fix: handle empty documents list in KnowledgeStorage.save() and asave()

Fixes #4277

When KnowledgeStorage.save() or asave() is called with an empty documents
list, the method now returns early instead of propagating a low-level
ValueError from ChromaDB's upsert operation.

This is a valid edge case that can occur in real-world workflows (e.g.,
after filtering, retrieval failures, or conditional logic), and should
be handled gracefully as a no-op.

Co-Authored-By: João <joao@crewai.com>

docs/en/concepts/memory.mdx

@@ -401,58 +401,23 @@ crew = Crew(
 
 ### Vertex AI Embeddings
 
-For Google Cloud users with Vertex AI access. Supports both legacy and new embedding models with automatic SDK selection.
+For Google Cloud users with Vertex AI access.
-
-<Note>
-**Deprecation Notice:** Legacy models (`textembedding-gecko*`) use the deprecated `vertexai.language_models` SDK which will be removed after June 24, 2026. Consider migrating to newer models like `gemini-embedding-001`. See the [Google migration guide](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk) for details.
-</Note>
 
 ```python
-# Recommended: Using new models with google-genai SDK
 crew = Crew(
     memory=True,
     embedder={
-        "provider": "google-vertex",
+        "provider": "vertexai",
         "config": {
             "project_id": "your-gcp-project-id",
-            "location": "us-central1",
+            "region": "us-central1",  # or your preferred region
-            "model_name": "gemini-embedding-001",  # or "text-embedding-005", "text-multilingual-embedding-002"
+            "api_key": "your-service-account-key",
-            "task_type": "RETRIEVAL_DOCUMENT",  # Optional
+            "model_name": "textembedding-gecko"
-            "output_dimensionality": 768  # Optional
-        }
-    }
-)
-
-# Using API key authentication (Exp)
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "google-vertex",
-        "config": {
-            "api_key": "your-google-api-key",
-            "model_name": "gemini-embedding-001"
-        }
-    }
-)
-
-# Legacy models (backwards compatible, emits deprecation warning)
-crew = Crew(
-    memory=True,
-    embedder={
-        "provider": "google-vertex",
-        "config": {
-            "project_id": "your-gcp-project-id",
-            "region": "us-central1",  # or "location" (region is deprecated)
-            "model_name": "textembedding-gecko"  # Legacy model
         }
     }
 )
 ```
 
-**Available models:**
-- **New SDK models** (recommended): `gemini-embedding-001`, `text-embedding-005`, `text-multilingual-embedding-002`
-- **Legacy models** (deprecated): `textembedding-gecko`, `textembedding-gecko@001`, `textembedding-gecko-multilingual`
-
 ### Ollama Embeddings (Local)
 
 Run embeddings locally for privacy and cost savings.
@@ -604,7 +569,7 @@ mem0_client_embedder_config = {
             "project_id": "my_project_id", # Optional
             "api_key": "custom-api-key"    # Optional - overrides env var
             "run_id": "my_run_id",        # Optional - for short-term memory
-            "includes": "include1",       # Optional
+            "includes": "include1",       # Optional 
             "excludes": "exclude1",       # Optional
             "infer": True                 # Optional defaults to True
             "custom_categories": new_categories  # Optional - custom categories for user memory
@@ -626,7 +591,7 @@ crew = Crew(
 
 ### Choosing the Right Embedding Provider
 
-When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs.
+When selecting an embedding provider, consider factors like performance, privacy, cost, and integration needs.  
 Below is a comparison to help you decide:
 
 | Provider       | Best For                       | Pros                              | Cons                      |
@@ -784,7 +749,7 @@ Entity Memory supports batching when saving multiple entities at once. When you
 
 This improves performance and observability when writing many entities in one operation.
 
-## 2. External Memory
+## 2. External Memory 
 External Memory provides a standalone memory system that operates independently from the crew's built-in memory. This is ideal for specialized memory providers or cross-application memory sharing.
 
 ### Basic External Memory with Mem0
@@ -854,7 +819,7 @@ external_memory = ExternalMemory(
             "project_id": "my_project_id", # Optional
             "api_key": "custom-api-key"    # Optional - overrides env var
             "run_id": "my_run_id",        # Optional - for short-term memory
-            "includes": "include1",       # Optional
+            "includes": "include1",       # Optional 
             "excludes": "exclude1",       # Optional
             "infer": True                 # Optional defaults to True
             "custom_categories": new_categories  # Optional - custom categories for user memory

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.9.0"
+__version__ = "1.8.1"

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.9.0",
+    "crewai==1.8.1",
     "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.9.0"
+__version__ = "1.8.1"

lib/crewai/pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.9.0",
+    "crewai-tools==1.8.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -90,7 +90,7 @@ azure-ai-inference = [
     "azure-ai-inference~=1.0.0b9",
 ]
 anthropic = [
-    "anthropic~=0.73.0",
+    "anthropic~=0.71.0",
 ]
 a2a = [
      "a2a-sdk~=0.3.10",

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.9.0"
+__version__ = "1.8.1"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.9.0"
+    "crewai[tools]==1.8.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.9.0"
+    "crewai[tools]==1.8.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/events/types/llm_events.py

@@ -84,4 +84,3 @@ class LLMStreamChunkEvent(LLMEventBase):
     chunk: str
     tool_call: ToolCall | None = None
     call_type: LLMCallType | None = None
-    response_id: str | None = None

lib/crewai/src/crewai/llm.py

@@ -768,10 +768,6 @@ class LLM(BaseLLM):
 
                 # Extract content from the chunk
                 chunk_content = None
-                response_id = None
-
-                if hasattr(chunk,'id'):
-                    response_id = chunk.id
 
                 # Safely extract content from various chunk formats
                 try:
@@ -827,7 +823,6 @@ class LLM(BaseLLM):
                                         available_functions=available_functions,
                                         from_task=from_task,
                                         from_agent=from_agent,
-                                        response_id=response_id
                                     )
 
                                     if result is not None:
@@ -849,7 +844,6 @@ class LLM(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             call_type=LLMCallType.LLM_CALL,
-                            response_id=response_id
                         ),
                     )
             # --- 4) Fallback to non-streaming if no content received
@@ -1027,7 +1021,6 @@ class LLM(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Task | None = None,
         from_agent: Agent | None = None,
-        response_id: str | None = None,
     ) -> Any:
         for tool_call in tool_calls:
             current_tool_accumulator = accumulated_tool_args[tool_call.index]
@@ -1048,7 +1041,6 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     call_type=LLMCallType.TOOL_CALL,
-                    response_id=response_id
                 ),
             )
 
@@ -1410,13 +1402,11 @@ class LLM(BaseLLM):
 
         params["stream"] = True
         params["stream_options"] = {"include_usage": True}
-        response_id = None
 
         try:
             async for chunk in await litellm.acompletion(**params):
                 chunk_count += 1
                 chunk_content = None
-                response_id = chunk.id if hasattr(chunk, "id") else None
 
                 try:
                     choices = None
@@ -1476,7 +1466,6 @@ class LLM(BaseLLM):
                             chunk=chunk_content,
                             from_task=from_task,
                             from_agent=from_agent,
-                            response_id=response_id
                         ),
                     )
 
@@ -1514,7 +1503,6 @@ class LLM(BaseLLM):
                         available_functions=available_functions,
                         from_task=from_task,
                         from_agent=from_agent,
-                        response_id=response_id,
                     )
                     if result is not None:
                         return result

lib/crewai/src/crewai/llms/base_llm.py

@@ -404,7 +404,6 @@ class BaseLLM(ABC):
         from_agent: Agent | None = None,
         tool_call: dict[str, Any] | None = None,
         call_type: LLMCallType | None = None,
-        response_id: str | None = None
     ) -> None:
         """Emit stream chunk event.
 
@@ -414,7 +413,6 @@ class BaseLLM(ABC):
             from_agent: The agent that initiated the call.
             tool_call: Tool call information if this is a tool call chunk.
             call_type: The type of LLM call (LLM_CALL or TOOL_CALL).
-            response_id: Unique ID for a particular LLM response, chunks have same response_id.
         """
         if not hasattr(crewai_event_bus, "emit"):
             raise ValueError("crewai_event_bus does not have an emit method") from None
@@ -427,7 +425,6 @@ class BaseLLM(ABC):
                 from_task=from_task,
                 from_agent=from_agent,
                 call_type=call_type,
-                response_id=response_id
             ),
         )
 

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -3,8 +3,9 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, Final, Literal, TypeGuard, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
+from anthropic.types import ThinkingBlock
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
@@ -21,9 +22,8 @@ if TYPE_CHECKING:
     from crewai.llms.hooks.base import BaseInterceptor
 
 try:
-    from anthropic import Anthropic, AsyncAnthropic, transform_schema
+    from anthropic import Anthropic, AsyncAnthropic
     from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
-    from anthropic.types.beta import BetaMessage
     import httpx
 except ImportError:
     raise ImportError(
@@ -31,62 +31,7 @@ except ImportError:
     ) from None
 
 
-ANTHROPIC_FILES_API_BETA: Final = "files-api-2025-04-14"
+ANTHROPIC_FILES_API_BETA = "files-api-2025-04-14"
-ANTHROPIC_STRUCTURED_OUTPUTS_BETA: Final = "structured-outputs-2025-11-13"
-
-NATIVE_STRUCTURED_OUTPUT_MODELS: Final[
-    tuple[
-        Literal["claude-sonnet-4-5"],
-        Literal["claude-sonnet-4.5"],
-        Literal["claude-opus-4-5"],
-        Literal["claude-opus-4.5"],
-        Literal["claude-opus-4-1"],
-        Literal["claude-opus-4.1"],
-        Literal["claude-haiku-4-5"],
-        Literal["claude-haiku-4.5"],
-    ]
-] = (
-    "claude-sonnet-4-5",
-    "claude-sonnet-4.5",
-    "claude-opus-4-5",
-    "claude-opus-4.5",
-    "claude-opus-4-1",
-    "claude-opus-4.1",
-    "claude-haiku-4-5",
-    "claude-haiku-4.5",
-)
-
-
-def _supports_native_structured_outputs(model: str) -> bool:
-    """Check if the model supports native structured outputs.
-
-    Native structured outputs are only available for Claude 4.5 models
-    (Sonnet 4.5, Opus 4.5, Opus 4.1, Haiku 4.5).
-    Other models require the tool-based fallback approach.
-
-    Args:
-        model: The model name/identifier.
-
-    Returns:
-        True if the model supports native structured outputs.
-    """
-    model_lower = model.lower()
-    return any(prefix in model_lower for prefix in NATIVE_STRUCTURED_OUTPUT_MODELS)
-
-
-def _is_pydantic_model_class(obj: Any) -> TypeGuard[type[BaseModel]]:
-    """Check if an object is a Pydantic model class.
-
-    This distinguishes between Pydantic model classes that support structured
-    outputs (have model_json_schema) and plain dicts like {"type": "json_object"}.
-
-    Args:
-        obj: The object to check.
-
-    Returns:
-        True if obj is a Pydantic model class.
-    """
-    return isinstance(obj, type) and issubclass(obj, BaseModel)
 
 
 def _contains_file_id_reference(messages: list[dict[str, Any]]) -> bool:
@@ -139,7 +84,6 @@ class AnthropicCompletion(BaseLLM):
         client_params: dict[str, Any] | None = None,
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
         thinking: AnthropicThinkingConfig | None = None,
-        response_format: type[BaseModel] | None = None,
         **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
@@ -157,8 +101,6 @@ class AnthropicCompletion(BaseLLM):
             stream: Enable streaming responses
             client_params: Additional parameters for the Anthropic client
             interceptor: HTTP interceptor for modifying requests/responses at transport level.
-            response_format: Pydantic model for structured output. When provided, responses
-                will be validated against this model schema.
             **kwargs: Additional parameters
         """
         super().__init__(
@@ -189,7 +131,6 @@ class AnthropicCompletion(BaseLLM):
         self.stop_sequences = stop_sequences or []
         self.thinking = thinking
         self.previous_thinking_blocks: list[ThinkingBlock] = []
-        self.response_format = response_format
         # Model-specific settings
         self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = True
@@ -290,8 +231,6 @@ class AnthropicCompletion(BaseLLM):
                 formatted_messages, system_message, tools
             )
 
-            effective_response_model = response_model or self.response_format
-
             # Handle streaming vs non-streaming
             if self.stream:
                 return self._handle_streaming_completion(
@@ -299,7 +238,7 @@ class AnthropicCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return self._handle_completion(
@@ -307,7 +246,7 @@ class AnthropicCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except Exception as e:
@@ -359,15 +298,13 @@ class AnthropicCompletion(BaseLLM):
                 formatted_messages, system_message, tools
             )
 
