test: fix llama converter tests to remove skip_external_api

.github/security.md

@@ -1,27 +1,19 @@
-## CrewAI Security Vulnerability Reporting Policy
+CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
+If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
 
-CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security issues:
+ ## Reporting a Vulnerability
+ Please do not report security vulnerabilities through public GitHub issues.
+ To report a vulnerability, please email us at security@crewai.com.
+ Please include the requested information listed below so that we can triage your report more quickly
 
-### Reporting Process
-Do **not** report vulnerabilities via public GitHub issues.
+ - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
+ - Full paths of source file(s) related to the manifestation of the issue
+ - The location of the affected source code (tag/branch/commit or direct URL)
+ - Any special configuration required to reproduce the issue
+ - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
+ - Proof-of-concept or exploit code (if possible)
+ - Impact of the issue, including how an attacker might exploit the issue
 
-Email all vulnerability reports directly to:  
-**security@crewai.com**
+ Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
 
-### Required Information
-To help us quickly validate and remediate the issue, your report must include:
-
-- **Vulnerability Type:** Clearly state the vulnerability type (e.g., SQL injection, XSS, privilege escalation).
-- **Affected Source Code:** Provide full file paths and direct URLs (branch, tag, or commit).
-- **Reproduction Steps:** Include detailed, step-by-step instructions. Screenshots are recommended.
-- **Special Configuration:** Document any special settings or configurations required to reproduce.
-- **Proof-of-Concept (PoC):** Provide exploit or PoC code (if available).
-- **Impact Assessment:** Clearly explain the severity and potential exploitation scenarios.
-
-### Our Response
-- We will acknowledge receipt of your report promptly via your provided email.
-- Confirmed vulnerabilities will receive priority remediation based on severity.
-- Patches will be released as swiftly as possible following verification.
-
-### Reward Notice
-Currently, we do not offer a bug bounty program. Rewards, if issued, are discretionary.
+ At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.

README.md

@@ -504,7 +504,7 @@ This example demonstrates how to:
 
 CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
 
-Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
 
 ## How CrewAI Compares
 

docs/concepts/crews.mdx

@@ -27,7 +27,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -246,7 +246,7 @@ print(f"Token Usage: {crew_output.token_usage}")
 You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
 In case of `True(Boolean)` will save as `logs.txt`.
 
-In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
 
 ```python Code
 # Save crew logs

docs/concepts/knowledge.mdx

@@ -397,53 +397,6 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 John is 30 years old and lives in San Francisco.
 ```
 </CodeGroup>
-
-## Query Rewriting
-
-CrewAI implements an intelligent query rewriting mechanism to optimize knowledge retrieval. When an agent needs to search through knowledge sources, the raw task prompt is automatically transformed into a more effective search query.
-
-### How Query Rewriting Works
-
-1. When an agent executes a task with knowledge sources available, the `_get_knowledge_search_query` method is triggered
-2. The agent's LLM is used to transform the original task prompt into an optimized search query
-3. This optimized query is then used to retrieve relevant information from knowledge sources
-
-### Benefits of Query Rewriting
-
-<CardGroup cols={2}>
-  <Card title="Improved Retrieval Accuracy" icon="bullseye-arrow">
-    By focusing on key concepts and removing irrelevant content, query rewriting helps retrieve more relevant information.
-  </Card>
-  <Card title="Context Awareness" icon="brain">
-    The rewritten queries are designed to be more specific and context-aware for vector database retrieval.
-  </Card>
-</CardGroup>
-
-### Implementation Details
-
-Query rewriting happens transparently using a system prompt that instructs the LLM to:
-
-- Focus on key words of the intended task
-- Make the query more specific and context-aware
-- Remove irrelevant content like output format instructions
-- Generate only the rewritten query without preamble or postamble
-
-<Tip>
-  This mechanism is fully automatic and requires no configuration from users. The agent's LLM is used to perform the query rewriting, so using a more capable LLM can improve the quality of rewritten queries.
-</Tip>
-
-### Example
-
-```python
-# Original task prompt
-task_prompt = "Answer the following questions about the user's favorite movies: What movie did John watch last week? Format your answer in JSON."
-
-# Behind the scenes, this might be rewritten as:
-rewritten_query = "What movies did John watch last week?"
-```
-
-The rewritten query is more focused on the core information need and removes irrelevant instructions about output formatting.
-
 ## Clearing Knowledge
 
 If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.

docs/concepts/llms.mdx

@@ -169,55 +169,19 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
-  <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+  <Accordion title="Google">
+    Set the following environment variables in your `.env` file:
 
-    ```toml .env
+    ```toml Code
+    # Option 1: Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
+
+    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
+    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7,
-    )
-    ```
-
-    ### Gemini models
-
-    Google offers a range of powerful models optimized for different use cases.
-
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
-  </Accordion>
-  <Accordion title="Google (Vertex AI)">
-    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
+    Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
     ```python Code
     import json
 
@@ -241,18 +205,14 @@ In this section, you'll find detailed examples that help you select, configure,
         vertex_credentials=vertex_credentials_json
     )
     ```
-
     Google offers a range of powerful models optimized for different use cases:
 
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | Model                  | Context Window | Best For                                                          |
+    |-----------------------|----------------|------------------------------------------------------------------|
+    | gemini-2.0-flash-exp  | 1M tokens      | Higher quality at faster speed, multimodal model, good for most tasks |
+    | gemini-1.5-flash      | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B   | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro        | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
   </Accordion>
 
   <Accordion title="Azure">

docs/guides/crews/first-crew.mdx

@@ -35,8 +35,7 @@ Let's get started building your first crew!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+2. Set up your OpenAI API key in your environment variables
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Project
@@ -93,8 +92,7 @@ For our research crew, we'll create two agents:
 1. A **researcher** who excels at finding and organizing information
 2. An **analyst** who can interpret research findings and create insightful reports
 
-Let's modify the `agents.yaml` file to define these specialized agents. Be sure
-to set `llm` to the provider you are using.
+Let's modify the `agents.yaml` file to define these specialized agents:
 
