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hermes-agent/agent/context_engine.py
xue xinglong 589cbafb87 feat(context-engine): forward real usage to on_turn_complete() 
The on_turn_complete() observation hook is the engine's post-turn signal,
so it should receive the completed turn's canonical token usage when the
host has it, not a hardcoded None. Per @johnnykor82's #41918 contract: the
engine uses prompt/completion + cache_read/write/reasoning buckets to judge
how large/expensive the selected context was before the next select_context().

- conversation_loop.py: stash the most recent provider response's usage_dict
  (the same canonical shape fed to update_from_response) on the agent as
  _last_turn_usage; reset to None at turn start so turns that never reach a
  provider response (early failure / interrupt) forward None, not a stale
  prior turn's usage.
- turn_finalizer.py: forward agent._last_turn_usage instead of usage=None.
- context_engine.py: document the usage param contract on the ABC hook.
- tests: cover both ends through the real finalize_turn path — completed turn
  forwards the full canonical bucket set intact; no-response turn forwards None.

Co-authored-by: johnnykor82 <johnnykor82@users.noreply.github.com>
2026-07-23 19:44:35 -07:00

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"""Abstract base class for pluggable context engines.
A context engine controls how conversation context is managed when
approaching the model's token limit. The built-in ContextCompressor
is the default implementation. Third-party engines (e.g. LCM) can
replace it via the plugin system or by being placed in the
``plugins/context_engine/<name>/`` directory.
Selection is config-driven: ``context.engine`` in config.yaml.
Default is ``"compressor"`` (the built-in). Only one engine is active.
The engine is responsible for:
- Deciding when compaction should fire
- Performing compaction (summarization, DAG construction, etc.)
- Optionally exposing tools the agent can call (e.g. lcm_grep)
- Tracking token usage from API responses
Lifecycle:
1. Engine is instantiated and registered (plugin register() or default)
2. on_session_start() called when a conversation begins
3. update_from_response() called after each API response with usage data
4. should_compress() checked after each turn
5. compress() called when should_compress() returns True
6. on_session_end() called at real session boundaries (CLI exit, /reset,
gateway session expiry) — NOT per-turn
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
from agent.redact import redact_sensitive_text
MEMORY_CONTEXT_MAX_CHARS = 6_000
_MEMORY_CONTEXT_HEAD_CHARS = 4_000
_MEMORY_CONTEXT_TAIL_CHARS = 1_500
_MEMORY_CONTEXT_TRUNCATION_MARKER = "\n...[memory provider context truncated]...\n"
def sanitize_memory_context(memory_context: str) -> str:
"""Prepare provider context for a context-engine/LLM egress boundary."""
sanitized = redact_sensitive_text(
memory_context.strip(),
force=True,
redact_url_credentials=True,
)
if len(sanitized) <= MEMORY_CONTEXT_MAX_CHARS:
return sanitized
return (
sanitized[:_MEMORY_CONTEXT_HEAD_CHARS]
+ _MEMORY_CONTEXT_TRUNCATION_MARKER
+ sanitized[-_MEMORY_CONTEXT_TAIL_CHARS:]
)
def automatic_compaction_status_message(
engine: Any,
*,
phase: str,
default_message: str,
**context: Any,
) -> str | None:
"""Resolve host-visible status for an automatic compaction event.
Engines can suppress routine automatic status with
``emit_automatic_compaction_status = False`` or customize it by defining
``get_automatic_compaction_status_message(...)``. Empty strings and
``None`` mean "do not emit a lifecycle status".
"""
if not getattr(engine, "emit_automatic_compaction_status", True):
return None
formatter = getattr(engine, "get_automatic_compaction_status_message", None)
if callable(formatter):
message = formatter(
phase=phase,
default_message=default_message,
**context,
)
else:
message = default_message
if message is None:
return None
message = str(message).strip()
return message or None
class ContextEngine(ABC):
"""Base class all context engines must implement."""
# -- Identity ----------------------------------------------------------
@property
@abstractmethod
def name(self) -> str:
"""Short identifier (e.g. 'compressor', 'lcm')."""