-            effective_response_model = response_model or self.response_format
-
             if self.stream:
                 return await self._ahandle_streaming_completion(
                     completion_params,
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return await self._ahandle_completion(
@@ -375,7 +312,7 @@ class AnthropicCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except Exception as e:
@@ -628,40 +565,22 @@ class AnthropicCompletion(BaseLLM):
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
+        if response_model:
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
+
+            params["tools"] = [structured_tool]
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
+
         uses_file_api = _contains_file_id_reference(params.get("messages", []))
-        betas: list[str] = []
-        use_native_structured_output = False
-
-        if uses_file_api:
-            betas.append(ANTHROPIC_FILES_API_BETA)
-
-        extra_body: dict[str, Any] | None = None
-        if _is_pydantic_model_class(response_model):
-            schema = transform_schema(response_model.model_json_schema())
-            if _supports_native_structured_outputs(self.model):
-                use_native_structured_output = True
-                betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
-                extra_body = {
-                    "output_format": {
-                        "type": "json_schema",
-                        "schema": schema,
-                    }
-                }
-            else:
-                structured_tool = {
-                    "name": "structured_output",
-                    "description": "Output the structured response",
-                    "input_schema": schema,
-                }
-                params["tools"] = [structured_tool]
-                params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         try:
-            if betas:
+            if uses_file_api:
-                params["betas"] = betas
+                params["betas"] = [ANTHROPIC_FILES_API_BETA]
-                response = self.client.beta.messages.create(
+                response = self.client.beta.messages.create(**params)
-                    **params, extra_body=extra_body
-                )
             else:
                 response = self.client.messages.create(**params)
 
@@ -674,34 +593,22 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(response)
         self._track_token_usage_internal(usage)
 
-        if _is_pydantic_model_class(response_model) and response.content:
+        if response_model and response.content:
-            if use_native_structured_output:
+            tool_uses = [
-                for block in response.content:
+                block for block in response.content if isinstance(block, ToolUseBlock)
-                    if isinstance(block, TextBlock):
+            ]
-                        structured_json = block.text
+            if tool_uses and tool_uses[0].name == "structured_output":
-                        self._emit_call_completed_event(
+                structured_data = tool_uses[0].input
-                            response=structured_json,
+                structured_json = json.dumps(structured_data)
-                            call_type=LLMCallType.LLM_CALL,
+                self._emit_call_completed_event(
-                            from_task=from_task,
+                    response=structured_json,
-                            from_agent=from_agent,
+                    call_type=LLMCallType.LLM_CALL,
-                            messages=params["messages"],
+                    from_task=from_task,
-                        )
+                    from_agent=from_agent,
-                        return structured_json
+                    messages=params["messages"],
-            else:
+                )
-                for block in response.content:
+
-                    if (
+                return structured_json
-                        isinstance(block, ToolUseBlock)
-                        and block.name == "structured_output"
-                    ):
-                        structured_json = json.dumps(block.input)
-                        self._emit_call_completed_event(
-                            response=structured_json,
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=params["messages"],
-                        )
-                        return structured_json
 
         # Check if Claude wants to use tools
         if response.content:
@@ -771,31 +678,17 @@ class AnthropicCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
+    ) -> str:
         """Handle streaming message completion."""
-        betas: list[str] = []
+        if response_model:
-        use_native_structured_output = False
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
 
-        extra_body: dict[str, Any] | None = None
+            params["tools"] = [structured_tool]
-        if _is_pydantic_model_class(response_model):
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
-            schema = transform_schema(response_model.model_json_schema())
-            if _supports_native_structured_outputs(self.model):
-                use_native_structured_output = True
-                betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
-                extra_body = {
-                    "output_format": {
-                        "type": "json_schema",
-                        "schema": schema,
-                    }
-                }
-            else:
-                structured_tool = {
-                    "name": "structured_output",
-                    "description": "Output the structured response",
-                    "input_schema": schema,
-                }
-                params["tools"] = [structured_tool]
-                params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         full_response = ""
 
@@ -803,22 +696,11 @@ class AnthropicCompletion(BaseLLM):
         # (the SDK sets it internally)
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
-        if betas:
-            stream_params["betas"] = betas
-
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
-        stream_context = (
+        # Make streaming API call
-            self.client.beta.messages.stream(**stream_params, extra_body=extra_body)
+        with self.client.messages.stream(**stream_params) as stream:
-            if betas
-            else self.client.messages.stream(**stream_params)
-        )
-        with stream_context as stream:
-            response_id = None
             for event in stream:
-                if hasattr(event, "message") and hasattr(event.message, "id"):
-                    response_id = event.message.id
-
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
                     text_delta = event.delta.text
                     full_response += text_delta
@@ -826,7 +708,6 @@ class AnthropicCompletion(BaseLLM):
                         chunk=text_delta,
                         from_task=from_task,
                         from_agent=from_agent,
-                        response_id=response_id,
                     )
 
                 if event.type == "content_block_start":
@@ -853,7 +734,6 @@ class AnthropicCompletion(BaseLLM):
                                 "index": block_index,
                             },
                             call_type=LLMCallType.TOOL_CALL,
-                            response_id=response_id,
                         )
                 elif event.type == "content_block_delta":
                     if event.delta.type == "input_json_delta":
@@ -877,10 +757,9 @@ class AnthropicCompletion(BaseLLM):
                                     "index": block_index,
                                 },
                                 call_type=LLMCallType.TOOL_CALL,
-                                response_id=response_id,
                             )
 
-            final_message = stream.get_final_message()
+            final_message: Message = stream.get_final_message()
 
         thinking_blocks: list[ThinkingBlock] = []
         if final_message.content:
@@ -895,30 +774,25 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
-        if _is_pydantic_model_class(response_model):
+        if response_model and final_message.content:
-            if use_native_structured_output:
+            tool_uses = [
+                block
+                for block in final_message.content
+                if isinstance(block, ToolUseBlock)
+            ]
+            if tool_uses and tool_uses[0].name == "structured_output":
+                structured_data = tool_uses[0].input
+                structured_json = json.dumps(structured_data)
+
                 self._emit_call_completed_event(
-                    response=full_response,
+                    response=structured_json,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
                 )
-                return full_response
+
-            for block in final_message.content:
+                return structured_json
-                if (
-                    isinstance(block, ToolUseBlock)
-                    and block.name == "structured_output"
-                ):
-                    structured_json = json.dumps(block.input)
-                    self._emit_call_completed_event(
-                        response=structured_json,
-                        call_type=LLMCallType.LLM_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-                    return structured_json
 
         if final_message.content:
             tool_uses = [
@@ -928,9 +802,11 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
+                # If no available_functions, return tool calls for executor to handle
                 if not available_functions:
                     return list(tool_uses)
 
+                # Handle tool use conversation flow internally
                 return self._handle_tool_use_conversation(
                     final_message,
                     tool_uses,
@@ -940,8 +816,10 @@ class AnthropicCompletion(BaseLLM):
                     from_agent,
                 )
 
+        # Apply stop words to full response
         full_response = self._apply_stop_words(full_response)
 
+        # Emit completion event and return full response
         self._emit_call_completed_event(
             response=full_response,
             call_type=LLMCallType.LLM_CALL,
@@ -999,7 +877,7 @@ class AnthropicCompletion(BaseLLM):
 
     def _handle_tool_use_conversation(
         self,
-        initial_response: Message | BetaMessage,
+        initial_response: Message,
         tool_uses: list[ToolUseBlock],
         params: dict[str, Any],
         available_functions: dict[str, Any],
@@ -1117,40 +995,22 @@ class AnthropicCompletion(BaseLLM):
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming async message completion."""
+        if response_model:
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
+
+            params["tools"] = [structured_tool]
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
+
         uses_file_api = _contains_file_id_reference(params.get("messages", []))
-        betas: list[str] = []
-        use_native_structured_output = False
-
-        if uses_file_api:
-            betas.append(ANTHROPIC_FILES_API_BETA)
-
-        extra_body: dict[str, Any] | None = None
-        if _is_pydantic_model_class(response_model):
-            schema = transform_schema(response_model.model_json_schema())
-            if _supports_native_structured_outputs(self.model):
-                use_native_structured_output = True
-                betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
-                extra_body = {
-                    "output_format": {
-                        "type": "json_schema",
-                        "schema": schema,
-                    }
-                }
-            else:
-                structured_tool = {
-                    "name": "structured_output",
-                    "description": "Output the structured response",
-                    "input_schema": schema,
-                }
-                params["tools"] = [structured_tool]
-                params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         try:
-            if betas:
+            if uses_file_api:
-                params["betas"] = betas
+                params["betas"] = [ANTHROPIC_FILES_API_BETA]
-                response = await self.async_client.beta.messages.create(
+                response = await self.async_client.beta.messages.create(**params)
-                    **params, extra_body=extra_body
-                )
             else:
                 response = await self.async_client.messages.create(**params)
 
@@ -1163,34 +1023,23 @@ class AnthropicCompletion(BaseLLM):
         usage = self._extract_anthropic_token_usage(response)
         self._track_token_usage_internal(usage)
 
-        if _is_pydantic_model_class(response_model) and response.content:
+        if response_model and response.content:
-            if use_native_structured_output:
+            tool_uses = [
-                for block in response.content:
+                block for block in response.content if isinstance(block, ToolUseBlock)
-                    if isinstance(block, TextBlock):
+            ]
-                        structured_json = block.text
+            if tool_uses and tool_uses[0].name == "structured_output":
-                        self._emit_call_completed_event(
+                structured_data = tool_uses[0].input
-                            response=structured_json,
+                structured_json = json.dumps(structured_data)
-                            call_type=LLMCallType.LLM_CALL,
+
-                            from_task=from_task,
+                self._emit_call_completed_event(
-                            from_agent=from_agent,
+                    response=structured_json,
-                            messages=params["messages"],
+                    call_type=LLMCallType.LLM_CALL,
-                        )
+                    from_task=from_task,
-                        return structured_json
+                    from_agent=from_agent,
-            else:
+                    messages=params["messages"],
-                for block in response.content:
+                )
-                    if (
+
-                        isinstance(block, ToolUseBlock)
+                return structured_json
-                        and block.name == "structured_output"
-                    ):
-                        structured_json = json.dumps(block.input)
-                        self._emit_call_completed_event(
-                            response=structured_json,
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=params["messages"],
-                        )
-                        return structured_json
 
         if response.content:
             tool_uses = [
@@ -1246,54 +1095,26 @@ class AnthropicCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
+    ) -> str:
         """Handle async streaming message completion."""
-        betas: list[str] = []
+        if response_model:
-        use_native_structured_output = False
+            structured_tool = {
+                "name": "structured_output",
+                "description": "Returns structured data according to the schema",
+                "input_schema": response_model.model_json_schema(),
+            }
 
-        extra_body: dict[str, Any] | None = None
+            params["tools"] = [structured_tool]
-        if _is_pydantic_model_class(response_model):
+            params["tool_choice"] = {"type": "tool", "name": "structured_output"}
-            schema = transform_schema(response_model.model_json_schema())
-            if _supports_native_structured_outputs(self.model):
-                use_native_structured_output = True
-                betas.append(ANTHROPIC_STRUCTURED_OUTPUTS_BETA)
-                extra_body = {
-                    "output_format": {
-                        "type": "json_schema",
-                        "schema": schema,
-                    }
-                }
-            else:
-                structured_tool = {
-                    "name": "structured_output",
-                    "description": "Output the structured response",
-                    "input_schema": schema,
-                }
-                params["tools"] = [structured_tool]
-                params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         full_response = ""
 
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
-        if betas:
-            stream_params["betas"] = betas
-
         current_tool_calls: dict[int, dict[str, Any]] = {}
 
-        stream_context = (
+        async with self.async_client.messages.stream(**stream_params) as stream:
-            self.async_client.beta.messages.stream(
-                **stream_params, extra_body=extra_body
-            )
-            if betas
-            else self.async_client.messages.stream(**stream_params)
-        )
-        async with stream_context as stream:
-            response_id = None
             async for event in stream:
-                if hasattr(event, "message") and hasattr(event.message, "id"):
-                    response_id = event.message.id
-
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
                     text_delta = event.delta.text
                     full_response += text_delta
@@ -1301,7 +1122,6 @@ class AnthropicCompletion(BaseLLM):
                         chunk=text_delta,
                         from_task=from_task,
                         from_agent=from_agent,
-                        response_id=response_id,
                     )
 
                 if event.type == "content_block_start":
@@ -1328,7 +1148,6 @@ class AnthropicCompletion(BaseLLM):
                                 "index": block_index,
                             },
                             call_type=LLMCallType.TOOL_CALL,
-                            response_id=response_id,
                         )
                 elif event.type == "content_block_delta":
                     if event.delta.type == "input_json_delta":
@@ -1352,38 +1171,32 @@ class AnthropicCompletion(BaseLLM):
                                     "index": block_index,
                                 },
                                 call_type=LLMCallType.TOOL_CALL,
-                                response_id=response_id,
                             )
 
