hermes-agent/docs/micro-compaction.md

# Micro-compaction

**A way to amortize the cost of compression.**

Long conversations eventually outgrow the model's context window, and something
has to be thrown away or summarized. Hermes has always done this in one batch:
when the transcript crosses a threshold, the session stops, a large chunk of the
middle is summarized in a single call, and the conversation resumes. That works,
but the whole bill comes due at once — one visible pause, one big summarization
request, at whatever moment you happened to cross the line.

Micro-compaction pays the same bill in instalments. After each completed turn,
Hermes folds the single oldest un-absorbed exchange into a running summary. The
work is the same work; it just happens continuously, a piece at a time, instead
of all at once in the middle of your session.

It is not free and it is not a magic bullet. Each pass is a real call to the
compression model, and it runs at the end of a turn — your answer has already
streamed, but the turn does not close until the pass finishes. What the feature
gives you is a **tuning option**: you choose how the compression cost is
distributed, and which model pays it. See
[Choosing a compression model](#choosing-a-compression-model), because that
choice matters more than anything else here.

**The tradeoff is that knowledge gets a little earlier than you may be used to.**
Because compaction is always running, older parts of the conversation become
summaries sooner than they would under batch compaction — which leaves
everything verbatim until the window actually fills. Detail from earlier in the
session turns second-hand faster. You trade some of that fidelity for never
eating one long stall, and for a context window that stays consistently smaller
rather than sawtoothing up to the threshold and back.

---

## What it does

After every turn that finishes normally, `finalize_turn` asks the context
compressor to absorb **one** exchange:

1. Find the oldest exchange that hasn't been summarized yet.
2. Send just that exchange, plus the current running summary, to the auxiliary
   summarization model.
3. Replace those messages in the transcript with a single summary marker
   carrying the updated running summary.

One exchange per turn. The per-turn cost stays bounded no matter how long the
conversation gets.

An **exchange** is an assistant message together with any tool results that
followed it. In tool-heavy work that's where the bulk of the tokens live — a
file read or a command's output dwarfs the surrounding prose — which is why
absorbing one exchange at a time is worth doing at all.

## Your messages are never compacted

An exchange deliberately starts at the *assistant* message. Micro-compaction
walks straight past user messages to get there, so **what you typed is never
summarized** — your prompts stay verbatim for the entire session, no matter how
long it runs or how many times compaction fires.

This is the most useful property of the whole design, and it's worth being
explicit about why. What the assistant produces is largely an account of what it
did: it read this file, it ran that command, it got this result. That kind of
narration survives summarising with very little loss — "it did it this way" is
about as informative compressed as it was in full. Your instructions are a
different kind of thing. They're the intent everything else is derived from, and
they cannot be reconstructed from the work that followed. Paraphrasing "use the
existing retry helper, don't add a new one" into a summary is exactly how an
agent ends up confidently doing the thing you told it not to, six turns later.

So the asymmetry is on purpose: compact the derived material, keep the source of
truth. The cost is a floor on how small the middle can get, since user turns
accumulate and are never absorbed. In practice that floor is low — a prompt is
normally a tiny fraction of what a single tool result costs — but it is a real
floor. If you routinely paste 10–20K-token prompts, that weight stays in context
by design.

## What it never touches

Two more regions are protected and stay verbatim:

- **The head** — the system prompt and the opening messages, so the session's
  founding instructions are never paraphrased.
- **The tail** — a token-budgeted window of the most recent messages, so
  everything that's immediately relevant is still there in full.

Micro-compaction only ever works in the middle, between those two.

## How it works

### The cursor

The compressor keeps a cursor: the index of the first message not yet absorbed.
Each successful pass advances it past the exchange it just summarized.

If that in-memory cursor is missing or out of range — a fresh process, a resumed
session — it's recovered by scanning the transcript for the last summary marker
and resuming just after it. The transcript itself is the source of truth, so
resuming a session doesn't re-summarize work already done.

### The rolling summary

Rather than keeping a pile of per-exchange summaries, there is exactly one
running summary that each new exchange is merged into. The summarizer is asked
to fold in the new material's decisions, requirements, file paths and open
questions, drop details that are no longer relevant, and preserve the existing
structure. It's also explicitly instructed to replace any credentials it
encounters with `[REDACTED]`.

