Add compaction type block in the output

litellm/litellm_core_utils/core_helpers.py

@@ -94,8 +94,8 @@ def map_finish_reason(
         return "length"
     elif finish_reason == "tool_use":  # anthropic
         return "tool_calls"
-    elif finish_reason == "content_filtered":
-        return "content_filter"
+    elif finish_reason == "compaction":
+        return "length"
     return finish_reason
 
 

litellm/llms/anthropic/chat/transformation.py

@@ -1028,9 +1028,37 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         if beta_value not in existing_values:
             headers["anthropic-beta"] = f"{existing_beta}, {beta_value}"
 
-    def _ensure_context_management_beta_header(self, headers: dict) -> None:
-        beta_value = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
-        self._ensure_beta_header(headers, beta_value)
+    def _ensure_context_management_beta_header(
+        self, headers: dict, context_management: dict
+    ) -> None:
+        """
+        Add appropriate beta headers based on context_management edits.
+        - If any edit has type "compact_20260112", add compact-2026-01-12 header
+        - For all other edits, add context-management-2025-06-27 header
+        """
+        edits = context_management.get("edits", [])
+        
+        has_compact = False
+        has_other = False
+        
+        for edit in edits:
+            edit_type = edit.get("type", "")
+            if edit_type == "compact_20260112":
+                has_compact = True
+            else:
+                has_other = True
+        
+        # Add compact header if any compact edits exist
+        if has_compact:
+            self._ensure_beta_header(
+                headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value
+            )
+        
+        # Add context management header if any other edits exist
+        if has_other:
+            self._ensure_beta_header(
+                headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
+            )
 
     def update_headers_with_optional_anthropic_beta(
         self, headers: dict, optional_params: dict
@@ -1058,7 +1086,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                     headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
                 )
         if optional_params.get("context_management") is not None:
-            self._ensure_context_management_beta_header(headers)
+            self._ensure_context_management_beta_header(
+                headers, optional_params["context_management"]
+            )
         if optional_params.get("output_format") is not None:
             self._ensure_beta_header(
                 headers, ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value
@@ -1227,6 +1257,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         List[ChatCompletionToolCallChunk],
         Optional[List[Any]],
         Optional[List[Any]],
+        Optional[List[Any]],
     ]:
         text_content = ""
         citations: Optional[List[Any]] = None
@@ -1239,6 +1270,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         tool_calls: List[ChatCompletionToolCallChunk] = []
         web_search_results: Optional[List[Any]] = None
         tool_results: Optional[List[Any]] = None
+        context_management: Optional[List[Any]] = None
+        compaction_blocks: Optional[List[Any]] = None
         for idx, content in enumerate(completion_response["content"]):
             if content["type"] == "text":
                 text_content += content["text"]
@@ -1280,6 +1313,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 thinking_blocks.append(
                     cast(ChatCompletionRedactedThinkingBlock, content)
                 )
+            
+            ## COMPACTION
+            elif content["type"] == "compaction":
+                if compaction_blocks is None:
+                    compaction_blocks = []
+                compaction_blocks.append(content)
 
             ## CITATIONS
             if content.get("citations") is not None:
@@ -1301,7 +1340,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 if thinking_content is not None:
                     reasoning_content += thinking_content
 
-        return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results
+        return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results, compaction_blocks
 
     def calculate_usage(
         self,
@@ -1444,6 +1483,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 tool_calls,
                 web_search_results,
                 tool_results,
+                compaction_blocks,
             ) = self.extract_response_content(completion_response=completion_response)
 
             if (
@@ -1471,6 +1511,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 provider_specific_fields["tool_results"] = tool_results
             if container is not None:
                 provider_specific_fields["container"] = container
+            if compaction_blocks is not None:
+                provider_specific_fields["compaction_blocks"] = compaction_blocks
                 
             _message = litellm.Message(
                 tool_calls=tool_calls,
@@ -1479,6 +1521,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 thinking_blocks=thinking_blocks,
                 reasoning_content=reasoning_content,
             )
+            _message.provider_specific_fields = provider_specific_fields
 
             ## HANDLE JSON MODE - anthropic returns single function call
             json_mode_message = self._transform_response_for_json_mode(
@@ -1509,18 +1552,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         model_response.created = int(time.time())
         model_response.model = completion_response["model"]
 
-        context_management_response = completion_response.get("context_management")
-        if context_management_response is not None:
-            _hidden_params["context_management"] = context_management_response
-            try:
-                model_response.__dict__["context_management"] = (
-                    context_management_response
-                )
-            except Exception:
-                pass
-
         model_response._hidden_params = _hidden_params
-
         return model_response
 
     def get_prefix_prompt(self, messages: List[AllMessageValues]) -> Optional[str]:

litellm/types/llms/anthropic.py

@@ -633,6 +633,7 @@ class ANTHROPIC_BETA_HEADER_VALUES(str, Enum):
     WEB_FETCH_2025_09_10 = "web-fetch-2025-09-10"
     WEB_SEARCH_2025_03_05 = "web-search-2025-03-05"
     CONTEXT_MANAGEMENT_2025_06_27 = "context-management-2025-06-27"
+    COMPACT_2026_01_12 = "compact-2026-01-12"
     STRUCTURED_OUTPUT_2025_09_25 = "structured-outputs-2025-11-13"
     ADVANCED_TOOL_USE_2025_11_20 = "advanced-tool-use-2025-11-20"