-            final_message = await stream.get_final_message()
+            final_message: Message = await stream.get_final_message()
 
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
-        if _is_pydantic_model_class(response_model):
+        if response_model and final_message.content:
-            if use_native_structured_output:
+            tool_uses = [
+                block
+                for block in final_message.content
+                if isinstance(block, ToolUseBlock)
+            ]
+            if tool_uses and tool_uses[0].name == "structured_output":
+                structured_data = tool_uses[0].input
+                structured_json = json.dumps(structured_data)
+
                 self._emit_call_completed_event(
-                    response=full_response,
+                    response=structured_json,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
                 )
-                return full_response
+
-            for block in final_message.content:
+                return structured_json
-                if (
-                    isinstance(block, ToolUseBlock)
-                    and block.name == "structured_output"
-                ):
-                    structured_json = json.dumps(block.input)
-                    self._emit_call_completed_event(
-                        response=structured_json,
-                        call_type=LLMCallType.LLM_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-                    return structured_json
 
         if final_message.content:
             tool_uses = [
@@ -1393,6 +1206,7 @@ class AnthropicCompletion(BaseLLM):
             ]
 
             if tool_uses:
+                # If no available_functions, return tool calls for executor to handle
                 if not available_functions:
                     return list(tool_uses)
 
@@ -1419,7 +1233,7 @@ class AnthropicCompletion(BaseLLM):
 
     async def _ahandle_tool_use_conversation(
         self,
-        initial_response: Message | BetaMessage,
+        initial_response: Message,
         tool_uses: list[ToolUseBlock],
         params: dict[str, Any],
         available_functions: dict[str, Any],
@@ -1528,9 +1342,7 @@ class AnthropicCompletion(BaseLLM):
         return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
 
     @staticmethod
-    def _extract_anthropic_token_usage(
+    def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
-        response: Message | BetaMessage,
-    ) -> dict[str, Any]:
         """Extract token usage from Anthropic response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -92,7 +92,6 @@ class AzureCompletion(BaseLLM):
         stop: list[str] | None = None,
         stream: bool = False,
         interceptor: BaseInterceptor[Any, Any] | None = None,
-        response_format: type[BaseModel] | None = None,
         **kwargs: Any,
     ):
         """Initialize Azure AI Inference chat completion client.
@@ -112,9 +111,6 @@ class AzureCompletion(BaseLLM):
             stop: Stop sequences
             stream: Enable streaming responses
             interceptor: HTTP interceptor (not yet supported for Azure).
-            response_format: Pydantic model for structured output. Used as default when
-                           response_model is not passed to call()/acall() methods.
-                           Only works with OpenAI models deployed on Azure.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -169,7 +165,6 @@ class AzureCompletion(BaseLLM):
         self.presence_penalty = presence_penalty
         self.max_tokens = max_tokens
         self.stream = stream
-        self.response_format = response_format
 
         self.is_openai_model = any(
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
@@ -303,7 +298,6 @@ class AzureCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
             )
-            effective_response_model = response_model or self.response_format
 
             # Format messages for Azure
             formatted_messages = self._format_messages_for_azure(messages)
@@ -313,7 +307,7 @@ class AzureCompletion(BaseLLM):
 
             # Prepare completion parameters
             completion_params = self._prepare_completion_params(
-                formatted_messages, tools, effective_response_model
+                formatted_messages, tools, response_model
             )
 
             # Handle streaming vs non-streaming
@@ -323,7 +317,7 @@ class AzureCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return self._handle_completion(
@@ -331,7 +325,7 @@ class AzureCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except Exception as e:
@@ -370,12 +364,11 @@ class AzureCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
             )
-            effective_response_model = response_model or self.response_format
 
             formatted_messages = self._format_messages_for_azure(messages)
 
             completion_params = self._prepare_completion_params(
-                formatted_messages, tools, effective_response_model
+                formatted_messages, tools, response_model
             )
 
             if self.stream:
@@ -384,7 +377,7 @@ class AzureCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return await self._ahandle_completion(
@@ -392,7 +385,7 @@ class AzureCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except Exception as e:
@@ -733,7 +726,6 @@ class AzureCompletion(BaseLLM):
         """
         if update.choices:
             choice = update.choices[0]
-            response_id = update.id if hasattr(update, "id") else None
             if choice.delta and choice.delta.content:
                 content_delta = choice.delta.content
                 full_response += content_delta
@@ -741,7 +733,6 @@ class AzureCompletion(BaseLLM):
                     chunk=content_delta,
                     from_task=from_task,
                     from_agent=from_agent,
-                    response_id=response_id,
                 )
 
             if choice.delta and choice.delta.tool_calls:
@@ -776,7 +767,6 @@ class AzureCompletion(BaseLLM):
                             "index": idx,
                         },
                         call_type=LLMCallType.TOOL_CALL,
-                        response_id=response_id,
                     )
 
         return full_response

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -172,7 +172,6 @@ class BedrockCompletion(BaseLLM):
         additional_model_request_fields: dict[str, Any] | None = None,
         additional_model_response_field_paths: list[str] | None = None,
         interceptor: BaseInterceptor[Any, Any] | None = None,
-        response_format: type[BaseModel] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize AWS Bedrock completion client.
@@ -193,8 +192,6 @@ class BedrockCompletion(BaseLLM):
             additional_model_request_fields: Model-specific request parameters
             additional_model_response_field_paths: Custom response field paths
             interceptor: HTTP interceptor (not yet supported for Bedrock).
-            response_format: Pydantic model for structured output. Used as default when
-                           response_model is not passed to call()/acall() methods.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -250,8 +247,7 @@ class BedrockCompletion(BaseLLM):
         self.top_p = top_p
         self.top_k = top_k
         self.stream = stream
-        self.stop_sequences = stop_sequences
+        self.stop_sequences = stop_sequences or []
-        self.response_format = response_format
 
         # Store advanced features (optional)
         self.guardrail_config = guardrail_config
@@ -271,7 +267,7 @@ class BedrockCompletion(BaseLLM):
     @property
     def stop(self) -> list[str]:
         """Get stop sequences sent to the API."""
-        return [] if self.stop_sequences is None else list(self.stop_sequences)
+        return list(self.stop_sequences)
 
     @stop.setter
     def stop(self, value: Sequence[str] | str | None) -> None:
@@ -303,8 +299,6 @@ class BedrockCompletion(BaseLLM):
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call AWS Bedrock Converse API."""
-        effective_response_model = response_model or self.response_format
-
         try:
             # Emit call started event
             self._emit_call_started_event(
@@ -381,7 +375,6 @@ class BedrockCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
                 )
 
             return self._handle_converse(
@@ -390,7 +383,6 @@ class BedrockCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
             )
 
         except Exception as e:
@@ -433,8 +425,6 @@ class BedrockCompletion(BaseLLM):
             NotImplementedError: If aiobotocore is not installed.
             LLMContextLengthExceededError: If context window is exceeded.
         """
-        effective_response_model = response_model or self.response_format
-
         if not AIOBOTOCORE_AVAILABLE:
             raise NotImplementedError(
                 "Async support for AWS Bedrock requires aiobotocore. "
@@ -504,21 +494,11 @@ class BedrockCompletion(BaseLLM):
 
             if self.stream:
                 return await self._ahandle_streaming_converse(
-                    formatted_messages,
+                    formatted_messages, body, available_functions, from_task, from_agent
-                    body,
-                    available_functions,
-                    from_task,
-                    from_agent,
-                    effective_response_model,
                 )
 
             return await self._ahandle_converse(
-                formatted_messages,
+                formatted_messages, body, available_functions, from_task, from_agent
-                body,
-                available_functions,
-                from_task,
-                from_agent,
-                effective_response_model,
             )
 
         except Exception as e:
@@ -540,29 +520,10 @@ class BedrockCompletion(BaseLLM):
         available_functions: Mapping[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
+    ) -> str:
-    ) -> str | Any:
         """Handle non-streaming converse API call following AWS best practices."""
-        if response_model:
-            structured_tool: ConverseToolTypeDef = {
-                "toolSpec": {
-                    "name": "structured_output",
-                    "description": "Returns structured data according to the schema",
-                    "inputSchema": {"json": response_model.model_json_schema()},
-                }
-            }
-            body["toolConfig"] = cast(
-                "ToolConfigurationTypeDef",
-                cast(
-                    object,
-                    {
-                        "tools": [structured_tool],
-                        "toolChoice": {"tool": {"name": "structured_output"}},
-                    },
-                ),
-            )
-
         try:
+            # Validate messages format before API call
             if not messages:
                 raise ValueError("Messages cannot be empty")
 
@@ -610,21 +571,6 @@ class BedrockCompletion(BaseLLM):
 
             # If there are tool uses but no available_functions, return them for the executor to handle
             tool_uses = [block["toolUse"] for block in content if "toolUse" in block]
-
-            if response_model and tool_uses:
-                for tool_use in tool_uses:
-                    if tool_use.get("name") == "structured_output":
-                        structured_data = tool_use.get("input", {})
-                        result = response_model.model_validate(structured_data)
-                        self._emit_call_completed_event(
-                            response=result.model_dump_json(),
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=messages,
-                        )
-                        return result
-
             if tool_uses and not available_functions:
                 self._emit_call_completed_event(
                     response=tool_uses,
@@ -771,28 +717,8 @@ class BedrockCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming converse API call with comprehensive event handling."""
-        if response_model:
-            structured_tool: ConverseToolTypeDef = {
-                "toolSpec": {
-                    "name": "structured_output",
-                    "description": "Returns structured data according to the schema",
-                    "inputSchema": {"json": response_model.model_json_schema()},
-                }
-            }
-            body["toolConfig"] = cast(
-                "ToolConfigurationTypeDef",
-                cast(
-                    object,
-                    {
-                        "tools": [structured_tool],
-                        "toolChoice": {"tool": {"name": "structured_output"}},
-                    },
-                ),
-            )
-
         full_response = ""
         current_tool_use: dict[str, Any] | None = None
         tool_use_id: str | None = None
@@ -810,7 +736,6 @@ class BedrockCompletion(BaseLLM):
             )
 
             stream = response.get("stream")
-            response_id = None
             if stream:
                 for event in stream:
                     if "messageStart" in event:
@@ -842,7 +767,6 @@ class BedrockCompletion(BaseLLM):
                                     "index": tool_use_index,
                                 },
                                 call_type=LLMCallType.TOOL_CALL,
-                                response_id=response_id,
                             )
                         logging.debug(
                             f"Tool use started in stream: {json.dumps(current_tool_use)} (ID: {tool_use_id})"
@@ -858,7 +782,6 @@ class BedrockCompletion(BaseLLM):
                                 chunk=text_chunk,
                                 from_task=from_task,
                                 from_agent=from_agent,
-                                response_id=response_id,
                             )
                         elif "toolUse" in delta and current_tool_use:
                             tool_input = delta["toolUse"].get("input", "")
@@ -879,7 +802,6 @@ class BedrockCompletion(BaseLLM):
                                         "index": tool_use_index,
                                     },
                                     call_type=LLMCallType.TOOL_CALL,
-                                    response_id=response_id,
                                 )
                     elif "contentBlockStop" in event:
                         logging.debug("Content block stopped in stream")
@@ -1003,28 +925,8 @@ class BedrockCompletion(BaseLLM):
         available_functions: Mapping[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
+    ) -> str:
-    ) -> str | Any:
         """Handle async non-streaming converse API call."""
-        if response_model:
-            structured_tool: ConverseToolTypeDef = {
-                "toolSpec": {
-                    "name": "structured_output",
-                    "description": "Returns structured data according to the schema",
-                    "inputSchema": {"json": response_model.model_json_schema()},
-                }
-            }
-            body["toolConfig"] = cast(
-                "ToolConfigurationTypeDef",
-                cast(
-                    object,
-                    {
-                        "tools": [structured_tool],
-                        "toolChoice": {"tool": {"name": "structured_output"}},
-                    },
-                ),
-            )
-
         try:
             if not messages:
                 raise ValueError("Messages cannot be empty")
@@ -1070,21 +972,6 @@ class BedrockCompletion(BaseLLM):
 
