OpenClaw Token Optimizer

Overview

Deliver a practical audit and configuration plan that cuts input tokens and unnecessary calls while keeping answer quality. Provide concrete config edits, workspace file trimming guidance, and a prioritized rollout plan.

Workflow

1) Scope and locate configuration

Identify the OpenClaw config file location (common paths include
```
~/.openclaw/openclaw.json
```
,
```
.openclaw/openclaw.json
```
, or project root config).

List injected workspace files in scope (e.g.,

AGENTS.md

SOUL.md

TOOLS.md

IDENTITY.md

USER.md

HEARTBEAT.md

MEMORY.md

, and

memory/YYYY-MM-DD.md

Confirm provider and model support for prompt caching and memory search to avoid proposing unsupported keys.

2) Baseline token sources

Break input cost into buckets: system prompt, tool schema, workspace files, memory files, and conversation history.
Use a rough sizing method if exact token counts are unavailable (e.g., characters/4 as a quick estimate) and call out that the estimate is approximate.

3) Input reduction (highest ROI)

Trim workspace files first. Target budgets:
- ```
AGENTS.md
```
  : keep only essential agent rules and policies.
- ```
SOUL.md
```
  : reduce to short persona bullets.
- ```
MEMORY.md
```
  : keep durable facts only; archive the rest.
- ```
memory/YYYY-MM-DD.md
```
  : prune or rotate daily logs.
Remove unused workspace injections in config (e.g., if
```
TOOLS.md
```
or
```
IDENTITY.md
```
is unused).
Prefer memory search over full-file injection for large memories. If using qmd, index only needed paths.

4) Cache and context control

Enable prompt caching for the primary model when supported. Set
```
cacheRetention
```
to a long window and keep a consistent system prompt to maximize cache hits.
Configure heartbeat to keep the cache warm (e.g., ~55 minutes), using a low-cost model and a minimal heartbeat prompt.
Enable context pruning with a TTL that matches the cache window to prevent unbounded history growth.
Add compaction with memory flush so long sessions preserve durable decisions while clearing history.

5) Call reduction

Audit cron and scheduled tasks. Consolidate overlapping checks, reduce frequency, and move non-creative tasks to cheaper models.
Configure delivery to be on-demand or only on change to avoid no-op calls.

6) Model strategy

Default to a cost-effective model for routine work and provide aliases for manual upgrades to premium models.
Use subagents for parallel, isolated tasks with cheaper models to avoid bloating the main context.

7) Deliverables

Provide:

A short audit summary and estimated savings.
A concrete config patch or JSON snippet for
```
openclaw.json
```
.
A list of files to trim, with before/after size targets.
A phased rollout plan (quick wins first, then advanced options).

References

Use
```
references/openclaw-token-optimization.md
```
for configuration snippets, checklists, and qmd guidance.

OpenClaw Token Optimizer

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OpenClaw Token Optimizer

Overview

Workflow

1) Scope and locate configuration

2) Baseline token sources

3) Input reduction (highest ROI)

4) Cache and context control

5) Call reduction

6) Model strategy

7) Deliverables

References

Quick Start

Manual Installation

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Tags

Multi-Agent Patterns

Memory Systems Design

Context Degradation Detection

Context Optimization

Tool Design for Agents

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