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Merge pull request #18945 from BerriAI/litellm_add_anthropic_tool_call_results
Add: missing anthropic tool results in response
This commit is contained in:
Sameer Kankute
GitHub
commit
c2fcc6aa92
3 changed files with 309 additions and 49 deletions
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@ -719,32 +719,40 @@ class ModelResponseIterator:
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content_block_start=content_block_start,
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provider_specific_fields=provider_specific_fields,
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)
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elif (
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content_block_start["content_block"]["type"]
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== "web_search_tool_result"
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):
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# Capture web_search_tool_result for multi-turn reconstruction
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# The full content comes in content_block_start, not in deltas
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# See: https://github.com/BerriAI/litellm/issues/17737
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self.web_search_results.append(
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content_block_start["content_block"]
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)
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provider_specific_fields["web_search_results"] = (
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self.web_search_results
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)
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elif (
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content_block_start["content_block"]["type"]
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== "web_fetch_tool_result"
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):
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# Capture web_fetch_tool_result for multi-turn reconstruction
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# The full content comes in content_block_start, not in deltas
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# Fixes: https://github.com/BerriAI/litellm/issues/18137
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self.web_search_results.append(
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content_block_start["content_block"]
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)
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provider_specific_fields["web_search_results"] = (
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self.web_search_results
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)
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elif content_block_start["content_block"]["type"].endswith("_tool_result"):
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# Handle all tool result types (web_search, bash_code_execution, text_editor, etc.)
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content_type = content_block_start["content_block"]["type"]
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# Special handling for web_search_tool_result for backwards compatibility
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if content_type == "web_search_tool_result":
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# Capture web_search_tool_result for multi-turn reconstruction
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# The full content comes in content_block_start, not in deltas
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# See: https://github.com/BerriAI/litellm/issues/17737
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self.web_search_results.append(
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content_block_start["content_block"]
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)
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provider_specific_fields["web_search_results"] = (
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self.web_search_results
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)
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elif content_type == "web_fetch_tool_result":
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# Capture web_fetch_tool_result for multi-turn reconstruction
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# The full content comes in content_block_start, not in deltas
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# Fixes: https://github.com/BerriAI/litellm/issues/18137
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self.web_search_results.append(
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content_block_start["content_block"]
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)
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provider_specific_fields["web_search_results"] = (
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self.web_search_results
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)
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elif content_type != "tool_search_tool_result":
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# Handle other tool results (code execution, etc.)
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# Skip tool_search_tool_result as it's internal metadata
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if not hasattr(self, "tool_results"):
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self.tool_results = []
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self.tool_results.append(content_block_start["content_block"])
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provider_specific_fields["tool_results"] = self.tool_results
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elif type_chunk == "content_block_stop":
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ContentBlockStop(**chunk) # type: ignore
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# check if tool call content block - only for tool_use and server_tool_use blocks
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@ -1138,6 +1138,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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Optional[str],
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List[ChatCompletionToolCallChunk],
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Optional[List[Any]],
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Optional[List[Any]],
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]:
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text_content = ""
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citations: Optional[List[Any]] = None
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@ -1149,6 +1150,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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reasoning_content: Optional[str] = None
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tool_calls: List[ChatCompletionToolCallChunk] = []
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web_search_results: Optional[List[Any]] = None
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tool_results: Optional[List[Any]] = None
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for idx, content in enumerate(completion_response["content"]):
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if content["type"] == "text":
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text_content += content["text"]
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@ -1159,22 +1161,27 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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index=idx,
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)
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tool_calls.append(tool_call)
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## TOOL SEARCH TOOL RESULT (skip - this is metadata about tool discovery)
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elif content["type"] == "tool_search_tool_result":
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# This block contains tool_references that were discovered
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# We don't need to include this in the response as it's internal metadata
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pass
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## WEB SEARCH TOOL RESULT - preserve web search results for multi-turn conversations
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elif content["type"] == "web_search_tool_result":
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if web_search_results is None:
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web_search_results = []
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web_search_results.append(content)
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## WEB FETCH TOOL RESULT - preserve web fetch results for multi-turn conversations
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## Fixes: https://github.com/BerriAI/litellm/issues/18137
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elif content["type"] == "web_fetch_tool_result":
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if web_search_results is None:
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web_search_results = []
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web_search_results.append(content)
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## TOOL RESULTS - handle all tool result types (code execution, etc.)
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elif content["type"].endswith("_tool_result"):
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# Skip tool_search_tool_result as it's internal metadata
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if content["type"] == "tool_search_tool_result":
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continue
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# Handle web_search_tool_result separately for backwards compatibility
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if content["type"] == "web_search_tool_result":
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if web_search_results is None:
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web_search_results = []
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web_search_results.append(content)
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elif content["type"] == "web_fetch_tool_result":
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if web_search_results is None:
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web_search_results = []
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web_search_results.append(content)
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else:
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# All other tool results (bash_code_execution_tool_result, text_editor_code_execution_tool_result, etc.)
