mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-25 08:45:09 +00:00
fix: detect anthropic preview tool-use blocks
This commit is contained in:
alex-clawd
@@ -124,6 +124,33 @@ def _contains_file_id_reference(messages: list[dict[str, Any]]) -> bool:
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return False
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def _is_tool_use_block(block: Any) -> bool:
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"""Return true for Anthropic tool-use blocks across SDK shapes.
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New/preview Anthropic models can return content blocks whose concrete SDK
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class is not one of the imported ``ToolUseBlock`` aliases. The stable part
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of the API shape is the block type plus ``name`` and ``input`` fields, so use
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duck typing instead of relying only on SDK classes.
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"""
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if isinstance(block, (ToolUseBlock, BetaToolUseBlock)):
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return True
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if isinstance(block, dict):
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return block.get("type") == "tool_use" and "name" in block and "input" in block
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return (
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getattr(block, "type", None) == "tool_use"
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and hasattr(block, "name")
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and hasattr(block, "input")
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)
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def _tool_use_blocks(blocks: list[Any]) -> list[Any]:
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return [block for block in blocks if _is_tool_use_block(block)]
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def _tool_use_attr(block: Any, attr: str) -> Any:
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return block.get(attr) if isinstance(block, dict) else getattr(block, attr)
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class AnthropicThinkingConfig(BaseModel):
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type: Literal["enabled", "disabled"]
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budget_tokens: int | None = None
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@@ -589,20 +616,41 @@ class AnthropicCompletion(BaseLLM):
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Returns:
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Dictionary with thinking block data including signature, or None if not a thinking block
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"""
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if content_block.type == "thinking":
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block_type = (
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content_block.get("type")
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if isinstance(content_block, dict)
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else getattr(content_block, "type", None)
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)
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if block_type == "thinking":
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thinking = (
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content_block.get("thinking")
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if isinstance(content_block, dict)
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else content_block.thinking
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)
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thinking_block = {
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"type": "thinking",
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"thinking": content_block.thinking,
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"thinking": thinking,
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}
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if hasattr(content_block, "signature"):
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thinking_block["signature"] = content_block.signature
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signature = (
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content_block.get("signature")
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if isinstance(content_block, dict)
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else getattr(content_block, "signature", None)
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)
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if signature:
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thinking_block["signature"] = signature
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return thinking_block
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if content_block.type == "redacted_thinking":
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if block_type == "redacted_thinking":
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redacted_block = {"type": "redacted_thinking"}
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if hasattr(content_block, "thinking"):
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redacted_block["thinking"] = content_block.thinking
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if hasattr(content_block, "signature"):
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redacted_block["signature"] = content_block.signature
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if isinstance(content_block, dict):
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if "thinking" in content_block:
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redacted_block["thinking"] = content_block["thinking"]
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if "signature" in content_block:
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redacted_block["signature"] = content_block["signature"]
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else:
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if hasattr(content_block, "thinking"):
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redacted_block["thinking"] = content_block.thinking
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if hasattr(content_block, "signature"):
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redacted_block["signature"] = content_block.signature
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return redacted_block
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return None
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@@ -944,10 +992,12 @@ class AnthropicCompletion(BaseLLM):
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else:
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for block in response.content:
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if (
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isinstance(block, (ToolUseBlock, BetaToolUseBlock))
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and block.name == "structured_output"
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_is_tool_use_block(block)
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and _tool_use_attr(block, "name") == "structured_output"
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):
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structured_data = response_model.model_validate(block.input)
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structured_data = response_model.model_validate(
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_tool_use_attr(block, "input")
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)
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self._emit_call_completed_event(
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response=structured_data.model_dump_json(),
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call_type=LLMCallType.LLM_CALL,
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@@ -962,11 +1012,7 @@ class AnthropicCompletion(BaseLLM):
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# Check if Claude wants to use tools
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if response.content:
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tool_uses = [
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block
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for block in response.content
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if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
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]
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tool_uses = _tool_use_blocks(list(response.content))
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if tool_uses:
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# Without available_functions, return tool calls so the executor can
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@@ -1093,11 +1139,13 @@ class AnthropicCompletion(BaseLLM):
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if event.type == "content_block_start":
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block = event.content_block
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if block.type == "tool_use":
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if _is_tool_use_block(block):
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block_index = event.index
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tool_use_id = _tool_use_attr(block, "id")
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tool_name = _tool_use_attr(block, "name")
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current_tool_calls[block_index] = {
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"id": block.id,
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"name": block.name,
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"id": tool_use_id,
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"name": tool_name,
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"arguments": "",
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"index": block_index,
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}
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@@ -1106,9 +1154,9 @@ class AnthropicCompletion(BaseLLM):
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from_task=from_task,
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from_agent=from_agent,
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tool_call={