             # If there are tool uses but no available_functions, return them for the executor to handle
             tool_uses = [block["toolUse"] for block in content if "toolUse" in block]
-
-            if response_model and tool_uses:
-                for tool_use in tool_uses:
-                    if tool_use.get("name") == "structured_output":
-                        structured_data = tool_use.get("input", {})
-                        result = response_model.model_validate(structured_data)
-                        self._emit_call_completed_event(
-                            response=result.model_dump_json(),
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=messages,
-                        )
-                        return result
-
             if tool_uses and not available_functions:
                 self._emit_call_completed_event(
                     response=tool_uses,
@@ -1215,28 +1102,8 @@ class BedrockCompletion(BaseLLM):
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle async streaming converse API call."""
-        if response_model:
-            structured_tool: ConverseToolTypeDef = {
-                "toolSpec": {
-                    "name": "structured_output",
-                    "description": "Returns structured data according to the schema",
-                    "inputSchema": {"json": response_model.model_json_schema()},
-                }
-            }
-            body["toolConfig"] = cast(
-                "ToolConfigurationTypeDef",
-                cast(
-                    object,
-                    {
-                        "tools": [structured_tool],
-                        "toolChoice": {"tool": {"name": "structured_output"}},
-                    },
-                ),
-            )
-
         full_response = ""
         current_tool_use: dict[str, Any] | None = None
         tool_use_id: str | None = None
@@ -1255,7 +1122,6 @@ class BedrockCompletion(BaseLLM):
             )
 
             stream = response.get("stream")
-            response_id = None
             if stream:
                 async for event in stream:
                     if "messageStart" in event:
@@ -1287,7 +1153,6 @@ class BedrockCompletion(BaseLLM):
                                     "index": tool_use_index,
                                 },
                                 call_type=LLMCallType.TOOL_CALL,
-                                response_id=response_id,
                             )
                             logging.debug(
                                 f"Tool use started in stream: {current_tool_use.get('name')} (ID: {tool_use_id})"
@@ -1303,7 +1168,6 @@ class BedrockCompletion(BaseLLM):
                                 chunk=text_chunk,
                                 from_task=from_task,
                                 from_agent=from_agent,
-                                response_id=response_id,
                             )
                         elif "toolUse" in delta and current_tool_use:
                             tool_input = delta["toolUse"].get("input", "")
@@ -1324,7 +1188,6 @@ class BedrockCompletion(BaseLLM):
                                         "index": tool_use_index,
                                     },
                                     call_type=LLMCallType.TOOL_CALL,
-                                    response_id=response_id,
                                 )
 
                     elif "contentBlockStop" in event:

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -56,7 +56,6 @@ class GeminiCompletion(BaseLLM):
         client_params: dict[str, Any] | None = None,
         interceptor: BaseInterceptor[Any, Any] | None = None,
         use_vertexai: bool | None = None,
-        response_format: type[BaseModel] | None = None,
         **kwargs: Any,
     ):
         """Initialize Google Gemini chat completion client.
@@ -87,8 +86,6 @@ class GeminiCompletion(BaseLLM):
                          - None (default): Check GOOGLE_GENAI_USE_VERTEXAI env var
                          When using Vertex AI with API key (Express mode), http_options with
                          api_version="v1" is automatically configured.
-            response_format: Pydantic model for structured output. Used as default when
-                           response_model is not passed to call()/acall() methods.
             **kwargs: Additional parameters
         """
         if interceptor is not None:
@@ -124,7 +121,6 @@ class GeminiCompletion(BaseLLM):
         self.safety_settings = safety_settings or {}
         self.stop_sequences = stop_sequences or []
         self.tools: list[dict[str, Any]] | None = None
-        self.response_format = response_format
 
         # Model-specific settings
         version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
@@ -296,7 +292,6 @@ class GeminiCompletion(BaseLLM):
                 from_agent=from_agent,
             )
             self.tools = tools
-            effective_response_model = response_model or self.response_format
 
             formatted_content, system_instruction = self._format_messages_for_gemini(
                 messages
@@ -308,7 +303,7 @@ class GeminiCompletion(BaseLLM):
                 raise ValueError("LLM call blocked by before_llm_call hook")
 
             config = self._prepare_generation_config(
-                system_instruction, tools, effective_response_model
+                system_instruction, tools, response_model
             )
 
             if self.stream:
@@ -318,7 +313,7 @@ class GeminiCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return self._handle_completion(
@@ -327,7 +322,7 @@ class GeminiCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except APIError as e:
@@ -379,14 +374,13 @@ class GeminiCompletion(BaseLLM):
                 from_agent=from_agent,
             )
             self.tools = tools
-            effective_response_model = response_model or self.response_format
 
             formatted_content, system_instruction = self._format_messages_for_gemini(
                 messages
             )
 
             config = self._prepare_generation_config(
-                system_instruction, tools, effective_response_model
+                system_instruction, tools, response_model
             )
 
             if self.stream:
@@ -396,7 +390,7 @@ class GeminiCompletion(BaseLLM):
                     available_functions,
                     from_task,
                     from_agent,
-                    effective_response_model,
+                    response_model,
                 )
 
             return await self._ahandle_completion(
@@ -405,7 +399,7 @@ class GeminiCompletion(BaseLLM):
                 available_functions,
                 from_task,
                 from_agent,
-                effective_response_model,
+                response_model,
             )
 
         except APIError as e:
@@ -561,11 +555,7 @@ class GeminiCompletion(BaseLLM):
 
                 response_data: dict[str, Any]
                 try:
-                    parsed = json.loads(text_content) if text_content else {}
+                    response_data = json.loads(text_content) if text_content else {}
-                    if isinstance(parsed, dict):
-                        response_data = parsed
-                    else:
-                        response_data = {"result": parsed}
                 except (json.JSONDecodeError, TypeError):
                     response_data = {"result": text_content}
 
@@ -576,10 +566,10 @@ class GeminiCompletion(BaseLLM):
                     types.Content(role="user", parts=[function_response_part])
                 )
             elif role == "assistant" and message.get("tool_calls"):
-                tool_parts: list[types.Part] = []
+                parts: list[types.Part] = []
 
                 if text_content:
-                    tool_parts.append(types.Part.from_text(text=text_content))
+                    parts.append(types.Part.from_text(text=text_content))
 
                 tool_calls: list[dict[str, Any]] = message.get("tool_calls") or []
                 for tool_call in tool_calls:
@@ -598,11 +588,11 @@ class GeminiCompletion(BaseLLM):
                     else:
                         func_args = func_args_raw
 
-                    tool_parts.append(
+                    parts.append(
                         types.Part.from_function_call(name=func_name, args=func_args)
                     )
 
-                contents.append(types.Content(role="model", parts=tool_parts))
+                contents.append(types.Content(role="model", parts=parts))
             else:
                 # Convert role for Gemini (assistant -> model)
                 gemini_role = "model" if role == "assistant" else "user"
@@ -796,7 +786,6 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Tuple of (updated full_response, updated function_calls, updated usage_data)
         """
-        response_id = chunk.response_id if hasattr(chunk, "response_id") else None
         if chunk.usage_metadata:
             usage_data = self._extract_token_usage(chunk)
 
@@ -806,7 +795,6 @@ class GeminiCompletion(BaseLLM):
                 chunk=chunk.text,
                 from_task=from_task,
                 from_agent=from_agent,
-                response_id=response_id,
             )
 
         if chunk.candidates:
@@ -843,7 +831,6 @@ class GeminiCompletion(BaseLLM):
                                 "index": call_index,
                             },
                             call_type=LLMCallType.TOOL_CALL,
-                            response_id=response_id,
                         )
 
         return full_response, function_calls, usage_data
@@ -978,7 +965,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
+    ) -> str:
         """Handle streaming content generation."""
         full_response = ""
         function_calls: dict[int, dict[str, Any]] = {}
@@ -1056,7 +1043,7 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
+    ) -> str:
         """Handle async streaming content generation."""
         full_response = ""
         function_calls: dict[int, dict[str, Any]] = {}

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -1047,12 +1047,8 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
 
         stream = self.client.responses.create(**params)
-        response_id_stream = None
 
         for event in stream:
-            if event.type == "response.created":
-                response_id_stream = event.response.id
-
             if event.type == "response.output_text.delta":
                 delta_text = event.delta or ""
                 full_response += delta_text
@@ -1060,7 +1056,6 @@ class OpenAICompletion(BaseLLM):
                     chunk=delta_text,
                     from_task=from_task,
                     from_agent=from_agent,
-                    response_id=response_id_stream
                 )
 
             elif event.type == "response.function_call_arguments.delta":
@@ -1175,12 +1170,8 @@ class OpenAICompletion(BaseLLM):
         final_response: Response | None = None
 
         stream = await self.async_client.responses.create(**params)
-        response_id_stream = None
 
         async for event in stream:
-            if event.type == "response.created":
-                response_id_stream = event.response.id
-
             if event.type == "response.output_text.delta":
                 delta_text = event.delta or ""
                 full_response += delta_text
@@ -1188,7 +1179,6 @@ class OpenAICompletion(BaseLLM):
                     chunk=delta_text,
                     from_task=from_task,
                     from_agent=from_agent,
-                    response_id=response_id_stream,
                 )
 
             elif event.type == "response.function_call_arguments.delta":
@@ -1709,8 +1699,6 @@ class OpenAICompletion(BaseLLM):
                 **parse_params, response_format=response_model
             ) as stream:
                 for chunk in stream:
-                    response_id_stream=chunk.id if hasattr(chunk,"id") else None
-
                     if chunk.type == "content.delta":
                         delta_content = chunk.delta
                         if delta_content:
@@ -1718,7 +1706,6 @@ class OpenAICompletion(BaseLLM):
                                 chunk=delta_content,
                                 from_task=from_task,
                                 from_agent=from_agent,
-                                response_id=response_id_stream
                             )
 
                 final_completion = stream.get_final_completion()
@@ -1748,8 +1735,6 @@ class OpenAICompletion(BaseLLM):
         usage_data = {"total_tokens": 0}
 
         for completion_chunk in completion_stream:
-            response_id_stream=completion_chunk.id if hasattr(completion_chunk,"id") else None
-
             if hasattr(completion_chunk, "usage") and completion_chunk.usage:
                 usage_data = self._extract_openai_token_usage(completion_chunk)
                 continue
@@ -1766,7 +1751,6 @@ class OpenAICompletion(BaseLLM):
                     chunk=chunk_delta.content,
                     from_task=from_task,
                     from_agent=from_agent,
-                    response_id=response_id_stream
                 )
 
             if chunk_delta.tool_calls:
@@ -1805,7 +1789,6 @@ class OpenAICompletion(BaseLLM):
                             "index": tool_calls[tool_index]["index"],
                         },
                         call_type=LLMCallType.TOOL_CALL,
-                        response_id=response_id_stream
                     )
 
         self._track_token_usage_internal(usage_data)
@@ -2017,8 +2000,6 @@ class OpenAICompletion(BaseLLM):
             accumulated_content = ""
             usage_data = {"total_tokens": 0}
             async for chunk in completion_stream:
-                response_id_stream=chunk.id if hasattr(chunk,"id") else None
-
                 if hasattr(chunk, "usage") and chunk.usage:
                     usage_data = self._extract_openai_token_usage(chunk)
                     continue
@@ -2035,7 +2016,6 @@ class OpenAICompletion(BaseLLM):
                         chunk=delta.content,
                         from_task=from_task,
                         from_agent=from_agent,
-                        response_id=response_id_stream
                     )
 
             self._track_token_usage_internal(usage_data)
@@ -2071,8 +2051,6 @@ class OpenAICompletion(BaseLLM):
         usage_data = {"total_tokens": 0}
 
         async for chunk in stream:
-            response_id_stream=chunk.id if hasattr(chunk,"id") else None
-
             if hasattr(chunk, "usage") and chunk.usage:
                 usage_data = self._extract_openai_token_usage(chunk)
                 continue
@@ -2089,7 +2067,6 @@ class OpenAICompletion(BaseLLM):
                     chunk=chunk_delta.content,
                     from_task=from_task,
                     from_agent=from_agent,
-                    response_id=response_id_stream
                 )
 
             if chunk_delta.tool_calls:
@@ -2128,7 +2105,6 @@ class OpenAICompletion(BaseLLM):
                             "index": tool_calls[tool_index]["index"],
                         },
                         call_type=LLMCallType.TOOL_CALL,
-                        response_id=response_id_stream
                     )
 
         self._track_token_usage_internal(usage_data)

lib/crewai/src/crewai/rag/chromadb/config.py

@@ -41,7 +41,6 @@ def _default_settings() -> Settings:
         persist_directory=DEFAULT_STORAGE_PATH,
         allow_reset=True,
         is_persistent=True,
-        anonymized_telemetry=False,
     )
 
 

lib/crewai/src/crewai/rag/embeddings/factory.py

@@ -18,6 +18,7 @@ if TYPE_CHECKING:
     )
     from chromadb.utils.embedding_functions.google_embedding_function import (
         GoogleGenerativeAiEmbeddingFunction,
+        GoogleVertexEmbeddingFunction,
     )
     from chromadb.utils.embedding_functions.huggingface_embedding_function import (
         HuggingFaceEmbeddingFunction,
@@ -51,9 +52,6 @@ if TYPE_CHECKING:
     from crewai.rag.embeddings.providers.aws.types import BedrockProviderSpec
     from crewai.rag.embeddings.providers.cohere.types import CohereProviderSpec
     from crewai.rag.embeddings.providers.custom.types import CustomProviderSpec
-    from crewai.rag.embeddings.providers.google.genai_vertex_embedding import (
-        GoogleGenAIVertexEmbeddingFunction,
-    )
     from crewai.rag.embeddings.providers.google.types import (
         GenerativeAiProviderSpec,
         VertexAIProviderSpec,
@@ -165,7 +163,7 @@ def build_embedder_from_dict(spec: OpenAIProviderSpec) -> OpenAIEmbeddingFunctio
 @overload
 def build_embedder_from_dict(
     spec: VertexAIProviderSpec,
-) -> GoogleGenAIVertexEmbeddingFunction: ...
+) -> GoogleVertexEmbeddingFunction: ...
 