 ```yaml
 # src/research_crew/config/agents.yaml
@@ -109,7 +107,7 @@ researcher:
     finding relevant information from various sources. You excel at
     organizing information in a clear and structured manner, making
     complex topics accessible to others.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 
 analyst:
   role: >
@@ -122,7 +120,7 @@ analyst:
     and technical writing. You have a talent for identifying patterns
     and extracting meaningful insights from research data, then
     communicating those insights effectively through well-crafted reports.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 ```
 
 Notice how each agent has a distinct role, goal, and backstory. These elements aren't just descriptive - they actively shape how the agent approaches its tasks. By crafting these carefully, you can create agents with specialized skills and perspectives that complement each other.
@@ -284,12 +282,12 @@ This script prepares the environment, specifies our research topic, and kicks of
 
 Create a `.env` file in your project root with your API keys:
 
-```sh
+```
+OPENAI_API_KEY=your_openai_api_key
 SERPER_API_KEY=your_serper_api_key
-# Add your provider's API key here too.
 ```
 
-See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
+You can get a Serper API key from [Serper.dev](https://serper.dev/).
 
 ## Step 8: Install Dependencies
 

docs/guides/flows/first-flow.mdx

@@ -45,8 +45,7 @@ Let's dive in and build your first flow!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+2. Set up your OpenAI API key in your environment variables
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Flow Project
@@ -108,8 +107,6 @@ Now, let's modify the generated files for the content writer crew. We'll set up
 
 1. First, update the agents configuration file to define our content creation team:
 
-   Remember to set `llm` to the provider you are using.
-
 ```yaml
 # src/guide_creator_flow/crews/content_crew/config/agents.yaml
 content_writer:
@@ -122,7 +119,7 @@ content_writer:
     You are a talented educational writer with expertise in creating clear, engaging
     content. You have a gift for explaining complex concepts in accessible language
     and organizing information in a way that helps readers build their understanding.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 
 content_reviewer:
   role: >
@@ -135,7 +132,7 @@ content_reviewer:
     content. You have an eye for detail, clarity, and coherence. You excel at
     improving content while maintaining the original author's voice and ensuring
     consistent quality across multiple sections.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 ```
 
 These agent definitions establish the specialized roles and perspectives that will shape how our AI agents approach content creation. Notice how each agent has a distinct purpose and expertise.
@@ -444,15 +441,10 @@ This is the power of flows - combining different types of processing (user inter
 
 ## Step 6: Set Up Your Environment Variables
 
-Create a `.env` file in your project root with your API keys. See the [LLM setup
-guide](/concepts/llms#setting-up-your-llm) for details on configuring a provider.
+Create a `.env` file in your project root with your API keys:
 
-```sh .env
+```
 OPENAI_API_KEY=your_openai_api_key
-# or
-GEMINI_API_KEY=your_gemini_api_key
-# or
-ANTHROPIC_API_KEY=your_anthropic_api_key
 ```
 
 ## Step 7: Install Dependencies
@@ -555,10 +547,7 @@ Let's break down the key components of flows to help you understand how to build
 Flows allow you to make direct calls to language models when you need simple, structured responses:
 
 ```python
-llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-    response_format=GuideOutline
-)
+llm = LLM(model="openai/gpt-4o-mini", response_format=GuideOutline)
 response = llm.call(messages=messages)
 ```
 

docs/how-to/arize-phoenix-observability.mdx

@@ -68,13 +68,7 @@ We'll create a CrewAI application where two agents collaborate to research and w
 ```python
 from crewai import Agent, Crew, Process, Task
 from crewai_tools import SerperDevTool
-from openinference.instrumentation.crewai import CrewAIInstrumentor
-from phoenix.otel import register
 
-# setup monitoring for your crew
-tracer_provider = register(
-    endpoint="http://localhost:6006/v1/traces")
-CrewAIInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_provider)
 search_tool = SerperDevTool()
 
