Update security.md

update policy for better readability
chore: bump version to 0.119.0 and update dependencies (#2778 )
2026-10-19 08:25:09 +02:00 · 2025-05-08 12:20:03 +08:00 · 2025-05-07 17:29:41 -07:00 · 2025-05-07 11:55:42 -07:00 · 2025-05-07 11:30:27 -04:00 · 2025-05-07 11:11:57 -04:00
39 changed files with 11355 additions and 1369 deletions
@@ -1,19 +1,27 @@
-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 Security Vulnerability Reporting Policy

- ## 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
+CrewAI prioritizes the security of our software products, services, and GitHub repositories. To promptly address vulnerabilities, follow these steps for reporting security 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
+### Reporting Process
+Do **not** report vulnerabilities via public GitHub issues.

- 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.
+Email all vulnerability reports directly to:  
+**security@crewai.com**

- At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.
+### 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.
@@ -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 you 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 your agents' connections to models.

 ## How CrewAI Compares

@@ -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. Defautls 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. Defaults 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(Booelan)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.

 ```python Code
 # Save crew logs
@@ -397,6 +397,53 @@ 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.
@@ -27,23 +27,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
  </Card>
 </CardGroup>

-## Setting Up Your LLM
+## Setting up your LLM

-There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
+There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.

 <Tabs>
  <Tab title="1. Environment Variables">
-    The simplest way to get started. Set these variables in your environment:
+    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.

-    ```bash
-    # Required: Your API key for authentication
-    OPENAI_API_KEY=<your-api-key>
+    ```bash .env
+    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...

-    # Optional: Default model selection
-    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
-
-    # Optional: Organization ID (if applicable)
-    OPENAI_ORGANIZATION_ID=<your-org-id>
+    # Be sure to set your API keys here too. See the Provider
+    # section below.
    ```

    <Warning>
@@ -53,13 +49,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
  <Tab title="2. YAML Configuration">
    Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:

-    ```yaml
+    ```yaml agents.yaml {6}
    researcher:
        role: Research Specialist
        goal: Conduct comprehensive research and analysis
        backstory: A dedicated research professional with years of experience
        verbose: true
-        llm: openai/gpt-4o-mini # your model here
+        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
        # (see provider configuration examples below for more)
    ```

@@ -74,23 +70,23 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
  <Tab title="3. Direct Code">
    For maximum flexibility, configure LLMs directly in your Python code:

-    ```python
+    ```python {4,8}
    from crewai import LLM

    # Basic configuration
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...

    # Advanced configuration with detailed parameters
    llm = LLM(
-        model="gpt-4o-mini",
+        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
        temperature=0.7,        # Higher for more creative outputs
-        timeout=120,           # Seconds to wait for response
-        max_tokens=4000,       # Maximum length of response
-        top_p=0.9,            # Nucleus sampling parameter
-        frequency_penalty=0.1, # Reduce repetition
-        presence_penalty=0.1,  # Encourage topic diversity
+        timeout=120,            # Seconds to wait for response
+        max_tokens=4000,        # Maximum length of response
+        top_p=0.9,              # Nucleus sampling parameter
+        frequency_penalty=0.1 , # Reduce repetition
+        presence_penalty=0.1,   # Encourage topic diversity
        response_format={"type": "json"},  # For structured outputs
-        seed=42               # For reproducible results
+        seed=42                 # For reproducible results
    )
    ```

@@ -110,7 +106,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi

 ## Provider Configuration Examples

-
 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.

@@ -383,7 +378,7 @@ In this section, you'll find detailed examples that help you select, configure,
    | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
    | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
    | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
    | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
    | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
    | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
@@ -407,19 +402,19 @@ In this section, you'll find detailed examples that help you select, configure,
  </Accordion>

  <Accordion title="Local NVIDIA NIM Deployed using WSL2">
-    
-    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux). 
-    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services. 
+
+    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
+    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
    Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
-    
+
    Here is a step-by-step guide to setting up a local NVIDIA NIM model:
-    
+
    1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)

    2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)

    3. Configure your crewai local models:
-  
+
    ```python Code
    from crewai.llm import LLM

