A Step-by-Step Process to Debug OpenClaw When `openclaw docker --fix` Is Not Enough

If you are in this situation, I understand that feeling very clearly: you run openclaw docker --fix many times, copy configs from elsewhere, and in the end OpenClaw still fails or, worse, won’t even start.

This article is the process I follow to debug in a way that is driven by logs, not guesswork.

When should you use this process?

Use it as soon as you see any of these symptoms:

• openclaw docker --fix finishes but the service still doesn’t come up.
• The container starts and then immediately goes down.
• The agent runs but responds inconsistently; routing models sometimes work, sometimes don’t.
• After you add more config, OpenClaw fails even harder than before.

Principles before making changes

Before you start, keep these 3 principles in mind to avoid “the more you fix, the more you break”:

1. Don’t make a lot of changes at once.
2. Every change must be justified by the logs.
3. After each change you must immediately verify it with a basic test.

Step 1: Open Claude Code in the right place

Start Claude Code right in the OpenClaw installation directory (where the config files and runtime logs are located).

Goal: so that Claude reads the correct local context on your machine, instead of guessing from a generic description.

Step 2: Collect enough error data before fixing

Don’t start fixing right away. Ask Claude to do these 3 things first:

• List the configuration files that are actually being used.
• Read the most recent startup log and group errors by how often they repeat.
• Point out the most likely root cause, not just surface-level errors.

Sample prompt:

“Read all OpenClaw logs and config files in the current directory. Group recurring errors, identify the root cause causing startup failure, and propose an order of checks from most to least likely.”

Step 3: Cross-check with the official documentation

After you have a hypothesis about the error, ask Claude to compare it against the official guide for the version you’re running.

The goals of this step are to:
- detect config keys with the wrong names,
- detect fields that changed between versions,
- detect configurations that are syntactically valid but semantically wrong at runtime.

Sample prompt:

“Compare the current configuration with the official guide for the version in use. Point out the differences and the impact level of each difference.”

Step 4: Fix using the minimal-change approach

This is extremely important. Don’t ask it to “auto-optimize everything”.

Ask Claude to:
- prioritize changes to the parts that cause startup failures first,
- attach a reason to every change,
- not touch parts that are currently working fine unless necessary.

Sample prompt:

“Apply only the minimal changes needed to restore stable startup. For each change, clearly specify: what is changed, why it’s changed, and the risk if it is not changed.”

Step 5: Configure a fallback model to avoid work stoppage

After the service is stable, configure a fallback model so that when the primary model fails you can still work continuously in the chat channel.

Your fallback checklist should include:

• A primary model plus at least 1–2 backup models.
• A clear fallback priority order.
• A description of fallback conditions (timeout, quota, provider error, etc.).
• Test calls to each model after configuration.

Step 6: Optimize the agent.md / AGENTS.md description

Many failure cases are not in Docker but in the way the agents’ roles are described too vaguely.

You should ask Claude to standardize the operating rules as follows:

• The main agent acts as orchestrator and is always on to receive user requests.
• Long/slow tasks are split out to sub-agents.
• Sub-agents must report checkpoints at each milestone.
• The main agent aggregates results and responds consistently.

This helps reduce main session blocking and limits context loss when handling long-running tasks.

Step 7: Post-fix verification (mandatory)

After each round of fixes, run a minimal test checklist:

• The service starts up stably after a restart.
• There are no recurring startup errors in the new logs.
• The primary model can be called.
• The fallback model can be called.
• The main-agent → sub-agent flow runs with the correct roles.

If something fails, go back to Step 2 and continue based on the new logs, do not rollback based on gut feeling.

Mistakes I made and lessons learned

My biggest mistake in the past was giving overly generic instructions so that OpenClaw would “auto-configure” itself. As a result, sometimes it changed too much and broke the configuration to the point where it couldn’t start.

After switching to the above process (log-first, minimal-change, continuous verification), the system has been much more stable.

All-in-one prompt you can copy and use

“Goal: fix OpenClaw failing to start even after running openclaw docker --fix.

Please follow this order:
1) Read current logs and configs, identify the root cause.
2) Cross-check with the official documentation for the correct version.
3) Propose minimal changes to restore startup.
4) Apply the changes, explaining each one.
5) Set up a fallback model (primary + backup).
6) Standardize agent.md/AGENTS.md so the main agent orchestrates and sub-agents handle long tasks.
7) Run the post-fix verification checklist and report the result for each item.”

Conclusion

When openclaw docker --fix is no longer helpful, the most effective approach is to switch to systematic debugging:

• read the logs,
• apply minimal fixes,
• test using a checklist,
• and only then optimize further with fallback models and agent descriptions.

Doing this will help you escape the fix-break loop and bring OpenClaw back to a stable state much faster.