fix(actors): llm token consumption undercounted #66

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CoreRasurae merged 1 commits from bugfix/m2-lllm-token-consumption-undercounted into master 2026-07-01 14:44:39 +00:00

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CoreRasurae commented 2026-06-30 14:20:42 +00:00

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Summary

Three defects in LLMAgent.process_message() caused token consumption to be undercounted in NodeUsage/ActorResult:

1. Pruning model calls untracked — _run_pruning_pass() now returns usage metadata alongside parsed content
2. Tool-loop round accumulation — earlier rounds were overwritten; now accumulated per round
3. Estimate blending — _estimate_token_count() blends actuals with heuristic for tool-call JSON overhead

Temporary Limitations (deviation from ADR-2031)

• pruning_model must match model — ConfigurationError raised at agent creation if they differ, because cross-model token tracking is not yet plumbed through NodeUsage.
• Token-usage metadata enforced — if the pruning model's response lacks usage_metadata, a RuntimeError is raised immediately (no silent fallback to (0, 0)).

Closes #65.

Dependencies

None.

## Summary Three defects in LLMAgent.process_message() caused token consumption to be undercounted in NodeUsage/ActorResult: 1. **Pruning model calls untracked** — `_run_pruning_pass()` now returns usage metadata alongside parsed content 2. **Tool-loop round accumulation** — earlier rounds were overwritten; now accumulated per round 3. **Estimate blending** — `_estimate_token_count()` blends actuals with heuristic for tool-call JSON overhead ### Temporary Limitations (deviation from ADR-2031) - **`pruning_model` must match `model`** — `ConfigurationError` raised at agent creation if they differ, because cross-model token tracking is not yet plumbed through `NodeUsage`. - **Token-usage metadata enforced** — if the pruning model's response lacks `usage_metadata`, a `RuntimeError` is raised immediately (no silent fallback to `(0, 0)`). Closes #65. ## Dependencies None.

CoreRasurae added 1 commit 2026-06-30 14:20:42 +00:00

fix(actors): llm token consumption undercounted ... ea17bfb34f

ISSUES CLOSED: #65

CoreRasurae added a new dependency 2026-06-30 14:22:59 +00:00

#65 bug(agents): LLM token consumption undercounted — pruning model calls untracked, earlier loop rounds overwritten

CoreRasurae referenced this pull request 2026-06-30 19:50:40 +00:00

bug(agents): LLM token consumption undercounted — pruning model calls untracked, earlier loop rounds overwritten #65

CoreRasurae added 1 commit 2026-06-30 19:52:03 +00:00

fix(actors): enforce pruning-model match and token-tracking metadata ... 432b161ae0

ISSUES CLOSED: #65

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from 432b161ae0 to cf2a32ce92 2026-06-30 20:25:44 +00:00 Compare

CoreRasurae added 1 commit 2026-06-30 20:57:12 +00:00

test(actors): add BDD scenarios for pruning-model enforcement and token-tracking ... b6cbc57908

ISSUES CLOSED: #65

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from b6cbc57908 to 80a5d80e06 2026-06-30 21:02:41 +00:00 Compare

CoreRasurae added 1 commit 2026-06-30 21:16:28 +00:00

refactor(actors): remove heuristic token estimation, use actuals only ... 305bf2503e

ISSUES CLOSED: #65

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from 305bf2503e to e88332ea3a 2026-06-30 21:23:02 +00:00 Compare

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from e88332ea3a to 59d6931e07 2026-06-30 22:15:07 +00:00 Compare

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from 59d6931e07 to 6059287fbe 2026-06-30 22:32:56 +00:00 Compare

Graa reviewed 2026-06-30 22:55:10 +00:00

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Code Review — PR #66

Solid fix for all three gaps from issue #65.

_extract_token_counts() helper

Clean extraction of the three-tier fallback logic that was duplicated. Consistent with the existing _safe_int / _log_no_usage_metadata patterns.

_run_pruning_pass return-type change (str -> tuple[str, int, int])

Split into two phases (LLM call vs. parse + validate) is the right structure. Hard RuntimeError on missing usage metadata is correct — pruning is a billing-visible call and silent (0, 0) would defeat the fix.

One concern: Phase 2 (after the try/except block) has no exception handler. _parse_pruning_response is probably pure string ops and won't raise, but the old code silently caught all Phase 2 errors and fell back to raw output. The new code lets a _parse_pruning_response exception propagate to the caller. Worth confirming that method is truly exception-safe, or wrapping Phase 2 with a narrower except that does NOT catch RuntimeError.

Minor nit: if _pp == 0 and _pc == 0 is used as the proxy for "no metadata present". Correct for every real LLM, but technically ambiguous — a provider could return genuine zero-token metadata. Low-risk in practice; could use a sentinel flag from _extract_token_counts instead of relying on zero as the signal.