 # Define your agents with roles and goals

docs/quickstart.mdx

@@ -180,9 +180,8 @@ Follow the steps below to get Crewing! 🚣‍♂️
   </Step>
   <Step title="Set your environment variables">
   Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
+    - An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
     - A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
-    - The configuration for your choice of model, such as an API key. See the
-        [LLM setup guide](/concepts/llms#setting-up-your-llm) to learn how to configure models from any provider.
   </Step>
   <Step title="Lock and install the dependencies">
     - Lock the dependencies and install them by using the CLI command:
@@ -318,7 +317,7 @@ email_summarizer:
       Summarize emails into a concise and clear summary
     backstory: >
       You will create a 5 bullet point summary of the report
-    llm: provider/model-id  # Add your choice of model here
+    llm: openai/gpt-4o
 ```
 
 <Tip>

docs/tools/nl2sqltool.mdx

@@ -8,10 +8,10 @@ icon: language
 
 ## Description
 
-This tool is used to convert natural language to SQL queries. When passed to the agent it will generate queries and then use them to interact with the database.
+This tool is used to convert natural language to SQL queries. When passsed to the agent it will generate queries and then use them to interact with the database.
 
 This enables multiple workflows like having an Agent to access the database fetch information based on the goal and then use the information to generate a response, report or any other output. 
-Along with that provides the ability for the Agent to update the database based on its goal.
+Along with that proivdes the ability for the Agent to update the database based on its goal.
 
 **Attention**: Make sure that the Agent has access to a Read-Replica or that is okay for the Agent to run insert/update queries on the database.
 
@@ -81,4 +81,4 @@ The Tool provides endless possibilities on the logic of the Agent and how it can
 
 ```md
  DB -> Agent -> ... -> Agent -> DB
-```
+```

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.119.0"
+version = "0.118.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.44.0"]
+tools = ["crewai-tools~=0.42.2"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.119.0"
+__version__ = "0.118.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -31,14 +31,6 @@ from crewai.utilities.events.agent_events import (
     AgentExecutionStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -130,10 +122,6 @@ class Agent(BaseAgent):
         default=None,
         description="Knowledge context for the crew.",
     )
-    knowledge_search_query: Optional[str] = Field(
-        default=None,
-        description="Knowledge search query for the agent dynamically generated by the agent.",
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -197,7 +185,7 @@ class Agent(BaseAgent):
         self,
         task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[List[BaseTool]] = None
     ) -> str:
         """Execute a task with the agent.
 
@@ -257,65 +245,27 @@ class Agent(BaseAgent):
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
-
         if self.knowledge:
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeRetrievalStartedEvent(
-                    agent=self,
-                ),
+            agent_knowledge_snippets = self.knowledge.query(
+                [task.prompt()], **knowledge_config
             )
-            try:
-                self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt
+            if agent_knowledge_snippets:
+                self.agent_knowledge_context = extract_knowledge_context(
+                    agent_knowledge_snippets
                 )
-                if self.knowledge_search_query:
-                    agent_knowledge_snippets = self.knowledge.query(
-                        [self.knowledge_search_query], **knowledge_config
-                    )
-                    if agent_knowledge_snippets:
-                        self.agent_knowledge_context = extract_knowledge_context(
-                            agent_knowledge_snippets
-                        )
-                        if self.agent_knowledge_context:
-                            task_prompt += self.agent_knowledge_context
-                    if self.crew:
-                        knowledge_snippets = self.crew.query_knowledge(
-                            [self.knowledge_search_query], **knowledge_config
-                        )
-                        if knowledge_snippets:
-                            self.crew_knowledge_context = extract_knowledge_context(
-                                knowledge_snippets
-                            )
-                            if self.crew_knowledge_context:
-                                task_prompt += self.crew_knowledge_context
+                if self.agent_knowledge_context:
+                    task_prompt += self.agent_knowledge_context
 
-                    crewai_event_bus.emit(
-                        self,
-                        event=KnowledgeRetrievalCompletedEvent(
-                            query=self.knowledge_search_query,
-                            agent=self,
-                            retrieved_knowledge=(
-                                (self.agent_knowledge_context or "")
-                                + (
-                                    "\n"
-                                    if self.agent_knowledge_context
-                                    and self.crew_knowledge_context
-                                    else ""
-                                )
-                                + (self.crew_knowledge_context or "")
-                            ),
-                        ),
-                    )
-            except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=KnowledgeSearchQueryFailedEvent(
-                        query=self.knowledge_search_query or "",
-                        agent=self,
-                        error=str(e),
-                    ),
+        if self.crew:
+            knowledge_snippets = self.crew.query_knowledge(
+                [task.prompt()], **knowledge_config
+            )
+            if knowledge_snippets:
+                self.crew_knowledge_context = extract_knowledge_context(
+                    knowledge_snippets
                 )
+                if self.crew_knowledge_context:
+                    task_prompt += self.crew_knowledge_context
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
@@ -338,19 +288,12 @@ class Agent(BaseAgent):
 
             # Determine execution method based on timeout setting
             if self.max_execution_time is not None:
-                if (
-                    not isinstance(self.max_execution_time, int)
-                    or self.max_execution_time <= 0
-                ):
-                    raise ValueError(
-                        "Max Execution time must be a positive integer greater than zero"
-                    )
-                result = self._execute_with_timeout(
-                    task_prompt, task, self.max_execution_time
-                )
+                if not isinstance(self.max_execution_time, int) or self.max_execution_time <= 0:
+                    raise ValueError("Max Execution time must be a positive integer greater than zero")
+                result = self._execute_with_timeout(task_prompt, task, self.max_execution_time)
             else:
                 result = self._execute_without_timeout(task_prompt, task)
-
+                
         except TimeoutError as e:
             # Propagate TimeoutError without retry
             crewai_event_bus.emit(
@@ -402,46 +345,54 @@ class Agent(BaseAgent):
         )
         return result
 
