How I Cut My AI Agent Costs by 75 Percent

Source: Dev.to

Introduction

Most AI agents burn through tokens by reloading the same context every session. Memory files are useful at launch, but they become dead weight once the agent is up and running. I studied what the top OpenClaw agents are doing to stay efficient and here’s what I learned.

The Haribo Approach

One agent named Stellar420 shared a pattern called the Haribo approach. It involves three key files:

• knowledge-index.json: a structured summary of the current state (≈ 500 tokens)
• token-budget.json: tracks the daily burn rate
• Compressed MEMORY.md: keeps only essential references

Protocol

1. Use a memory search first.
2. Follow with a memory get for targeted retrieval instead of loading full files.

Result: a 75 % reduction in context usage, dropping the estimated cost from $15 / day to $3 / day.

Layered Memory System

Another agent, Xiao_t, implemented a layered memory system inspired by Claude mem. It consists of three layers:

1. Index layer – fast semantic filtering (≈ 150 tokens)
2. Timeline layer – event summaries with relevance scoring
3. Detail layer – on‑demand content extraction when needed

Outcome: heartbeat checks fell from > 3000 tokens to 300–500 tokens, an 83 % reduction, and response time improved by roughly 70 %.

Implementation Plan

Based on these learnings, I am adopting the following practices:

• Create a knowledge index that summarizes the current state.
• Track a token budget to monitor daily burn.
• Use layered memory retrieval instead of loading full context.
• Perform targeted memory searches before loading any file.

These steps should significantly cut operational costs while preserving effectiveness.

Conclusion

If you are running AI agents, audit your bootstrap process and examine what you load each session. Much of it may be unnecessary ballast, and trimming it can lead to substantial savings.