Files
hermes-agent/agent/turn_usage.py

313 lines
15 KiB
Python

"""Per-response usage accounting for the conversation turn loop.
After every successful model API call, ``run_conversation`` folds the provider's
``response.usage`` into: the context compressor (``update_from_response`` + the
compression-budget rearm latch), the usage anchor for display/compression math,
per-session token/cost counters, the state.db token-delta queue, and the
observability log line. MoA sessions additionally fold advisor fan-out usage into
the reported counts and price the aggregator at its REAL model/provider.
``record_response_usage`` owns that block. It mutates ``agent`` exactly as the inline
code did and returns the loop-visible verdict (compression budget counter, and
whether a provider-confirmed recovery rearmed it) as ``ResponseUsageOutcome``.
Logger name stays ``agent.conversation_loop`` for caplog/log-routing parity.
"""
from __future__ import annotations
import logging
from dataclasses import dataclass
from typing import Any, Dict, List
from agent.model_metadata import capture_usage_anchor
from agent.usage_pricing import estimate_usage_cost, normalize_usage
logger = logging.getLogger("agent.conversation_loop")
@dataclass
class ResponseUsageOutcome:
"""What the loop reads back after usage accounting.
``compression_attempts`` is the (possibly rearmed-to-zero) budget counter;
``rearmed`` tells the loop to also clear its preflight-block latch."""
compression_attempts: int
rearmed: bool = False
def _loop_mod():
"""Lazy ``agent.conversation_loop`` so tests patching
``agent.conversation_loop.save_context_length`` still intercept, and so this
module never imports the loop at load time (cycle)."""
import agent.conversation_loop as _cl
return _cl
def record_response_usage(
agent: Any,
response: Any,
*,
messages: List[Dict[str, Any]],
api_call_count: int,
api_duration: float,
compression_attempts: int,
max_compression_attempts: int,
) -> ResponseUsageOutcome:
"""Fold ``response.usage`` into compressor, anchors, session counters, state.db
and the API-call log line (see module docstring). No-usage responses only
consume a pending compaction verdict. Returns the loop-visible outcome."""
rearmed = False
# Track actual token usage from response for context management
if hasattr(response, 'usage') and response.usage:
canonical_usage = normalize_usage(
response.usage,
provider=agent.provider,
api_mode=agent.api_mode,
)
# Aggregator-only usage kept for pricing: advisor tokens are priced
# at each advisor's OWN model rate and added as dollars below.
aggregator_usage = canonical_usage
# MoA: fold advisor fan-out usage into REPORTED token counts — only
# aggregator usage is returned, so advisor spend would be invisible.
_moa_ref_cost = None
_moa_client = getattr(agent, "client", None)
if _moa_client is not None and hasattr(_moa_client, "consume_reference_usage"):
try:
_ref_usage, _moa_ref_cost = _moa_client.consume_reference_usage()
if _ref_usage is not None:
canonical_usage = canonical_usage + _ref_usage
except Exception as _moa_acct_exc: # pragma: no cover - defensive
logger.debug("MoA reference usage accounting failed: %s", _moa_acct_exc)
# Flush the full-turn MoA trace when moa.save_traces is on; on the
# streaming path pass the streamed acting text so the trace is self-
# contained.
if _moa_client is not None and hasattr(_moa_client, "consume_and_save_trace"):
try:
_agg_streamed_text = (
getattr(agent, "_current_streamed_assistant_text", "") or ""
)
_moa_client.consume_and_save_trace(
agent.session_id,
aggregator_output_fallback=_agg_streamed_text or None,
)
except Exception as _moa_trace_exc: # pragma: no cover - defensive
logger.debug("MoA trace flush failed: %s", _moa_trace_exc)
prompt_tokens = canonical_usage.prompt_tokens
completion_tokens = canonical_usage.output_tokens
total_tokens = canonical_usage.total_tokens
# Forward canonical token + cache buckets for context engines;
# legacy keys stay for back-compat.
usage_dict = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"input_tokens": canonical_usage.input_tokens,