-    def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> str:
+    def _execute_with_timeout(
+        self,
+        task_prompt: str,
+        task: Task,
+        timeout: int
+    ) -> str:
         """Execute a task with a timeout.
-
+        
         Args:
             task_prompt: The prompt to send to the agent.
             task: The task being executed.
             timeout: Maximum execution time in seconds.
-
+            
         Returns:
             The output of the agent.
-
+            
         Raises:
             TimeoutError: If execution exceeds the timeout.
             RuntimeError: If execution fails for other reasons.
         """
         import concurrent.futures
-
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future = executor.submit(
-                self._execute_without_timeout, task_prompt=task_prompt, task=task
+                self._execute_without_timeout,
+                task_prompt=task_prompt,
+                task=task
             )
-
+            
             try:
                 return future.result(timeout=timeout)
             except concurrent.futures.TimeoutError:
                 future.cancel()
-                raise TimeoutError(
-                    f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task."
-                )
+                raise TimeoutError(f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task.")
             except Exception as e:
                 future.cancel()
                 raise RuntimeError(f"Task execution failed: {str(e)}")
 
-    def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
+    def _execute_without_timeout(
+        self,
+        task_prompt: str,
+        task: Task
+    ) -> str:
         """Execute a task without a timeout.
-
+        
         Args:
             task_prompt: The prompt to send to the agent.
             task: The task being executed.
-
+            
         Returns:
             The output of the agent.
         """
@@ -609,61 +560,6 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
 
-    def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
-        """Generate a search query for the knowledge base based on the task description."""
-        crewai_event_bus.emit(
-            self,
-            event=KnowledgeQueryStartedEvent(
-                task_prompt=task_prompt,
-                agent=self,
-            ),
-        )
-        query = self.i18n.slice("knowledge_search_query").format(
-            task_prompt=task_prompt
-        )
-        rewriter_prompt = self.i18n.slice("knowledge_search_query_system_prompt")
-        if not isinstance(self.llm, BaseLLM):
-            self._logger.log(
-                "warning",
-                f"Knowledge search query failed: LLM for agent '{self.role}' is not an instance of BaseLLM",
-            )
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryFailedEvent(
-                    agent=self,
-                    error="LLM is not compatible with knowledge search queries",
-                ),
-            )
-            return None
-
-        try:
-            rewritten_query = self.llm.call(
-                [
-                    {
-                        "role": "system",
-                        "content": rewriter_prompt,
-                    },
-                    {"role": "user", "content": query},
-                ]
-            )
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryCompletedEvent(
-                    query=query,
-                    agent=self,
-                ),
-            )
-            return rewritten_query
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryFailedEvent(
-                    agent=self,
-                    error=str(e),
-                ),
-            )
-            return None
-
     def kickoff(
         self,
         messages: Union[str, List[Dict[str, str]]],

src/crewai/cli/constants.py

@@ -13,7 +13,7 @@ ENV_VARS = {
     ],
     "gemini": [
         {
-            "prompt": "Enter your GEMINI API key from https://ai.dev/apikey (press Enter to skip)",
+            "prompt": "Enter your GEMINI API key (press Enter to skip)",
             "key_name": "GEMINI_API_KEY",
         }
     ],

src/crewai/cli/provider.py

@@ -1,6 +1,4 @@
 import json
-import logging
-import os
 import time
 from collections import defaultdict
 from pathlib import Path
@@ -10,8 +8,6 @@ import requests
 
 from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
 
-logger = logging.getLogger(__name__)
-
 
 def select_choice(prompt_message, choices):
     """
@@ -161,74 +157,22 @@ def fetch_provider_data(cache_file):
     """
     Fetches provider data from a specified URL and caches it to a file.
 
-    Warning: This function includes a fallback that disables SSL verification.
-    This should only be used in development environments or when absolutely necessary.
-    Production deployments should resolve SSL certificate issues properly.
-
     Args:
     - cache_file (Path): The path to the cache file.
 
     Returns:
     - dict or None: The fetched provider data or None if the operation fails.
     """
-    allow_insecure = os.getenv("CREW_ALLOW_INSECURE_SSL", "false").lower() == "true"
-
     try:
-        verify = not allow_insecure
-        if not verify:
-            logger.warning(
-                "SSL verification disabled via CREW_ALLOW_INSECURE_SSL environment variable. "
-                "This is less secure and should only be used in development environments."
-            )
-            click.secho(
-                "SSL verification disabled via environment variable. "
-                "This is less secure and should only be used in development environments.",
-                fg="yellow",
-            )
-
-        response = requests.get(JSON_URL, stream=True, timeout=60, verify=verify)
+        response = requests.get(JSON_URL, stream=True, timeout=60)
         response.raise_for_status()
         data = download_data(response)
         with open(cache_file, "w") as f:
             json.dump(data, f)
         return data
-    except requests.exceptions.SSLError:
-        if not allow_insecure:
-            logger.warning(
-                "SSL certificate verification failed. Retrying with verification disabled. "
-                "This is less secure but may be necessary on some systems."
-            )
-            click.secho(
-                "SSL certificate verification failed. Retrying with verification disabled. "
-                "This is less secure but may be necessary on some systems.",
-                fg="yellow",
-            )
-            try:
-                os.environ["CREW_TEMP_ALLOW_INSECURE"] = "true"
-                response = requests.get(
-                    JSON_URL,
-                    stream=True,
-                    timeout=60,
-                    verify=False,  # nosec B501
-                )
-                os.environ.pop("CREW_TEMP_ALLOW_INSECURE", None)
-                
-                response.raise_for_status()
-                data = download_data(response)
-                with open(cache_file, "w") as f:
-                    json.dump(data, f)
-                return data
-            except requests.RequestException as e:
-                logger.error(f"Error fetching provider data: {e}")
-                click.secho(f"Error fetching provider data: {e}", fg="red")
-                return None
-            finally:
-                os.environ.pop("CREW_TEMP_ALLOW_INSECURE", None)
     except requests.RequestException as e:
-        logger.error(f"Error fetching provider data: {e}")
         click.secho(f"Error fetching provider data: {e}", fg="red")
     except json.JSONDecodeError:
-        logger.error("Error parsing provider data. Invalid JSON format.")
         click.secho("Error parsing provider data. Invalid JSON format.", fg="red")
     return None
 