@@ -441,7 +436,7 @@ In this section, you'll find detailed examples that help you select, configure,
                config=self.agents_config['researcher'], # type: ignore[index]
                llm=local_nvidia_nim_llm
            )
-        
+
        # ...
    ```
  </Accordion>
@@ -637,19 +632,19 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece

    When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
  </Tab>
-  
+
  <Tab title="Event Handling">
    CrewAI emits events for each chunk received during streaming:
-    
+
    ```python
    from crewai import LLM
    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
-    
+
    class MyEventHandler(EventHandler):
        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
            # Process each chunk as it arrives
            print(f"Received chunk: {event.chunk}")
-    
+
    # Register the event handler
    from crewai.utilities.events import crewai_event_bus
    crewai_event_bus.register_handler(MyEventHandler())
@@ -785,7 +780,7 @@ Learn how to get the most out of your LLM configuration:
    <Tip>
      Use larger context models for extensive tasks
    </Tip>
-    
+
    ```python
    # Large context model
    llm = LLM(model="openai/gpt-4o")  # 128K tokens
@@ -35,7 +35,8 @@ 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 OpenAI API key in your environment variables
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python

 ## Step 1: Create a New CrewAI Project
@@ -92,7 +93,8 @@ 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:
+Let's modify the `agents.yaml` file to define these specialized agents. Be sure
+to set `llm` to the provider you are using.

 ```yaml
 # src/research_crew/config/agents.yaml
@@ -107,7 +109,7 @@ 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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...

 analyst:
  role: >
@@ -120,7 +122,7 @@ 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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 ```

 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.
@@ -282,12 +284,12 @@ This script prepares the environment, specifies our research topic, and kicks of

 Create a `.env` file in your project root with your API keys:

-```
-OPENAI_API_KEY=your_openai_api_key
+```sh
 SERPER_API_KEY=your_serper_api_key
+# Add your provider's API key here too.
 ```

-You can get a Serper API key from [Serper.dev](https://serper.dev/).
+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/).

 ## Step 8: Install Dependencies

@@ -45,7 +45,8 @@ 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 OpenAI API key in your environment variables
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python

 ## Step 1: Create a New CrewAI Flow Project
@@ -107,6 +108,8 @@ 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:
@@ -119,7 +122,7 @@ 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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...

 content_reviewer:
  role: >
@@ -132,7 +135,7 @@ 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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 ```

 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.
@@ -441,10 +444,15 @@ 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:
+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.

-```
+```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
@@ -547,7 +555,10 @@ 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="openai/gpt-4o-mini", response_format=GuideOutline)
+llm = LLM(
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    response_format=GuideOutline
+)
 response = llm.call(messages=messages)
 ```

@@ -71,6 +71,10 @@ If you haven't installed `uv` yet, follow **step 1** to quickly get it set up on
            ```
          </Warning>

+          <Warning>
+            If you encounter the `chroma-hnswlib==0.7.6` build error (`fatal error C1083: Cannot open include file: 'float.h'`) on Windows, install (Visual Studio Build Tools)[https://visualstudio.microsoft.com/downloads/] with *Desktop development with C++*.
+          </Warning>
+
      - To verify that `crewai` is installed, run:
        ```shell
        uv tool list
@@ -180,8 +180,9 @@ 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:
@@ -317,7 +318,7 @@ email_summarizer:
      Summarize emails into a concise and clear summary
    backstory: >
      You will create a 5 bullet point summary of the report
-    llm: openai/gpt-4o
+    llm: provider/model-id  # Add your choice of model here
 ```

 <Tip>
@@ -22,7 +22,7 @@ streamlining the process of finding specific information within large document c
 Install the crewai_tools package by running the following command in your terminal:

 ```shell
-pip install 'crewai[tools]'
+uv pip install docx2txt 'crewai[tools]'
 ```