"output_tokens": canonical_usage.output_tokens,
"cache_read_tokens": canonical_usage.cache_read_tokens,
"cache_write_tokens": canonical_usage.cache_write_tokens,
"reasoning_tokens": canonical_usage.reasoning_tokens,
}
# Capture the boundary latch before update_from_response() consumes
# it: only the real prompt count right after a compaction rearms the
# budget.
_completed_compaction_pending = bool(
getattr(
agent.context_compressor,
"_verify_compaction_cleared_threshold",
False,
)
)
agent.context_compressor.update_from_response(usage_dict)
# Usage-anchored accounting: snapshot exact provider usage against
# the durable transcript; main-loop ONLY. MoA uses pre-fold
# aggregator usage.
_new_anchor = capture_usage_anchor(
aggregator_usage.prompt_tokens,
aggregator_usage.output_tokens,
messages,
)
if _new_anchor is not None:
agent._usage_anchor = _new_anchor
# Anchor the display meter on the turn's FIRST response:
# later same-turn responses inflate prompt_tokens with replayed
# thinking. Display-only; compression math uses real usage.
if api_call_count == 1:
agent._turn_base_usage_anchor = _new_anchor
_compression_threshold = int(
getattr(agent.context_compressor, "threshold_tokens", 0)
or 0
)
if _loop_mod()._should_rearm_compression_budget(
compression_attempts,
completed_compaction_pending=_completed_compaction_pending,
prompt_tokens=prompt_tokens,
threshold_tokens=_compression_threshold,
):
logger.info(
"Compression budget rearmed after provider-confirmed "
"recovery: prompt=%s < threshold=%s (attempts were %s/%s)",
f"{prompt_tokens:,}",
f"{_compression_threshold:,}",
compression_attempts,
max_compression_attempts,
)
compression_attempts = 0
# Confirmed recovery also clears the loop's stale insufficient-progress
# verdict (``_preflight_compression_blocked``), else it stays armed all
# turn and a later pressure spike grows unchecked.
rearmed = True
# Stash canonical usage for on_turn_complete() (same shape as
# update_from_response); keep the latest call's — last request.
agent._last_turn_usage = dict(usage_dict)
elif getattr(
agent.context_compressor,
"awaiting_real_usage_after_compression",
False,
):
# No usage -> cannot adjudicate the prior compaction; consume the
# pending verdict so later readings aren't charged to it and
# preflight deferral isn't latched indefinitely.
agent.context_compressor.update_from_response({})
if hasattr(response, 'usage') and response.usage:
# Persist only provider-confirmed context lengths, not probe tiers.
if getattr(agent.context_compressor, "_context_probed", False):
ctx = agent.context_compressor.context_length
if getattr(agent.context_compressor, "_context_probe_persistable", False):
_loop_mod().save_context_length(agent.model, agent.base_url, ctx)
agent._safe_print(f"{agent.log_prefix}💾 Cached context length: {ctx:,} tokens for {agent.model}")
agent.context_compressor._context_probed = False
agent.context_compressor._context_probe_persistable = False
agent.session_prompt_tokens += prompt_tokens
agent.session_completion_tokens += completion_tokens
agent.session_total_tokens += total_tokens
agent.session_api_calls += 1
agent.session_input_tokens += canonical_usage.input_tokens
agent.session_output_tokens += canonical_usage.output_tokens
agent.session_cache_read_tokens += canonical_usage.cache_read_tokens
agent.session_cache_write_tokens += canonical_usage.cache_write_tokens
agent.session_reasoning_tokens += canonical_usage.reasoning_tokens
# Rolling history for status-bar averages (last 10).
try:
hist = getattr(agent, "_api_latency_history", None)
if hist is not None:
hist.append(float(api_duration))
ohist = getattr(agent, "_api_output_history", None)
if ohist is not None:
ohist.append(int(canonical_usage.output_tokens or 0))
except Exception:
pass
# Log API call details for debugging/observability
_cache_pct = ""
if canonical_usage.cache_read_tokens and prompt_tokens:
_cache_pct = f" cache={canonical_usage.cache_read_tokens}/{prompt_tokens} ({100*canonical_usage.cache_read_tokens/prompt_tokens:.0f}%)"
logger.info(
"API call #%d: model=%s provider=%s in=%d out=%d total=%d latency=%.1fs%s",
agent.session_api_calls, agent.model, agent.provider or "unknown",
prompt_tokens, completion_tokens, total_tokens,
api_duration, _cache_pct,
)
