Files
hermes-agent/agent/native_compaction.py
Teknium e00965a7e8 fix(compression): correct prune boundary + exempt native compaction checkpoints
Two corrections on top of the #71077 base (the whole bug class):

1. Turn boundary = last USER message, not last assistant message. A Codex
   turn spans several assistant messages (assistant+tool_calls -> tool ->
   ... -> final assistant) whose reasoning items must replay together; the
   last-assistant boundary would strip reasoning mid-chain from the active
   turn (the gap flagged in PR #71077 review).

2. type="compaction" checkpoints (native server-side compaction, PR #81747)
   are exempt: they carry already-pruned history, not per-turn reasoning.
   Pruning filters items instead of popping the sidecar key.

Sibling site fixed in the same class: the Codex incomplete-continuation
dedup path blind-overwrote codex_reasoning_items on visually-duplicate
interim messages, which would drop the only copy of a checkpoint captured
on the earlier response. Extracted merge_interim_reasoning_items() into
agent/native_compaction.py; newer reasoning wins, prior checkpoints are
preserved unless the newer payload carries its own.
2026-08-08 14:09:41 -07:00

187 lines
7.7 KiB
Python

"""Native OpenAI Responses server-side compaction — gpt-5.6 on direct OpenAI routes only.
OpenAI's Responses API supports server-side compaction: include
``context_management=[{"type": "compaction", "compact_threshold": N}]`` in a
``/v1/responses`` request and, when the rendered input crosses N tokens, the
server summarizes older context into an opaque ``compaction`` output item
(``encrypted_content``, sealed to the issuing endpoint). Replaying that item
as an input item on later requests stands in for the pruned history, so the
model keeps long-horizon recall without the client ever seeing a summary.
Docs: https://developers.openai.com/api/docs/guides/compaction
Hermes' support is deliberately narrow (live verification, Aug 2026):
* **gpt-5.6 family only.** gpt-5.6 and its variants compact correctly.
Sending the field to gpt-5.1 / gpt-5.2 reliably fails server-side —
HTTP 500 on the blocking path and a permanent stall on the streaming
path (90s watchdog x 3 retries = a dead turn). There is no structured
"unsupported" rejection to downgrade on, so the only safe gate is an
explicit model-family check.
* **Direct OpenAI routes only:** api.openai.com (API key) or the ChatGPT
Codex backend (subscription OAuth). Every other Responses surface
(xAI, GitHub/Copilot, relays, local servers) never sees the field —
most would 400 on the unknown parameter, and none can mint or decrypt
the compaction blob.
Ownership model: Hermes' local compression stays fully armed as the
fallback owner. The native threshold is clamped safely below the local
compressor's trigger so the server compacts first; if it doesn't (native
disabled mid-session, provider hiccup, non-eligible route), the local
summarizer fires exactly as before. There is no new custody state — the
captured compaction items ride the existing ``codex_reasoning_items``
sidecar, which already handles persistence (state.db), gateway session
replay, cross-issuer stamping, and the encrypted-replay kill switch.
This module is dependency-free on purpose so the transport, adapter, and
conversation loop can share the gate without import cycles.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from urllib.parse import urlsplit
# Native compaction fires this many tokens below the local compressor's
# trigger so the server always gets the first shot at compaction.
LOCAL_TRIGGER_SAFETY_MARGIN = 8_192
DEFAULT_COMPACT_THRESHOLD = 200_000
# Model-family gate. Substring match on the lowercased model id so dated
# snapshots (gpt-5.6-2026-07-xx) and variants (gpt-5.6-mini) stay eligible.
_ELIGIBLE_MODEL_MARKER = "gpt-5.6"
def is_native_compaction_model(model: Optional[str]) -> bool:
"""True when the model is in the gpt-5.6 family."""
return _ELIGIBLE_MODEL_MARKER in (model or "").lower()
def is_direct_openai_route(
base_url: Optional[str],
*,
is_codex_backend: bool = False,
) -> bool:
"""True for api.openai.com or the ChatGPT Codex backend — nothing else."""
if is_codex_backend:
return True
try:
hostname = (urlsplit(base_url or "").hostname or "").lower()