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0,<1.0.0"
+    "crewai[tools]>=0.118.0,<1.0.0"
 ]
 
 [project.scripts]

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0,<1.0.0",
+    "crewai[tools]>=0.118.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.119.0"
+    "crewai[tools]>=0.118.0"
 ]
 
 [tool.crewai]

src/crewai/translations/en.json

@@ -27,9 +27,7 @@
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
     "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
-    "knowledge_search_query": "The original query is: {task_prompt}.",
-    "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
+    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",

src/crewai/utilities/events/event_listener.py

@@ -8,14 +8,6 @@ from crewai.telemetry.telemetry import Telemetry
 from crewai.utilities import Logger
 from crewai.utilities.constants import EMITTER_COLOR
 from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from crewai.utilities.events.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
@@ -65,8 +57,6 @@ class EventListener(BaseEventListener):
     execution_spans: Dict[Task, Any] = Field(default_factory=dict)
     next_chunk = 0
     text_stream = StringIO()
-    knowledge_retrieval_in_progress = False
-    knowledge_query_in_progress = False
 
     def __new__(cls):
         if cls._instance is None:
@@ -352,59 +342,5 @@ class EventListener(BaseEventListener):
         def on_crew_test_failed(source, event: CrewTestFailedEvent):
             self.formatter.handle_crew_test_failed(event.crew_name or "Crew")
 
-        @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
-        def on_knowledge_retrieval_started(
-            source, event: KnowledgeRetrievalStartedEvent
-        ):
-            if self.knowledge_retrieval_in_progress:
-                return
-
-            self.knowledge_retrieval_in_progress = True
-
-            self.formatter.handle_knowledge_retrieval_started(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
-        def on_knowledge_retrieval_completed(
-            source, event: KnowledgeRetrievalCompletedEvent
-        ):
-            if not self.knowledge_retrieval_in_progress:
-                return
-
-            self.knowledge_retrieval_in_progress = False
-            self.formatter.handle_knowledge_retrieval_completed(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-                event.retrieved_knowledge,
-            )
-
-        @crewai_event_bus.on(KnowledgeQueryStartedEvent)
-        def on_knowledge_query_started(source, event: KnowledgeQueryStartedEvent):
-            pass
-
-        @crewai_event_bus.on(KnowledgeQueryFailedEvent)
-        def on_knowledge_query_failed(source, event: KnowledgeQueryFailedEvent):
-            self.formatter.handle_knowledge_query_failed(
-                self.formatter.current_agent_branch,
-                event.error,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(KnowledgeQueryCompletedEvent)
-        def on_knowledge_query_completed(source, event: KnowledgeQueryCompletedEvent):
-            pass
-
-        @crewai_event_bus.on(KnowledgeSearchQueryFailedEvent)
-        def on_knowledge_search_query_failed(
-            source, event: KnowledgeSearchQueryFailedEvent
-        ):
-            self.formatter.handle_knowledge_search_query_failed(
-                self.formatter.current_agent_branch,
-                event.error,
-                self.formatter.current_crew_tree,
-            )
-
 
 event_listener = EventListener()

src/crewai/utilities/events/knowledge_events.py

@@ -1,56 +0,0 @@
-from typing import TYPE_CHECKING, Any
-
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.utilities.events.base_events import BaseEvent
-
-if TYPE_CHECKING:
-    from crewai.agents.agent_builder.base_agent import BaseAgent
-
-
-class KnowledgeRetrievalStartedEvent(BaseEvent):
-    """Event emitted when a knowledge retrieval is started."""
-
-    type: str = "knowledge_search_query_started"
-    agent: BaseAgent
-
-
-class KnowledgeRetrievalCompletedEvent(BaseEvent):
-    """Event emitted when a knowledge retrieval is completed."""
-
-    query: str
-    type: str = "knowledge_search_query_completed"
-    agent: BaseAgent
-    retrieved_knowledge: Any
-
-
-class KnowledgeQueryStartedEvent(BaseEvent):
-    """Event emitted when a knowledge query is started."""
-
-    task_prompt: str
-    type: str = "knowledge_query_started"
-    agent: BaseAgent
-
-
-class KnowledgeQueryFailedEvent(BaseEvent):
-    """Event emitted when a knowledge query fails."""
-
-    type: str = "knowledge_query_failed"
-    agent: BaseAgent
-    error: str
-
-
-class KnowledgeQueryCompletedEvent(BaseEvent):
-    """Event emitted when a knowledge query is completed."""
-
-    query: str
-    type: str = "knowledge_query_completed"
-    agent: BaseAgent
-
-
-class KnowledgeSearchQueryFailedEvent(BaseEvent):
-    """Event emitted when a knowledge search query fails."""
-
-    query: str
-    type: str = "knowledge_search_query_failed"
-    agent: BaseAgent
-    error: str

