feat(prompt-caching): include system parameter in cache affinity key

Add prepend_system_prompt helper to merge system parameter into messages for cache key computation. Update async_filter_deployments to prepend system prompt before computing affinity and pass tools to async_get_model_id. Update async_log_success_event to prepend system prompt and pass tools to async_add_model_id. Add test coverage for system parameter in cache affinity.

litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py

@@ -74,7 +74,7 @@ class PromptCachingDeploymentCheck(CustomLogger):
             ## AUTO PROMPT CACHING - the breakpoints this request will carry are injected inside
             ## `litellm.acompletion`, after a deployment has been picked, so the affinity key has to
             ## be derived from the messages as they will be sent, not as they arrive here.
-            affinity_messages: Final = AnthropicCacheControlHook.messages_with_default_injections(
+            injected_messages: Final = AnthropicCacheControlHook.messages_with_default_injections(
                 messages=cast(list[AllMessageValues], messages),
                 models=(
                     deployment["litellm_params"]["model"]
@@ -93,10 +93,18 @@ class PromptCachingDeploymentCheck(CustomLogger):
                 ),
                 request_kwargs=request_kwargs,
             )
+            affinity_messages: Final = PromptCachingCache.prepend_system_prompt(
+                injected_messages,
+                request_kwargs.get("system") if request_kwargs is not None else None,
+            )
 
             model_id_dict: Final = await prompt_cache.async_get_model_id(
                 messages=affinity_messages,
-                tools=None,
+                tools=(
+                    cast(list[AllToolParamValues] | None, request_kwargs.get("tools"))
+                    if request_kwargs is not None
+                    else None
+                ),
             )
             if model_id_dict is not None:
                 model_id: Final = model_id_dict["model_id"]
@@ -139,18 +147,23 @@ class PromptCachingDeploymentCheck(CustomLogger):
             )
             return
 
+        logged_messages: Final = PromptCachingCache.prepend_system_prompt(
+            cast(list[AllMessageValues], messages),
+            kwargs.get("system"),
+        )
+
         ## PROMPT CACHING - cache model id, if prompt caching valid prompt + provider
         if await offload_token_count(is_prompt_caching_valid_prompt)(
             model=model,
-            messages=cast(list[AllMessageValues], messages),
+            messages=logged_messages,
         ):
             cache: Final = PromptCachingCache(
                 cache=self.cache,
             )
             await cache.async_add_model_id(
                 model_id=model_id,
-                messages=messages,
-                tools=None,  # [TODO]: add tools once standard_logging_object supports it
+                messages=logged_messages,
+                tools=cast(list[AllToolParamValues] | None, kwargs.get("tools")),
             )
 
         return

litellm/router_utils/prompt_caching_cache.py

@@ -139,6 +139,18 @@ class PromptCachingCache:
 
         return cacheable_prefix
 
+    @staticmethod
+    def prepend_system_prompt(
+        messages: list[AllMessageValues],
+        system: object | None,
+    ) -> list[AllMessageValues]:
+        if system is None:
+            return messages
+        return cast(
+            list[AllMessageValues],
+            [{"role": "system", "content": system}, *messages],
+        )
+
     @staticmethod
     def get_prompt_caching_ttl(
         messages: list[AllMessageValues] | None,

tests/test_litellm/router_utils/pre_call_checks/test_prompt_caching_deployment_check.py

@@ -61,6 +61,27 @@ def _messages(word_count: int) -> List[AllMessageValues]:
     )
 
 
+@pytest.mark.asyncio
+async def test_system_parameter_is_part_of_prompt_cache_affinity():
+    cache = DualCache()
+    check = PromptCachingDeploymentCheck(cache=cache)
+    deployments = _deployments("anthropic/claude-opus-4-6", "anthropic/claude-opus-4-6")
+    messages = _messages(word_count=5000)
+    system = [{"type": "text", "text": "system", "cache_control": {"type": "ephemeral", "ttl": "1h"}}]
+    cached_messages = PromptCachingCache.prepend_system_prompt(messages, system)
+
+    await PromptCachingCache(cache=cache).async_add_model_id("dep-2", cached_messages, None)
+
+    filtered = await check.async_filter_deployments(
+        model=MODEL_GROUP_ALIAS,
+        healthy_deployments=deployments,
+        messages=messages,
+        request_kwargs={"system": system},
+    )
+
+    assert filtered == [deployments[1]]
+
+
 @pytest.mark.parametrize(
     ("ttl", "expected_affinity_ttl"),
     ((None, 300), ("5m", 300), ("1h", 3600)),

type-discipline-budget.json

@@ -27,7 +27,7 @@
     "limit": 0
   },
   "LIT010": {
-    "limit": 16396
+    "limit": 16394
   },
   "LIT011": {
     "limit": 5504