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if tool_results is None:
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tool_results = []
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tool_results.append(content)
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elif content.get("thinking", None) is not None:
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if thinking_blocks is None:
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thinking_blocks = []
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@ -1206,7 +1213,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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if thinking_content is not None:
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reasoning_content += thinking_content
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return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results
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return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results
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def calculate_usage(
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self,
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@ -1348,6 +1355,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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reasoning_content,
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tool_calls,
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web_search_results,
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tool_results,
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) = self.extract_response_content(completion_response=completion_response)
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if (
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@ -1371,6 +1379,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
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provider_specific_fields["context_management"] = context_management
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if web_search_results is not None:
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provider_specific_fields["web_search_results"] = web_search_results
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if tool_results is not None:
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provider_specific_fields["tool_results"] = tool_results
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if container is not None:
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provider_specific_fields["container"] = container
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@ -185,7 +185,7 @@ def test_extract_response_content_with_citations():
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},
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}
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_, citations, _, _, _, _ = config.extract_response_content(completion_response)
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_, citations, _, _, _, _ , _= config.extract_response_content(completion_response)
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assert citations == [
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[
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{
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@ -342,7 +342,7 @@ def test_web_search_tool_result_extraction():
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}
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}
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results = config.extract_response_content(
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results = config.extract_response_content(
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completion_response
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)
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@ -474,7 +474,7 @@ def test_multiple_web_search_tool_results():
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]
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}
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results = config.extract_response_content(
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results = config.extract_response_content(
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completion_response
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)
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@ -923,7 +923,7 @@ def test_server_tool_use_in_response():
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]
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}
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results = config.extract_response_content(
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results = config.extract_response_content(
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completion_response
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)
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@ -1051,7 +1051,7 @@ def test_tool_search_complete_response_parsing():
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}
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# Extract content
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results = config.extract_response_content(
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text, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results = config.extract_response_content(
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completion_response
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)
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@ -1171,7 +1171,7 @@ def test_caller_field_in_response():
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"usage": {"input_tokens": 100, "output_tokens": 50}
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}
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text, citations, thinking, reasoning, tool_calls, web_search_results = config.extract_response_content(completion_response)
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text, citations, thinking, reasoning, tool_calls, web_search_results, tool_results = config.extract_response_content(completion_response)
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assert len(tool_calls) == 1
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assert tool_calls[0]["id"] == "toolu_123"
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@ -1812,3 +1812,245 @@ def test_calculate_usage_completion_tokens_details_with_reasoning():
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expected_text_tokens = 500 - usage.completion_tokens_details.reasoning_tokens
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assert usage.completion_tokens_details.text_tokens == expected_text_tokens
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assert usage.completion_tokens == 500
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def test_code_execution_tool_results_extraction():
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"""
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Test that code execution tool results (bash_code_execution_tool_result,
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text_editor_code_execution_tool_result) are properly extracted and exposed
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in provider_specific_fields.
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Related to: https://github.com/BerriAI/litellm/issues/xxxxx
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"""
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import httpx
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from litellm.types.utils import ModelResponse
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config = AnthropicConfig()
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# Mock Anthropic response with code execution tool results
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mock_anthropic_response = {
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"id": "msg_01XYZ",
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"type": "message",
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"role": "assistant",
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"model": "claude-sonnet-4-5-20250929",
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"content": [
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{
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"type": "text",
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"text": "I'll calculate that for you."
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},
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{
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"type": "server_tool_use",
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"id": "srvtoolu_01ABC",
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"name": "bash_code_execution",
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"input": {
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"command": "python3 << 'EOF'\nprint(2 + 2)\nEOF\n"
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}
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},
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{
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"type": "bash_code_execution_tool_result",
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"tool_use_id": "srvtoolu_01ABC",
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"content": {
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"type": "bash_code_execution_result",
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"stdout": "4\n",
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"stderr": "",
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"return_code": 0
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}
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},
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{
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"type": "server_tool_use",
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"id": "srvtoolu_01DEF",
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"name": "text_editor_code_execution",
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"input": {
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"command": "create",
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"path": "test.txt",
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"file_text": "Hello"
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}
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},
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{
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"type": "text_editor_code_execution_tool_result",
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"tool_use_id": "srvtoolu_01DEF",
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"content": {
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"type": "text_editor_code_execution_result",
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"is_file_update": False
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}
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},
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{
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"type": "text",
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"text": "Done!"
