Context Budgeting

Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.

Install
$clawhub install context-budgeting

Context Budgeting Skill

This skill provides a systematic framework for managing the finite context window (RAM) of an OpenClaw agent.

Core Concepts

1. Information Partitioning

  • Objective/Goal (10%): Core task instructions and active constraints.

  • Short-term History (40%): Recent 5-10 turns of raw dialogue.

  • Decision Logs (20%): Summarized outcomes of past steps ("Tried X, failed because Y").

  • Background/Knowledge (20%): High-relevance snippets from MEMORY.md.

2. Pre-compression Checkpointing (Mandatory)

Before any compaction (manual or automatic), the agent MUST:

  1. Generate Checkpoint: Update memory/hot/HOT_MEMORY.md with:

    • Status: Current task progress.
    • Key Decision: Significant choices made.
    • Next Step: Immediate action required.

  2. Run Automation: Execute scripts/gc_and_checkpoint.sh to trigger the physical cleanup.

Automation Tool: gc_and_checkpoint.sh

Located at: skills/context-budgeting/scripts/gc_and_checkpoint.sh

Usage:

  • Run this script after updating HOT_MEMORY.md to finalize the compaction process without restarting the session.

Integration with Heartbeat

Heartbeat (every 30m) acts as the Garbage Collector (GC):

  1. Check /status. If Context > 80%, trigger the Checkpointing procedure.

  2. Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.

Details

Version
v1.0.0
Downloads
1,318
Stars
2
ClawHub
View on ClawHub

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