OpenClaw Secrets Management - Auditing and Securing Your AI Agent Credentials

If you're running AI agents in production, you have credentials scattered somewhere. API keys for language models, database connection strings, authentication tokens for external services. The question isn't whether you have secrets to manage - it's whether you're managing them well or just hoping nobody notices the plaintext API key sitting in a config file.

OpenClaw takes this seriously with its openclaw secrets CLI. It's not just a key-value store. It's a proper secrets management workflow with auditing, migration planning, and atomic runtime updates. For anyone operating AI agents at scale, this is the kind of tooling that separates a proof of concept from a production deployment.

The Problem This Solves

Here's what we see regularly when we start working with organisations on their AI agent deployments. Someone got an agent prototype working. They pasted an API key directly into a config file. It worked, so they moved on. Six months later, that config file has been committed to a repo, copied across environments, and the original key has been shared with three different team members via Slack messages.

This isn't carelessness. It's how things naturally evolve when you're moving fast and the tooling doesn't provide a better path. OpenClaw's secrets system provides that better path.

The core concept is SecretRefs - instead of storing actual credential values in your configuration, you store references that point to a secrets provider (like a vault, environment variable, or command-line tool). The runtime resolves these references at startup, and the actual values never touch disk in plaintext.

The Operator Loop

OpenClaw recommends a specific workflow for managing secrets, and it's worth following. The loop goes:

openclaw secrets audit --check
openclaw secrets configure
openclaw secrets apply --from /tmp/openclaw-secrets-plan.json --dry-run
openclaw secrets apply --from /tmp/openclaw-secrets-plan.json
openclaw secrets audit --check
openclaw secrets reload

Each step has a specific purpose, and skipping steps is how you end up with half-migrated credentials and a runtime that can't resolve its secrets.

Let me walk through each one.

Auditing - Finding What's Wrong

Start with openclaw secrets audit. This scans your OpenClaw configuration for problems:

• Plaintext secrets stored directly in config files
• Unresolved refs that point to providers that can't deliver the value
• Precedence drift where credentials in auth-profiles.json shadow SecretRefs in openclaw.json
• Generated model residues where provider API keys ended up in generated agent model files
• Legacy residues from older OpenClaw versions

The --check flag makes it CI-friendly - it returns exit code 1 if there are findings, exit code 2 for unresolved refs. You can drop openclaw secrets audit --check into your deployment pipeline and fail the build if someone has accidentally committed a plaintext key.

Run it without --check first to see the full report. The output includes finding codes like PLAINTEXT_FOUND, REF_UNRESOLVED, REF_SHADOWED, and LEGACY_RESIDUE. Each one tells you exactly what's wrong and where.

One thing I appreciate about the audit: it also scans generated files. When OpenClaw generates agent model configurations, it's possible for provider API keys to leak into those generated files. The audit catches provider apiKey values and sensitive headers (anything matching patterns like authorization, x-api-key, token, secret, password, or credential). This is heuristic-based, so it's not perfect, but it catches the common cases.

The --allow-exec Flag

If you're using exec-type SecretRef providers (where OpenClaw runs a command to retrieve a secret), the audit skips those by default. Makes sense - you don't want an audit command executing arbitrary binaries without explicit permission. Pass --allow-exec if you want the audit to verify exec refs resolve correctly.

Configure - Building a Plan

openclaw secrets configure is an interactive wizard that walks you through setting up providers and mapping credentials. It requires a TTY, so you can't script it - but that's the point. This is a decision-making step where you need to think about which secrets provider to use for each credential.

The flow goes:

1. Provider setup - add, edit, or remove entries in secrets.providers. This is where you define your secrets backends (vault services, environment variables, exec commands).
2. Credential mapping - select which fields in your configuration should use SecretRefs, and assign each one to a provider.
3. Preflight and optional apply - validate everything resolves before writing changes.

You can scope it with --providers-only (just set up providers, skip credential mapping) or --skip-provider-setup (map credentials to providers you've already configured). The --agent flag scopes the work to a single agent, which is useful when you're migrating one agent at a time.