 ## Example
@@ -76,4 +76,4 @@ tool = DOCXSearchTool(
        ),
    )
 )
-```
+```
@@ -8,10 +8,10 @@ icon: language

 ## Description

-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 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 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 proivdes the ability for the Agent to update the database based on its goal.
+Along with that provides 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
-```
+```
@@ -143,12 +143,30 @@ config = {
        "config": {
            "model": "text-embedding-ada-002"
        }
+    },
+    "vectordb": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "my-collection",
+            "cloud_id": "deployment-name:xxxx",
+            "api_key": "your-key",
+            "verify_certs": False
+        }
+    },
+    "chunker": {
+        "chunk_size": 400,
+        "chunk_overlap": 100,
+        "length_function": "len",
+        "min_chunk_size": 0
    }
 }

 rag_tool = RagTool(config=config, summarize=True)
 ```

-## Conclusion
+The internal RAG tool utilizes the Embedchain adapter, allowing you to pass any configuration options that are supported by Embedchain.
+You can refer to the [Embedchain documentation](https://docs.embedchain.ai/components/introduction) for details.
+Make sure to review the configuration options available in the .yaml file.

+## Conclusion
 The `RagTool` provides a powerful way to create and query knowledge bases from various data sources. By leveraging Retrieval-Augmented Generation, it enables agents to access and retrieve relevant information efficiently, enhancing their ability to provide accurate and contextually appropriate responses.
@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.118.0"
+version = "0.119.0"
 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"
@@ -11,7 +11,7 @@ dependencies = [
    # Core Dependencies
    "pydantic>=2.4.2",
    "openai>=1.13.3",
-    "litellm==1.67.1",
+    "litellm==1.68.0",
    "instructor>=1.3.3",
    # Text Processing
    "pdfplumber>=0.11.4",
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
-tools = ["crewai-tools~=0.42.2"]
+tools = ["crewai-tools~=0.44.0"]
 embeddings = [
    "tiktoken~=0.7.0"
 ]
@@ -17,7 +17,7 @@ warnings.filterwarnings(
    category=UserWarning,
    module="pydantic.main",
 )
-__version__ = "0.118.0"
+__version__ = "0.119.0"
 __all__ = [
    "Agent",
    "Crew",
@@ -31,6 +31,14 @@ 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
@@ -122,6 +130,10 @@ 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):
@@ -185,7 +197,7 @@ 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.

@@ -245,27 +257,65 @@ class Agent(BaseAgent):
        knowledge_config = (
            self.knowledge_config.model_dump() if self.knowledge_config else {}
        )
-        if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query(
-                [task.prompt()], **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(
-                [task.prompt()], **knowledge_config
+        if self.knowledge:
+            crewai_event_bus.emit(
+                self,
+                event=KnowledgeRetrievalStartedEvent(
+                    agent=self,
+                ),
            )
-            if knowledge_snippets:
-                self.crew_knowledge_context = extract_knowledge_context(
-                    knowledge_snippets
+            try:
+                self.knowledge_search_query = self._get_knowledge_search_query(
+                    task_prompt
+                )
+                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
+
+                    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_context:
-                    task_prompt += self.crew_knowledge_context

        tools = tools or self.tools or []
        self.create_agent_executor(tools=tools, task=task)
@@ -288,12 +338,19 @@ 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(
@@ -345,54 +402,46 @@ 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.
        """
@@ -560,6 +609,61 @@ 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]]],
@@ -4,7 +4,7 @@ import click


 # Be mindful about changing this.
-# on some enviorments we don't use this command but instead uv sync directly
+# on some environments we don't use this command but instead uv sync directly
 # so if you expect this to support more things you will need to replicate it there
 # ask @joaomdmoura if you are unsure
 def install_crew(proxy_options: list[str]) -> None:
@@ -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]>=0.118.0,<1.0.0"
+    "crewai[tools]>=0.119.0,<1.0.0"
 ]

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

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

 [tool.crewai]
@@ -2,6 +2,7 @@ from __future__ import annotations

 import asyncio
 import json
+import logging
 import os
 import platform
 import warnings
@@ -14,6 +15,8 @@ from crewai.telemetry.constants import (
    CREWAI_TELEMETRY_SERVICE_NAME,
 )

+logger = logging.getLogger(__name__)
+

@contextmanager
 def suppress_warnings():