 
 @overload
@@ -298,9 +296,7 @@ def build_embedder(spec: OpenAIProviderSpec) -> OpenAIEmbeddingFunction: ...
 
 
 @overload
-def build_embedder(
+def build_embedder(spec: VertexAIProviderSpec) -> GoogleVertexEmbeddingFunction: ...
-    spec: VertexAIProviderSpec,
-) -> GoogleGenAIVertexEmbeddingFunction: ...
 
 
 @overload

lib/crewai/src/crewai/rag/embeddings/providers/google/__init__.py

@@ -1,8 +1,5 @@
 """Google embedding providers."""
 
-from crewai.rag.embeddings.providers.google.genai_vertex_embedding import (
-    GoogleGenAIVertexEmbeddingFunction,
-)
 from crewai.rag.embeddings.providers.google.generative_ai import (
     GenerativeAiProvider,
 )
@@ -21,7 +18,6 @@ __all__ = [
     "GenerativeAiProvider",
     "GenerativeAiProviderConfig",
     "GenerativeAiProviderSpec",
-    "GoogleGenAIVertexEmbeddingFunction",
     "VertexAIProvider",
     "VertexAIProviderConfig",
     "VertexAIProviderSpec",

lib/crewai/src/crewai/rag/embeddings/providers/google/genai_vertex_embedding.py

@@ -1,237 +0,0 @@
-"""Google Vertex AI embedding function implementation.
-
-This module supports both the new google-genai SDK and the deprecated
-vertexai.language_models module for backwards compatibility.
-
-The deprecated vertexai.language_models module will be removed after June 24, 2026.
-Migration guide: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk
-"""
-
-from typing import Any, ClassVar, cast
-import warnings
-
-from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
-from typing_extensions import Unpack
-
-from crewai.rag.embeddings.providers.google.types import VertexAIProviderConfig
-
-
-class GoogleGenAIVertexEmbeddingFunction(EmbeddingFunction[Documents]):
-    """Embedding function for Google Vertex AI with dual SDK support.
-
-    This class supports both:
-    - Legacy models (textembedding-gecko*) using the deprecated vertexai.language_models SDK
-    - New models (gemini-embedding-*, text-embedding-*) using the google-genai SDK
-
-    The SDK is automatically selected based on the model name. Legacy models will
-    emit a deprecation warning.
-
-    Supports two authentication modes:
-    1. Vertex AI backend: Set project_id and location/region (uses Application Default Credentials)
-    2. API key: Set api_key for direct API access
-
-    Example:
-        # Using legacy model (will emit deprecation warning)
-        embedder = GoogleGenAIVertexEmbeddingFunction(
-            project_id="my-project",
-            region="us-central1",
-            model_name="textembedding-gecko"
-        )
-
-        # Using new model with google-genai SDK
-        embedder = GoogleGenAIVertexEmbeddingFunction(
-            project_id="my-project",
-            location="us-central1",
-            model_name="gemini-embedding-001"
-        )
-
-        # Using API key (new SDK only)
-        embedder = GoogleGenAIVertexEmbeddingFunction(
-            api_key="your-api-key",
-            model_name="gemini-embedding-001"
-        )
-    """
-
-    # Models that use the legacy vertexai.language_models SDK
-    LEGACY_MODELS: ClassVar[set[str]] = {
-        "textembedding-gecko",
-        "textembedding-gecko@001",
-        "textembedding-gecko@002",
-        "textembedding-gecko@003",
-        "textembedding-gecko@latest",
-        "textembedding-gecko-multilingual",
-        "textembedding-gecko-multilingual@001",
-        "textembedding-gecko-multilingual@latest",
-    }
-
-    # Models that use the new google-genai SDK
-    GENAI_MODELS: ClassVar[set[str]] = {
-        "gemini-embedding-001",
-        "text-embedding-005",
-        "text-multilingual-embedding-002",
-    }
-
-    def __init__(self, **kwargs: Unpack[VertexAIProviderConfig]) -> None:
-        """Initialize Google Vertex AI embedding function.
-
-        Args:
-            **kwargs: Configuration parameters including:
-                - model_name: Model to use for embeddings (default: "textembedding-gecko")
-                - api_key: Optional API key for authentication (new SDK only)
-                - project_id: GCP project ID (for Vertex AI backend)
-                - location: GCP region (default: "us-central1")
-                - region: Deprecated alias for location
-                - task_type: Task type for embeddings (default: "RETRIEVAL_DOCUMENT", new SDK only)
-                - output_dimensionality: Optional output embedding dimension (new SDK only)
-        """
-        # Handle deprecated 'region' parameter (only if it has a value)
-        region_value = kwargs.pop("region", None)  # type: ignore[typeddict-item]
-        if region_value is not None:
-            warnings.warn(
-                "The 'region' parameter is deprecated, use 'location' instead. "
-                "See: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            if "location" not in kwargs or kwargs.get("location") is None:
-                kwargs["location"] = region_value  # type: ignore[typeddict-unknown-key]
-
-        self._config = kwargs
-        self._model_name = str(kwargs.get("model_name", "textembedding-gecko"))
-        self._use_legacy = self._is_legacy_model(self._model_name)
-
-        if self._use_legacy:
-            self._init_legacy_client(**kwargs)
-        else:
-            self._init_genai_client(**kwargs)
-
-    def _is_legacy_model(self, model_name: str) -> bool:
-        """Check if the model uses the legacy SDK."""
-        return model_name in self.LEGACY_MODELS or model_name.startswith(
-            "textembedding-gecko"
-        )
-
-    def _init_legacy_client(self, **kwargs: Any) -> None:
-        """Initialize using the deprecated vertexai.language_models SDK."""
-        warnings.warn(
-            f"Model '{self._model_name}' uses the deprecated vertexai.language_models SDK "
-            "which will be removed after June 24, 2026. Consider migrating to newer models "
-            "like 'gemini-embedding-001'. "
-            "See: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk",
-            DeprecationWarning,
-            stacklevel=3,
-        )
-
-        try:
-            import vertexai
-            from vertexai.language_models import TextEmbeddingModel
-        except ImportError as e:
-            raise ImportError(
-                "vertexai is required for legacy embedding models (textembedding-gecko*). "
-                "Install it with: pip install google-cloud-aiplatform"
-            ) from e
-
-        project_id = kwargs.get("project_id")
-        location = str(kwargs.get("location", "us-central1"))
-
-        if not project_id:
-            raise ValueError(
-                "project_id is required for legacy models. "
-                "For API key authentication, use newer models like 'gemini-embedding-001'."
-            )
-
-        vertexai.init(project=str(project_id), location=location)
-        self._legacy_model = TextEmbeddingModel.from_pretrained(self._model_name)
-
-    def _init_genai_client(self, **kwargs: Any) -> None:
-        """Initialize using the new google-genai SDK."""
-        try:
-            from google import genai
-            from google.genai.types import EmbedContentConfig
-        except ImportError as e:
-            raise ImportError(
-                "google-genai is required for Google Gen AI embeddings. "
-                "Install it with: uv add 'crewai[google-genai]'"
-            ) from e
-
-        self._genai = genai
-        self._EmbedContentConfig = EmbedContentConfig
-        self._task_type = kwargs.get("task_type", "RETRIEVAL_DOCUMENT")
-        self._output_dimensionality = kwargs.get("output_dimensionality")
-
-        # Initialize client based on authentication mode
-        api_key = kwargs.get("api_key")
-        project_id = kwargs.get("project_id")
-        location: str = str(kwargs.get("location", "us-central1"))
-
-        if api_key:
-            self._client = genai.Client(api_key=api_key)
-        elif project_id:
-            self._client = genai.Client(
-                vertexai=True,
-                project=str(project_id),
-                location=location,
-            )
-        else:
-            raise ValueError(
-                "Either 'api_key' (for API key authentication) or 'project_id' "
-                "(for Vertex AI backend with ADC) must be provided."
-            )
-
-    @staticmethod
-    def name() -> str:
-        """Return the name of the embedding function for ChromaDB compatibility."""
-        return "google-vertex"
-
-    def __call__(self, input: Documents) -> Embeddings:
-        """Generate embeddings for input documents.
-
-        Args:
-            input: List of documents to embed.
-
-        Returns:
-            List of embedding vectors.
-        """
-        if isinstance(input, str):
-            input = [input]
-
-        if self._use_legacy:
-            return self._call_legacy(input)
-        return self._call_genai(input)
-
-    def _call_legacy(self, input: list[str]) -> Embeddings:
-        """Generate embeddings using the legacy SDK."""
-        import numpy as np
-
-        embeddings_list = []
-        for text in input:
-            embedding_result = self._legacy_model.get_embeddings([text])
-            embeddings_list.append(
-                np.array(embedding_result[0].values, dtype=np.float32)
-            )
-
-        return cast(Embeddings, embeddings_list)
-
-    def _call_genai(self, input: list[str]) -> Embeddings:
-        """Generate embeddings using the new google-genai SDK."""
-        # Build config for embed_content
-        config_kwargs: dict[str, Any] = {
-            "task_type": self._task_type,
-        }
-        if self._output_dimensionality is not None:
-            config_kwargs["output_dimensionality"] = self._output_dimensionality
-
-        config = self._EmbedContentConfig(**config_kwargs)
-
-        # Call the embedding API
-        response = self._client.models.embed_content(
-            model=self._model_name,
-            contents=input,  # type: ignore[arg-type]
-            config=config,
-        )
-
-        # Extract embeddings from response
-        if response.embeddings is None:
-            raise ValueError("No embeddings returned from the API")
-        embeddings = [emb.values for emb in response.embeddings]
-        return cast(Embeddings, embeddings)

lib/crewai/src/crewai/rag/embeddings/providers/google/types.py

@@ -34,47 +34,12 @@ class GenerativeAiProviderSpec(TypedDict):
 
 
 class VertexAIProviderConfig(TypedDict, total=False):
-    """Configuration for Vertex AI provider with dual SDK support.
+    """Configuration for Vertex AI provider."""
-
-    Supports both legacy models (textembedding-gecko*) using the deprecated
-    vertexai.language_models SDK and new models using google-genai SDK.
-
-    Attributes:
-        api_key: Google API key (optional if using project_id with ADC). Only for new SDK models.
-        model_name: Embedding model name (default: "textembedding-gecko").
-            Legacy models: textembedding-gecko, textembedding-gecko@001, etc.
-            New models: gemini-embedding-001, text-embedding-005, text-multilingual-embedding-002
-        project_id: GCP project ID (required for Vertex AI backend and legacy models).
-        location: GCP region/location (default: "us-central1").
-        region: Deprecated alias for location (kept for backwards compatibility).
-        task_type: Task type for embeddings (default: "RETRIEVAL_DOCUMENT"). Only for new SDK models.
-        output_dimensionality: Output embedding dimension (optional). Only for new SDK models.
-    """
 
     api_key: str
-    model_name: Annotated[
+    model_name: Annotated[str, "textembedding-gecko"]
-        Literal[
+    project_id: Annotated[str, "cloud-large-language-models"]
-            # Legacy models (deprecated vertexai.language_models SDK)
+    region: Annotated[str, "us-central1"]
-            "textembedding-gecko",
-            "textembedding-gecko@001",
-            "textembedding-gecko@002",
-            "textembedding-gecko@003",
-            "textembedding-gecko@latest",
-            "textembedding-gecko-multilingual",
-            "textembedding-gecko-multilingual@001",
-            "textembedding-gecko-multilingual@latest",
-            # New models (google-genai SDK)
-            "gemini-embedding-001",
-            "text-embedding-005",
-            "text-multilingual-embedding-002",
-        ],
-        "textembedding-gecko",
-    ]
-    project_id: str
-    location: Annotated[str, "us-central1"]
-    region: Annotated[str, "us-central1"]  # Deprecated alias for location
-    task_type: Annotated[str, "RETRIEVAL_DOCUMENT"]
-    output_dimensionality: int
 
 
 class VertexAIProviderSpec(TypedDict, total=False):

lib/crewai/src/crewai/rag/embeddings/providers/google/vertex.py

@@ -1,126 +1,46 @@
-"""Google Vertex AI embeddings provider.
+"""Google Vertex AI embeddings provider."""
-
-This module supports both the new google-genai SDK and the deprecated
-vertexai.language_models module for backwards compatibility.
-
-The SDK is automatically selected based on the model name:
-- Legacy models (textembedding-gecko*) use vertexai.language_models (deprecated)
-- New models (gemini-embedding-*, text-embedding-*) use google-genai
-
-Migration guide: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk
-"""
-
-from __future__ import annotations
 