The configure step outputs a plan file (use --plan-out /tmp/openclaw-secrets-plan.json to save it explicitly). This plan is what you apply in the next step.

Apply - Making Changes

openclaw secrets apply executes a saved plan. Always run it with --dry-run first. The dry run validates everything without writing files.

openclaw secrets apply --from /tmp/openclaw-secrets-plan.json --dry-run
openclaw secrets apply --from /tmp/openclaw-secrets-plan.json

What apply can update:

• openclaw.json - SecretRef targets and provider configuration
• auth-profiles.json - scrubbing plaintext values that have been migrated
• Legacy auth.json residues from older versions
• ~/.openclaw/.env entries for migrated keys

There's an important design choice here: apply does not create rollback backups. This sounds alarming until you think about it. A rollback backup containing old plaintext values would itself be a security risk. Instead, OpenClaw uses strict preflight validation and an atomic-ish apply process. If something fails during apply, it attempts an in-memory restore. If the plan validates in dry-run, the actual apply should succeed.

The --allow-exec flag matters here too. If your plan includes exec-type providers, write mode (non-dry-run) will reject the plan unless you explicitly pass --allow-exec. This is a sensible guardrail - you want to consciously opt into running external commands during a secrets migration.

Reload - Swapping the Runtime Snapshot

After applying changes, run openclaw secrets reload to tell the running gateway to re-resolve all SecretRefs and swap its runtime snapshot.

This uses the gateway's RPC method (secrets.reload). If resolution fails for any ref, the gateway keeps the previous working snapshot and returns an error. No partial activation, no half-working state. This is the kind of design that matters in production.

openclaw secrets reload --json

The JSON response includes a warningCount, so you can check whether there were non-fatal issues during reload.

Exec Provider Safety

If you're using exec providers (where OpenClaw runs a command to retrieve a secret), there are platform-specific safety considerations.

On macOS, Homebrew installs often put binaries under /opt/homebrew/bin as symlinks. OpenClaw's exec provider is cautious about symlinked commands by default. If your provider binary is a Homebrew symlink, set allowSymlinkCommand: true on that provider, and pair it with trustedDirs pointing to /opt/homebrew.

On Windows, if ACL verification fails for a provider path, OpenClaw fails closed (won't execute). For trusted paths, you can set allowInsecurePath: true to bypass this check. Only do this for paths you genuinely trust.

Where This Fits in a Production Setup

For organisations running AI agents as managed services, secrets management is non-negotiable. You need:

1. No plaintext credentials in config files - audit ensures this
2. Credential rotation without downtime - update the secret in your provider, then reload
3. Visibility into what's stored where - audit gives you a complete picture
4. Safe migration from messy to clean - configure + apply handles this

The audit-configure-apply-reload loop is something we recommend running on a schedule, not just during initial setup. Secrets drift. Someone adds a new integration and pastes in an API key directly because they're in a hurry. A monthly audit catches this before it becomes a problem.

Integrating With CI/CD

The --check exit codes make secrets audit a natural fit for deployment pipelines. We recommend:

• Run openclaw secrets audit --check as a gate before any deployment
• Fail the pipeline on exit code 1 (plaintext findings) or 2 (unresolved refs)
• Include --json output in your pipeline logs for debugging

This way, plaintext credentials never make it to production. The pipeline catches them before deployment happens.

Getting Started

If you're running OpenClaw agents and haven't set up proper secrets management yet, start with the audit. Just run openclaw secrets audit and see what it finds. You might be surprised.

From there, work through the operator loop. Take your time with the configure step - think about which secrets provider makes sense for your environment. For most of our clients, we recommend a cloud vault (Azure Key Vault if you're already in the Microsoft ecosystem) combined with environment variables for local development.

We help Australian organisations deploy and secure AI agent platforms, including OpenClaw. If you'd like help setting up secrets management or auditing your existing agent infrastructure, get in touch.

For the full CLI reference, see the OpenClaw secrets documentation.