test: Fix o4-mini test to use mocking instead of real API calls

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

docs/concepts/cli.mdx

@@ -179,78 +179,7 @@ def crew(self) -> Crew:
 ```
 </Note>
 
-### 10. Deploy
-
-Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
-
-- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
-    ```shell Terminal
-    crewai signup
-    ```
-    If you already have an account, you can login with:
-    ```shell Terminal
-    crewai login
-    ```
-
-- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
-    ```shell Terminal
-    crewai deploy create
-    ```
-    - Reads your local project configuration.
-    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
-    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
-
-- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
-    ```shell Terminal
-    crewai deploy push
-    ``` 
-    - Initiates the deployment process on the CrewAI Enterprise platform.
-    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
-
-- **Deployment Status**: You can check the status of your deployment with:
-    ```shell Terminal
-    crewai deploy status
-    ```
-    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
-
-- **Deployment Logs**: You can check the logs of your deployment with:
-    ```shell Terminal
-    crewai deploy logs
-    ```
-    This streams the deployment logs to your terminal.
-
-- **List deployments**: You can list all your deployments with:
-    ```shell Terminal
-    crewai deploy list
-    ```
-    This lists all your deployments.
-
-- **Delete a deployment**: You can delete a deployment with:
-    ```shell Terminal
-    crewai deploy remove
-    ```
-    This deletes the deployment from the CrewAI Enterprise platform.
-
-- **Help Command**: You can get help with the CLI with:
-    ```shell Terminal
-    crewai deploy --help
-    ```
-    This shows the help message for the CrewAI Deploy CLI.
-
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-### 11. API Keys
+### 10. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 

docs/docs.json

@@ -8,9 +8,6 @@
     "dark": "#C94C3C"
   },
   "favicon": "favicon.svg",
-  "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
-  },
   "navigation": {
     "tabs": [
       {
@@ -247,12 +244,7 @@
     "prompt": "Search CrewAI docs"
   },
   "seo": {
-    "indexing": "all"
-  },
-  "errors": {
-    "404": {
-      "redirect": true
-    }
+    "indexing": "navigable"
   },
   "footer": {
     "socials": {

docs/tools/codeinterpretertool.mdx

@@ -8,29 +8,11 @@ icon: code-simple
 
 ## Description
 
-The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
+The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. The code is run in a secure, isolated Docker container, ensuring safety regardless of the content. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.
 
-There are several ways to use this tool:
-
-### Docker Container (Recommended)
-
-This is the primary option. The code runs in a secure, isolated Docker container, ensuring safety regardless of its content.
-Make sure Docker is installed and running on your system. If you don’t have it, you can install it from [here](https://docs.docker.com/get-docker/).
-
-### Sandbox environment
-
-If Docker is unavailable — either not installed or not accessible for any reason — the code will be executed in a restricted Python environment - called sandbox.
-This environment is very limited, with strict restrictions on many modules and built-in functions.
-
-### Unsafe Execution
-
-**NOT RECOMMENDED FOR PRODUCTION** 
-This mode allows execution of any Python code, including dangerous calls to `sys, os..` and similar modules. [Check out](/tools/codeinterpretertool#enabling-unsafe-mode) how to enable this mode
-
-## Logging
-
-The `CodeInterpreterTool` logs the selected execution strategy to STDOUT
+## Requirements
 
+- Docker must be installed and running on your system. If you don't have it, you can install it from [here](https://docs.docker.com/get-docker/).
 
 ## Installation
 
@@ -92,32 +74,18 @@ programmer_agent = Agent(
 )
 ```
 
-### Enabling `unsafe_mode`
-
-```python Code
-from crewai_tools import CodeInterpreterTool
-
-code = """
-import os
-os.system("ls -la")
-"""
-
-CodeInterpreterTool(unsafe_mode=True).run(code=code)
-```
-
 ## Parameters
 
 The `CodeInterpreterTool` accepts the following parameters during initialization:
 
 - **user_dockerfile_path**: Optional. Path to a custom Dockerfile to use for the code interpreter container.
 - **user_docker_base_url**: Optional. URL to the Docker daemon to use for running the container.
-- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container or sandbox. Default is `False`. Use with caution!
-- **default_image_tag**: Optional. Default Docker image tag. Default is `code-interpreter:latest`
+- **unsafe_mode**: Optional. Whether to run code directly on the host machine instead of in a Docker container. Default is `False`. Use with caution!
 
 When using the tool with an agent, the agent will need to provide:
 
 - **code**: Required. The Python 3 code to execute.
-- **libraries_used**: Optional. A list of libraries used in the code that need to be installed. Default is `[]`
+- **libraries_used**: Required. A list of libraries used in the code that need to be installed.
 
