# AI Agent Profiler — Measure agent cost, cache waste, and context bloat

> Source: <https://dev.to/rguiu/ai-agent-profiler-measure-agent-cost-cache-waste-and-context-bloat-p86>
> Published: 2026-07-21 22:14:18+00:00

I built a local-first profiler that sits as a transparent reverse proxy between your coding agent (Claude Code, OpenCode) and the LLM provider, recording every request without adding latency. It's like `perf`

for your agent — showing you exactly where your tokens go.

[Live demo (read-only dashboard with sample data).](https://rguiu.github.io/ai-agent-profiler/)

**Only ~60% of my API cost was my actual prompts.** The rest was agent overhead I never saw — subagent exploration (`search`

), context compaction, catch-up summaries when I stepped away, and session-title generation. The profiler classifies every request into 11 kinds (`main`

, `search`

, `compact`

, `recap`

, `title`

, `subagent`

, `webfetch`

, `quota`

, `tool_result`

, `guide`

, `unknown`

) and shows a "Cost by kind" table. The `search`

subagent alone ate ~25-30% of tokens when I told the agent "explore thoroughly."

**The 5-minute cache TTL penalty is real and expensive.** Claude Code places `cache_control: {"type": "ephemeral"}`

markers without an explicit TTL — so your cached prefix (system prompt, tools, message history) expires after ~5 minutes of inactivity. Step away to read docs or grab coffee, come back, and the next request pays a full cache write on potentially 200K+ tokens. On Opus models, a cache write is a massive multiplier on your first request back. The profiler detects cold regenerations, marks them with severity badges, and shows exactly what each idle gap cost you.

**Long sessions compound it.** Every turn grows the cached prefix, so cold-refresh costs grow linearly. A 3-hour session can re-write 200K+ tokens on each expiry. The profiler tracks context growth over time and flags when tool definitions alone account for 10-15K tokens re-sent on every call.

**I tried to optimize and mostly failed.** I built a full optimization layer (pruning stale tool results, collapsing system prompts, removing unused tools) and benchmarked it. Early numbers looked spectacular — but they were wrong. Cross-session cache warming inflated baselines, and the cost model initially ignored cache-write tokens. Once both were fixed, savings vanished. Editing the cached prefix turns cheap reads into expensive writes, and the client re-sends the pristine full history every turn anyway. I wrote up the autopsy in the repo under docs/optimization/FINDINGS.md.

```
npm install -g ai-agent-profiler
aap install              # seeds config
aap serve                # terminal 1: proxy + dashboard at :8080/ui
aap run claude           # terminal 2: launch Claude Code through the proxy
```

Supports Anthropic, OpenAI, DeepSeek, AWS Bedrock (SigV4), and Ollama. Zero telemetry, secrets redacted. All metrics rebuildable from raw traces.

Repo: [https://github.com/rguiu/ai-agent-profiler](https://github.com/rguiu/ai-agent-profiler)