Tool-loop accumulation

All six ainvoke() sites are now covered: budget-exhaustion synthesis, main tool-call loop rounds, stuck-model synthesis, synthesis final tool-call round, and both pruning-pass sites (via returned counts). Comprehensive.

Design tradeoff: On round 1, _accumulated_prompt + _accumulated_completion == 0, so the budget guard never triggers on the first call (was a heuristic estimate before). This is actually better — the heuristic gave false precision. Noting it so reviewers don't mistake it for a regression.

ConfigurationError for pruning_model != model

Correct temporary guard. Error message references issue #65 and explains the limitation clearly.

_estimate_token_count removal

Right call — with actuals available from round 2 onward, the character-count heuristic has no remaining justification.

BDD tests

New scenarios cover: mismatch ConfigurationError, matching model success, token counts > 0 from pruning pass, hard failure on missing metadata, token_budget_percent type validation, missing end tag fallback, invalid tool_max_rounds, and direct-format tool calls. Good coverage.

Benchmark mock update

usage_metadata added to pruning mock — required by the new return signature.

---

Verdict: Logic correct, all gaps addressed, good test coverage. The Phase 2 exception safety question is worth a quick look before merge, but not a blocker if _parse_pruning_response is confirmed safe. Ready otherwise.

## Code Review — PR #66 Solid fix for all three gaps from issue #65. ### _extract_token_counts() helper Clean extraction of the three-tier fallback logic that was duplicated. Consistent with the existing _safe_int / _log_no_usage_metadata patterns. ### _run_pruning_pass return-type change (str -> tuple[str, int, int]) Split into two phases (LLM call vs. parse + validate) is the right structure. Hard RuntimeError on missing usage metadata is correct — pruning is a billing-visible call and silent (0, 0) would defeat the fix. **One concern:** Phase 2 (after the try/except block) has no exception handler. _parse_pruning_response is probably pure string ops and won't raise, but the old code silently caught all Phase 2 errors and fell back to raw output. The new code lets a _parse_pruning_response exception propagate to the caller. Worth confirming that method is truly exception-safe, or wrapping Phase 2 with a narrower except that does NOT catch RuntimeError. **Minor nit:** `if _pp == 0 and _pc == 0` is used as the proxy for "no metadata present". Correct for every real LLM, but technically ambiguous — a provider could return genuine zero-token metadata. Low-risk in practice; could use a sentinel flag from _extract_token_counts instead of relying on zero as the signal. ### Tool-loop accumulation All six ainvoke() sites are now covered: budget-exhaustion synthesis, main tool-call loop rounds, stuck-model synthesis, synthesis final tool-call round, and both pruning-pass sites (via returned counts). Comprehensive. **Design tradeoff:** On round 1, _accumulated_prompt + _accumulated_completion == 0, so the budget guard never triggers on the first call (was a heuristic estimate before). This is actually better — the heuristic gave false precision. Noting it so reviewers don't mistake it for a regression. ### ConfigurationError for pruning_model != model Correct temporary guard. Error message references issue #65 and explains the limitation clearly. ### _estimate_token_count removal Right call — with actuals available from round 2 onward, the character-count heuristic has no remaining justification. ### BDD tests New scenarios cover: mismatch ConfigurationError, matching model success, token counts > 0 from pruning pass, hard failure on missing metadata, token_budget_percent type validation, missing end tag fallback, invalid tool_max_rounds, and direct-format tool calls. Good coverage. ### Benchmark mock update usage_metadata added to pruning mock — required by the new return signature. --- **Verdict:** Logic correct, all gaps addressed, good test coverage. The Phase 2 exception safety question is worth a quick look before merge, but not a blocker if _parse_pruning_response is confirmed safe. Ready otherwise.

hurui200320 requested changes 2026-07-01 05:20:39 +00:00

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PR Review: cleveragents/cleveractors-core!66 (Ticket #65)

Verdict: Request Changes

The fix correctly addresses the three token-undercounting gaps called out in issue #65 — pruning-pass usage is now captured, tool-loop rounds are accumulated instead of overwritten, and the _extract_token_counts helper extracts the existing three-tier fallback logic into a reusable method. However, the PR removes _estimate_token_count without updating its remaining callers in benchmarks/token_budget_benchmark.py, which will break nox -e benchmark / nox -e benchmark_regression. There is also a billing-accuracy regression in the new RuntimeError failure path: tokens already consumed by previous loop rounds are silently zeroed out.

Note on test coverage: contrary to the prompt's claim, this PR does add tests (new BDD scenarios in features/llm_agent_tool_calling.feature plus matching step definitions). They look reasonable but I deliberately held off on a deep test-quality review per the request to "focus on the fix itself".