 ## Agent Integration Example
 
@@ -184,7 +152,7 @@ class CodeInterpreterTool(BaseTool):
         if self.unsafe_mode:
             return self.run_code_unsafe(code, libraries_used)
         else:
-            return self.run_code_safety(code, libraries_used)
+            return self.run_code_in_docker(code, libraries_used)
 ```
 
 The tool performs the following steps:
@@ -200,9 +168,8 @@ The tool performs the following steps:
 By default, the `CodeInterpreterTool` runs code in an isolated Docker container, which provides a layer of security. However, there are still some security considerations to keep in mind:
 
 1. The Docker container has access to the current working directory, so sensitive files could potentially be accessed.
-2. If the Docker container is unavailable and the code needs to run safely, it will be executed in a sandbox environment. For security reasons, installing arbitrary libraries is not allowed
-3. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
-4. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
+2. The `unsafe_mode` parameter allows code to be executed directly on the host machine, which should only be used in trusted environments.
+3. Be cautious when allowing agents to install arbitrary libraries, as they could potentially include malicious code.
 
 ## Conclusion
 

src/crewai/cli/constants.py

@@ -122,16 +122,7 @@ PROVIDERS = [
 ]
 
 MODELS = {
-    "openai": [
-        "gpt-4",
-        "gpt-4.1",
-        "gpt-4.1-mini-2025-04-14",
-        "gpt-4.1-nano-2025-04-14",
-        "gpt-4o",
-        "gpt-4o-mini",
-        "o1-mini",
-        "o1-preview",
-    ],
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
     "anthropic": [
         "claude-3-5-sonnet-20240620",
         "claude-3-sonnet-20240229",
@@ -141,17 +132,8 @@ MODELS = {
     "gemini": [
         "gemini/gemini-1.5-flash",
         "gemini/gemini-1.5-pro",
-        "gemini/gemini-2.0-flash-lite-001",
-        "gemini/gemini-2.0-flash-001",
-        "gemini/gemini-2.0-flash-thinking-exp-01-21",
-        "gemini/gemini-2.5-flash-preview-04-17",
-        "gemini/gemini-2.5-pro-exp-03-25",
         "gemini/gemini-gemma-2-9b-it",
         "gemini/gemini-gemma-2-27b-it",
-        "gemini/gemma-3-1b-it",
-        "gemini/gemma-3-4b-it",
-        "gemini/gemma-3-12b-it",
-        "gemini/gemma-3-27b-it",
     ],
     "nvidia_nim": [
         "nvidia_nim/nvidia/mistral-nemo-minitron-8b-8k-instruct",

src/crewai/cli/tools/main.py

@@ -117,9 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
 
         published_handle = publish_response.json()["handle"]
         console.print(
-            f"Successfully published `{published_handle}` ({project_version}).\n\n"
-            + "⚠️ Security checks are running in the background. Your tool will be available once these are complete.\n"
-            + f"You can monitor the status or access your tool here:\nhttps://app.crewai.com/crewai_plus/tools/{published_handle}",
+            f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
             style="bold green",
         )
 
@@ -155,12 +153,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         login_response_json = login_response.json()
 
         settings = Settings()
-        settings.tool_repository_username = login_response_json["credential"][
-            "username"
-        ]
-        settings.tool_repository_password = login_response_json["credential"][
-            "password"
-        ]
+        settings.tool_repository_username = login_response_json["credential"]["username"]
+        settings.tool_repository_password = login_response_json["credential"]["password"]
         settings.dump()
 
         console.print(
@@ -185,7 +179,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             capture_output=False,
             env=self._build_env_with_credentials(repository_handle),
             text=True,
-            check=True,
+            check=True
         )
 
         if add_package_result.stderr:
@@ -210,11 +204,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings = Settings()
 
         env = os.environ.copy()
-        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
-            settings.tool_repository_username or ""
-        )
-        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
-            settings.tool_repository_password or ""
-        )
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
 
         return env

src/crewai/llm.py

@@ -37,7 +37,6 @@ with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
     from litellm import Choices
-    from litellm.exceptions import ContextWindowExceededError
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
@@ -82,26 +81,14 @@ LLM_CONTEXT_WINDOW_SIZES = {
     "gpt-4o": 128000,
     "gpt-4o-mini": 128000,
     "gpt-4-turbo": 128000,
-    "gpt-4.1": 1047576,  # Based on official docs
-    "gpt-4.1-mini-2025-04-14": 1047576,
-    "gpt-4.1-nano-2025-04-14": 1047576,
     "o1-preview": 128000,
     "o1-mini": 128000,
     "o3-mini": 200000,  # Based on official o3-mini specifications
     # gemini
     "gemini-2.0-flash": 1048576,
-    "gemini-2.0-flash-thinking-exp-01-21": 32768,
-    "gemini-2.0-flash-lite-001": 1048576,
-    "gemini-2.0-flash-001": 1048576,
-    "gemini-2.5-flash-preview-04-17": 1048576,
-    "gemini-2.5-pro-exp-03-25": 1048576,
     "gemini-1.5-pro": 2097152,
     "gemini-1.5-flash": 1048576,
     "gemini-1.5-flash-8b": 1048576,
-    "gemini/gemma-3-1b-it": 32000,
-    "gemini/gemma-3-4b-it": 128000,
-    "gemini/gemma-3-12b-it": 128000,
-    "gemini/gemma-3-27b-it": 128000,
     # deepseek
     "deepseek-chat": 128000,
     # groq
