How to Cut OpenClaw Token Costs by 80%: Memory, Cache, and Model Tips

Power users report dropping OpenClaw spend from ~$300/mo to under $80 without killing automation quality. The lever is not "use AI less" — it is stop paying to re-read the same context.

1. Fix memory bloat

Agents re-send huge histories by default if you let threads grow forever.

• Summarize completed tasks into short system notes
• Archive channels that are no longer active
• Split "research" and "execution" into separate tasks

2. Route models by task type

One Claw's credit pools make this easier to reason about than juggling five API dashboards.

3. Cache stable context

Put static instructions in skills (SKILL.md), not repeated chat preambles.

• Brand voice → skill
• Support macros → skill
• API shapes → skill

The agent loads skills on demand instead of re-ingesting 2k tokens every message.

4. Shrink tool payloads (MCP vs CLI lesson)

Tool definitions can dominate context. Prefer:

• Narrow tools with clear schemas
• CLI wrappers for bulky operations
• Post-process results before they re-enter chat

5. Use hosted credits when predictability matters

Raw API billing rewards spikes. Managed OpenClaw plans bundle monthly credits so finance can forecast.

Quick audit checklist

Run this monthly:

1. Top 10 longest threads — can they be summarized?
2. Skills duplicated in chat — migrate to library
3. Models used per task type — any overkill?
4. Failed tool loops — burning tokens on retries?
5. Channels with no owner — mute or archive

Ship cheaper automations this week

Start on One Claw pricing, apply the audit on a real workspace, and read best models for OpenClaw for routing ideas.