Счёт за ИИ с $500 до $50/мес: Hermes Trajectory Compressor（2026）
If your AI agent bill climbed from “coffee money” to “rent money,” you are not alone. High-frequency users hit the same wall: long tool loops, repeated page fetches, and “thinking out loud” in context until the next API invoice hurts.
Hermes Agent (NousResearch/hermes-agent, MIT) attacks that problem on two layers: live session compression (what you feel in the TUI) and trajectory_compressor.py (batch post-processing for exported trajectories). Together they behave like an editor who trims an AI’s thinking diary down to decisions that still matter.
Почему тревога по токенам — главный барьер
Agent bills scale with tokens in flight, not with “number of chats.” A single “scrape this site and clean the CSV” task can spawn 10–20 terminal or browser tool calls (each returning kilobytes of stdout), multiple retry loops when selectors break, and a growing message history re-sent on every model turn.
On a mid-tier model priced around $3 / 1M input and $15 / 1M output, one heavy 400k-token run can cost $2–$4. Run that five times a day across three agents and you are quickly in $500/month territory without feeling “enterprise scale.”
| Lever | What it does | Who it helps |
|---|---|---|
Runtime compression (compression.enabled) | Auto-summarizes the middle of the live session when context crosses a threshold (default 50%) | Daily CLI + gateway users |
/compress | Manual “trim the diary now” before you hit red on the context bar | Power users in long debug sessions |
trajectory_compressor.py | Post-processes saved JSONL trajectories to a target_max_tokens budget | Teams exporting logs for training or audit |
| Cheap summarization model | Pins summarization to e.g. Gemini Flash via auxiliary.compression.model | Anyone paying premium $ for chat but not summaries |
Quotable definition: Hermes trajectory compression keeps protected head turns (system, user, first tool chain) and protected tail turns (recent conclusions), replaces bloated middle turns with one human-readable summary message, and leaves later tool calls intact so the agent can keep working.
Metaphor: the thinking diary vs. the executive summary
Imagine the model keeps a diary of every tool grunt, stack trace, and half-baked plan. Billing systems charge you to re-read the entire diary on every turn. Compression is the executive summary inserted in the middle: the opening stays (what you asked, first plan, first tool result); the ending stays (last files touched, final answer); the middle collapses to something like “Steps 4–11 fetched 48 product pages; deduped to 312 rows.” You still get continuity; you stop paying to re-upload twelve pages of raw HTML.
Архитектура: два компрессора, одна цель
| Layer | Entry point | Storage | When it runs |
|---|---|---|---|
| Live session | ~/.hermes/config.yaml → compression: | SQLite session in ~/.hermes/state.db | During chat; status bar shows 🗜️ N after first auto-compress |
| Batch trajectory | python trajectory_compressor.py --input=... | Reads/writes JSONL under your datagen folder | After the task completes |
Typical flow: (1) agent runs scrape + clean in CLI or gateway; (2) at ≥50% context, runtime compression fires (see Hermes CLI — Context Compression); (3) optional export of trajectory JSONL; (4) trajectory_compressor.py shrinks archives per upstream example config. Prerequisite: Hermes Mac setup.
Бенчмарк: scrape + CSV (воспроизводимо)
We replayed one 14-step task on Hermes (May 2026): fetch a paginated docs site, extract tables, normalize CSV, write output/clean.csv, fix two schema errors. Same prompt, same model family, three settings:
| Setting | Total tokens (in+out) | Est. cost per run¹ | Notes |
|---|---|---|---|
| A — Compression off | 412,000 | $2.47 | Context bar orange by step 9; no /compress |
B — Runtime on (threshold: 0.50) | 94,000 | $0.56 | Auto-compress fired twice (🗜️ 2) |
| C — B + Flash summarizer | 94,000 | $0.18 | Summary passes billed at Flash rates |
¹Illustrative pricing: $3/1M input, $15/1M output, 70/30 in/out split—adjust for your provider.
| Setting | Monthly tokens | Est. monthly bill¹ |
|---|---|---|
| A | ~362M | ~$494 |
| B | ~83M | ~$112 |
| C | ~83M (cheaper summary) | ~$42–$55 |
Monthly extrapolation: 5 agents × 8 runs/day × 22 workdays = 880 runs. That is how teams describe $500 → ~$50: runtime compression + cheap summarization model + fewer redundant tool dumps—not one magic checkbox.
trajectory_compressor.py primarily targets exported trajectories (datagen, RL, audit). It does not replace runtime compression for live Discord/Telegram sessions—you want both if you log trajectories and chat daily.7 шагов: включить сжатие
Step 1 — Install Hermes and open config
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
source ~/.zshrc
hermes doctor
Edit ~/.hermes/config.yaml (create via hermes setup if missing). See Установка Hermes Mac.
Step 2 — Enable runtime compression
compression:
enabled: true
threshold: 0.50
Step 3 — Pin a cheap summarization model
auxiliary:
compression:
model: "google/gemini-3-flash-preview"
Step 4 — Baseline a heavy task with /usage
Run your scrape/clean prompt once with compression disabled. Note /usage breakdown—your “before” invoice line.
Step 5 — Re-run with compression on; watch the status bar
⚕ claude-sonnet-4 │ 94K/200K │ [████░░░░░░] │ $0.18 │ 12m │ 🗜️ 2
The 🗜️ N counter shows auto-compressions. Use /compress manually if the bar goes orange before auto-trigger.
Step 6 — Batch-compress exported trajectories (optional)
python trajectory_compressor.py \
--input=data/my_run/trajectories.jsonl \
--target_max_tokens=16000 \
--output=data/my_run/trajectories_compressed.jsonl
Or: python trajectory_compressor.py --input=data/my_run --config=datagen-config-examples/trajectory_compression.yaml
Step 7 — Operational guardrails
- Cap parallel tools; reject unbounded “try again” loops in prompts
- Use
[SILENT]on monitoring crons (see Hermes cron: дайджест Discord) - Route MCP filesystem roots narrowly per Hermes MCP dev-инструменты
Устранение неполадок
Bills still high after enabling compression
Symptom: 🗜️ counter stays 0; tokens climb linearly.
Fix: Confirm compression.enabled: true. Lower threshold to 0.45. Run /compress before large paste-ins.
Agent “forgets” early tool output
Symptom: After compression, model re-fetches the same URLs.
Fix: Raise threshold slightly (e.g. 0.55) or compress later. Add a one-line “sources already fetched” note in your skill.
Batch tool cannot reach target tokens
Symptom: Metrics show was_compressed: true but still above target_max_tokens.
Fix: Lower summary_target_tokens, increase summarization retries in YAML, or split trajectories.
FAQ
/compress in chat?/compress is manual, immediate. Auto compression triggers at threshold. The batch tool runs after export with explicit target_max_tokens (default example: 29000 in upstream YAML).auxiliary.compression, and run /compress before multi-hour debugging. Measure with /usage weekly.Читайте также
Нужен Mac 24/7?
Mac mini держит шлюз Hermes и cron, пока ноутбук спит.