src/crewai/utilities/events/utils/console_formatter.py

@@ -783,202 +783,3 @@ class ConsoleFormatter:
             self.update_lite_agent_status(
                 self.current_lite_agent_branch, lite_agent_role, status, **fields
             )
-
-    def handle_knowledge_retrieval_started(
-        self,
-        agent_branch: Optional[Tree],
-        crew_tree: Optional[Tree],
-    ) -> Optional[Tree]:
-        """Handle knowledge retrieval started event."""
-        if not self.verbose:
-            return None
-
-        branch_to_use = agent_branch or self.current_lite_agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None or tree_to_use is None:
-            # If we don't have a valid branch, use crew_tree as the branch if available
-            if crew_tree is not None:
-                branch_to_use = tree_to_use = crew_tree
-            else:
-                return None
-
-        knowledge_branch = branch_to_use.add("")
-        self.update_tree_label(
-            knowledge_branch, "🔍", "Knowledge Retrieval Started", "blue"
-        )
-
-        self.print(tree_to_use)
-        self.print()
-        return knowledge_branch
-
-    def handle_knowledge_retrieval_completed(
-        self,
-        agent_branch: Optional[Tree],
-        crew_tree: Optional[Tree],
-        retrieved_knowledge: Any,
-    ) -> None:
-        """Handle knowledge retrieval completed event."""
-        if not self.verbose:
-            return None
-
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None and tree_to_use is not None:
-            branch_to_use = tree_to_use
-
-        if branch_to_use is None or tree_to_use is None:
-            if retrieved_knowledge:
-                knowledge_text = str(retrieved_knowledge)
-                if len(knowledge_text) > 500:
-                    knowledge_text = knowledge_text[:497] + "..."
-
-                knowledge_panel = Panel(
-                    Text(knowledge_text, style="white"),
-                    title="📚 Retrieved Knowledge",
-                    border_style="green",
-                    padding=(1, 2),
-                )
-                self.print(knowledge_panel)
-                self.print()
-            return None
-
-        knowledge_branch_found = False
-        for child in branch_to_use.children:
-            if "Knowledge Retrieval Started" in str(child.label):
-                self.update_tree_label(
-                    child, "✅", "Knowledge Retrieval Completed", "green"
-                )
-                knowledge_branch_found = True
-                break
-
-        if not knowledge_branch_found:
-            for child in branch_to_use.children:
-                if (
-                    "Knowledge Retrieval" in str(child.label)
-                    and "Started" not in str(child.label)
-                    and "Completed" not in str(child.label)
-                ):
-                    self.update_tree_label(
-                        child, "✅", "Knowledge Retrieval Completed", "green"
-                    )
-                    knowledge_branch_found = True
-                    break
-
-        if not knowledge_branch_found:
-            knowledge_branch = branch_to_use.add("")
-            self.update_tree_label(
-                knowledge_branch, "✅", "Knowledge Retrieval Completed", "green"
-            )
-
-        self.print(tree_to_use)
-
-        if retrieved_knowledge:
-            knowledge_text = str(retrieved_knowledge)
-            if len(knowledge_text) > 500:
-                knowledge_text = knowledge_text[:497] + "..."
-
-            knowledge_panel = Panel(
-                Text(knowledge_text, style="white"),
-                title="📚 Retrieved Knowledge",
-                border_style="green",
-                padding=(1, 2),
-            )
-            self.print(knowledge_panel)
-
-        self.print()
-
-    def handle_knowledge_query_started(
-        self,
-        agent_branch: Optional[Tree],
-        task_prompt: str,
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle knowledge query generated event."""
-        if not self.verbose:
-            return None
-
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
-        if branch_to_use is None or tree_to_use is None:
-            return None
-
-        query_branch = branch_to_use.add("")
-        self.update_tree_label(
-            query_branch, "🔎", f"Query: {task_prompt[:50]}...", "yellow"
-        )
-
-        self.print(tree_to_use)
-        self.print()
-
-    def handle_knowledge_query_failed(
-        self,
-        agent_branch: Optional[Tree],
-        error: str,
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle knowledge query failed event."""
-        if not self.verbose:
-            return
-
-        tree_to_use = self.current_lite_agent_branch or crew_tree
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-
-        if branch_to_use and tree_to_use:
-            query_branch = branch_to_use.add("")
-            self.update_tree_label(query_branch, "❌", "Knowledge Query Failed", "red")
-            self.print(tree_to_use)
-            self.print()
-
-        # Show error panel
-        error_content = self.create_status_content(
-            "Knowledge Query Failed", "Query Error", "red", Error=error
-        )
-        self.print_panel(error_content, "Knowledge Error", "red")
-
-    def handle_knowledge_query_completed(
-        self,
-        agent_branch: Optional[Tree],
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle knowledge query completed event."""
-        if not self.verbose:
-            return None
-
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None or tree_to_use is None:
-            return None
-
-        query_branch = branch_to_use.add("")
-        self.update_tree_label(query_branch, "✅", "Knowledge Query Completed", "green")
-
-        self.print(tree_to_use)
-        self.print()
-
-    def handle_knowledge_search_query_failed(
-        self,
-        agent_branch: Optional[Tree],
-        error: str,
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle knowledge search query failed event."""
-        if not self.verbose:
-            return
-
-        tree_to_use = self.current_lite_agent_branch or crew_tree
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-
-        if branch_to_use and tree_to_use:
-            query_branch = branch_to_use.add("")
-            self.update_tree_label(query_branch, "❌", "Knowledge Search Failed", "red")
-            self.print(tree_to_use)
-            self.print()
-
-        # Show error panel
-        error_content = self.create_status_content(
-            "Knowledge Search Failed", "Search Error", "red", Error=error
-        )
-        self.print_panel(error_content, "Search Error", "red")

