CLI Reference¶
Complete reference for all openclaw learning commands.
Overview¶
openclaw learning <command> [options]
Commands¶
learning status¶
Display learning layer summary with posteriors and baseline comparison.
openclaw learning status [--host <host>] [--port <port>]
When the gateway is reachable, data is fetched from the gateway API (live data). If the gateway is unreachable, falls back to the local SQLite database.
Options:
| Option | Default | Description |
|---|---|---|
--host |
127.0.0.1 |
Gateway host (or set OPENCLAW_GATEWAY_HOST) |
--port |
18789 |
Gateway port |
Output:
- Phase badge (PASSIVE or ACTIVE)
- Config summary (budget, baseline rate, min pulls)
- Trace count, arm count, total tokens, date range
- Run distribution (baseline vs. selected, with percentages)
- Token savings (percentage reduction vs. baseline)
- Top 5 arms (highest posterior mean)
- Bottom 5 arms (candidates for exclusion)
Example output:
Learning Layer Status [PASSIVE]
Budget: 8,000 | Baseline: 10% | Min pulls: 5
Traces: 247 Arms: 18 Tokens: 1,284,000 Range: 1/15/2025 – 2/5/2025
Run Distribution
Baseline: 24 (9.7%) Selected: 223 (90.3%)
Token Savings: +12.3% (baseline avg: 5200, selected avg: 4560)
Top Arms (highest posterior mean)
Arm Mean Pulls Last Updated
tool:fs:Read 0.923 187 2/5/2025
tool:exec:Bash 0.891 165 2/5/2025
tool:fs:Edit 0.845 142 2/4/2025
tool:fs:Grep 0.812 128 2/4/2025
tool:fs:Write 0.798 119 2/3/2025
Bottom Arms (candidates for exclusion)
Arm Mean Pulls Last Updated
file:workspace:old-notes.md 0.124 31 2/1/2025
memory:project:legacy-api 0.187 22 1/30/2025
skill:debug:verbose 0.234 18 1/29/2025
file:workspace:scratch.ts 0.267 15 1/28/2025
memory:project:draft-spec 0.312 12 1/27/2025
learning reset¶
Reset all arm posteriors back to uninformative priors Beta(1,1).
openclaw learning reset [--host <host>] [--port <port>] [--confirm]
This is a destructive operation -- all learned data is lost. The bandit starts fresh as if no observations had been recorded. Use this when:
- You have significantly changed your tool inventory or skills
- Poisoned data has skewed posteriors (e.g., a bug caused incorrect reward signals)
- You want to re-run the learning phase from scratch after a major config change
Options:
| Option | Default | Description |
|---|---|---|
--host |
127.0.0.1 |
Gateway host (or set OPENCLAW_GATEWAY_HOST) |
--port |
18789 |
Gateway port |
--confirm |
— | Skip the confirmation prompt |
Without --confirm, you will be prompted to confirm before the reset proceeds.
Example:
$ openclaw learning reset
? Reset all arm posteriors to Beta(1,1)? (Y/n) Y
Reset 18 arm(s) for learner "openclaw".
# Non-interactive (CI, scripts)
openclaw learning reset --confirm
learning export¶
Export traces and posteriors to stdout.
openclaw learning export [options]
Options:
| Option | Default | Description |
|---|---|---|
--format |
json |
Export format: json or csv |
--traces |
true |
Include run traces in export |
--posteriors |
true |
Include arm posteriors in export |
Examples:
# Full JSON export
openclaw learning export --format json
# CSV posteriors only
openclaw learning export --format csv --no-traces
# JSON traces only
openclaw learning export --format json --no-posteriors
# Pipe to file
openclaw learning export --format json > learning-data.json
learning dashboard¶
Print the URL of the Learning dashboard.
openclaw learning dashboard [--host <host>] [--port <port>]
The gateway serves the dashboard HTML on-the-fly at /__openclaw__/api/learning/dashboard. No files are written to disk.
Options:
| Option | Default | Description |
|---|---|---|
--host |
127.0.0.1 |
Gateway host (or set OPENCLAW_GATEWAY_HOST) |
--port |
18789 |
Gateway port |
Example:
$ openclaw learning dashboard
Dashboard: http://localhost:18789/__openclaw__/api/learning/dashboard
See the Dashboard Guide for details on dashboard sections, themes, and troubleshooting.
Global Options¶
| Option | Description |
|---|---|
--help |
Show help for command |
Exit Codes¶
| Code | Description |
|---|---|
0 |
Success |
1 |
General error |
2 |
Invalid arguments |
Examples¶
Daily Workflow¶
# Check learning layer status
openclaw learning status
# Open the dashboard for visual analysis
openclaw learning dashboard
# Export data for offline analysis
openclaw learning export --format json > daily-snapshot.json
Monitoring Convergence¶
# Watch posterior means stabilize over time
openclaw learning status
# Check if bottom arms have enough pulls for confident exclusion
# Look for "high" confidence in the posteriors table on the dashboard
openclaw learning dashboard
# When ready, switch to active mode in openclaw.json
# Then monitor token savings
openclaw learning status
Resetting After Config Changes¶
# You've added several new tools and removed old ones.
# Existing posteriors no longer reflect the current inventory.
# Reset all posteriors to Beta(1,1)
openclaw learning reset --confirm
# Verify the reset
openclaw learning status
# Should show 0 traces and all arms at mean 0.500
Remote Gateway¶
# Point at a remote gateway
openclaw learning status --host 10.0.0.5 --port 9999
# Or set the environment variable
export OPENCLAW_GATEWAY_HOST=10.0.0.5
openclaw learning status
openclaw learning dashboard