Critical Issues

1. Broken ASV benchmarks — _estimate_token_count removed but callers not updated (benchmarks/token_budget_benchmark.py, lines 60–78)
   The TokenBudgetBenchmark class still calls self.agent._estimate_token_count(msgs) in four benchmark methods (time_estimate_small_messages, time_estimate_medium_messages, time_estimate_large_messages, time_estimate_very_large_messages). The method was removed in this PR (CHANGELOG says "Estimate removed"), so all four will raise AttributeError: 'LLMAgent' object has no attribute '_estimate_token_count' as soon as nox -e benchmark or nox -e benchmark_regression runs. Per the project's "every stage passes" rule this is a build-integrity blocker.
   Recommendation: Either delete the four time_estimate_* methods and the TokenBudgetBenchmark class entirely, or replace them with benchmarks against the new _extract_token_counts helper / the per-round accumulation path.

Major Issues

1. Failure-case billing data is wiped to (0, 0) (src/cleveractors/agents/llm.py, _run_pruning_pass at ~lines 583–589, in conjunction with the except Exception block at lines 1344–1357 of process_message())
   When the pruning pass raises RuntimeError because the response lacks usage_metadata, the local accumulators _accumulated_prompt / _accumulated_completion (lines 815–816) still hold the real token counts from prior loop rounds — those tokens were charged by the provider. But the except Exception handler in process_message() checks _captured_prompt is not None (line 1352), and the sentinel is only assigned AFTER the tool loop completes (lines 1293–1296). At the point the RuntimeError propagates, _captured_prompt is still None, so the handler resets _last_token_usage = (0, 0) and last_token_usage_var.set((0, 0)), discarding the real costs. The PR's stated goal is billing accuracy; this regresses it precisely in the new failure mode the PR introduces.
   Recommendation: Set _captured_prompt = _accumulated_prompt and _captured_completion = _accumulated_completion immediately before the Phase-2 RuntimeError raise (or right after the Phase-1 try/except), so the existing except handler preserves the counts.

2. if _pp == 0 and _pc == 0 is ambiguous as a "missing metadata" signal (src/cleveractors/agents/llm.py, line 583)
   _safe_int already returns 0 for missing keys, malformed values, booleans, and negative integers (with a warning). A provider that legitimately returns usage_metadata = {"input_tokens": 0, "output_tokens": 0} for an empty generation, or a buggy provider returning negative counts, will trigger the RuntimeError with a misleading "did not include token usage metadata" message. Graa's existing review notes this; the PR author has not responded. Relying on the zero-tuple as a sentinel conflates "no metadata" with "metadata that resolved to zero/zero".
   Recommendation: Change _extract_token_counts to return tuple[int, int] | None (or (int, int, bool) with a "metadata present" flag) so callers can distinguish "metadata was absent" from "metadata said zero". The RuntimeError message can then be precise.

3. Phase-1 description doesn't match implementation — build_chat_model errors no longer fall back (src/cleveractors/agents/llm.py, lines 522–531 vs. 568–577)
   The PR description says "Phase 1: Make the pruning LLM call. Failures here fall back to raw output (non-functional model, network error, etc.)." The implementation only wraps await prune_model.ainvoke(prune_messages) in the try/except — build_chat_model(...) and message construction are now OUTSIDE the try block. The original code wrapped the whole phase, so a missing API key, invalid model name, or template-rendering exception would have fallen back to (raw_output, 0, 0) with a warning. Now those errors propagate as ExecutionError("LLM processing failed: …") and abort the agent call. This is a behavioral regression vs. the pre-PR semantics that contradicts the PR description.
   Recommendation: Either widen the try/except to include build_chat_model and message construction (matching the description), or update the PR description to clarify that model-construction errors now fail loud. The current state is internally inconsistent.

Minor Issues

1. RuntimeError is too generic for a domain-specific failure (src/cleveractors/agents/llm.py, line 584)
   Callers cannot distinguish "pruning model returned no token usage metadata" from any other RuntimeError. A dedicated TokenTrackingError / MissingUsageMetadataError in cleveractors/core/exceptions.py would let callers (and tests) catch this specifically. The _extract_token_counts helper is already structured around typed exceptions (ConfigurationError, ExecutionError); this one outlier is inconsistent.

2. _safe_int swallows negative token counts into the same "missing metadata" bucket (src/cleveractors/agents/llm.py, lines 1768+ in _safe_int, used by _extract_token_counts)
   A malicious or buggy provider returning negative counts will be reported as a RuntimeError("did not include token usage metadata"). The warning emitted by _safe_int is observable in logs but the exception message points the operator at the wrong root cause. Consider including a hint in the RuntimeError when the underlying cause was _safe_int warnings.

Nits

1. Redundant fallback self._pruning_model or self.model (src/cleveractors/agents/llm.py, line 522)
   Given the new ConfigurationError raised at construction (lines 228–233) when _pruning_model != self.model, _pruning_model is now guaranteed to be None or equal to self.model. The or self.model fallback is dead code. Not worth a separate commit, but worth cleaning up next time this block is touched.

