can audit what happened. Alert on repeated failures, auto-disabled jobs, authentication errors, and sudden increases in API usage.
Troubleshooting common failures
| Symptom | Likely cause | Action |
|---|---|---|
| Doctor reports missing skill requirements | Required binary, environment variable, configuration, or OS capability is absent | Install or configure the dependency, then rerun Doctor before starting an automation |
| GitHub actions fail with permission errors | Token lacks contents or pull-request write access, or the repository is outside its scope | Inspect the fine-grained token’s repository and permission selections; do not jump to administrator rights |
| Issues are detected late | Polling interval or API backoff is too long | Check automation timing and rate-limit usage; shorten the interval only if the quota allows it |
| No scheduled jobs run | Disable flags remain set, Gateway was not restarted, or a job was auto-disabled | Inspect configuration, restart the Gateway, run Doctor, and check automation history |
| The agent waits forever for approval | The run is headless and contains an interactive prompt | Move the approval decision into explicit pre-run policy and stop conditions |
| Tests pass locally but not in review | Different commit, dependencies, environment, or generated files were used | Record the commit SHA, dependency state, exact command, and exit code in the pull request |
| Doctor changes configuration unexpectedly | --fix was used when only diagnosis was intended |
Restore the backup if needed, review the diff, and use --lint, --json, or bare Doctor for read-only checks |
A staged rollout that is easier to trust
Stage 1: diagnosis only
Feed known failures to OpenClaw and require a written diagnosis without repository write access. Compare its suspected root cause with the known answer and measure how often it correctly identifies the failing component.
Stage 2: branch and test
Allow edits only in a sandbox repository. Require the agent to run the test suite and report failures, but do not connect live issue polling yet.
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Grant the narrow GitHub token and allow one repository. Review every generated pull request and record false fixes, missed regressions, and unnecessary changes.
Stage 4: controlled intake
Enable polling or a webhook for a selected label or queue. Keep concurrency limits, protected branches, and the same review gate.
Stage 5: maintenance
After every OpenClaw update or plugin installation, run Doctor, verify skill availability, inspect automation state, and confirm that token scopes and branch protections are unchanged. Back up configuration before upgrades so a failed migration is recoverable.
When automatic fixing is the wrong tool
Do not delegate a bug when the report involves a production incident still in progress, irreversible data changes, authentication or payment logic without a reproducible test, legal or privacy-sensitive records, or a migration that cannot be rolled back. In these cases OpenClaw can summarize logs or propose investigation steps, while a responsible engineer controls the actual change.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe safest default is therefore straightforward: let OpenClaw automate diagnosis, testing, and pull-request preparation; keep merge, deployment, permission changes, and ambiguous decisions with a human. That division captures the speed benefit without turning an unverified patch into an unreviewed release.
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