+from chromadb.utils.embedding_functions.google_embedding_function import (
+    GoogleVertexEmbeddingFunction,
+)
 from pydantic import AliasChoices, Field
 
 from crewai.rag.core.base_embeddings_provider import BaseEmbeddingsProvider
-from crewai.rag.embeddings.providers.google.genai_vertex_embedding import (
-    GoogleGenAIVertexEmbeddingFunction,
-)
 
 
-class VertexAIProvider(BaseEmbeddingsProvider[GoogleGenAIVertexEmbeddingFunction]):
+class VertexAIProvider(BaseEmbeddingsProvider[GoogleVertexEmbeddingFunction]):
-    """Google Vertex AI embeddings provider with dual SDK support.
+    """Google Vertex AI embeddings provider."""
 
-    Supports both legacy models (textembedding-gecko*) using the deprecated
+    embedding_callable: type[GoogleVertexEmbeddingFunction] = Field(
-    vertexai.language_models SDK and new models (gemini-embedding-*, text-embedding-*)
+        default=GoogleVertexEmbeddingFunction,
-    using the google-genai SDK.
+        description="Vertex AI embedding function class",
-
-    The SDK is automatically selected based on the model name. Legacy models will
-    emit a deprecation warning.
-
-    Authentication modes:
-    1. Vertex AI backend: Set project_id and location/region (uses Application Default Credentials)
-    2. API key: Set api_key for direct API access (new SDK models only)
-
-    Example:
-        # Legacy model (backwards compatible, will emit deprecation warning)
-        provider = VertexAIProvider(
-            project_id="my-project",
-            region="us-central1",  # or location="us-central1"
-            model_name="textembedding-gecko"
-        )
-
-        # New model with Vertex AI backend
-        provider = VertexAIProvider(
-            project_id="my-project",
-            location="us-central1",
-            model_name="gemini-embedding-001"
-        )
-
-        # New model with API key
-        provider = VertexAIProvider(
-            api_key="your-api-key",
-            model_name="gemini-embedding-001"
-        )
-    """
-
-    embedding_callable: type[GoogleGenAIVertexEmbeddingFunction] = Field(
-        default=GoogleGenAIVertexEmbeddingFunction,
-        description="Google Vertex AI embedding function class",
     )
     model_name: str = Field(
         default="textembedding-gecko",
-        description=(
+        description="Model name to use for embeddings",
-            "Model name to use for embeddings. Legacy models (textembedding-gecko*) "
-            "use the deprecated SDK. New models (gemini-embedding-001, text-embedding-005) "
-            "use the google-genai SDK."
-        ),
         validation_alias=AliasChoices(
             "EMBEDDINGS_GOOGLE_VERTEX_MODEL_NAME",
             "GOOGLE_VERTEX_MODEL_NAME",
             "model",
         ),
     )
-    api_key: str | None = Field(
+    api_key: str = Field(
-        default=None,
+        description="Google API key",
-        description="Google API key (optional if using project_id with Application Default Credentials)",
         validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_CLOUD_API_KEY",
+            "EMBEDDINGS_GOOGLE_CLOUD_API_KEY", "GOOGLE_CLOUD_API_KEY"
-            "GOOGLE_CLOUD_API_KEY",
-            "GOOGLE_API_KEY",
         ),
     )
-    project_id: str | None = Field(
+    project_id: str = Field(
-        default=None,
+        default="cloud-large-language-models",
-        description="GCP project ID (required for Vertex AI backend and legacy models)",
+        description="GCP project ID",
         validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_CLOUD_PROJECT",
+            "EMBEDDINGS_GOOGLE_CLOUD_PROJECT", "GOOGLE_CLOUD_PROJECT"
-            "GOOGLE_CLOUD_PROJECT",
         ),
     )
-    location: str = Field(
+    region: str = Field(
         default="us-central1",
-        description="GCP region/location",
+        description="GCP region",
         validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_CLOUD_LOCATION",
+            "EMBEDDINGS_GOOGLE_CLOUD_REGION", "GOOGLE_CLOUD_REGION"
-            "EMBEDDINGS_GOOGLE_CLOUD_REGION",
-            "GOOGLE_CLOUD_LOCATION",
-            "GOOGLE_CLOUD_REGION",
-        ),
-    )
-    region: str | None = Field(
-        default=None,
-        description="Deprecated: Use 'location' instead. GCP region (kept for backwards compatibility)",
-        validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_VERTEX_REGION",
-            "GOOGLE_VERTEX_REGION",
-        ),
-    )
-    task_type: str = Field(
-        default="RETRIEVAL_DOCUMENT",
-        description="Task type for embeddings (e.g., RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY). Only used with new SDK models.",
-        validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_VERTEX_TASK_TYPE",
-            "GOOGLE_VERTEX_TASK_TYPE",
-        ),
-    )
-    output_dimensionality: int | None = Field(
-        default=None,
-        description="Output embedding dimensionality (optional). Only used with new SDK models.",
-        validation_alias=AliasChoices(
-            "EMBEDDINGS_GOOGLE_VERTEX_OUTPUT_DIMENSIONALITY",
-            "GOOGLE_VERTEX_OUTPUT_DIMENSIONALITY",
         ),
     )

lib/crewai/src/crewai/utilities/file_store.py

@@ -5,29 +5,17 @@ from __future__ import annotations
 import asyncio
 from collections.abc import Coroutine
 import concurrent.futures
-import logging
 from typing import TYPE_CHECKING, TypeVar
 from uuid import UUID
 
+from aiocache import Cache  # type: ignore[import-untyped]
+from aiocache.serializers import PickleSerializer  # type: ignore[import-untyped]
+
 
 if TYPE_CHECKING:
-    from aiocache import Cache
     from crewai_files import FileInput
 
-logger = logging.getLogger(__name__)
+_file_store = Cache(Cache.MEMORY, serializer=PickleSerializer())
-
-_file_store: Cache | None = None
-
-try:
-    from aiocache import Cache
-    from aiocache.serializers import PickleSerializer
-
-    _file_store = Cache(Cache.MEMORY, serializer=PickleSerializer())
-except ImportError:
-    logger.debug(
-        "aiocache is not installed. File store features will be disabled. "
-        "Install with: uv add aiocache"
-    )
 
 T = TypeVar("T")
 
@@ -71,8 +59,6 @@ async def astore_files(
         files: Dictionary mapping names to file inputs.
         ttl: Time-to-live in seconds.
     """
-    if _file_store is None:
-        return
     await _file_store.set(f"{_CREW_PREFIX}{execution_id}", files, ttl=ttl)
 
 
@@ -85,8 +71,6 @@ async def aget_files(execution_id: UUID) -> dict[str, FileInput] | None:
     Returns:
         Dictionary of files or None if not found.
     """
-    if _file_store is None:
-        return None
     result: dict[str, FileInput] | None = await _file_store.get(
         f"{_CREW_PREFIX}{execution_id}"
     )
@@ -99,8 +83,6 @@ async def aclear_files(execution_id: UUID) -> None:
     Args:
         execution_id: Unique identifier for the crew execution.
     """
-    if _file_store is None:
-        return
     await _file_store.delete(f"{_CREW_PREFIX}{execution_id}")
 
 
@@ -116,8 +98,6 @@ async def astore_task_files(
         files: Dictionary mapping names to file inputs.
         ttl: Time-to-live in seconds.
     """
-    if _file_store is None:
-        return
     await _file_store.set(f"{_TASK_PREFIX}{task_id}", files, ttl=ttl)
 
 
@@ -130,8 +110,6 @@ async def aget_task_files(task_id: UUID) -> dict[str, FileInput] | None:
     Returns:
         Dictionary of files or None if not found.
     """
-    if _file_store is None:
-        return None
     result: dict[str, FileInput] | None = await _file_store.get(
         f"{_TASK_PREFIX}{task_id}"
     )
@@ -144,8 +122,6 @@ async def aclear_task_files(task_id: UUID) -> None:
     Args:
         task_id: Unique identifier for the task.
     """
-    if _file_store is None:
-        return
     await _file_store.delete(f"{_TASK_PREFIX}{task_id}")
 
 

lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml

@@ -1,8 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Output
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Returns structured data according to the schema","input_schema":{"description":"Response model for greeting test.","properties":{"greeting":{"title":"Greeting","type":"string"},"language":{"title":"Language","type":"string"}},"required":["greeting","language"],"title":"GreetingResponse","type":"object"}}]}'
-      the structured response","input_schema":{"type":"object","description":"Response
-      model for greeting test.","title":"GreetingResponse","properties":{"greeting":{"type":"string","title":"Greeting"},"language":{"type":"string","title":"Language"}},"additionalProperties":false,"required":["greeting","language"]}}]}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -15,7 +13,7 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '551'
+      - '539'
       content-type:
       - application/json
       host:
@@ -31,7 +29,7 @@ interactions:
       x-stainless-os:
       - X-STAINLESS-OS-XXX
       x-stainless-package-version:
-      - 0.76.0
+      - 0.75.0
       x-stainless-retry-count:
       - '0'
       x-stainless-runtime:
@@ -44,7 +42,7 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01CKTyVmak15L5oQ36mv4sL9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0174BYmn6xiSnUwVhFD8S7EW","name":"structured_output","input":{"greeting":"Bonjour","language":"French"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":436,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":53,"service_tier":"standard"}}'
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01XjvX2nCho1knuucbwwgCpw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019rfPRSDmBb7CyCTdGMv5rK","name":"structured_output","input":{"greeting":"Bonjour","language":"French"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":432,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":53,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
@@ -53,7 +51,7 @@ interactions:
       Content-Type:
       - application/json
       Date:
-      - Mon, 26 Jan 2026 14:59:34 GMT
+      - Mon, 01 Dec 2025 11:19:38 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -84,10 +82,12 @@ interactions:
       - DYNAMIC
       request-id:
       - REQUEST-ID-XXX
+      retry-after:
+      - '24'
       strict-transport-security:
       - STS-XXX
       x-envoy-upstream-service-time:
-      - '968'
+      - '2101'
     status:
       code: 200
       message: OK