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"id": block.id,
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"id": tool_use_id,
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"function": {
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"name": block.name,
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"name": tool_name,
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"arguments": "",
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},
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"type": "function",
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@@ -1177,10 +1225,12 @@ class AnthropicCompletion(BaseLLM):
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return structured_data
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for block in final_message.content:
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if (
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isinstance(block, ToolUseBlock)
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and block.name == "structured_output"
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_is_tool_use_block(block)
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and _tool_use_attr(block, "name") == "structured_output"
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):
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structured_data = response_model.model_validate(block.input)
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structured_data = response_model.model_validate(
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_tool_use_attr(block, "input")
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)
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self._emit_call_completed_event(
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response=structured_data.model_dump_json(),
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call_type=LLMCallType.LLM_CALL,
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@@ -1194,11 +1244,7 @@ class AnthropicCompletion(BaseLLM):
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return structured_data
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if final_message.content:
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tool_uses = [
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block
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for block in final_message.content
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if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
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]
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tool_uses = _tool_use_blocks(list(final_message.content))
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if tool_uses:
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if not available_functions:
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@@ -1229,7 +1275,7 @@ class AnthropicCompletion(BaseLLM):
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def _execute_tools_and_collect_results(
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self,
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tool_uses: list[ToolUseBlock | BetaToolUseBlock],
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tool_uses: list[Any],
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available_functions: dict[str, Any],
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from_task: Any | None = None,
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from_agent: Any | None = None,
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@@ -1248,8 +1294,8 @@ class AnthropicCompletion(BaseLLM):
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tool_results = []
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for tool_use in tool_uses:
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function_name = tool_use.name
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function_args = tool_use.input
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function_name = _tool_use_attr(tool_use, "name")
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function_args = _tool_use_attr(tool_use, "input")
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result = self._handle_tool_execution(
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function_name=function_name,
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@@ -1261,7 +1307,7 @@ class AnthropicCompletion(BaseLLM):
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tool_result = {
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"type": "tool_result",
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"tool_use_id": tool_use.id,
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"tool_use_id": _tool_use_attr(tool_use, "id"),
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"content": str(result)
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if result is not None
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else "Tool execution completed",
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@@ -1272,7 +1318,7 @@ class AnthropicCompletion(BaseLLM):
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def _execute_first_tool(
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self,
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tool_uses: list[ToolUseBlock | BetaToolUseBlock],
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tool_uses: list[Any],
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available_functions: dict[str, Any],
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from_task: Any | None = None,
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from_agent: Any | None = None,
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@@ -1293,8 +1339,8 @@ class AnthropicCompletion(BaseLLM):
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The result of the first tool execution, or None if execution failed
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"""
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tool_use = tool_uses[0]
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function_name = tool_use.name
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function_args = cast(dict[str, Any], tool_use.input)
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function_name = _tool_use_attr(tool_use, "name")
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function_args = cast(dict[str, Any], _tool_use_attr(tool_use, "input"))
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return self._handle_tool_execution(
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function_name=function_name,
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@@ -1308,7 +1354,7 @@ class AnthropicCompletion(BaseLLM):
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def _handle_tool_use_conversation(
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self,
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initial_response: Message | BetaMessage,
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tool_uses: list[ToolUseBlock | BetaToolUseBlock],
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tool_uses: list[Any],
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params: dict[str, Any],
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available_functions: dict[str, Any],
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from_task: Any | None = None,
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@@ -1335,13 +1381,13 @@ class AnthropicCompletion(BaseLLM):
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thinking_block = self._extract_thinking_block(block)
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if thinking_block:
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assistant_content.append(thinking_block)
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elif block.type == "tool_use":
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elif _is_tool_use_block(block):
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assistant_content.append(
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{
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"type": "tool_use",
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"id": block.id,
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"name": block.name,
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"input": block.input,
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"id": _tool_use_attr(block, "id"),
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"name": _tool_use_attr(block, "name"),
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"input": _tool_use_attr(block, "input"),
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}
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)
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elif hasattr(block, "text"):
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@@ -1494,10 +1540,12 @@ class AnthropicCompletion(BaseLLM):
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else:
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for block in response.content:
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if (
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isinstance(block, ToolUseBlock)
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and block.name == "structured_output"
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_is_tool_use_block(block)
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and _tool_use_attr(block, "name") == "structured_output"
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):
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structured_data = response_model.model_validate(block.input)
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structured_data = response_model.model_validate(
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_tool_use_attr(block, "input")
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)
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self._emit_call_completed_event(
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response=structured_data.model_dump_json(),
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call_type=LLMCallType.LLM_CALL,
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@@ -1510,13 +1558,9 @@ class AnthropicCompletion(BaseLLM):
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)
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return structured_data
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# Handle both ToolUseBlock (regular API) and BetaToolUseBlock (beta API features)
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# Handle Anthropic tool-use blocks across stable, beta, and preview SDK shapes.