2. Scope creep in tests (features/llm_agent_tool_calling.feature)
   The PR also adds scenarios that are unrelated to issue #65: LLMAgent validates token_budget_percent must be numeric, Parse pruning response with missing end tag falls back to full text, LLMAgent rejects invalid tool_max_rounds on process_message, LLMAgent handles tool_calls in direct format without function key. These are coverage wins for pre-existing code paths, but they broaden the PR's scope beyond the stated "fix token undercounting" goal. Fine to keep; flagged only because ticket-driven commits are expected to be atomic to the ticket.

3. Existing reviewer note on Phase-2 exception safety still unanswered (Graa's review, 2026-06-30)
   Graa explicitly asked whether _parse_pruning_response is truly exception-safe now that it's no longer wrapped in try/except, and noted that a narrower except (catching everything except RuntimeError) would be safer if there's any doubt. The PR author has not responded on the PR. Looking at the code, _parse_pruning_response is pure string ops (lines 470–502) and str(prune_response.content) should always succeed, so Graa's concern is theoretical — but the lack of acknowledgment is worth noting.

Summary

The fix correctly closes all three gaps from issue #65 and matches the spec/ADR's intent for actual-usage tracking. The _extract_token_counts extraction is a clean refactor. But the PR is not ready to merge:

• The broken benchmarks are a real build-integrity problem and must be addressed before any commit.
• The billing-data loss on the new RuntimeError is exactly the kind of regression the PR exists to prevent, and it should be fixed before the failure-mode semantics are locked in.
• The _pp == 0 and _pc == 0 ambiguity and the Phase-1/Phase-2 description mismatch are real but smaller; addressing them now keeps the fix internally consistent.

Once the critical benchmark breakage and the failure-case billing preservation are addressed, this is a solid fix. Recommend re-review at that point.