# MoA: agent.model/provider are the virtual preset/"moa" with no
# pricing entry, silently dropping aggregator spend. Price at the
# REAL model/provider from the MoA client's aggregator slot.
_agg_cost_model = agent.model
_agg_cost_provider = agent.provider
_agg_cost_base_url = agent.base_url
_agg_slot = getattr(_moa_client, "last_aggregator_slot", None) if _moa_client is not None else None
if _agg_slot and _agg_slot.get("model"):
_agg_cost_model = _agg_slot["model"]
_agg_cost_provider = _agg_slot.get("provider") or agent.provider
_agg_cost_base_url = _agg_slot.get("base_url") or agent.base_url
cost_result = estimate_usage_cost(
_agg_cost_model,
aggregator_usage,
provider=_agg_cost_provider,
base_url=_agg_cost_base_url,
api_key=getattr(agent, "api_key", ""),
)
if cost_result.amount_usd is not None:
agent.session_estimated_cost_usd += float(cost_result.amount_usd)
# Add MoA advisor cost (already priced per-advisor at each
# advisor's own model rate) on top of the aggregator cost.
if _moa_ref_cost is not None:
try:
agent.session_estimated_cost_usd += float(_moa_ref_cost)
except (TypeError, ValueError): # pragma: no cover - defensive
pass
agent.session_cost_status = cost_result.status
agent.session_cost_source = cost_result.source
# Persist per-call token deltas for any session_id so non-CLI runs
# can't lose accounting; gateway/session-store writes use absolute
# totals and safely overwrite these deltas.
if agent._session_db and agent.session_id:
try:
# Ensure the row exists: under concurrent SQLite load the
# initial _ensure_db_session() may fail, and UPDATE on a
# missing row silently affects 0 rows.
if not agent._session_db_created:
agent._ensure_db_session()
# Cost delta = aggregator + MoA advisor cost so state.db's
# estimated_cost_usd matches the folded token counts.
_cost_delta = None
if cost_result.amount_usd is not None:
_cost_delta = float(cost_result.amount_usd)
if _moa_ref_cost is not None:
try:
_cost_delta = (_cost_delta or 0.0) + float(_moa_ref_cost)
except (TypeError, ValueError): # pragma: no cover
pass
# Enqueued, not written: a cold state.db UPDATE here stalled
# the tool loop. Drained at finalize via _persist_session.
agent._session_db.queue_token_counts(
agent.session_id,
input_tokens=canonical_usage.input_tokens,
output_tokens=canonical_usage.output_tokens,
cache_read_tokens=canonical_usage.cache_read_tokens,
cache_write_tokens=canonical_usage.cache_write_tokens,
reasoning_tokens=canonical_usage.reasoning_tokens,
estimated_cost_usd=_cost_delta,
cost_status=cost_result.status,
cost_source=cost_result.source,
billing_provider=agent.provider,
billing_base_url=agent.base_url,
billing_mode="subscription_included"
if cost_result.status == "included" else None,
model=agent.model,
api_call_count=1,
)
except Exception as e:
# Log failures — silent loss here undercounts analytics.
logger.debug(
"Token persistence failed (session=%s, tokens=%d): %s",
agent.session_id, total_tokens, e,
)
if agent.verbose_logging:
logging.debug(f"Token usage: prompt={usage_dict['prompt_tokens']:,}, completion={usage_dict['completion_tokens']:,}, total={usage_dict['total_tokens']:,}")
# Report cache stats for any provider that returns
# ``prompt_tokens_details.cached_tokens``, not only when we inject
# cache_control markers. ``canonical_usage`` is already normalised.
cached = canonical_usage.cache_read_tokens
written = canonical_usage.cache_write_tokens
prompt = usage_dict["prompt_tokens"]
if (cached or written) and not agent.quiet_mode:
hit_pct = (cached / prompt * 100) if prompt > 0 else 0
agent._vprint(
f"{agent.log_prefix} 💾 Cache: "
f"{cached:,}/{prompt:,} tokens "
f"({hit_pct:.0f}% hit, {written:,} written)"
)
return ResponseUsageOutcome(
compression_attempts=compression_attempts,
rearmed=rearmed,
)