Cut OpenClaw Agent Costs 15x: Config Tuning Guide

TL;DR

OpenClaw agents balloon token costs because sessions accumulate full conversation history on every request—tune HEARTBEAT.md intervals, scope agents narrowly, prune context deliberately, and filter tool output to cut costs by 15x without sacrificing functionality.

Why Continuous Sessions Get Expensive Fast

Every agent request includes the entire session context: all prior messages, tool results, and memory entries. A five-day untuned session easily carries 80,000 tokens per request. If your agent polls via HEARTBEAT.md every 15 minutes, that's 96 requests daily at 80k tokens each—the arithmetic compounds quickly into substantial API bills.

The three primary cost drivers are long-running sessions without context pruning, aggressive HEARTBEAT.md polling intervals (checking every 5 minutes for hourly changes), and broad tool access that returns verbose results. Most developers don't configure these deliberately; they inherit inefficient defaults.

Fix 1: Tune HEARTBEAT.md Intervals Aggressively

Your HEARTBEAT.md likely contains checks running every 5 minutes when they should run every 60 minutes or longer. Match the interval to the actual change frequency of what you're monitoring. A deployment pipeline that takes 20 minutes needs a 15-minute health check, not a 5-minute one—that alone cuts token usage by 66% for that task.

Critical addition: include conditional reporting. Instead of logging success on every healthy check, instruct the agent to report only when status is abnormal:

• Server health: 15-minute interval, report only if status ≠ 200
• Disk usage: 60-minute interval, alert only if above 80%
• SSL expiry: 24-hour interval, alert only if under 14 days

Silent success prevents verbose context inflation from accumulating in memory on every routine check.

Fix 2: Scope Agents to Single Responsibilities

Running one agent for server monitoring AND customer support AND email triage AND daily digests creates a bloated session that accumulates context from all four jobs simultaneously. The fix: give each agent a single, scoped role with explicit boundary clauses in AGENTS.md.

A monitoring agent watches servers. A support agent handles users. A digest agent runs once daily and exits cleanly. Tight scoping prevents context bleed—off-topic conversations no longer drag unrelated tokens into every subsequent request. This architectural choice is cheaper and easier to debug than generalist agents.

Fix 3: Prune Context Deliberately with Memory Rules

OpenClaw's memory system offers pruning tools most developers never use. Add explicit rules to AGENTS.md to manage what persists:

• Summarize daily notes older than 7 days into one line, then delete originals
• Never keep raw tool output longer than 24 hours
• Mark resolved alerts as resolved and exclude them from active context after 2 hours
• Summarize completed tasks weekly instead of retaining step-by-step logs

A daily cleanup task in HEARTBEAT.md (running at 03:00) can summarize and delete weekly files, preventing context from growing indefinitely. This keeps MEMORY.md useful for recall without becoming a token dump.

Fix 4: Filter Tool Output Before Context Ingestion

When agents call APIs, scrape pages, or read logs, the entire payload lands in context and gets re-sent on every subsequent request. Instruction-level filtering in AGENTS.md stops this:

• Extract only necessary fields from API responses; discard full JSON
• Summarize last 10 lines only from log files; never paste full output
• Extract key points from web pages; don't quote entire articles

These instructions tell the agent to process and discard, not process and retain. Your effective context window stays lean.

Real-World Cost Reduction Example

A realistic monitoring agent configured for efficiency generates roughly a 15x cost reduction compared to an unoptimized version. Using GPT-4 pricing as a baseline:

• Unoptimized agent: 5-minute intervals, verbose logging, full tool output retention = ~$600/month
• Optimized agent: 15–60-minute intervals, silent success, filtered output, daily pruning = ~$40/month

Same functionality, vastly different bills. The difference is configuration discipline, not model choice.

Common Mistakes to Avoid

• 5-minute intervals on everything: Most checks don't need that frequency; align intervals to actual signal change rates
• Raw tool output in memory: API responses and logs are expensive to carry; always summarize
• One agent for multiple jobs: Scoped agents with narrow contexts are cheaper and easier to debug
• No pruning rules: Without explicit instructions, agents retain everything indefinitely
• Verbose success messages: Tell your agent to stay silent when checks pass; only write context when something needs attention

Security Guardrails for Cost-Optimized Agents

Never embed API credentials, tokens, or passwords in HEARTBEAT.md or AGENTS.md—use environment variable references. Apply least-privilege principles: a health-check agent doesn't need file write access or exec permissions beyond its specific checks. Review MEMORY.md before running automated pruning to ensure unresolved security incidents aren't deleted.

Start With One Change Today

If your agent bill is higher than expected, open HEARTBEAT.md and identify every interval under 15 minutes. Ask whether the underlying signal actually changes that fast. For most checks, it doesn't. Push intervals to 15 or 60 minutes. That single change cuts token usage significantly without reliability impact.

For new agents, build cost-efficiency in from the start: use tight scoping, sensible HEARTBEAT defaults, explicit pruning rules, and instruction-level output filtering.

Key Takeaways

• Session context balloons costs because full conversation history gets re-sent every request; a five-day untuned session carries 80k tokens per call
• Tune HEARTBEAT.md intervals to actual signal change rates (most checks need 15–60 minutes, not 5) and only report on abnormal conditions
• Scope agents to single responsibilities and add explicit boundary clauses to prevent off-topic context accumulation
• Use deliberate memory pruning rules and daily cleanup tasks to prevent context from growing indefinitely
• Filter tool output (APIs, logs, web pages) at the instruction level so agents summarize and discard rather than retain full results
• Realistic cost reduction is 15x with zero reduction in functionality—optimize configuration, not capability

Source: Roberto Capodieci, Medium, March 2026