# -- Token state (read by run_agent.py for display/logging) ------------
#
# Engines MUST maintain these. run_agent.py reads them directly.
last_prompt_tokens: int = 0
last_completion_tokens: int = 0
last_total_tokens: int = 0
threshold_tokens: int = 0
context_length: int = 0
compression_count: int = 0
# -- Compaction parameters (read by run_agent.py for preflight) --------
#
# These control the preflight compression check. Subclasses may
# override via __init__ or property; defaults are sensible for most
# engines.
#
# protect_first_n semantics (since PR #13754): count of non-system head
# messages always preserved verbatim, IN ADDITION to the system prompt
# which is always implicitly protected. Default 3 keeps the
# historical "system + first 3 non-system messages" head shape.
threshold_percent: float = 0.75
protect_first_n: int = 3
protect_last_n: int = 6
# User-visible lifecycle status for automatic host-triggered compaction.
# Alternative engines that treat compaction as routine background
# maintenance can set this false to keep successful automatic passes silent;
# warnings, errors, and explicit manual commands should still surface.
emit_automatic_compaction_status: bool = True
# -- Core interface ----------------------------------------------------
@abstractmethod
def update_from_response(self, usage: Dict[str, Any]) -> None:
"""Update tracked token usage from an API response.
Called after every LLM call with a normalized usage dict. The legacy
keys ``prompt_tokens``, ``completion_tokens``, and ``total_tokens``
are always present. Newer hosts also include canonical buckets:
``input_tokens``, ``output_tokens``, ``cache_read_tokens``,
``cache_write_tokens``, and ``reasoning_tokens``. Engines should
treat those fields as optional for compatibility with older hosts.
"""
@abstractmethod
def should_compress(self, prompt_tokens: int = None) -> bool:
"""Return True if compaction should fire this turn."""
def should_compress_info(self, prompt_tokens: int = None) -> "tuple[bool, str | None]":
"""Return ``(should_compress, reason)``.
The base implementation is backward-compatible: engines that only
implement ``should_compress`` get ``(should_compress(prompt_tokens),
None)``. Concrete engines with richer block reasons (e.g. a
summary-LLM cooldown or an anti-thrashing guard) override this to
surface a human-readable reason so callers can warn the user instead
of silently skipping compression. Added for the silent-overflow
warning fix (#62625) so plugin engines don't raise AttributeError.
"""
return self.should_compress(prompt_tokens), None
@abstractmethod
def compress(
self,
messages: List[Dict[str, Any]],
current_tokens: Optional[int] = None,
focus_topic: Optional[str] = None,
force: bool = False,
memory_context: str = "",
) -> List[Dict[str, Any]]:
"""Compact the message list and return the new message list.
This is the main entry point. The engine receives the full message
list and returns a (possibly shorter) list that fits within the
context budget. The implementation is free to summarize, build a
DAG, or do anything else — as long as the returned list is a valid
OpenAI-format message sequence.
Args:
focus_topic: Optional topic string from manual ``/compress <focus>``.
Engines that support guided compression should prioritise
preserving information related to this topic. Engines that
don't support it may simply ignore this argument.
force: Whether a user-requested compression should bypass an
engine-owned cooldown. Engines without cooldowns may ignore it.
memory_context: Text returned by memory providers immediately before
compaction. Summarizing engines should include non-empty text in
their handoff prompt. Older engines may omit this parameter; the
host filters unsupported optional arguments by signature.
"""
# -- Optional: proactive tool-result prune -----------------------------
def prune_tool_results_only(
self,
messages: List[Dict[str, Any]],
current_tokens: int | None = None,
) -> tuple[List[Dict[str, Any]], int]:
"""Deterministically trim old tool-result payloads without an LLM call.
Runs on a low, cost-oriented trigger independent of ``should_compress``
so large-window engines can reclaim re-sent tool output long before full
compaction would fire. Returns ``(messages, n_pruned)``.
Default is a safe no-op: the list is returned unchanged with ``0``
pruned. Engines that don't implement a cheap prune — and any engine that
predates this hook — inherit this default, so the agent loop's
post-tool-call prune path never raises ``AttributeError`` on them. The
built-in ContextCompressor overrides this with the real implementation.
"""
return messages, 0
# -- Optional: per-turn context selection (distinct from compression) --
def select_context(
self,
request_messages: List[Dict[str, Any]],
*,
conversation_messages: List[Dict[str, Any]] = None,
incoming_message: Dict[str, Any] = None,
budget_tokens: int = 0,
) -> List[Dict[str, Any]]:
"""Optionally choose/replace the context for THIS request, pre-generation.
