hataa
c9fb9768d4
fix(subagents): unify guardrail caps on additive stop_reason + add token_budget ( #3875 Phase 2) ( #3980 )
...
Phase 2 of #3875 . Two guardrail axes can end a subagent run early — the turn
budget (GraphRecursionError) and the token budget (TokenBudgetMiddleware) —
and both now surface *why* through one additive `subagent_stop_reason` field
instead of a status enum.
This completes and course-corrects Phase 1 (#3949 ), which shipped the
turn-budget cap as a `max_turns_reached` status enum. The agreed Phase 2
design replaces that enum with an optional `stop_reason` field
(token_capped | turn_capped | loop_capped): a new enum value would break v1
consumers, while an additive field is ignored by older frontends and ledger
readers. `max_turns_reached` and SubagentStatus.MAX_TURNS_REACHED are removed.
- subagents.token_budget config (default enabled, 2,000,000 tokens, warn 0.7)
with per-agent override; TokenBudgetMiddleware is now attached in
build_subagent_runtime_middlewares so the cost-ceiling backstop engages for
every subagent. The hard-stop does not raise — it strips tool_calls and
lets the run finish with a final answer, recording the cap on a per-run
consume_stop_reason() accessor.
- executor.py: on normal completion it reads consume_stop_reason() and stamps
completed + token_capped when the budget fired; on GraphRecursionError it
recovers the last AIMessage partial (completed + turn_capped) or, if nothing
usable survived, failed + turn_capped. SubagentResult gains stop_reason.
- status_contract.py / contracts/subagent_status_contract.json (v2) /
frontend subtask-result.ts: additive subagent_stop_reason field, pinned by
test_status_values_match_contract / test_stop_reason_values_match_contract.
- task_tool.py + delegation_ledger.py: drop the max_turns_reached paths; the
ledger captures stop_reason and renders model-facing "capped" guidance so
the lead reuses a capped completion knowingly.
The 2,000,000-token default is deliberately loose (tighten to taste) — it
would have roughly halved the reported 4.4M burn while leaving legitimate
deep-research runs (max_turns=150) room. Subagent summarization is a follow-up.
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