Issue Station
Sign In
Tools hereToolbox

openclaw - 💡(How to fix) Fix Feature: command-aware output compaction / token reduction layer [1 comments, 1 participants]

Official PRs (…)
ON THIS PAGE

Recommended Tools

×6

Utilities matched from this issue’s tags and category — try them while you read without losing context.

GitHub issue graph ai analysis

Paste a GitHub issue URL. We fetch that issue, discover linked issues from bodies/comments/timeline, collect linked pull requests, and produce a structured English report.

The report is written in English Markdown for sharing and archival.

Helpful · Quick feedback

Loading…
GitHub stats
openclaw/openclaw#63292•Fetched 2026-04-09 07:55:44
View on GitHub
Comments
1
Participants
1
Timeline
2
Reactions
0
Author
Sotired001Sotired001
Participants
Sotired001Sotired001
Timeline (top)
closed ×1commented ×1

Root Cause

This would improve coding-agent loops, long-running diagnostics, and tool-heavy workflows by lowering token burn and reducing context pollution.

RAW_BUFFERClick to expand / collapse

Pattern to steal

Inspired by token-reduction / command-compaction ideas (for example, RTK-style command compression).

Problem

A lot of OpenClaw cost and context waste comes from noisy command output, repeated logs, and verbose tool payloads that carry low semantic value.

Proposed feature

Add a command-aware output compaction layer that can:

  • summarize repetitive command output
  • dedupe repeated warnings/errors
  • collapse low-value lines while preserving failure signal
  • keep raw logs available on demand
  • reduce token use in coding/debugging workflows without hiding the important bits

Why this matters

This would improve coding-agent loops, long-running diagnostics, and tool-heavy workflows by lowering token burn and reducing context pollution.

Acceptance ideas

  • define safe compaction rules by tool/command type
  • preserve raw-output drilldown paths
  • ensure important failure lines are never lost
  • start with high-noise command classes (tests, lint, builds, logs)

extent analysis

TL;DR

Implement a command-aware output compaction layer to reduce noisy command output and improve coding workflows.

Guidance

  • Identify high-noise command classes (e.g., tests, lint, builds, logs) to prioritize for output compaction
  • Define safe compaction rules by tool/command type to ensure important failure lines are preserved
  • Develop a mechanism to keep raw logs available on demand for debugging purposes
  • Start by summarizing repetitive command output and deduplicating repeated warnings/errors

Example

# Pseudocode example of output compaction
def compact_output(output_lines):
    # Define compaction rules
    rules = {
        'test': lambda line: line.startswith('TEST'),
        'lint': lambda line: line.startswith('LINT')
    }
    
    # Apply compaction rules
    compacted_lines = []
    for line in output_lines:
        for rule in rules.values():
            if rule(line):
                # Summarize or deduplicate line
                compacted_lines.append(line)
                break
        else:
            # Preserve important failure lines
            compacted_lines.append(line)
    
    return compacted_lines

Notes

This solution assumes that the output compaction layer can be implemented as a separate module or component, and that the compaction rules can be defined and applied dynamically.

Recommendation

Apply a workaround by implementing a basic output compaction layer, focusing on high-noise command classes, to reduce token burn and context pollution.

Vote matrix · Quick signals

Works
Did the solution work? Tap to confirm.
Easy Fix
Was it a quick fix?
Time Saver
Did it save you time?
Blocking
Was it severely blocking?
Common Issue
Are others likely hitting this too?
Flaky / Intermittent
Is it intermittent?
Verified / Reproducible
Can you reproduce it reliably?
Loading…
#api#ssr#memory leak#API versioning#request timeout

Still need to ship something?

×6

Another batch ranked right after the header list — different links, same matching logic.

Back to top recommendations

TRENDING