OpenClaw Cost Tracking: Monitor Token Usage & Optimize Spending
OpenClaw users report $1,000-$3,600/month in surprise API bills. Sub-agents spawn sub-agents, context windows fill up, and costs compound with zero visibility. Here's how to take control.
The Hidden Cost Problem in OpenClaw
OpenClaw's power is also its cost risk. A single agent can spawn sub-agents, each making LLM calls, tool calls, and memory lookups. One user session might trigger dozens of API calls across multiple models. Without tracking, you have no idea what anything costs until the bill arrives.
The most common cost traps:
- Runaway sub-agent chains — An agent spawns a sub-agent that spawns another sub-agent. Each step multiplies token usage. A 3-level chain can 10x your expected cost.
- Bloated context windows — Agents accumulate memory, conversation history, and tool results. The context window grows with each turn, and you pay for every token in it.
- Retry loops — When a tool call fails, agents often retry. Without loop detection, a broken tool can trigger hundreds of retries, each costing tokens.
- Model selection blindness — Using GPT-4 for tasks that GPT-4o-mini handles equally well. Without per-task cost data, you can't optimize model selection.
5 Ways to Reduce OpenClaw Agent Costs
1. Track Per-Agent, Per-Session Costs
You can't optimize what you can't measure. The first step is granular cost attribution — knowing exactly which agent, which session, and which model is responsible for every dollar spent. Krabb breaks down costs by agent, session, model, and channel in real-time.
2. Set Budget Alerts
Define daily and monthly cost thresholds. Get notified via Slack, email, or webhook the moment an agent exceeds its budget. Catch runaway sessions in minutes instead of discovering them on your next invoice.
3. Identify Token-Heavy Sessions
Sort sessions by token consumption. The top 10% of sessions often account for 50%+ of total cost. Investigate these outliers — they're usually retry loops, bloated contexts, or sub-agent chains you can optimize.
4. Optimize Model Selection
Compare cost-per-task across models. Many agent tasks (simple tool calls, classification, formatting) work equally well with smaller models at a fraction of the cost. Use per-task cost data to make informed model choices.
5. Monitor Cost Trends
Track daily and weekly cost trends. A 5% daily increase compounds fast. Visualize spending over time to spot gradual increases before they become bill shock. Compare costs across agents, channels, and time periods.
OpenClaw Cost Benchmarks
Based on publicly reported data from OpenClaw users:
| Use Case | Avg Cost/Session | Avg Tokens |
|---|---|---|
| Simple Q&A agent | $0.01 - $0.05 | 1K - 5K |
| Tool-using agent | $0.05 - $0.30 | 5K - 30K |
| Multi-agent workflow | $0.20 - $2.00 | 20K - 200K |
| Code review agent | $0.10 - $1.00 | 10K - 100K |
| Research agent (long-running) | $0.50 - $5.00+ | 50K - 500K+ |
Note: Costs vary significantly based on model, prompt length, and agent complexity. These are approximate ranges based on community reports.
How Krabb Tracks OpenClaw Costs
Krabb receives cost and token data directly from your OpenClaw agents via OTLP. No sampling, no estimation — actual usage data from every session.
- Per-agent breakdown — See exactly what each agent costs daily, weekly, monthly
- Per-model attribution — Know which LLM model drives your costs
- Session drill-down — Click into any session to see token usage at every step
- Cost trends — Daily and weekly spending visualizations with change indicators
- Budget alerts — Slack, email, or webhook when thresholds are exceeded
Start Tracking Costs Now
Set up cost tracking in under 2 minutes. No code changes required.