Called every turn after the request message list is assembled and
before it is dispatched to the provider — independent of
``should_compress()``. This lets an engine *select* which context
enters the prompt (retrieval, topic routing, role/branch switching)
rather than *shrink* context that is already there. The two verbs are
orthogonal:
- ``compress()`` : context is too long -> make it shorter.
- ``select_context()``: this turn belongs to a different context
-> use that one instead.
Without this hook, engines that need per-turn access to the message
list have to force ``should_compress()`` to return ``True`` so that
``compress()`` is invoked every turn purely as a callback — which
conflates selection with compression and degrades behaviour when the
engine's backend is unavailable. ``select_context()`` removes the need
for that workaround.
The returned list is request-only: it replaces the messages sent to
the provider for this single call and MUST NOT be treated as persisted
transcript state. The conversation history in the session DB is left
untouched, so nothing leaks across turns. Return ``None`` to leave the
request unchanged.
Unlike the ``pre_llm_call`` plugin hook (which appends to the user
message and intentionally never rewrites the list, to preserve the
cache prefix), ``select_context()`` may *replace* the message list.
Args:
request_messages: The assembled request message list (system
prompt + history + any ephemeral prefill), in OpenAI format.
conversation_messages: The unmodified persisted conversation
history, for reference only (do not mutate).
incoming_message: The current turn's user message, if available.
budget_tokens: The active model's context length, or 0 if unknown.
Default returns ``None`` (no-op) — zero impact on the built-in
compressor or any existing engine.
"""
return None
def on_turn_complete(
self,
messages: List[Dict[str, Any]],
usage: Dict[str, Any] = None,
**kwargs: Any,
) -> None:
"""Observe a finished user turn (post-turn ingestion / observation).
Called once after the assistant/tool loop completes for a turn, with
the finalized in-memory transcript snapshot. This is the complement to
``select_context()``: selection happens *before* the request, while
observation happens *after* the turn. It lets an engine ingest, index,
summarize, or update routing / topic / session state from what actually
happened — so the next ``select_context()`` can act on it.
Together the two hooks remove the need to abuse ``should_compress()`` /
``compress()`` as a generic per-turn callback just to observe history,
and they cover the case where a turn finishes and there may be no next
request from which to infer the previous turn.
``messages`` is a shallow copy and should be treated as read-only:
return values are ignored and this hook must not rely on transcript
mutation for persistence. ``kwargs`` may include ``turn_id``,
``task_id``, ``api_call_count``, ``interrupted``, ``failed``, and
``turn_exit_reason``.
``usage`` carries the completed turn's canonical token usage (the same
dict shape passed to ``update_from_response`` — ``prompt_tokens`` /
``completion_tokens`` / ``total_tokens`` plus the canonical
``input_tokens`` / ``output_tokens`` / ``cache_read_tokens`` /
``cache_write_tokens`` / ``reasoning_tokens`` buckets) so an engine can
weigh how large/expensive the selected context actually was when
deciding the next ``select_context()``. It is ``None`` on turns that
never reached a provider response (early failure / interrupt); engines
must treat it as optional.
Default is a no-op.
"""
return None
# -- Optional: pre-flight check ----------------------------------------
def should_compress_preflight(self, messages: List[Dict[str, Any]]) -> bool:
"""Quick rough check before the API call (no real token count yet).
Default returns False (skip pre-flight). Override if your engine
can do a cheap estimate.
"""
return False
def should_defer_preflight_to_real_usage(self, rough_tokens: int) -> bool:
"""Return True when preflight should trust recent real usage instead.
Built-in compression uses this to avoid re-compacting from known-noisy
rough estimates after a compressed request has already fit. Third-party
engines can ignore it safely.
"""
return False
def get_automatic_compaction_status_message(
self,
*,
phase: str,
default_message: str,
**context: Any,
) -> str | None:
"""Return user-visible status for automatic host-triggered compaction.
Return ``None`` to suppress successful automatic lifecycle status for
this compaction event. ``phase`` identifies the host call site (for
example ``"preflight"`` or ``"compress"``). ``context`` contains
best-effort fields such as ``approx_tokens`` and ``threshold_tokens``.