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if response.content:
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tool_uses = [
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block
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for block in response.content
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if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
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]
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tool_uses = _tool_use_blocks(list(response.content))
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if tool_uses:
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if not available_functions:
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@@ -1629,11 +1673,13 @@ class AnthropicCompletion(BaseLLM):
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if event.type == "content_block_start":
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block = event.content_block
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if block.type == "tool_use":
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if _is_tool_use_block(block):
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block_index = event.index
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tool_use_id = _tool_use_attr(block, "id")
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tool_name = _tool_use_attr(block, "name")
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current_tool_calls[block_index] = {
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"id": block.id,
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"name": block.name,
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"id": tool_use_id,
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"name": tool_name,
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"arguments": "",
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"index": block_index,
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}
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@@ -1642,9 +1688,9 @@ class AnthropicCompletion(BaseLLM):
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from_task=from_task,
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from_agent=from_agent,
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tool_call={
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"id": block.id,
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"id": tool_use_id,
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"function": {
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"name": block.name,
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"name": tool_name,
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"arguments": "",
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},
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"type": "function",
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@@ -1703,10 +1749,12 @@ class AnthropicCompletion(BaseLLM):
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return structured_data
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for block in final_message.content:
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if (
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isinstance(block, ToolUseBlock)
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and block.name == "structured_output"
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_is_tool_use_block(block)
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and _tool_use_attr(block, "name") == "structured_output"
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):
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structured_data = response_model.model_validate(block.input)
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structured_data = response_model.model_validate(
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_tool_use_attr(block, "input")
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)
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self._emit_call_completed_event(
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response=structured_data.model_dump_json(),
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call_type=LLMCallType.LLM_CALL,
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@@ -1720,11 +1768,7 @@ class AnthropicCompletion(BaseLLM):
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return structured_data
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if final_message.content:
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tool_uses = [
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block
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for block in final_message.content
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if isinstance(block, (ToolUseBlock, BetaToolUseBlock))
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]
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tool_uses = _tool_use_blocks(list(final_message.content))
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if tool_uses:
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if not available_functions:
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@@ -1754,7 +1798,7 @@ class AnthropicCompletion(BaseLLM):
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async def _ahandle_tool_use_conversation(
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self,
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initial_response: Message | BetaMessage,
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tool_uses: list[ToolUseBlock | BetaToolUseBlock],
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tool_uses: list[Any],
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params: dict[str, Any],
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available_functions: dict[str, Any],
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from_task: Any | None = None,
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@@ -1,7 +1,7 @@
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import os
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import sys
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import types
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from unittest.mock import patch, MagicMock
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from unittest.mock import AsyncMock, patch, MagicMock
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import pytest
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from crewai.llm import LLM
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@@ -1358,6 +1358,205 @@ _MANY_TOOLS = [
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]
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def _dict_tool_use_response():
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mock_response = MagicMock()
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mock_response.content = [
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{
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"type": "tool_use",
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"id": "toolu_123",
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"name": "search_web",
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"input": {"query": "CrewAI"},
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}
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]
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mock_response.usage = MagicMock(input_tokens=10, output_tokens=2)
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mock_response.stop_reason = "tool_use"
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mock_response.id = "msg_123"
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return mock_response
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class _SyncAnthropicStream:
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def __init__(self, events, final_message):
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self.events = events
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self.final_message = final_message
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def __enter__(self):
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return self
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def __exit__(self, *_args):
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return False
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def __iter__(self):
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return iter(self.events)
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def get_final_message(self):
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return self.final_message
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|
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class _AsyncAnthropicStream:
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def __init__(self, events, final_message):
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self.events = list(events)
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self.final_message = final_message
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async def __aenter__(self):
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return self
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async def __aexit__(self, *_args):
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return False
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def __aiter__(self):
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self._iter = iter(self.events)
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return self
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async def __anext__(self):
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try:
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return next(self._iter)
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except StopIteration:
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raise StopAsyncIteration
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async def get_final_message(self):
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return self.final_message
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|
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|
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def _dict_tool_use_stream_events():
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return [
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types.SimpleNamespace(
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type="content_block_start",
|
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index=0,
|
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content_block={
|
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"type": "tool_use",
|
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"id": "toolu_123",
|
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"name": "search_web",
|
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"input": {},
|
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},
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),
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types.SimpleNamespace(
|
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type="content_block_delta",
|
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index=0,
|
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delta=types.SimpleNamespace(
|
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type="input_json_delta",
|
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partial_json='{"query":"CrewAI"}',
|
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),
|
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),
|
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]
|
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|
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|
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def test_anthropic_tool_use_dict_blocks_are_returned_as_tool_calls():
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"""Preview Anthropic models may return dict-shaped tool_use blocks."""