tests/agent_test.py

@@ -9,6 +9,7 @@ import pytest
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
+from crewai.agents.parser import CrewAgentParser, OutputParserException
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -72,7 +73,6 @@ def test_agent_creation():
     assert agent.goal == "test goal"
     assert agent.backstory == "test backstory"
 
-
 def test_agent_with_only_system_template():
     """Test that an agent with only system_template works without errors."""
     agent = Agent(
@@ -88,7 +88,6 @@ def test_agent_with_only_system_template():
     assert agent.goal == "Test Goal"
     assert agent.backstory == "Test Backstory"
 
-
 def test_agent_with_only_prompt_template():
     """Test that an agent with only system_template works without errors."""
     agent = Agent(
@@ -120,8 +119,7 @@ def test_agent_with_missing_response_template():
     assert agent.role == "Test Role"
     assert agent.goal == "Test Goal"
     assert agent.backstory == "Test Backstory"
-
-
+    
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
     assert agent.llm.model == "gpt-4o-mini"
@@ -1632,10 +1630,13 @@ def test_agent_with_knowledge_sources():
     # Create a knowledge source with some content
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
-    with patch("crewai.knowledge") as MockKnowledge:
+
+    with patch(
+        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.search.return_value = [{"content": content}]
+        mock_knowledge_instance.query.return_value = [{"content": content}]
 
         agent = Agent(
             role="Information Agent",
@@ -1689,7 +1690,7 @@ def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold():
 
             assert agent.knowledge is not None
             mock_knowledge_query.assert_called_once_with(
-                ["Brandon's favorite color"],
+                [task.prompt()],
                 **knowledge_config.model_dump(),
             )
 
@@ -1726,7 +1727,7 @@ def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_defau
 
             assert agent.knowledge is not None
             mock_knowledge_query.assert_called_once_with(
-                ["Brandon's favorite color"],
+                [task.prompt()],
                 **knowledge_config.model_dump(),
             )
 
@@ -1736,7 +1737,9 @@ def test_agent_with_knowledge_sources_extensive_role():
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
 
-    with patch("crewai.knowledge") as MockKnowledge:
+    with patch(
+        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
         mock_knowledge_instance.query.return_value = [{"content": content}]
@@ -1800,40 +1803,6 @@ def test_agent_with_knowledge_sources_works_with_copy():
             assert isinstance(agent_copy.llm, LLM)
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_generate_search_query():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-
-    with patch("crewai.knowledge") as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-
-        agent = Agent(
-            role="Information Agent with extensive role description that is longer than 80 characters",
-            goal="Provide information based on knowledge sources",
-            backstory="You have access to specific knowledge sources.",
-            llm=LLM(model="gpt-4o-mini"),
-            knowledge_sources=[string_source],
-        )
-
-        task = Task(
-            description="What is Brandon's favorite color?",
-            expected_output="The answer to the question, in a format like this: `{{name: str, favorite_color: str}}`",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        # Updated assertion to check the JSON content
-        assert "Brandon" in str(agent.knowledge_search_query)
-        assert "favorite color" in str(agent.knowledge_search_query)
-
-        assert "red" in result.raw.lower()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_litellm_auth_error_handling():
     """Test that LiteLLM authentication errors are handled correctly and not retried."""
@@ -1971,57 +1940,3 @@ def test_litellm_anthropic_error_handling():
 
