fix(context_management): count the system prompt toward the clear_tool_uses input_tokens trigger

Anthropic's API compares an input_tokens trigger with the whole prompt, system
included; the polyfill counted messages and tools only, so it cleared late by the
system prompt's size. compact_20260112 already counts it: share its system_to_text.

litellm/llms/anthropic/pass_through/context_management/editors/clear_tool_uses.py

@@ -31,6 +31,7 @@ from ..constants import (
 )
 from ..errors import AnthropicContextManagementError
 from ..placeholders import build_cleared_tool_result_content
+from .compact import system_to_text
 
 T = TypeVar("T")
 # api.anthropic.com, measured 2026-10-19: text results of 1 to 46 bytes are never cleared or counted toward keep
@@ -195,11 +196,20 @@ def _select_in_batches(candidates: Sequence[str], tokens: Mapping[str, int], cle
     return tuple(candidates[:batch_end])
 
 
+def _with_system(
+    messages: list[dict[str, object]], system: str | list[dict[str, object]] | None
+) -> list[dict[str, object]]:
+    """The messages to count: like the API, an ``input_tokens`` trigger counts the system prompt too."""
+    system_text: Final = system_to_text(system)
+    return [{"role": "system", "content": system_text}, *messages] if system_text else messages
+
+
 def _trigger_met(
     trigger: dict[str, object],
     model: str,
     messages: list[dict[str, object]],
     tools: list[dict[str, object]] | None,
+    system: str | list[dict[str, object]] | None,
     counted_tool_uses: int,
 ) -> tuple[bool, int | None]:
     """Return (trigger_met, input_tokens if counted for reuse)."""
@@ -215,7 +225,7 @@ def _trigger_met(
         threshold = DEFAULT_INPUT_TOKENS_TRIGGER
     current_tokens: Final = litellm.token_counter(
         model=model,
-        messages=messages,
+        messages=_with_system(messages, system),
         tools=cast(Any, tools),
     )
     verbose_logger.debug("context_management polyfill: current_tokens: %s", current_tokens)
@@ -332,7 +342,7 @@ def apply_clear_tool_uses_20250919(
         for tool_use_id, name in _tool_uses_in_order(blocks)
         if name not in exclude_tools and (tool_use_id not in tiny or _clears_input(name, input_tools))
     )
-    met, counted_tokens = _trigger_met(trigger, model, messages, tools, len(clearable))
+    met, counted_tokens = _trigger_met(trigger, model, messages, tools, system, len(clearable))
     if not met:
         return messages, None
 
@@ -366,9 +376,11 @@ def apply_clear_tool_uses_20250919(
     tokens_before: Final = (
         counted_tokens
         if counted_tokens is not None
-        else litellm.token_counter(model=model, messages=messages, tools=cast(Any, tools))
+        else litellm.token_counter(model=model, messages=_with_system(messages, system), tools=cast(Any, tools))
+    )
+    tokens_after: Final = litellm.token_counter(
+        model=model, messages=_with_system(final_messages, system), tools=cast(Any, tools)
     )
-    tokens_after: Final = litellm.token_counter(model=model, messages=final_messages, tools=cast(Any, tools))
     cleared_input_tokens: Final = max(tokens_before - tokens_after, 0)
 
     return final_messages, {

litellm/llms/anthropic/pass_through/context_management/editors/compact.py

@@ -320,13 +320,13 @@ def _count_effective_tokens(
         content: Final = compaction_block.get("content") or ""
         if content:
             total += litellm.token_counter(model=model, text=content)
-    system_text: Final = _system_to_text(system)
+    system_text: Final = system_to_text(system)
     if system_text:
         total += litellm.token_counter(model=model, text=system_text)
     return total
 
 
-def _system_to_text(
+def system_to_text(
     system: str | list[dict[str, object]] | None,
 ) -> str:
     """Flatten an Anthropic-style ``system`` value into a single string for

tests/unit/llms/anthropic/pass_through/context_management/test_clear_tool_uses.py

@@ -690,6 +690,31 @@ def test_tool_uses_trigger_does_not_count_excluded_tools(trigger_value: int, tri
     assert (applied is not None) == triggers
 
 
+@pytest.mark.parametrize(
+    "system",
+    ["Reference notes. " * 400, [{"type": "text", "text": "Reference notes. " * 400}]],
+)
+def test_input_tokens_trigger_counts_the_system_prompt(system: str | list[dict[str, object]]) -> None:
+    """Like the API, the trigger compares the whole prompt: a system prompt can push it over."""
+    messages = _make_history(n_pairs=3)
+    history_tokens: Final = litellm.token_counter(model=MODEL, messages=messages)
+    edit_spec: Final = {
+        "type": "clear_tool_uses_20250919",
+        "trigger": {"type": "input_tokens", "value": history_tokens + 100},
+        "keep": {"type": "tool_uses", "value": 1},
+    }
+    _, without_system = apply_clear_tool_uses_20250919(
+        model=MODEL, messages=deepcopy(messages), tools=None, system=None, edit_spec=edit_spec
+    )
+    _, with_system = apply_clear_tool_uses_20250919(
+        model=MODEL, messages=deepcopy(messages), tools=None, system=system, edit_spec=edit_spec
+    )
+    assert without_system is None
+    assert with_system is not None
+    assert with_system["cleared_tool_uses"] == 2
+    assert with_system["cleared_input_tokens"] < history_tokens
+
+
 @pytest.mark.parametrize(("clear_tool_inputs", "triggers"), [(False, False), (True, True)])
 def test_tool_uses_trigger_counts_tiny_results_only_when_their_inputs_are_cleared(
     clear_tool_inputs: bool, triggers: bool