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}
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],
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"stop_reason": "stop",
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"stop_sequence": None,
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"usage": {
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"input_tokens": 100,
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"output_tokens": 50
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}
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}
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# Create mock HTTP response
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mock_raw_response = MagicMock(spec=httpx.Response)
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mock_raw_response.json.return_value = mock_anthropic_response
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mock_raw_response.status_code = 200
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mock_raw_response.headers = {}
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model_response = ModelResponse()
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transformed_response = config.transform_parsed_response(
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completion_response=mock_anthropic_response,
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raw_response=mock_raw_response,
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model_response=model_response,
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json_mode=False,
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prefix_prompt=None,
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)
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# Verify tool calls are present
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assert transformed_response.choices[0].message.tool_calls is not None
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assert len(transformed_response.choices[0].message.tool_calls) == 2
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# Verify first tool call
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assert transformed_response.choices[0].message.tool_calls[0].id == "srvtoolu_01ABC"
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assert transformed_response.choices[0].message.tool_calls[0].function.name == "bash_code_execution"
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# Verify second tool call
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assert transformed_response.choices[0].message.tool_calls[1].id == "srvtoolu_01DEF"
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assert transformed_response.choices[0].message.tool_calls[1].function.name == "text_editor_code_execution"
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# Verify tool results are in provider_specific_fields
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provider_fields = transformed_response.choices[0].message.provider_specific_fields
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assert provider_fields is not None
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assert "tool_results" in provider_fields
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assert provider_fields["tool_results"] is not None
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assert len(provider_fields["tool_results"]) == 2
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# Verify bash_code_execution_tool_result
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bash_result = provider_fields["tool_results"][0]
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assert bash_result["type"] == "bash_code_execution_tool_result"
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assert bash_result["tool_use_id"] == "srvtoolu_01ABC"
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assert bash_result["content"]["stdout"] == "4\n"
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assert bash_result["content"]["return_code"] == 0
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# Verify text_editor_code_execution_tool_result
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editor_result = provider_fields["tool_results"][1]
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assert editor_result["type"] == "text_editor_code_execution_tool_result"
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assert editor_result["tool_use_id"] == "srvtoolu_01DEF"
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assert editor_result["content"]["is_file_update"] is False
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# Verify text content is properly concatenated
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assert "I'll calculate that for you." in transformed_response.choices[0].message.content
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assert "Done!" in transformed_response.choices[0].message.content
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def test_tool_search_tool_result_not_in_tool_results():
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"""
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Test that tool_search_tool_result is NOT included in tool_results
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since it's internal metadata, not actual tool execution results.
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"""
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import httpx
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from litellm.types.utils import ModelResponse
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config = AnthropicConfig()
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mock_anthropic_response = {
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"id": "msg_01XYZ",
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"type": "message",
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"role": "assistant",
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"model": "claude-sonnet-4-5-20250929",
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"content": [
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{
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"type": "text",
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"text": "Found tools."
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},
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{
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"type": "tool_search_tool_result",
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"tool_references": ["tool1", "tool2"]
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}
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],
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"stop_reason": "stop",
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"stop_sequence": None,
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"usage": {
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"input_tokens": 100,
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"output_tokens": 50
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}
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}
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mock_raw_response = MagicMock(spec=httpx.Response)
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mock_raw_response.json.return_value = mock_anthropic_response
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mock_raw_response.status_code = 200
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mock_raw_response.headers = {}
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model_response = ModelResponse()
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transformed_response = config.transform_parsed_response(
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completion_response=mock_anthropic_response,
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raw_response=mock_raw_response,
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model_response=model_response,
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json_mode=False,
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prefix_prompt=None,
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)
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# Verify tool_search_tool_result is NOT in tool_results
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provider_fields = transformed_response.choices[0].message.provider_specific_fields
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assert provider_fields.get("tool_results") is None
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def test_web_search_tool_result_backwards_compatibility():
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"""
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Test that web_search_tool_result continues to be stored in web_search_results
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for backwards compatibility, not in tool_results.
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"""
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import httpx
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from litellm.types.utils import ModelResponse
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config = AnthropicConfig()
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mock_anthropic_response = {
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"id": "msg_01XYZ",
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"type": "message",
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"role": "assistant",
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"model": "claude-sonnet-4-5-20250929",
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"content": [
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{
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"type": "text",
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"text": "Here are the results."
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},
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{
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"type": "web_search_tool_result",
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"search_query": "test query",
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"results": [{"title": "Result 1", "url": "https://example.com"}]
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}
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],
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"stop_reason": "stop",
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"stop_sequence": None,
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"usage": {
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"input_tokens": 100,
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"output_tokens": 50
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}
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}
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mock_raw_response = MagicMock(spec=httpx.Response)
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mock_raw_response.json.return_value = mock_anthropic_response
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mock_raw_response.status_code = 200
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mock_raw_response.headers = {}
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model_response = ModelResponse()
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transformed_response = config.transform_parsed_response(
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completion_response=mock_anthropic_response,
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raw_response=mock_raw_response,
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model_response=model_response,
|
||||
json_mode=False,
|
||||
prefix_prompt=None,
|
||||
)
|
||||
|
||||
# Verify web_search_tool_result is in web_search_results (not tool_results)
|
||||
provider_fields = transformed_response.choices[0].message.provider_specific_fields
|
||||
assert "web_search_results" in provider_fields
|
||||
assert provider_fields["web_search_results"] is not None
|
||||
assert len(provider_fields["web_search_results"]) == 1
|
||||
assert provider_fields["web_search_results"][0]["type"] == "web_search_tool_result"
|
||||
|
||||
# Should NOT be in tool_results
|
||||
assert provider_fields.get("tool_results") is None
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue