Tokenoptimizer

**Quick verdict** Tokenoptimizer is a focused, high-leverage utility for teams using OpenClaw who care about cost control. It operates entirely via local configuration under `~/.openclaw`, provides dry-run previews and backups, and targets the largest cost drivers (model choice, heartbeats, context size, caching). For typical OpenClaw workflows overprovisioned on expensive models, it can plausibly deliver large savings with minimal code changes. **Strengths** - **Cost impact:** Uses cheaper models (Haiku) by default, restricting Sonnet/Opus to when they’re truly needed; adds caching and context trimming to reduce token waste. - **Safe by design:** Dry-run mode by default, diff previews before applying changes, and automatic timestamped backups under `~/.openclaw/backups/` with rollback support. - **Operational tooling:** Health checks, verification commands, and heartbeat routing to local or alternative providers (Ollama, LM Studio, Groq) help keep costs and infrastructure under control. - **Low adoption friction:** Install via `clawhub`, then run a small set of clear CLI commands (`analyze`, `optimize`, `verify`, `health`, `rollback`). **Key risks / limitations** - **Config drift / schema changes:** It directly edits `~/.openclaw/openclaw.json`. If OpenClaw changes its config schema or you have elaborate custom configs, there’s a non-zero risk of misconfiguration until the tool is updated. Backups mitigate this but don’t prevent downtime. - **Heuristic routing:** Routing “Haiku by default, Sonnet/Opus only when needed” is powerful but not magical. Some workloads may suffer quality regressions if they implicitly rely on higher-end models and you don’t explicitly adjust routing rules. - **Provider dependencies:** Heartbeat routing to Ollama/LM Studio/Groq assumes those services are correctly installed, reachable, and maintained; failures there may surface as degraded monitoring or health checks. - **Limited visibility into internals:** Without reviewing the code, you should assume its cost estimates and savings reports are approximate and validate them against your own billing or telemetry. **Best-fit scenarios** - You run multiple OpenClaw agents or services and **LLM cost has become significant** (hundreds to thousands of dollars per month). - Your workloads **don’t always need top-tier models**, and you’re comfortable with a Haiku-first strategy plus selective escalation to more capable models. - You can tolerate brief configuration experimentation and have the ability to roll back if something isn’t right. - You want to **standardize cost-conscious defaults** (budgets, caching, heartbeat providers) across a team or CI pipelines. **Less ideal scenarios** - Workloads that **strictly require maximum-capability models (Sonnet/Opus) at all times**, where cost is secondary to quality or compliance. - Highly customized or frequently changing OpenClaw setups where an external tool rewriting configs may conflict with your own automation. - Environments with strict change-management or compliance rules where any auto-rewriting of config files must be heavily audited or approved first. **Bottom line** Tokenoptimizer is a strong, pragmatic choice for OpenClaw users who want a fast, reversible way to harden cost controls at the config level. Use it first in a staging environment, review the diff previews carefully, and validate observed savings against real billing before relying on its reports in critical production systems.