OpenClaw Cost Optimizer#

Analyzes the user's OpenClaw config and rewrites it to minimize Anthropic API spend. Works on YAML and JSON configs. Reads pricing tables from references/pricing.md before starting.

Step 0 — Load pricing reference#

Read references/pricing.md now. It contains current model rates and the savings formulas used for estimates. Do this before any analysis.

Step 1 — Locate the config#

Check these locations in order, stop at the first match:

1. Path the user explicitly provides
2. ./openclaw.yaml or ./openclaw.json in the current working directory
3. ~/.openclaw/config.yaml
4. ~/.openclaw/openclaw.yaml
5. ~/openclaw.yaml

If none found, ask the user to paste their config directly.

Step 2 — Audit: the five cost levers#

Work through every lever. Flag each issue with severity HIGH / MEDIUM / LOW.

Lever 1 — Prompt caching (HIGH impact, ~60–90% input cost reduction)#

Flag HIGH if any of these are missing for Anthropic models:

• cacheRetention not set, or set to "none"

Fix: Add cacheRetention: "long" for the primary model. Use "short" (5 min) for agents that handle fast-changing context, "long" (1 hr) for everything else.

yaml

agents:
  defaults:
    models:
      anthropic/claude-opus-4-6:
        params:
          cacheRetention: "long"

Note: caching is API-key only. Subscription setup-tokens do not honor it.

---

Lever 2 — Model routing (HIGH impact)#

Flag HIGH if: a single model (especially Opus) is used for ALL agents with no per-agent overrides.

Fix: Route by task complexity:

Agent typeRecommended modelMain reasoning / planninganthropic/claude-opus-4-6Coding, editing, reviewanthropic/claude-sonnet-4-6Linting, search, triage, classifyanthropic/claude-haiku-4-5yaml

agents:
  list:
    - id: planner
      params:
        model: anthropic/claude-opus-4-6
    - id: coder
      params:
        model: anthropic/claude-sonnet-4-6
    - id: reviewer
      params:
        model: anthropic/claude-sonnet-4-6
    - id: triage
      params:
        model: anthropic/claude-haiku-4-5

---

Lever 3 — Thinking level (MEDIUM impact)#

Flag MEDIUM if: thinking: "adaptive" or thinking: "high" is set as the default for ALL agents, including simple ones.

Fix: Scope thinking: "adaptive" only to the primary reasoning agent. Set thinking: "low" or omit for utility agents.

yaml

agents:
  defaults:
    models:
      anthropic/claude-opus-4-6:
        params:
          thinking: "low"   # default: low
  list:
    - id: planner
      params:
        thinking: "adaptive"  # override only where needed

---

Lever 4 — 1M context window (MEDIUM impact)#

Flag MEDIUM if: context1m: true is set globally or for agents that don't need it.

The 1M context beta header triggers surcharge pricing for Anthropic API calls. Most agents don't need it.

Fix: Remove context1m: true from the global defaults. Re-add only to agents that demonstrably require >200K tokens of context.

---

Lever 5 — Fast mode (LOW impact, but easy win)#

Flag LOW if: fastMode is not enabled for Sonnet agents doing quick tasks.

Fix: Add fastMode: true to Sonnet model params for agents doing rapid back-and-forth tasks (search, triage, review). This reduces latency and can lower costs on high-frequency agents.

yaml

models:
  anthropic/claude-sonnet-4-6:
    params:
      fastMode: true
      cacheRetention: "long"

---

Step 3 — Generate the optimized config#

Rewrite the full config applying all fixes. Preserve every key the user had that is not being changed. Add comments above each changed block explaining what changed and why.

Format:

yaml

# COST OPTIMIZER — changed blocks annotated below
# Original: <what it was>  →  Optimized: <what it is now>

Step 4 — Show the diff and cost estimate#

Before writing anything, show the user:

1. Issues found — list each one with severity and one-line description
2. Estimated monthly savings — use the formula from references/pricing.md with assumed 500K tokens/day baseline
3. Diff — show only the changed blocks (not the full file unless it's short)

Example output format:

text

Issues found (3):
  [HIGH]   No prompt caching — estimated +$340/mo at current usage
  [HIGH]   All agents using Opus — Haiku suitable for triage/review agents
  [MEDIUM] thinking: adaptive set globally — limits to reasoning agent only

Estimated savings: ~$290/mo (68% reduction)
  Before: ~$430/mo  →  After: ~$140/mo

Changed blocks:
  + agents.defaults.models.anthropic/claude-opus-4-6.params.cacheRetention: "long"
  + agents.list[triage].params.model: anthropic/claude-haiku-4-5
  ~ agents.list[planner].params.thinking: adaptive → low (default); kept adaptive on planner only

Step 5 — Confirm and write#

Ask the user: "Apply these changes to your config?"

• If yes: write the optimized config back to the same file path. Then tell the user to run openclaw gateway restart (or it will auto-restart if config watch is enabled).
• If no / partial: apply only confirmed changes.
• If they want to review first: show the full optimized YAML before writing.

Edge cases#

• Subscription setup-token auth: Note that cacheRetention has no effect with subscription tokens — it's API-key only. Tell the user if their auth config uses sk-ant-oat-* tokens.
• Multiple agents with no IDs: If the config has unnamed agents, suggest adding IDs to enable per-agent model routing.
• Non-Anthropic providers: Only audit Anthropic model blocks. Leave OpenAI, Gemini, and other provider blocks untouched.
• Config not found: If no config file is found and no paste provided, offer to generate a fresh cost-optimized starter config instead.