lib/crewai/tests/cassettes/llms/google/test_gemini_tool_returning_float.yaml

@@ -1,319 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"parts": [{"text": "\nCurrent Task: What is 10000 + 20000?
-      Use the sum_numbers tool to calculate this.\n\nThis is the expected criteria
-      for your final answer: The sum of the two numbers\nyou MUST return the actual
-      complete content as the final answer, not a summary.\n\nThis is VERY important
-      to you, your job depends on it!"}], "role": "user"}], "systemInstruction": {"parts":
-      [{"text": "You are Calculator. You are a calculator that adds numbers.\nYour
-      personal goal is: Calculate numbers accurately"}], "role": "user"}, "tools":
-      [{"functionDeclarations": [{"description": "Add two numbers together and return
-      the result", "name": "sum_numbers", "parameters": {"properties": {"a": {"description":
-      "The first number to add", "title": "A", "type": "NUMBER"}, "b": {"description":
-      "The second number to add", "title": "B", "type": "NUMBER"}}, "required": ["a",
-      "b"], "type": "OBJECT"}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - '*/*'
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '962'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      x-goog-api-client:
-      - google-genai-sdk/1.49.0 gl-python/3.13.3
-      x-goog-api-key:
-      - X-GOOG-API-KEY-XXX
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: "{\n  \"candidates\": [\n    {\n      \"content\": {\n        \"parts\":
-        [\n          {\n            \"functionCall\": {\n              \"name\": \"sum_numbers\",\n
-        \             \"args\": {\n                \"a\": 10000,\n                \"b\":
-        20000\n              }\n            }\n          }\n        ],\n        \"role\":
-        \"model\"\n      },\n      \"finishReason\": \"STOP\",\n      \"avgLogprobs\":
-        -0.00059548033667462211\n    }\n  ],\n  \"usageMetadata\": {\n    \"promptTokenCount\":
-        127,\n    \"candidatesTokenCount\": 7,\n    \"totalTokenCount\": 134,\n    \"promptTokensDetails\":
-        [\n      {\n        \"modality\": \"TEXT\",\n        \"tokenCount\": 127\n
-        \     }\n    ],\n    \"candidatesTokensDetails\": [\n      {\n        \"modality\":
-        \"TEXT\",\n        \"tokenCount\": 7\n      }\n    ]\n  },\n  \"modelVersion\":
-        \"gemini-2.0-flash-001\",\n  \"responseId\": \"bLBzabiACaP3-8YP7s-P6QI\"\n}\n"
-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Fri, 23 Jan 2026 17:31:24 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=673
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - X-CONTENT-TYPE-XXX
-      X-Frame-Options:
-      - X-FRAME-OPTIONS-XXX
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"contents": [{"parts": [{"text": "\nCurrent Task: What is 10000 + 20000?
-      Use the sum_numbers tool to calculate this.\n\nThis is the expected criteria
-      for your final answer: The sum of the two numbers\nyou MUST return the actual
-      complete content as the final answer, not a summary.\n\nThis is VERY important
-      to you, your job depends on it!"}], "role": "user"}, {"parts": [{"functionCall":
-      {"args": {"a": 10000, "b": 20000}, "name": "sum_numbers"}}], "role": "model"},
-      {"parts": [{"functionResponse": {"name": "sum_numbers", "response": {"result":
-      30000}}}], "role": "user"}, {"parts": [{"text": "Analyze the tool result. If
-      requirements are met, provide the Final Answer. Otherwise, call the next tool.
-      Deliver only the answer without meta-commentary."}], "role": "user"}], "systemInstruction":
-      {"parts": [{"text": "You are Calculator. You are a calculator that adds numbers.\nYour
-      personal goal is: Calculate numbers accurately"}], "role": "user"}, "tools":
-      [{"functionDeclarations": [{"description": "Add two numbers together and return
-      the result", "name": "sum_numbers", "parameters": {"properties": {"a": {"description":
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-      "The second number to add", "title": "B", "type": "NUMBER"}}, "required": ["a",
-      "b"], "type": "OBJECT"}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - '*/*'
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '1374'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      x-goog-api-client:
-      - google-genai-sdk/1.49.0 gl-python/3.13.3
-      x-goog-api-key:
-      - X-GOOG-API-KEY-XXX
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: "{\n  \"candidates\": [\n    {\n      \"content\": {\n        \"parts\":
-        [\n          {\n            \"text\": \"\"\n          }\n        ],\n        \"role\":
-        \"model\"\n      },\n      \"finishReason\": \"STOP\"\n    }\n  ],\n  \"usageMetadata\":
-        {\n    \"promptTokenCount\": 171,\n    \"totalTokenCount\": 171,\n    \"promptTokensDetails\":
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-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Type:
-      - application/json; charset=UTF-8
-      Date:
-      - Fri, 23 Jan 2026 17:31:25 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=382
-      Transfer-Encoding:
-      - chunked
-      Vary:
-      - Origin
-      - X-Origin
-      - Referer
-      X-Content-Type-Options:
-      - X-CONTENT-TYPE-XXX
-      X-Frame-Options:
-      - X-FRAME-OPTIONS-XXX
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"contents": [{"parts": [{"text": "\nCurrent Task: What is 10000 + 20000?
-      Use the sum_numbers tool to calculate this.\n\nThis is the expected criteria
-      for your final answer: The sum of the two numbers\nyou MUST return the actual
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-      to you, your job depends on it!"}], "role": "user"}, {"parts": [{"functionCall":
-      {"args": {"a": 10000, "b": 20000}, "name": "sum_numbers"}}], "role": "model"},
-      {"parts": [{"functionResponse": {"name": "sum_numbers", "response": {"result":
-      30000}}}], "role": "user"}, {"parts": [{"text": "Analyze the tool result. If
-      requirements are met, provide the Final Answer. Otherwise, call the next tool.
-      Deliver only the answer without meta-commentary."}], "role": "user"}, {"parts":
-      [{"text": "\nCurrent Task: What is 10000 + 20000? Use the sum_numbers tool to
-      calculate this.\n\nThis is the expected criteria for your final answer: The
-      sum of the two numbers\nyou MUST return the actual complete content as the final
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-      Calculator. You are a calculator that adds numbers.\nYour personal goal is:
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-      adds numbers.\nYour personal goal is: Calculate numbers accurately"}], "role":
-      "user"}, "tools": [{"functionDeclarations": [{"description": "Add two numbers
-      together and return the result", "name": "sum_numbers", "parameters": {"properties":
-      {"a": {"description": "The first number to add", "title": "A", "type": "NUMBER"},
-      "b": {"description": "The second number to add", "title": "B", "type": "NUMBER"}},
-      "required": ["a", "b"], "type": "OBJECT"}}]}], "generationConfig": {"stopSequences":
-      ["\nObservation:"]}}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - '*/*'
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '1837'
-      content-type:
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-      host:
-      - generativelanguage.googleapis.com
-      x-goog-api-client:
-      - google-genai-sdk/1.49.0 gl-python/3.13.3
-      x-goog-api-key:
-      - X-GOOG-API-KEY-XXX
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: "{\n  \"candidates\": [\n    {\n      \"content\": {\n        \"parts\":
-        [\n          {\n            \"text\": \"\"\n          }\n        ],\n        \"role\":
-        \"model\"\n      },\n      \"finishReason\": \"STOP\"\n    }\n  ],\n  \"usageMetadata\":
-        {\n    \"promptTokenCount\": 271,\n    \"totalTokenCount\": 271,\n    \"promptTokensDetails\":
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-    headers:
-      Alt-Svc:
-      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
-      Content-Type:
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-      Date:
-      - Fri, 23 Jan 2026 17:31:25 GMT
-      Server:
-      - scaffolding on HTTPServer2
-      Server-Timing:
-      - gfet4t7; dur=410
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-      - chunked
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-      - Origin
-      - X-Origin
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-      - X-FRAME-OPTIONS-XXX
-      X-XSS-Protection:
-      - '0'
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"contents": [{"parts": [{"text": "\nCurrent Task: What is 10000 + 20000?
-      Use the sum_numbers tool to calculate this.\n\nThis is the expected criteria
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-      complete content as the final answer, not a summary.\n\nThis is VERY important
-      to you, your job depends on it!"}], "role": "user"}, {"parts": [{"functionCall":
-      {"args": {"a": 10000, "b": 20000}, "name": "sum_numbers"}}], "role": "model"},
-      {"parts": [{"functionResponse": {"name": "sum_numbers", "response": {"result":
-      30000}}}], "role": "user"}, {"parts": [{"text": "Analyze the tool result. If
-      requirements are met, provide the Final Answer. Otherwise, call the next tool.
-      Deliver only the answer without meta-commentary."}], "role": "user"}, {"parts":
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-      {"parts": [{"text": "You are Calculator. You are a calculator that adds numbers.\nYour
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-      is: Calculate numbers accurately"}], "role": "user"}, "tools": [{"functionDeclarations":
-      [{"description": "Add two numbers together and return the result", "name": "sum_numbers",
-      "parameters": {"properties": {"a": {"description": "The first number to add",
-      "title": "A", "type": "NUMBER"}, "b": {"description": "The second number to
-      add", "title": "B", "type": "NUMBER"}}, "required": ["a", "b"], "type": "OBJECT"}}]}],
-      "generationConfig": {"stopSequences": ["\nObservation:"]}}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - '*/*'
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '2300'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      x-goog-api-client:
-      - google-genai-sdk/1.49.0 gl-python/3.13.3
-      x-goog-api-key:
-      - X-GOOG-API-KEY-XXX
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: "{\n  \"candidates\": [\n    {\n      \"content\": {\n        \"parts\":
-        [\n          {\n            \"text\": \"\\n{\\\"sum_numbers_response\\\":
-        {\\\"result\\\": 30000}}\\n\"\n          }\n        ],\n        \"role\":
-        \"model\"\n      },\n      \"finishReason\": \"STOP\",\n      \"avgLogprobs\":
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-        \     }\n    ],\n    \"candidatesTokensDetails\": [\n      {\n        \"modality\":
-        \"TEXT\",\n        \"tokenCount\": 19\n      }\n    ]\n  },\n  \"modelVersion\":
-        \"gemini-2.0-flash-001\",\n  \"responseId\": \"bbBzaauxJ_SgjrEP7onK2Ak\"\n}\n"
-    headers:
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lib/crewai/tests/knowledge/test_knowledge_storage_integration.py

@@ -193,3 +193,40 @@ def test_dimension_mismatch_error_handling(mock_get_client: MagicMock) -> None:
 
     with pytest.raises(ValueError, match="Embedding dimension mismatch"):
         storage.save(["test document"])
+
+
+@patch("crewai.knowledge.storage.knowledge_storage.get_rag_client")
+def test_save_empty_documents_list(mock_get_client: MagicMock) -> None:
+    """Test that save() handles empty documents list gracefully.
+
+    Calling save() with an empty documents list should be a no-op and not
+    propagate low-level storage exceptions from ChromaDB.
+    """
+    mock_client = MagicMock()
+    mock_get_client.return_value = mock_client
+
+    storage = KnowledgeStorage(collection_name="empty_docs_test")
+
+    storage.save([])
+
+    mock_client.get_or_create_collection.assert_not_called()
+    mock_client.add_documents.assert_not_called()
+
+
+@pytest.mark.asyncio
+@patch("crewai.knowledge.storage.knowledge_storage.get_rag_client")
+async def test_asave_empty_documents_list(mock_get_client: MagicMock) -> None:
+    """Test that asave() handles empty documents list gracefully.
+
+    Calling asave() with an empty documents list should be a no-op and not
+    propagate low-level storage exceptions from ChromaDB.
+    """
+    mock_client = MagicMock()
+    mock_get_client.return_value = mock_client
+
+    storage = KnowledgeStorage(collection_name="empty_docs_async_test")
+
+    await storage.asave([])
+
+    mock_client.aget_or_create_collection.assert_not_called()
+    mock_client.aadd_documents.assert_not_called()

lib/crewai/tests/llms/google/test_google.py

@@ -635,54 +635,6 @@ def test_gemini_token_usage_tracking():
     assert usage.total_tokens > 0
 
 
-@pytest.mark.vcr()
-def test_gemini_tool_returning_float():
-    """
-    Test that Gemini properly handles tools that return non-dict values like floats.
-
-    This is an end-to-end test that verifies the agent can use a tool that returns
-    a float (which gets wrapped in {"result": value} for Gemini's FunctionResponse).
-    """
-    from pydantic import BaseModel, Field
-    from typing import Type
-    from crewai.tools import BaseTool
-
-    class SumNumbersToolInput(BaseModel):
-        a: float = Field(..., description="The first number to add")
-        b: float = Field(..., description="The second number to add")
-
-    class SumNumbersTool(BaseTool):
-        name: str = "sum_numbers"
-        description: str = "Add two numbers together and return the result"
-        args_schema: Type[BaseModel] = SumNumbersToolInput
-
-        def _run(self, a: float, b: float) -> float:
-            return a + b
-
-    sum_tool = SumNumbersTool()
-
-    agent = Agent(
-        role="Calculator",
-        goal="Calculate numbers accurately",
-        backstory="You are a calculator that adds numbers.",
-        llm=LLM(model="google/gemini-2.0-flash-001"),
-        tools=[sum_tool],
-        verbose=True,
-    )
-
-    task = Task(
-        description="What is 10000 + 20000? Use the sum_numbers tool to calculate this.",
-        expected_output="The sum of the two numbers",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task], verbose=True)
-    result = crew.kickoff()
-
-    # The result should contain 30000 (the sum)
-    assert "30000" in result.raw
-
-
 def test_gemini_stop_sequences_sync():
     """Test that stop and stop_sequences attributes stay synchronized."""
     llm = LLM(model="google/gemini-2.0-flash-001")

lib/crewai/tests/llms/openai/test_openai.py

@@ -511,13 +511,10 @@ def test_openai_streaming_with_response_model():
         mock_chunk1 = MagicMock()
         mock_chunk1.type = "content.delta"
         mock_chunk1.delta = '{"answer": "test", '
-        mock_chunk1.id = "response-1"
 