@@ -371,7 +358,12 @@ class LLM(BaseLLM):
         }
 
         # Remove None values from params
-        return {k: v for k, v in params.items() if v is not None}
+        params = {k: v for k, v in params.items() if v is not None}
+        
+        if "o4-mini" in self.model:
+            params.pop("stop", None)
+            
+        return params
 
     def _handle_streaming_response(
         self,
@@ -598,11 +590,6 @@ class LLM(BaseLLM):
             self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
             return full_response
 
-        except ContextWindowExceededError as e:
-            # Catch context window errors from litellm and convert them to our own exception type.
-            # This exception is handled by CrewAgentExecutor._invoke_loop() which can then
-            # decide whether to summarize the content or abort based on the respect_context_window flag.
-            raise LLMContextLengthExceededException(str(e))
         except Exception as e:
             logging.error(f"Error in streaming response: {str(e)}")
             if full_response.strip():
@@ -717,16 +704,7 @@ class LLM(BaseLLM):
             str: The response text
         """
         # --- 1) Make the completion call
-        try:
-            # Attempt to make the completion call, but catch context window errors
-            # and convert them to our own exception type for consistent handling
-            # across the codebase. This allows CrewAgentExecutor to handle context
-            # length issues appropriately.
-            response = litellm.completion(**params)
-        except ContextWindowExceededError as e:
-            # Convert litellm's context window error to our own exception type
-            # for consistent handling in the rest of the codebase
-            raise LLMContextLengthExceededException(str(e))
+        response = litellm.completion(**params)
 
         # --- 2) Extract response message and content
         response_message = cast(Choices, cast(ModelResponse, response).choices)[
@@ -885,17 +863,15 @@ class LLM(BaseLLM):
                         params, callbacks, available_functions
                     )
 
-            except LLMContextLengthExceededException:
-                # Re-raise LLMContextLengthExceededException as it should be handled
-                # by the CrewAgentExecutor._invoke_loop method, which can then decide
-                # whether to summarize the content or abort based on the respect_context_window flag
-                raise
             except Exception as e:
                 crewai_event_bus.emit(
                     self,
                     event=LLMCallFailedEvent(error=str(e)),
                 )
-                logging.error(f"LiteLLM call failed: {str(e)}")
+                if not LLMContextLengthExceededException(
+                    str(e)
+                )._is_context_limit_error(str(e)):
+                    logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
     def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):

src/crewai/utilities/embedding_configurator.py

@@ -156,7 +156,7 @@ class EmbeddingConfigurator:
         )
 
         # Allow custom model_name override with backwards compatibility
-        kwargs = {"session": config.get("session"), "operation_name": "InvokeModel"}
+        kwargs = {"session": config.get("session")}
         if model_name is not None:
             kwargs["model_name"] = model_name
         return AmazonBedrockEmbeddingFunction(**kwargs)

tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-001].yaml

@@ -1,59 +0,0 @@
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-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
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-      accept-encoding:
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-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-001:generateContent
-  response:
-    body:
-      string: !!binary |
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-      Date:
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-      message: OK
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tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-lite-001].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-lite-001:generateContent
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAC/61RTU+EMBC98yuanhdSiMjq1Y+Txo0SY6J7GGGARmhJ2zUawn+3wMIWvdpDM5n3
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tests/cassettes/test_gemini_models[gemini-gemini-2.0-flash-thinking-exp-01-21].yaml

@@ -1,58 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-thinking-exp-01-21:generateContent
-  response:
-    body:
-      string: !!binary |
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tests/cassettes/test_gemini_models[gemini-gemini-2.5-flash-preview-04-17].yaml

@@ -1,59 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "What is the capital
-      of France?"}]}], "generationConfig": {"stop_sequences": []}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '131'
-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
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-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
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-    status:
-      code: 200
-      message: OK
-version: 1

tests/cassettes/test_gpt_4_1[gpt-4.1].yaml

@@ -1,101 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "user", "content": "What is the capital of France?"}],
-      "model": "gpt-4.1", "stop": []}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '109'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.68.2