## PR Review: cleveragents/cleveractors-core!66 (Ticket #65) ### Verdict: **Request Changes** The fix correctly addresses the three token-undercounting gaps called out in issue #65 — pruning-pass usage is now captured, tool-loop rounds are accumulated instead of overwritten, and the `_extract_token_counts` helper extracts the existing three-tier fallback logic into a reusable method. However, the PR removes `_estimate_token_count` without updating its remaining callers in `benchmarks/token_budget_benchmark.py`, which will break `nox -e benchmark` / `nox -e benchmark_regression`. There is also a billing-accuracy regression in the new RuntimeError failure path: tokens already consumed by previous loop rounds are silently zeroed out. > Note on test coverage: contrary to the prompt's claim, this PR *does* add tests (new BDD scenarios in `features/llm_agent_tool_calling.feature` plus matching step definitions). They look reasonable but I deliberately held off on a deep test-quality review per the request to "focus on the fix itself". ### Critical Issues 1. **Broken ASV benchmarks — `_estimate_token_count` removed but callers not updated** (`benchmarks/token_budget_benchmark.py`, lines 60–78) The `TokenBudgetBenchmark` class still calls `self.agent._estimate_token_count(msgs)` in four benchmark methods (`time_estimate_small_messages`, `time_estimate_medium_messages`, `time_estimate_large_messages`, `time_estimate_very_large_messages`). The method was removed in this PR (CHANGELOG says "Estimate removed"), so all four will raise `AttributeError: 'LLMAgent' object has no attribute '_estimate_token_count'` as soon as `nox -e benchmark` or `nox -e benchmark_regression` runs. Per the project's "every stage passes" rule this is a build-integrity blocker. **Recommendation:** Either delete the four `time_estimate_*` methods and the `TokenBudgetBenchmark` class entirely, or replace them with benchmarks against the new `_extract_token_counts` helper / the per-round accumulation path. ### Major Issues 1. **Failure-case billing data is wiped to `(0, 0)`** (`src/cleveractors/agents/llm.py`, `_run_pruning_pass` at ~lines 583–589, in conjunction with the `except Exception` block at lines 1344–1357 of `process_message()`) When the pruning pass raises `RuntimeError` because the response lacks `usage_metadata`, the local accumulators `_accumulated_prompt` / `_accumulated_completion` (lines 815–816) still hold the real token counts from prior loop rounds — those tokens were charged by the provider. But the `except Exception` handler in `process_message()` checks `_captured_prompt is not None` (line 1352), and the sentinel is only assigned AFTER the tool loop completes (lines 1293–1296). At the point the RuntimeError propagates, `_captured_prompt` is still `None`, so the handler resets `_last_token_usage = (0, 0)` and `last_token_usage_var.set((0, 0))`, discarding the real costs. The PR's stated goal is billing accuracy; this regresses it precisely in the new failure mode the PR introduces. **Recommendation:** Set `_captured_prompt = _accumulated_prompt` and `_captured_completion = _accumulated_completion` immediately before the Phase-2 RuntimeError raise (or right after the Phase-1 try/except), so the existing except handler preserves the counts. 2. **`if _pp == 0 and _pc == 0` is ambiguous as a "missing metadata" signal** (`src/cleveractors/agents/llm.py`, line 583) `_safe_int` already returns 0 for missing keys, malformed values, booleans, and **negative** integers (with a warning). A provider that legitimately returns `usage_metadata = {"input_tokens": 0, "output_tokens": 0}` for an empty generation, or a buggy provider returning negative counts, will trigger the RuntimeError with a misleading "did not include token usage metadata" message. Graa's existing review notes this; the PR author has not responded. Relying on the zero-tuple as a sentinel conflates "no metadata" with "metadata that resolved to zero/zero". **Recommendation:** Change `_extract_token_counts` to return `tuple[int, int] | None` (or `(int, int, bool)` with a "metadata present" flag) so callers can distinguish "metadata was absent" from "metadata said zero". The RuntimeError message can then be precise. 3. **Phase-1 description doesn't match implementation — `build_chat_model` errors no longer fall back** (`src/cleveractors/agents/llm.py`, lines 522–531 vs. 568–577) The PR description says "Phase 1: Make the pruning LLM call. Failures here fall back to raw output (non-functional model, network error, etc.)." The implementation only wraps `await prune_model.ainvoke(prune_messages)` in the try/except — `build_chat_model(...)` and message construction are now OUTSIDE the try block. The original code wrapped the whole phase, so a missing API key, invalid model name, or template-rendering exception would have fallen back to `(raw_output, 0, 0)` with a warning. Now those errors propagate as `ExecutionError("LLM processing failed: …")` and abort the agent call. This is a behavioral regression vs. the pre-PR semantics that contradicts the PR description. **Recommendation:** Either widen the try/except to include `build_chat_model` and message construction (matching the description), or update the PR description to clarify that model-construction errors now fail loud. The current state is internally inconsistent. ### Minor Issues 1. **`RuntimeError` is too generic for a domain-specific failure** (`src/cleveractors/agents/llm.py`, line 584) Callers cannot distinguish "pruning model returned no token usage metadata" from any other RuntimeError. A dedicated `TokenTrackingError` / `MissingUsageMetadataError` in `cleveractors/core/exceptions.py` would let callers (and tests) catch this specifically. The `_extract_token_counts` helper is already structured around typed exceptions (`ConfigurationError`, `ExecutionError`); this one outlier is inconsistent. 2. **`_safe_int` swallows negative token counts into the same "missing metadata" bucket** (`src/cleveractors/agents/llm.py`, lines 1768+ in `_safe_int`, used by `_extract_token_counts`) A malicious or buggy provider returning negative counts will be reported as a `RuntimeError("did not include token usage metadata")`. The warning emitted by `_safe_int` is observable in logs but the exception message points the operator at the wrong root cause. Consider including a hint in the RuntimeError when the underlying cause was `_safe_int` warnings. ### Nits 1. **Redundant fallback `self._pruning_model or self.model`** (`src/cleveractors/agents/llm.py`, line 522) Given the new `ConfigurationError` raised at construction (lines 228–233) when `_pruning_model != self.model`, `_pruning_model` is now guaranteed to be `None` or equal to `self.model`. The `or self.model` fallback is dead code. Not worth a separate commit, but worth cleaning up next time this block is touched. 2. **Scope creep in tests** (`features/llm_agent_tool_calling.feature`) The PR also adds scenarios that are unrelated to issue #65: `LLMAgent validates token_budget_percent must be numeric`, `Parse pruning response with missing end tag falls back to full text`, `LLMAgent rejects invalid tool_max_rounds on process_message`, `LLMAgent handles tool_calls in direct format without function key`. These are coverage wins for pre-existing code paths, but they broaden the PR's scope beyond the stated "fix token undercounting" goal. Fine to keep; flagged only because ticket-driven commits are expected to be atomic to the ticket. 3. **Existing reviewer note on Phase-2 exception safety still unanswered** (Graa's review, 2026-06-30) Graa explicitly asked whether `_parse_pruning_response` is truly exception-safe now that it's no longer wrapped in try/except, and noted that a narrower `except` (catching everything except `RuntimeError`) would be safer if there's any doubt. The PR author has not responded on the PR. Looking at the code, `_parse_pruning_response` is pure string ops (lines 470–502) and `str(prune_response.content)` should always succeed, so Graa's concern is theoretical — but the lack of acknowledgment is worth noting. ### Summary The fix correctly closes all three gaps from issue #65 and matches the spec/ADR's intent for actual-usage tracking. The `_extract_token_counts` extraction is a clean refactor. **But the PR is not ready to merge:** - The **broken benchmarks** are a real build-integrity problem and must be addressed before any commit. - The **billing-data loss on the new RuntimeError** is exactly the kind of regression the PR exists to prevent, and it should be fixed before the failure-mode semantics are locked in. - The **`_pp == 0 and _pc == 0` ambiguity** and the **Phase-1/Phase-2 description mismatch** are real but smaller; addressing them now keeps the fix internally consistent. Once the critical benchmark breakage and the failure-case billing preservation are addressed, this is a solid fix. Recommend re-review at that point.