except ValueError:
return False
return hostname == "api.openai.com"
def resolve_compact_threshold(
configured_threshold: Any,
local_trigger_tokens: Any = None,
) -> int:
"""Clamp the configured native threshold below the local compressor trigger.
Without the clamp a native threshold above the local trigger would let the
local summarizer fire first every time, making native compaction dead
config. ``local_trigger_tokens`` is ``ContextCompressor.threshold_tokens``
when a compressor is attached, else None.
"""
try:
configured = int(configured_threshold)
except (TypeError, ValueError):
configured = DEFAULT_COMPACT_THRESHOLD
if isinstance(configured_threshold, bool) or configured <= 0:
configured = DEFAULT_COMPACT_THRESHOLD
local = None
try:
if local_trigger_tokens is not None and not isinstance(local_trigger_tokens, bool):
local = int(local_trigger_tokens)
except (TypeError, ValueError):
local = None
if local is None or local <= 0:
return configured
if local > LOCAL_TRIGGER_SAFETY_MARGIN:
upper = local - LOCAL_TRIGGER_SAFETY_MARGIN
else:
upper = max(1_024, int(local * 0.8))
return max(1_024, min(configured, upper))
def native_compaction_context_management(
agent: Any,
*,
is_codex_backend: bool,
is_xai_responses: bool = False,
is_github_responses: bool = False,
) -> Optional[List[Dict[str, Any]]]:
"""Return the ``context_management`` payload for this request, or None.
None means "do not send the field" — the request is byte-identical to
pre-feature behavior. All gates are re-checked per request so a
mid-session model switch or the in-session kill switch
(``agent.codex_responses_native_compaction = False``, set by the
conversation loop's rejection recovery) takes effect on the next call.
"""
if not bool(getattr(agent, "codex_responses_native_compaction", False)):
return None
# compression.enabled: false disables ALL automatic compaction, native
# included — mirrors the codex_app_server_auto contract.
if not bool(getattr(agent, "compression_enabled", True)):
return None
if is_xai_responses or is_github_responses:
return None
if not is_native_compaction_model(getattr(agent, "model", None)):
return None
if not is_direct_openai_route(
getattr(agent, "base_url", None), is_codex_backend=is_codex_backend
):
return None
compressor = getattr(agent, "context_compressor", None)
threshold = resolve_compact_threshold(
getattr(agent, "codex_responses_compact_threshold", DEFAULT_COMPACT_THRESHOLD),
getattr(compressor, "threshold_tokens", None) if compressor is not None else None,
)
return [{"type": "compaction", "compact_threshold": threshold}]
def is_native_compaction_rejection(error: Any) -> bool:
"""True when a provider error names the context_management field.
Used by the conversation loop's one-shot recovery: strip the field,
disable native compaction for the rest of the session, retry. Matching
is deliberately narrow — generic 4xx/5xx/timeouts must NOT permanently
downgrade native compaction, they take the normal retry path.
"""
text = str(error or "").lower()
return "context_management" in text or "compact_threshold" in text
def merge_interim_reasoning_items(
prior_items: Any,
new_items: Any,
) -> List[Dict[str, Any]]:
"""Merge ``codex_reasoning_items`` across Codex incomplete-continuation
dedup, preserving native compaction checkpoints.
The incomplete-retry path updates a visually-duplicate interim assistant
message in place with the newer response's replay payload. A checkpoint
captured on the EARLIER response is a cumulative context carrier the
continuation won't re-emit (the replayed checkpoint keeps the server
render under threshold), so a blind overwrite drops the only copy and the
next request balloons back to full history. Rule: newer items win, but
prior checkpoints are prepended unless the newer payload carries its own.
"""
kept_checkpoints = [
item
for item in (prior_items if isinstance(prior_items, list) else [])
if isinstance(item, dict) and item.get("type") == "compaction"
]
new_list = list(new_items) if isinstance(new_items, list) else []
new_has_checkpoint = any(
isinstance(item, dict) and item.get("type") == "compaction"
for item in new_list
)
if new_has_checkpoint or not kept_checkpoints:
return new_list
return kept_checkpoints + new_list