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.68.2
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.11.12
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
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-      x-ratelimit-remaining-tokens:
-      - '29999989'
-      x-ratelimit-reset-requests:
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-      x-ratelimit-reset-tokens:
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-      x-request-id:
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-    status:
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-      message: OK
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tests/llm_test.py

@@ -256,52 +256,6 @@ def test_validate_call_params_no_response_format():
     llm._validate_call_params()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
-@pytest.mark.parametrize(
-    "model",
-    [
-        "gemini/gemini-2.0-flash-thinking-exp-01-21",
-        "gemini/gemini-2.0-flash-001",
-        "gemini/gemini-2.0-flash-lite-001",
-        "gemini/gemini-2.5-flash-preview-04-17",
-        "gemini/gemini-2.5-pro-exp-03-25",
-    ],
-)
-def test_gemini_models(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"], filter_query_parameters=["key"])
-@pytest.mark.parametrize(
-    "model",
-    [
-        "gemini/gemma-3-1b-it",
-        "gemini/gemma-3-4b-it",
-        "gemini/gemma-3-12b-it",
-        "gemini/gemma-3-27b-it",
-    ],
-)
-def test_gemma3(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.parametrize(
-    "model", ["gpt-4.1", "gpt-4.1-mini-2025-04-14", "gpt-4.1-nano-2025-04-14"]
-)
-def test_gpt_4_1(model):
-    llm = LLM(model=model)
-    result = llm.call("What is the capital of France?")
-    assert isinstance(result, str)
-    assert "Paris" in result
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_o3_mini_reasoning_effort_high():
     llm = LLM(
@@ -373,45 +327,6 @@ def get_weather_tool_schema():
         },
     }
 
-def test_context_window_exceeded_error_handling():
-    """Test that litellm.ContextWindowExceededError is converted to LLMContextLengthExceededException."""
-    from litellm.exceptions import ContextWindowExceededError
-
-    from crewai.utilities.exceptions.context_window_exceeding_exception import (
-        LLMContextLengthExceededException,
-    )
-
-    llm = LLM(model="gpt-4")
-
-    # Test non-streaming response
-    with patch("litellm.completion") as mock_completion:
-        mock_completion.side_effect = ContextWindowExceededError(
-            "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
-            model="gpt-4",
-            llm_provider="openai"
-        )
-
-        with pytest.raises(LLMContextLengthExceededException) as excinfo:
-            llm.call("This is a test message")
-
-        assert "context length exceeded" in str(excinfo.value).lower()
-        assert "8192 tokens" in str(excinfo.value)
-
-    # Test streaming response
-    llm = LLM(model="gpt-4", stream=True)
-    with patch("litellm.completion") as mock_completion:
-        mock_completion.side_effect = ContextWindowExceededError(
-            "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
-            model="gpt-4",
-            llm_provider="openai"
-        )
-
-        with pytest.raises(LLMContextLengthExceededException) as excinfo:
-            llm.call("This is a test message")
-
-        assert "context length exceeded" in str(excinfo.value).lower()
-        assert "8192 tokens" in str(excinfo.value)
-
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.fixture
@@ -618,3 +533,39 @@ def test_handle_streaming_tool_calls_no_tools(mock_emit):
         expected_completed_llm_call=1,
         expected_final_chunk_result=response,
     )
+
+
+def test_llm_o4_mini_stop_parameter():
+    """Test that o4-mini model works correctly without stop parameter."""
+    llm = LLM(model="o4-mini", stop=["STOP", "END"])
+    
+    # Check that stop parameter is set
+    assert llm.stop == ["STOP", "END"]
+    
+    params = llm._prepare_completion_params(messages=[{"role": "user", "content": "Hello"}])
+    
+    assert "stop" not in params
+    
+    with patch("litellm.completion") as mock_completion:
+        # Create a mock response
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 5,
+            "completion_tokens": 5,
+            "total_tokens": 10,
+        }
+        
+        # Set up the mock to return our response
+        mock_completion.return_value = mock_response
+        
+        response = llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+        
+        assert response == "Test response"
+        
+        call_args = mock_completion.call_args[1]
+        assert "stop" not in call_args

tests/utilities/test_embedding_configurator.py

@@ -1,21 +0,0 @@
-import unittest
-from unittest.mock import MagicMock, patch
-
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
-
-
-class TestEmbeddingConfigurator(unittest.TestCase):
-    @patch("chromadb.utils.embedding_functions.amazon_bedrock_embedding_function.AmazonBedrockEmbeddingFunction")
-    def test_configure_bedrock(self, mock_bedrock_embedder):
-        """Test that the Bedrock embedder is configured correctly."""
-        config = {"session": MagicMock()}
-        model_name = "amazon.titan-embed-text-v1"
-        
-        embedder = EmbeddingConfigurator()._configure_bedrock(config, model_name)
-        
-        mock_bedrock_embedder.assert_called_once_with(
-            session=config["session"],
-            model_name=model_name,
-            operation_name="InvokeModel",
-        )
-        self.assertEqual(embedder, mock_bedrock_embedder.return_value)