Because that summary is cumulative, only the newest marker is kept in the
transcript. Earlier markers are strictly redundant — the current summary already
contains everything they held — so they're dropped as they're superseded. This
matters more than it sounds: leaving them in place stacks near-duplicate copies
of the same text, each with its own heading and end-marker scaffolding, and the
transcript grows on every turn instead of shrinking.

### Defrag

Merge into a summary often enough and it gets baggy — repetitive, and larger
than the material justifies. When the running summary crosses a token threshold
(2000 by default), the next pass **defrags**: it re-summarizes the summary and
whatever middle remains in one shot, replacing it with a fresh compact version
and advancing the cursor to the tail.

This is still much cheaper than full batch compaction. It only ever processes the
summary plus the un-absorbed middle, never the whole transcript.

### Staying in step with the session database

The in-memory splice alone isn't enough. Hermes's normal session flush is
append-only, so the original rows would stay marked active and a resume would
load *both* the summary and the messages it replaced — putting the session
straight over the context limit.

So each pass also calls `archive_and_compact`, which atomically soft-archives the
active rows and inserts the compacted set. The messages are then stamped as
already-persisted so the append-only flush that follows skips them. If that
database step fails, it's logged and the session continues; the resume would
double-load until the next batch compression cleans up.

### When the summarizer fails

A summarization call can fail — the auxiliary model is unreachable, out of quota,
or the exchange itself is somehow unsummarizable. The transcript is left
untouched and the failure is counted.

If the *same* exchange fails three times in a row, the cursor is advanced past it
anyway. Without that, one bad exchange would be retried on every single turn
forever. Those skipped messages stay in the transcript and get picked up by the
next defrag or batch compaction.

## Interaction with batch compaction

Micro-compaction doesn't replace batch compaction — it defers it. Threshold-based
compaction is still there and still fires if the window fills anyway, and its
summary markers are the same format, so the two interoperate. In practice
micro-compaction keeps the transcript far enough below the threshold that the
batch path fires much less often.

## Configuration

```yaml
compression:
  micro_compact: true   # default
```

Set it to `false` to disable micro-compaction and return to batch-only
compaction. Everything else about compression is unchanged.

## Choosing a compression model

Micro-compaction uses the `auxiliary.compression` model:

```yaml
auxiliary:
  compression:
    provider: openai-api
    model: <your choice>
    base_url: <endpoint>
```

This is the single most important knob, and there is no universally right
answer — it depends on your hardware and what you are willing to trade.

Each pass sends the running summary plus one exchange, so the prompt is small
(a few thousand tokens) but the call happens **every turn**, at the end of the
turn. Two properties matter:

- **Latency dominates.** Because a pass runs per turn, its wall-clock cost is
  felt repeatedly. A model that takes 30 seconds turns every turn into a turn
  plus 30 seconds.
- **Reasoning models are a poor fit.** Merging one exchange into a summary is
  mechanical work. A thinking model will spend reasoning tokens on it and be
  substantially slower than a plain instruct model of similar size, for no
  benefit to the output.

Some measured points, on one particular setup — treat them as illustrations of
the shape, not as recommendations:

| model | observed |
|---|---|
| 7B 4-bit instruct, local (MLX, Apple Silicon) | ~31s per pass; box also serving other work |
| large MoE reasoning model, remote GPU | noticeably slower still — thinking tokens on a summarisation task |

The pattern is that a small, fast, non-reasoning instruct model is usually the
right shape, and that a bigger or "smarter" model is often worse here rather
than better. Where that lands for you depends on what you have to run it on.

If passes feel too slow, your options in rough order of effect are: pick a
faster or smaller compression model; give it a less contended host; or turn
micro-compaction off and go back to batch compaction.

## Measuring it

Micro-compaction is not primarily a token-saving or time-saving optimisation,
and judging it on tokens saved will undersell it. The two things it actually
buys you are:

1. **The long pause is amortized.** The same summarization work happens, but as
   small increments after turns instead of one stall in the middle of a session.
2. **Your context lasts longer.** Because the middle is continuously reclaimed,
   occupancy stays low instead of sawtoothing up to the threshold. A session
   runs much further — often indefinitely — before it needs a hard compaction
   at all.

So the number that matters is **occupancy**: how full the window is being kept,
as a percentage of the compaction threshold. A session that holds steady around
40% has headroom to keep going; one climbing through 90% is about to stall. The
second number is **how many batch compactions actually fired** — ideally none.