     # Verify the LLM call was only made once (no retries)
     mock_llm_call.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_get_knowledge_search_query():
-    """Test that _get_knowledge_search_query calls the LLM with the correct prompts."""
-    from crewai.utilities.i18n import I18N
-
-    content = "The capital of France is Paris."
-    string_source = StringKnowledgeSource(content=content)
-
-    agent = Agent(
-        role="Information Agent",
-        goal="Provide information based on knowledge sources",
-        backstory="I have access to knowledge sources",
-        llm=LLM(model="gpt-4"),
-        knowledge_sources=[string_source],
-    )
-
-    task = Task(
-        description="What is the capital of France?",
-        expected_output="The capital of France is Paris.",
-        agent=agent,
-    )
-
-    i18n = I18N()
-    task_prompt = task.prompt()
-
-    with patch.object(agent, "_get_knowledge_search_query") as mock_get_query:
-        mock_get_query.return_value = "Capital of France"
-
-        crew = Crew(agents=[agent], tasks=[task])
-        crew.kickoff()
-
-        mock_get_query.assert_called_once_with(task_prompt)
-
-    with patch.object(agent.llm, "call") as mock_llm_call:
-        agent._get_knowledge_search_query(task_prompt)
-
-        mock_llm_call.assert_called_once_with(
-            [
-                {
-                    "role": "system",
-                    "content": i18n.slice(
-                        "knowledge_search_query_system_prompt"
-                    ).format(task_prompt=task.description),
-                },
-                {
-                    "role": "user",
-                    "content": i18n.slice("knowledge_search_query").format(
-                        task_prompt=task_prompt
-                    ),
-                },
-            ]
-        )

tests/cassettes/test_agent_with_knowledge_sources_generate_search_query.yaml

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-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '137'
-      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-read-timeout:
-      - '600'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/embeddings
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-import json
-import os
-import tempfile
-from pathlib import Path
-from unittest import mock
-
-import pytest
-import requests
-from requests.exceptions import SSLError
-
-from crewai.cli.provider import fetch_provider_data, get_provider_data
-
-
-class TestProviderFunctions:
-    @mock.patch("crewai.cli.provider.requests.get")
-    def test_fetch_provider_data_success(self, mock_get):
-        mock_response = mock.MagicMock()
-        mock_response.headers.get.return_value = "100"
-        mock_response.iter_content.return_value = [b'{"test": "data"}']
-        mock_get.return_value = mock_response
-
-        with tempfile.NamedTemporaryFile() as temp_file:
-            cache_file = Path(temp_file.name)
-            result = fetch_provider_data(cache_file)
-
-        assert result == {"test": "data"}
-        mock_get.assert_called_once()
-
-    @mock.patch("crewai.cli.provider.requests.get")
-    @mock.patch("crewai.cli.provider.click.secho")
-    def test_fetch_provider_data_ssl_error_fallback(self, mock_secho, mock_get):
-        mock_response = mock.MagicMock()
-        mock_response.headers.get.return_value = "100"
-        mock_response.iter_content.return_value = [b'{"test": "data"}']
-        
-        mock_get.side_effect = [
-            SSLError("certificate verify failed: unable to get local issuer certificate"),
-            mock_response
-        ]
-
-        with tempfile.NamedTemporaryFile() as temp_file:
-            cache_file = Path(temp_file.name)
-            result = fetch_provider_data(cache_file)
-
-        assert result == {"test": "data"}
-        assert mock_get.call_count == 2
-        
-        assert mock_get.call_args_list[1][1]["verify"] is False
-        
-        mock_secho.assert_any_call(
-            "SSL certificate verification failed. Retrying with verification disabled. "
-            "This is less secure but may be necessary on some systems.",
-            fg="yellow"
-        )
-
-    @mock.patch("crewai.cli.provider.requests.get")
-    @mock.patch("crewai.cli.provider.click.secho")
-    @mock.patch.dict(os.environ, {"CREW_ALLOW_INSECURE_SSL": "true"})
-    def test_fetch_provider_data_with_insecure_env_var(self, mock_secho, mock_get):
-        mock_response = mock.MagicMock()
-        mock_response.headers.get.return_value = "100"
-        mock_response.iter_content.return_value = [b'{"test": "data"}']
-        mock_get.return_value = mock_response
-
-        with tempfile.NamedTemporaryFile() as temp_file:
-            cache_file = Path(temp_file.name)
-            result = fetch_provider_data(cache_file)
-
-        assert result == {"test": "data"}
-        mock_get.assert_called_once()
-        
-        assert mock_get.call_args[1]["verify"] is False
-        
-        mock_secho.assert_any_call(
-            "SSL verification disabled via environment variable. "
-            "This is less secure and should only be used in development environments.",
-            fg="yellow"
-        )
-
-    @mock.patch("crewai.cli.provider.requests.get")
-    def test_fetch_provider_data_with_empty_response(self, mock_get):
-        mock_response = mock.MagicMock()
-        mock_response.headers.get.return_value = "0"
-        mock_response.iter_content.return_value = [b'{}']
-        mock_get.return_value = mock_response
-
-        with tempfile.NamedTemporaryFile() as temp_file:
-            cache_file = Path(temp_file.name)
-            result = fetch_provider_data(cache_file)
-
-        assert result == {}
-        mock_get.assert_called_once()
-
-    @mock.patch("crewai.cli.provider.requests.get")
-    @mock.patch("crewai.cli.provider.click.secho")
-    def test_fetch_provider_data_request_exception(self, mock_secho, mock_get):
-        mock_get.side_effect = requests.RequestException("Connection error")
-
-        with tempfile.NamedTemporaryFile() as temp_file:
-            cache_file = Path(temp_file.name)
-            result = fetch_provider_data(cache_file)
-
-        assert result is None
-        mock_get.assert_called_once()
-        
-        mock_secho.assert_any_call(
-            "Error fetching provider data: Connection error",
-            fg="red"
-        )

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@@ -738,7 +738,7 @@ wheels = [
 
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+version = "0.118.0"
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+version = "0.42.2"
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