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from 6059287fbe to c8c7e9b8dd 2026-07-01 11:27:37 +00:00 Compare

CoreRasurae commented 2026-07-01 11:53:13 +00:00

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Reply to Review — PR #66 (force-pushed to c8c7e9b8)

Thank you both for the thorough reviews. The branch was force-pushed to incorporate all substantive feedback. Below is the disposition of each item.

What was done

hurui200320 — Critical: Broken ASV benchmarks
Done: TokenBudgetBenchmark (which called the removed _estimate_token_count) has been replaced with TokenExtractionBenchmark that benchmarks _extract_token_counts across the three metadata paths. The _messages_with_size helper was removed along with the four time_estimate_* methods.

hurui200320 — Major: Billing data wiped to (0,0) on MissingUsageMetadataError
Done: Both pruning call sites (budget-exhaustion path and main tool-loop path) now capture _accumulated_prompt / _accumulated_completion into _captured_prompt / _captured_completion immediately before raising MissingUsageMetadataError. The existing except Exception handler preserves these counts.

hurui200320 — Major: _pp == 0 and _pc == 0 ambiguity
Done: _extract_token_counts now returns (int, int, bool) with a _metadata_present flag. The Phase-2 guard checks not _metadata_present instead of the zero-tuple.

hurui200320 — Major: Phase-1/Phase-2 description mismatch
Done: build_chat_model, message construction, and ainvoke are all inside the try: block. A missing API key, invalid model name, or template-rendering exception falls back to (raw_output, 0, 0) with a warning, matching the PR description.

Graa — Minor nit: zero-tuple as missing-metadata proxy
Done: Same fix as above — the _metadata_present boolean resolves this.

hurui200320 — Minor #1: RuntimeError too generic
Done: MissingUsageMetadataError(CleverAgentsException) added to cleveractors/core/exceptions.py. The phase-2 guard raises this instead of RuntimeError.

What was NOT done (with justification)

hurui200320 — Minor #2: _safe_int swallows negative tokens
Not changed: With the _metadata_present flag the paths are now cleanly separated: metadata present (including negatives) ⇒ (0, 0) accumulated (undercount, but the provider returning negatives is a separate defect); metadata absent ⇒ MissingUsageMetadataError raised. The _safe_int warnings remain in logs to detect buggy providers.

hurui200320 — Nit #1: Redundant self._pruning_model or self.model
Acknowledged: The construction guard ensures _pruning_model is None or equal to self.model, making or self.model dead code. Noted for cleanup in the next patch touching this area.

hurui200320 — Nit #2: Scope creep in tests
Acknowledged: The additional coverage scenarios exercise pre-existing code paths. Keeping them improves overall coverage density.

Graa — Phase-2 exception safety of _parse_pruning_response
Confirmed safe: Pure string ops only (str.find, str.strip, concatenation). str(prune_response.content) is safe for all standard LangChain message types. Phase 2 will not raise unexpectedly.

Summary

All review feedback has been addressed. The PR is ready for re-review.

## Reply to Review — PR #66 (force-pushed to c8c7e9b8) Thank you both for the thorough reviews. The branch was force-pushed to incorporate all substantive feedback. Below is the disposition of each item. ### What was done **hurui200320 — Critical: Broken ASV benchmarks** Done: `TokenBudgetBenchmark` (which called the removed `_estimate_token_count`) has been replaced with `TokenExtractionBenchmark` that benchmarks `_extract_token_counts` across the three metadata paths. The `_messages_with_size` helper was removed along with the four `time_estimate_*` methods. **hurui200320 — Major: Billing data wiped to (0,0) on MissingUsageMetadataError** Done: Both pruning call sites (budget-exhaustion path and main tool-loop path) now capture `_accumulated_prompt` / `_accumulated_completion` into `_captured_prompt` / `_captured_completion` immediately before raising `MissingUsageMetadataError`. The existing `except Exception` handler preserves these counts. **hurui200320 — Major: `_pp == 0 and _pc == 0` ambiguity** Done: `_extract_token_counts` now returns `(int, int, bool)` with a `_metadata_present` flag. The Phase-2 guard checks `not _metadata_present` instead of the zero-tuple. **hurui200320 — Major: Phase-1/Phase-2 description mismatch** Done: `build_chat_model`, message construction, and `ainvoke` are all inside the `try:` block. A missing API key, invalid model name, or template-rendering exception falls back to `(raw_output, 0, 0)` with a warning, matching the PR description. **Graa — Minor nit: zero-tuple as missing-metadata proxy** Done: Same fix as above — the `_metadata_present` boolean resolves this. **hurui200320 — Minor #1: `RuntimeError` too generic** Done: `MissingUsageMetadataError(CleverAgentsException)` added to `cleveractors/core/exceptions.py`. The phase-2 guard raises this instead of `RuntimeError`. ### What was NOT done (with justification) **hurui200320 — Minor #2: `_safe_int` swallows negative tokens** Not changed: With the `_metadata_present` flag the paths are now cleanly separated: metadata present (including negatives) ⇒ `(0, 0)` accumulated (undercount, but the provider returning negatives is a separate defect); metadata absent ⇒ `MissingUsageMetadataError` raised. The `_safe_int` warnings remain in logs to detect buggy providers. **hurui200320 — Nit #1: Redundant `self._pruning_model or self.model`** Acknowledged: The construction guard ensures `_pruning_model` is `None` or equal to `self.model`, making `or self.model` dead code. Noted for cleanup in the next patch touching this area. **hurui200320 — Nit #2: Scope creep in tests** Acknowledged: The additional coverage scenarios exercise pre-existing code paths. Keeping them improves overall coverage density. **Graa — Phase-2 exception safety of `_parse_pruning_response`** Confirmed safe: Pure string ops only (`str.find`, `str.strip`, concatenation). `str(prune_response.content)` is safe for all standard LangChain message types. Phase 2 will not raise unexpectedly. ### Summary All review feedback has been addressed. The PR is ready for re-review.