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from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
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|
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llm = AnthropicCompletion(model="claude-fable-5")
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mock_response = _dict_tool_use_response()
|
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|
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mock_client = MagicMock()
|
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mock_client.messages.create.return_value = mock_response
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llm._client = mock_client
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|
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result = llm.call("Search for CrewAI", tools=_MANY_TOOLS)
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|
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assert result == mock_response.content
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|
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|
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@pytest.mark.asyncio
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async def test_anthropic_acall_returns_dict_tool_use_blocks_as_tool_calls():
|
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from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
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|
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llm = AnthropicCompletion(model="claude-fable-5")
|
||||
mock_response = _dict_tool_use_response()
|
||||
|
||||
mock_client = MagicMock()
|
||||
mock_client.messages.create = AsyncMock(return_value=mock_response)
|
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llm._async_client = mock_client
|
||||
|
||||
result = await llm.acall("Search for CrewAI", tools=_MANY_TOOLS)
|
||||
|
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assert result == mock_response.content
|
||||
|
||||
|
||||
def test_anthropic_streaming_returns_dict_tool_use_blocks_as_tool_calls():
|
||||
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
||||
|
||||
llm = AnthropicCompletion(model="claude-fable-5", stream=True)
|
||||
mock_response = _dict_tool_use_response()
|
||||
mock_client = MagicMock()
|
||||
mock_client.messages.stream.return_value = _SyncAnthropicStream(
|
||||
_dict_tool_use_stream_events(), mock_response
|
||||
)
|
||||
llm._client = mock_client
|
||||
llm._emit_stream_chunk_event = MagicMock()
|
||||
|
||||
result = llm.call("Search for CrewAI", tools=_MANY_TOOLS)
|
||||
|
||||
assert result == mock_response.content
|
||||
assert any(
|
||||
call.kwargs.get("tool_call", {}).get("id") == "toolu_123"
|
||||
for call in llm._emit_stream_chunk_event.call_args_list
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_anthropic_async_streaming_returns_dict_tool_use_blocks_as_tool_calls():
|
||||
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
||||
|
||||
llm = AnthropicCompletion(model="claude-fable-5", stream=True)
|
||||
mock_response = _dict_tool_use_response()
|
||||
mock_client = MagicMock()
|
||||
mock_client.messages.stream.return_value = _AsyncAnthropicStream(
|
||||
_dict_tool_use_stream_events(), mock_response
|
||||
)
|
||||
llm._async_client = mock_client
|
||||
llm._emit_stream_chunk_event = MagicMock()
|
||||
|
||||
result = await llm.acall("Search for CrewAI", tools=_MANY_TOOLS)
|
||||
|
||||
assert result == mock_response.content
|
||||
assert any(
|
||||
call.kwargs.get("tool_call", {}).get("id") == "toolu_123"
|
||||
for call in llm._emit_stream_chunk_event.call_args_list
|
||||
)
|
||||
|
||||
|
||||
def test_anthropic_dict_tool_use_blocks_execute_available_function():
|
||||
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
||||
|
||||
llm = AnthropicCompletion(model="claude-fable-5")
|
||||
mock_response = _dict_tool_use_response()
|
||||
mock_client = MagicMock()
|
||||
mock_client.messages.create.return_value = mock_response
|
||||
llm._client = mock_client
|
||||
|
||||
result = llm.call(
|
||||
"Search for CrewAI",
|
||||
tools=_MANY_TOOLS,
|
||||
available_functions={"search_web": lambda query: f"found {query}"},
|
||||
)
|
||||
|
||||
assert result == "found CrewAI"
|
||||
|
||||
|
||||
def test_anthropic_dict_tool_use_blocks_work_in_follow_up_conversation():
|
||||
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
|
||||
|
||||
llm = AnthropicCompletion(model="claude-fable-5")
|
||||
initial_response = _dict_tool_use_response()
|
||||
final_response = MagicMock()
|
||||
final_response.content = [types.SimpleNamespace(text="Final answer")]
|
||||
final_response.usage = MagicMock(input_tokens=4, output_tokens=3)
|
||||
final_response.stop_reason = "end_turn"
|
||||
final_response.id = "msg_final"
|
||||
mock_client = MagicMock()
|
||||
mock_client.messages.create.return_value = final_response
|
||||
llm._client = mock_client
|
||||
|
||||
result = llm._handle_tool_use_conversation(
|
||||
initial_response,
|
||||
initial_response.content,
|
||||
params={"messages": []},
|
||||
available_functions={"search_web": lambda query: f"found {query}"},
|
||||
)
|
||||
|
||||
assert result == "Final answer"
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_tool_search_discovers_and_calls_tool():
|
||||
"""Tool search should discover the right tool and return a tool_use block."""
|
||||
|
||||
Reference in New Issue
Block a user