-        # Second chunk
         mock_chunk2 = MagicMock()
         mock_chunk2.type = "content.delta"
         mock_chunk2.delta = '"confidence": 0.95}'
-        mock_chunk2.id = "response-2"
 
         # Create mock final completion with parsed result
         mock_parsed = TestResponse(answer="test", confidence=0.95)

lib/crewai/tests/rag/embeddings/test_embedding_factory.py

@@ -272,100 +272,3 @@ class TestEmbeddingFactory:
         mock_build_from_provider.assert_called_once_with(mock_provider)
         assert result == mock_embedding_function
         mock_import.assert_not_called()
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_google_vertex_with_genai_model(self, mock_import):
-        """Test routing to Google Vertex provider with new genai model."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "google-vertex",
-            "config": {
-                "api_key": "test-google-api-key",
-                "model_name": "gemini-embedding-001",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.google.vertex.VertexAIProvider"
-        )
-        mock_provider_class.assert_called_once()
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["api_key"] == "test-google-api-key"
-        assert call_kwargs["model_name"] == "gemini-embedding-001"
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_google_vertex_with_legacy_model(self, mock_import):
-        """Test routing to Google Vertex provider with legacy textembedding-gecko model."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "google-vertex",
-            "config": {
-                "project_id": "my-gcp-project",
-                "region": "us-central1",
-                "model_name": "textembedding-gecko",
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.google.vertex.VertexAIProvider"
-        )
-        mock_provider_class.assert_called_once()
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["project_id"] == "my-gcp-project"
-        assert call_kwargs["region"] == "us-central1"
-        assert call_kwargs["model_name"] == "textembedding-gecko"
-
-    @patch("crewai.rag.embeddings.factory.import_and_validate_definition")
-    def test_build_embedder_google_vertex_with_location(self, mock_import):
-        """Test routing to Google Vertex provider with location parameter."""
-        mock_provider_class = MagicMock()
-        mock_provider_instance = MagicMock()
-        mock_embedding_function = MagicMock()
-
-        mock_import.return_value = mock_provider_class
-        mock_provider_class.return_value = mock_provider_instance
-        mock_provider_instance.embedding_callable.return_value = mock_embedding_function
-
-        config = {
-            "provider": "google-vertex",
-            "config": {
-                "project_id": "my-gcp-project",
-                "location": "europe-west1",
-                "model_name": "gemini-embedding-001",
-                "task_type": "RETRIEVAL_DOCUMENT",
-                "output_dimensionality": 768,
-            },
-        }
-
-        build_embedder(config)
-
-        mock_import.assert_called_once_with(
-            "crewai.rag.embeddings.providers.google.vertex.VertexAIProvider"
-        )
-
-        call_kwargs = mock_provider_class.call_args.kwargs
-        assert call_kwargs["project_id"] == "my-gcp-project"
-        assert call_kwargs["location"] == "europe-west1"
-        assert call_kwargs["model_name"] == "gemini-embedding-001"
-        assert call_kwargs["task_type"] == "RETRIEVAL_DOCUMENT"
-        assert call_kwargs["output_dimensionality"] == 768

lib/crewai/tests/rag/embeddings/test_google_vertex_memory_integration.py

@@ -1,176 +0,0 @@
-"""Integration tests for Google Vertex embeddings with Crew memory.
-
-These tests make real API calls and use VCR to record/replay responses.
-"""
-
-import os
-import threading
-from collections import defaultdict
-from unittest.mock import patch
-
-import pytest
-
-from crewai import Agent, Crew, Task
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.memory_events import (
-    MemorySaveCompletedEvent,
-    MemorySaveStartedEvent,
-)
-
-
-@pytest.fixture(autouse=True)
-def setup_vertex_ai_env():
-    """Set up environment for Vertex AI tests.
-    
-    Sets GOOGLE_GENAI_USE_VERTEXAI=true to ensure the SDK uses the Vertex AI
-    backend (aiplatform.googleapis.com) which matches the VCR cassettes.
-    Also mocks GOOGLE_API_KEY if not already set.
-    """
-    env_updates = {"GOOGLE_GENAI_USE_VERTEXAI": "true"}
-    
-    # Add a mock API key if none exists
-    if "GOOGLE_API_KEY" not in os.environ and "GEMINI_API_KEY" not in os.environ:
-        env_updates["GOOGLE_API_KEY"] = "test-key"
-    
-    with patch.dict(os.environ, env_updates):
-        yield
-
-
-@pytest.fixture
-def google_vertex_embedder_config():
-    """Fixture providing Google Vertex embedder configuration."""
-    return {
-        "provider": "google-vertex",
-        "config": {
-            "api_key": os.getenv("GOOGLE_API_KEY", "test-key"),
-            "model_name": "gemini-embedding-001",
-        },
-    }
-
-
-@pytest.fixture
-def simple_agent():
-    """Fixture providing a simple test agent."""
-    return Agent(
-        role="Research Assistant",
-        goal="Help with research tasks",
-        backstory="You are a helpful research assistant.",
-        verbose=False,
-    )
-
-
-@pytest.fixture
-def simple_task(simple_agent):
-    """Fixture providing a simple test task."""
-    return Task(
-        description="Summarize the key points about artificial intelligence in one sentence.",
-        expected_output="A one sentence summary about AI.",
-        agent=simple_agent,
-    )
-
-
-@pytest.mark.vcr()
-@pytest.mark.timeout(120)  # Longer timeout for VCR recording
-def test_crew_memory_with_google_vertex_embedder(
-    google_vertex_embedder_config, simple_agent, simple_task
-) -> None:
-    """Test that Crew with memory=True works with google-vertex embedder and memory is used."""
-    # Track memory events
-    events: dict[str, list] = defaultdict(list)
-    condition = threading.Condition()
-
-    @crewai_event_bus.on(MemorySaveStartedEvent)
-    def on_save_started(source, event):
-        with condition:
-            events["MemorySaveStartedEvent"].append(event)
-            condition.notify()
-
-    @crewai_event_bus.on(MemorySaveCompletedEvent)
-    def on_save_completed(source, event):
-        with condition:
-            events["MemorySaveCompletedEvent"].append(event)
-            condition.notify()
-
-    crew = Crew(
-        agents=[simple_agent],
-        tasks=[simple_task],
-        memory=True,
-        embedder=google_vertex_embedder_config,
-        verbose=False,
-    )
-
-    result = crew.kickoff()
-
-    assert result is not None
-    assert result.raw is not None
-    assert len(result.raw) > 0
-
-    with condition:
-        success = condition.wait_for(
-            lambda: len(events["MemorySaveCompletedEvent"]) >= 1,
-            timeout=10,
-        )
-
-    assert success, "Timeout waiting for memory save events - memory may not be working"
-    assert len(events["MemorySaveStartedEvent"]) >= 1, "No memory save started events"
-    assert len(events["MemorySaveCompletedEvent"]) >= 1, "Memory save completed events"
-
-
-@pytest.mark.vcr()
-@pytest.mark.timeout(120)
-def test_crew_memory_with_google_vertex_project_id(simple_agent, simple_task) -> None:
-    """Test Crew memory with Google Vertex using project_id authentication."""
-    project_id = os.getenv("GOOGLE_CLOUD_PROJECT")
-    if not project_id:
-        pytest.skip("GOOGLE_CLOUD_PROJECT environment variable not set")
-
-    # Track memory events
-    events: dict[str, list] = defaultdict(list)
-    condition = threading.Condition()
-
-    @crewai_event_bus.on(MemorySaveStartedEvent)
-    def on_save_started(source, event):
-        with condition:
-            events["MemorySaveStartedEvent"].append(event)
-            condition.notify()
-
-    @crewai_event_bus.on(MemorySaveCompletedEvent)
-    def on_save_completed(source, event):
-        with condition:
-            events["MemorySaveCompletedEvent"].append(event)
-            condition.notify()
-
-    embedder_config = {
-        "provider": "google-vertex",
-        "config": {
-            "project_id": project_id,
-            "location": "us-central1",
-            "model_name": "gemini-embedding-001",
-        },
-    }
-
-    crew = Crew(
-        agents=[simple_agent],
-        tasks=[simple_task],
-        memory=True,
-        embedder=embedder_config,
-        verbose=False,
-    )
-
-    result = crew.kickoff()
-
-    # Verify basic result
-    assert result is not None
-    assert result.raw is not None
-
-    # Wait for memory save events
-    with condition:
-        success = condition.wait_for(
-            lambda: len(events["MemorySaveCompletedEvent"]) >= 1,
-            timeout=10,
-        )
-
-    # Verify memory was actually used
-    assert success, "Timeout waiting for memory save events - memory may not be working"
-    assert len(events["MemorySaveStartedEvent"]) >= 1, "No memory save started events"
-    assert len(events["MemorySaveCompletedEvent"]) >= 1, "No memory save completed events"

lib/crewai/tests/utilities/test_events.py

@@ -984,8 +984,8 @@ def test_streaming_fallback_to_non_streaming():
     def mock_call(messages, tools=None, callbacks=None, available_functions=None):
         nonlocal fallback_called
         # Emit a couple of chunks to simulate partial streaming
-        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1", response_id = "Id"))
+        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 1"))
-        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2", response_id = "Id"))
+        crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="Test chunk 2"))
 
         # Mark that fallback would be called
         fallback_called = True
@@ -1041,7 +1041,7 @@ def test_streaming_empty_response_handling():
     def mock_call(messages, tools=None, callbacks=None, available_functions=None):
         # Emit a few empty chunks
         for _ in range(3):
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="",response_id="id"))
+            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk=""))
 
         # Return the default message for empty responses
         return "I apologize, but I couldn't generate a proper response. Please try again or rephrase your request."

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.9.0"
+__version__ = "1.8.1"

uv.lock

@@ -310,7 +310,7 @@ wheels = [
 
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-version = "0.73.0"
+version = "0.71.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -322,9 +322,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f0/07/f550112c3f5299d02f06580577f602e8a112b1988ad7c98ac1a8f7292d7e/anthropic-0.73.0.tar.gz", hash = "sha256:30f0d7d86390165f86af6ca7c3041f8720bb2e1b0e12a44525c8edfdbd2c5239", size = 425168, upload-time = "2025-11-14T18:47:52.635Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/05/4b/19620875841f692fdc35eb58bf0201c8ad8c47b8443fecbf1b225312175b/anthropic-0.71.1.tar.gz", hash = "sha256:a77d156d3e7d318b84681b59823b2dee48a8ac508a3e54e49f0ab0d074e4b0da", size = 493294, upload-time = "2025-10-28T17:28:42.213Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/15/b1/5d4d3f649e151e58dc938cf19c4d0cd19fca9a986879f30fea08a7b17138/anthropic-0.73.0-py3-none-any.whl", hash = "sha256:0d56cd8b3ca3fea9c9b5162868bdfd053fbc189b8b56d4290bd2d427b56db769", size = 367839, upload-time = "2025-11-14T18:47:51.195Z" },
+    { url = "https://files.pythonhosted.org/packages/4b/68/b2f988b13325f9ac9921b1e87f0b7994468014e1b5bd3bdbd2472f5baf45/anthropic-0.71.1-py3-none-any.whl", hash = "sha256:6ca6c579f0899a445faeeed9c0eb97aa4bdb751196262f9ccc96edfc0bb12679", size = 355020, upload-time = "2025-10-28T17:28:40.653Z" },
 ]
 
 [[package]]
@@ -1276,7 +1276,7 @@ requires-dist = [
     { name = "aiobotocore", marker = "extra == 'aws'", specifier = "~=2.25.2" },
     { name = "aiocache", extras = ["memcached", "redis"], marker = "extra == 'a2a'", specifier = "~=0.12.3" },
     { name = "aiosqlite", specifier = "~=0.21.0" },
-    { name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.73.0" },
+    { name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.71.0" },
     { name = "appdirs", specifier = "~=1.4.4" },
     { name = "azure-ai-inference", marker = "extra == 'azure-ai-inference'", specifier = "~=1.0.0b9" },
     { name = "boto3", marker = "extra == 'aws'", specifier = "~=1.40.38" },