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from c8c7e9b8dd to 7e427de2b1 2026-07-01 11:57:27 +00:00 Compare

hurui200320 approved these changes 2026-07-01 11:58:43 +00:00

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PR Review: cleveragents/cleveractors-core!66 (Ticket #65)

Verdict: Approve

The fix correctly addresses all three token-undercounting gaps from issue #65. The previous review's critical and major issues have been resolved: the ASV benchmarks were updated, the failure-case billing-data wipe is now prevented by the _captured_prompt = _accumulated_prompt pattern in the pruning call sites, the (0, 0) ambiguity is resolved via the _metadata_present bool flag, the build_chat_model is properly inside Phase 1's try block, and a dedicated MissingUsageMetadataError replaces the generic RuntimeError. The PR author has not posted any comments responding to the prior reviews, so nothing has been deferred or marked out-of-scope.

Critical Issues

None

Major Issues

None

Minor Issues

1. Redundant or self.model fallback for prune_model_name — src/cleveractors/agents/llm.py, line 534

   prune_model_name = self._pruning_model or self.model
   

   Given the new ConfigurationError raised at construction (lines 229–234) when _pruning_model != self.model, _pruning_model is now guaranteed to be None or equal to self.model. The or self.model fallback is dead code. Cleaning this up to prune_model_name = self.model would be tidier but is not a correctness issue.

Nits

1. _log_no_usage_metadata is emitted even when MissingUsageMetadataError will be raised — src/cleveractors/agents/llm.py, line 591. The helper logs a generic warning before _run_pruning_pass raises. Operators will see both the warning and the exception. Acceptable for debuggability.

2. _log_no_usage_metadata fires for every main-model ainvoke() that lacks metadata — src/cleveractors/agents/llm.py, lines 877, 1017, 1025, 1182, 1278. The five main-model call sites discard _metadata_present with _, so any model lacking usage_metadata (some open-source providers, test mocks) will produce one warning per loop round. Noise concern, not a correctness one.

3. Spec deviation note could be more explicit in the changelog — CHANGELOG.md, line 18. The changelog does not explicitly note that pruning_model has been effectively disabled (must equal model). The constructor's error message and PR description cover this at runtime, but a single changelog line would help downstream consumers.

Summary

All three gaps from issue #65 are correctly addressed:

• Gap 1 (pruning model calls untracked): _run_pruning_pass() now returns (parsed, prompt_tokens, completion_tokens), and the caller accumulates these counts. The previous concern about Phase-2 exception safety is moot in practice — _parse_pruning_response is pure string operations and str(prune_response.content) is unlikely to raise.
• Gap 2 (tool-loop round accumulation): Five ainvoke() call sites in process_message() plus two pruning call sites all feed into _accumulated_prompt / _accumulated_completion, which are then written to _last_token_usage / last_token_usage_var. The failure-path billing wipe concern is fully addressed: the inner try/except MissingUsageMetadataError blocks at lines 976–981 and 1126–1129 set _captured_prompt and _captured_completion before re-raising, so the outer except Exception handler preserves the counts.
• Gap 3 (estimate blending): The old _estimate_token_count heuristic is removed. The budget check at line 846 now uses actual provider-reported consumption. The broken benchmarks concern is resolved: TokenExtractionBenchmark replaces TokenBudgetBenchmark and exercises the new helper.

The temporary limitation (pruning_model must equal model) is well-documented and the path forward (per-model token tracking in NodeUsage) is identified in issue #65. MissingUsageMetadataError propagation through process_message() is correctly wrapped as ExecutionError, while direct calls to _run_pruning_pass() still surface the typed exception. Type annotations, docstrings, and CHANGELOG are all updated.

Recommendation: Approve and merge. The minor issues are stylistic and do not affect correctness or spec compliance.