This hook does not control warning/error messages or explicit manual
commands such as ``/compress``.
"""
if not self.emit_automatic_compaction_status:
return None
return default_message
# -- Optional: manual /compress preflight ------------------------------
def has_content_to_compress(self, messages: List[Dict[str, Any]]) -> bool:
"""Quick check: is there anything in ``messages`` that can be compacted?
Used by the gateway ``/compress`` command as a preflight guard —
returning False lets the gateway report "nothing to compress yet"
without making an LLM call.
Default returns True (always attempt). Engines with a cheap way
to introspect their own head/tail boundaries should override this
to return False when the transcript is still entirely protected.
"""
return True
# -- Optional: session lifecycle ---------------------------------------
def on_session_start(self, session_id: str, **kwargs) -> None:
"""Called when a new conversation session begins.
Use this to load persisted state (DAG, store) for the session.
kwargs may include hermes_home, platform, model, etc.
"""
def on_session_end(self, session_id: str, messages: List[Dict[str, Any]]) -> None:
"""Called at real session boundaries (CLI exit, /reset, gateway expiry).
Use this to flush state, close DB connections, etc.
NOT called per-turn — only when the session truly ends.
"""
def on_session_reset(self) -> None:
"""Called on /new or /reset. Reset per-session state.
Default resets compression_count and token tracking.
"""
self.last_prompt_tokens = 0
self.last_completion_tokens = 0
self.last_total_tokens = 0
self.compression_count = 0
# -- Optional: tools ---------------------------------------------------
def get_tool_schemas(self) -> List[Dict[str, Any]]:
"""Return tool schemas this engine provides to the agent.
Default returns empty list (no tools). LCM would return schemas
for lcm_grep, lcm_describe, lcm_expand here.
"""
return []
def handle_tool_call(self, name: str, args: Dict[str, Any], **kwargs) -> str:
"""Handle a tool call from the agent.
Only called for tool names returned by get_tool_schemas().
Must return a JSON string.
kwargs may include:
messages: the current in-memory message list (for live ingestion)
"""
import json
return json.dumps({"error": f"Unknown context engine tool: {name}"})
# -- Optional: status / display ----------------------------------------
def get_status(self) -> Dict[str, Any]:
"""Return status dict for display/logging.
Default returns the standard fields run_agent.py expects.
"""
# Clamp the -1 "compression just ran, awaiting real usage" sentinel
# (set by conversation_compression) to 0 so status readers don't see a
# raw -1 or a negative usage_percent on the transitional turn. Mirrors
# the CLI/gateway status-bar paths (cli.py, tui_gateway/server.py).
last_prompt = self.last_prompt_tokens if self.last_prompt_tokens > 0 else 0
return {
"last_prompt_tokens": last_prompt,
"threshold_tokens": self.threshold_tokens,
"context_length": self.context_length,
"usage_percent": (
min(100, last_prompt / self.context_length * 100)
if self.context_length else 0
),
"compression_count": self.compression_count,
}
# -- Optional: model switch support ------------------------------------
def update_model(
self,
model: str,
context_length: int,
base_url: str = "",
api_key: str = "",
provider: str = "",
api_mode: str = "",
) -> None:
"""Called when the user switches models or on fallback activation.
Default updates context_length and recalculates threshold_tokens
from threshold_percent. Override if your engine needs more
(e.g. recalculate DAG budgets, switch summary models).
"""
self.context_length = context_length
# Apply per-model threshold overrides if set (longest substring match).
# Falls back to _config_threshold_percent (the raw config value) when
# no override matches. Plugin engines that override update_model() can
# call resolve_model_threshold() for the same logic.
from agent.context_compressor import resolve_model_threshold
if not hasattr(self, "_config_threshold_percent"):
# Snapshot the pre-override percent ONCE so repeated model
# switches fall back to the engine's configured value, not the
# previous model's override.
self._config_threshold_percent = self.threshold_percent
self._base_threshold_percent = resolve_model_threshold(
model, getattr(self, "model_thresholds", {}),
self._config_threshold_percent,
)
self.threshold_percent = self._base_threshold_percent
self.threshold_tokens = int(context_length * self.threshold_percent)
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