A session can save nothing on paper and still be a clear win on both counts.

Every pass emits one content-free JSON line, in the same style as the batch
compaction telemetry:

```
micro compaction telemetry: {"event":"micro_compaction","outcome":"absorbed",
"tokens_before":12739,"tokens_after":12060,"tokens_delta":-679,
"occupancy_pct":38.4,"threshold_tokens":34816,"context_limit":40960,
"exchange_tokens":868,"rolling_summary_tokens":31,"passes_total":1,
"tokens_saved_total":679,"duration_ms":14,...}
```

`occupancy_pct` is `tokens_after` as a share of the compaction threshold -- the
headroom figure. It is null when the model's window has not been resolved yet:
the telemetry reads only the cached value, because resolving it can issue a
synchronous `/models` probe and telemetry must never be what blocks a turn.

`tokens_delta` is negative when the pass shrank the transcript.
`tokens_saved_total` and `passes_total` accumulate across the session, so a whole
run can be summarised from its last line. No transcript content appears in the
payload — only counts.

To turn a log into an answer:

```
python scripts/micro_compaction_report.py [--per-session] [LOGFILE ...]
```

Defaults to `$HERMES_HOME/logs/agent.log`. It reports passes, outcome mix, net
tokens saved, mean absorbed-exchange size and pass durations.

### What it looks like when it is working

One real session — a 3.5 hour whole-project code review, ~75K tokens of
transcript, 400K window, compaction threshold at 320K:

| pass | messages | tokens | delta | occupancy | duration |
|---|---|---|---|---|---|
| 1 | 40 -> 39 | 27,479 -> 27,778 | +299 | 8.7% | 2.2s |
| 2 | 61 -> 59 | 48,676 -> 48,128 | -548 | 15.0% | 4.5s |
| 3 | 70 -> 67 | 58,309 -> 55,915 | -2,394 | 17.5% | 9.1s |
| 4 | 84 -> 80 | 75,251 -> 69,818 | -5,433 | 21.8% | 36.2s |
| 5 | 84 -> 80 | 74,659 -> 70,264 | -4,395 | 22.0% | 31.2s |

Three things to read off it.

**Occupancy flattened.** It climbed to about 22% and stopped. The last two
passes are identical (84 -> 80 messages); between them the conversation added
4,841 tokens and micro-compaction reclaimed 4,395. That is equilibrium: the
window holds steady instead of marching toward the threshold.

**No batch compaction fired.** Across the whole session the long pause never
happened.

**Reclamation only ramps after the tail budget.** The first passes recovered
almost nothing, because below the tail budget (here 64,000 tokens, 16% of the
window) nearly the whole transcript is protected tail and there is very little
that may be touched. Early sessions legitimately show no passes at all.

And the cost, stated plainly: passes ran 2 to 37 seconds, median around 31, on
a small local model that was also serving other work. Roughly two minutes of
summarisation spread across three and a half hours. Against one batch
compaction of a 75K-token middle that is still the better trade, but a
37-second increment is not a rounding error. See
[Choosing a compression model](#choosing-a-compression-model).

### Reading the numbers honestly

**The first pass in a session usually costs tokens rather than saving them.**
Inserting the summary marker carries a fixed ~400 tokens of scaffolding — the
compaction preamble, the historical heading, the end marker — and on pass one
that is paid against a single absorbed exchange. A first pass showing
`tokens_delta: +330` is not a malfunction.

From the second pass on, the marker is *replaced* rather than added, so the
scaffolding is already paid for and each absorbed exchange is close to pure
saving. The break-even is normally the second or third pass. This is why the
per-session view matters more than any single line: judge the feature on a
session's trajectory, not on one turn.

The plainer human-readable lines are still there too:

```
Micro-compaction: 37 -> 36 messages
Micro-compaction defrag: rolling summary re-summarized (1843 chars)
Micro-compaction: skipping exchange at cursor 12 after 3 consecutive failures
```

Message counts move by small amounts — that's expected. The token count is where
the effect shows: absorbing one tool-heavy exchange can drop hundreds of tokens
while changing the message count by one or two.

## Failure behaviour

Micro-compaction is best-effort throughout. The call in `finalize_turn` is wrapped
so that any exception is logged and swallowed — a failure returns the conversation
unchanged and the turn completes normally. It can degrade, but it shouldn't be
able to break a session.