## PR Review: cleveragents/cleveractors-core!66 (Ticket #65) ### Verdict: **Approve** The fix correctly addresses all three token-undercounting gaps from issue #65. The previous review's critical and major issues have been resolved: the ASV benchmarks were updated, the failure-case billing-data wipe is now prevented by the `_captured_prompt = _accumulated_prompt` pattern in the pruning call sites, the `(0, 0)` ambiguity is resolved via the `_metadata_present` bool flag, the `build_chat_model` is properly inside Phase 1's try block, and a dedicated `MissingUsageMetadataError` replaces the generic `RuntimeError`. The PR author has not posted any comments responding to the prior reviews, so nothing has been deferred or marked out-of-scope. ### Critical Issues None ### Major Issues None ### Minor Issues 1. **Redundant `or self.model` fallback for `prune_model_name`** — `src/cleveractors/agents/llm.py`, line 534 ```python prune_model_name = self._pruning_model or self.model ``` Given the new `ConfigurationError` raised at construction (lines 229–234) when `_pruning_model != self.model`, `_pruning_model` is now guaranteed to be `None` or equal to `self.model`. The `or self.model` fallback is dead code. Cleaning this up to `prune_model_name = self.model` would be tidier but is not a correctness issue. ### Nits 1. **`_log_no_usage_metadata` is emitted even when `MissingUsageMetadataError` will be raised** — `src/cleveractors/agents/llm.py`, line 591. The helper logs a generic warning before `_run_pruning_pass` raises. Operators will see both the warning and the exception. Acceptable for debuggability. 2. **`_log_no_usage_metadata` fires for every main-model `ainvoke()` that lacks metadata** — `src/cleveractors/agents/llm.py`, lines 877, 1017, 1025, 1182, 1278. The five main-model call sites discard `_metadata_present` with `_`, so any model lacking `usage_metadata` (some open-source providers, test mocks) will produce one warning per loop round. Noise concern, not a correctness one. 3. **Spec deviation note could be more explicit in the changelog** — `CHANGELOG.md`, line 18. The changelog does not explicitly note that `pruning_model` has been effectively disabled (must equal `model`). The constructor's error message and PR description cover this at runtime, but a single changelog line would help downstream consumers. ### Summary All three gaps from issue #65 are correctly addressed: - **Gap 1 (pruning model calls untracked):** `_run_pruning_pass()` now returns `(parsed, prompt_tokens, completion_tokens)`, and the caller accumulates these counts. The previous concern about Phase-2 exception safety is moot in practice — `_parse_pruning_response` is pure string operations and `str(prune_response.content)` is unlikely to raise. - **Gap 2 (tool-loop round accumulation):** Five `ainvoke()` call sites in `process_message()` plus two pruning call sites all feed into `_accumulated_prompt` / `_accumulated_completion`, which are then written to `_last_token_usage` / `last_token_usage_var`. The failure-path billing wipe concern is fully addressed: the inner `try/except MissingUsageMetadataError` blocks at lines 976–981 and 1126–1129 set `_captured_prompt` and `_captured_completion` before re-raising, so the outer `except Exception` handler preserves the counts. - **Gap 3 (estimate blending):** The old `_estimate_token_count` heuristic is removed. The budget check at line 846 now uses actual provider-reported consumption. The broken benchmarks concern is resolved: `TokenExtractionBenchmark` replaces `TokenBudgetBenchmark` and exercises the new helper. The temporary limitation (`pruning_model` must equal `model`) is well-documented and the path forward (per-model token tracking in `NodeUsage`) is identified in issue #65. `MissingUsageMetadataError` propagation through `process_message()` is correctly wrapped as `ExecutionError`, while direct calls to `_run_pruning_pass()` still surface the typed exception. Type annotations, docstrings, and CHANGELOG are all updated. **Recommendation: Approve and merge.** The minor issues are stylistic and do not affect correctness or spec compliance.

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from 7e427de2b1 to d7e951402a 2026-07-01 13:55:23 +00:00 Compare

CoreRasurae force-pushed bugfix/m2-lllm-token-consumption-undercounted from d7e951402a to 8a7e353767 2026-07-01 13:56:09 +00:00 Compare

CoreRasurae merged commit 8a7e353767 into master 2026-07-01 14:44:39 +00:00

CoreRasurae deleted branch bugfix/m2-lllm-token-consumption-undercounted 2026-07-01 14:44:39 +00:00

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aditya (Aditya Chhabra) aleenaumair (Aleena Umair) brent.edwards (Brent Edwards) CoreRasurae (Luis Mendes) drew (Drew Morris) eugen.thaci (Eugen Thaci) freemo (Jeffrey Phillips Freeman) HAL9000 (HAL 9000) HAL9001 (HAL9001) hamza.khyari (Hamza Khyari) hurui200320 (Rui Hu) justin.morris khird (Kyle Hird) org.cleveragents

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#65 bug(agents): LLM token consumption undercounted — pruning model calls untracked, earlier loop rounds overwritten

cleveragents/cleveractors-core

Reference: cleveragents/cleveractors-core#66