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, andmemory/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.mdorIDENTITY.mdis 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
cacheRetentionto 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.mdfor configuration snippets, checklists, and qmd guidance.