@@ -28,7 +31,10 @@ from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
 )
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
-from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
+from opentelemetry.sdk.trace.export import (  # noqa: E402
+    BatchSpanProcessor,
+    SpanExportResult,
+)
 from opentelemetry.trace import Span, Status, StatusCode  # noqa: E402

 if TYPE_CHECKING:
@@ -36,6 +42,15 @@ if TYPE_CHECKING:
    from crewai.task import Task


+class SafeOTLPSpanExporter(OTLPSpanExporter):
+    def export(self, spans) -> SpanExportResult:
+        try:
+            return super().export(spans)
+        except Exception as e:
+            logger.error(e)
+            return SpanExportResult.FAILURE
+
+
 class Telemetry:
    """A class to handle anonymous telemetry for the crewai package.

@@ -64,7 +79,7 @@ class Telemetry:
                self.provider = TracerProvider(resource=self.resource)

            processor = BatchSpanProcessor(
-                OTLPSpanExporter(
+                SafeOTLPSpanExporter(
                    endpoint=f"{CREWAI_TELEMETRY_BASE_URL}/v1/traces",
                    timeout=30,
                )
@@ -27,7 +27,9 @@
    "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."
+    "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."
  },
  "errors": {
    "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
@@ -8,6 +8,14 @@ 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,
@@ -57,6 +65,8 @@ 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:
@@ -342,5 +352,59 @@ 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()
@@ -0,0 +1,56 @@
+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
@@ -783,3 +783,202 @@ 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")
@@ -9,7 +9,6 @@ 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
@@ -73,6 +72,7 @@ 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(
@@ -88,6 +88,7 @@ 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(
@@ -119,7 +120,8 @@ 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"
@@ -1630,13 +1632,10 @@ 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.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
+    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}]
+        mock_knowledge_instance.search.return_value = [{"content": content}]

        agent = Agent(
            role="Information Agent",
@@ -1690,7 +1689,7 @@ 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(
-                [task.prompt()],
+                ["Brandon's favorite color"],
                **knowledge_config.model_dump(),
            )

@@ -1727,7 +1726,7 @@ 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(
-                [task.prompt()],
+                ["Brandon's favorite color"],
                **knowledge_config.model_dump(),
            )

@@ -1737,9 +1736,7 @@ 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.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
+    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}]
@@ -1803,6 +1800,40 @@ 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."""
@@ -1940,3 +1971,57 @@ 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
+                    ),
+                },
+            ]
+        )
@@ -16,7 +16,7 @@ interactions:
      host:
      - api.openai.com
      user-agent:
-      - OpenAI/Python 1.52.1
+      - OpenAI/Python 1.68.2
      x-stainless-arch:
      - arm64
      x-stainless-async:
@@ -26,25 +26,132 @@ interactions:
      x-stainless-os:
      - MacOS
      x-stainless-package-version:
-      - 1.52.1
+      - 1.68.2
+      x-stainless-read-timeout:
+      - '600'
      x-stainless-retry-count:
      - '0'
      x-stainless-runtime:
      - CPython
      x-stainless-runtime-version:
-      - 3.11.9
+      - 3.12.9
    method: POST
    uri: https://api.openai.com/v1/embeddings
  response:
-    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\":
-      \"embedding\",\n      \"index\": 0,\n      \"embedding\": 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truncated
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+    body:
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+version: 1
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+import os
+from unittest.mock import patch
+
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.telemetry import Telemetry
+
+
+@pytest.mark.parametrize(
+    "env_var,value,expected_ready",
+    [
+        ("OTEL_SDK_DISABLED", "true", False),
+        ("OTEL_SDK_DISABLED", "TRUE", False),
+        ("CREWAI_DISABLE_TELEMETRY", "true", False),
+        ("CREWAI_DISABLE_TELEMETRY", "TRUE", False),
+        ("OTEL_SDK_DISABLED", "false", True),
+        ("CREWAI_DISABLE_TELEMETRY", "false", True),
+    ],
+)
+def test_telemetry_environment_variables(env_var, value, expected_ready):
+    """Test telemetry state with different environment variable configurations."""
+    with patch.dict(os.environ, {env_var: value}):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is expected_ready
+
+
+def test_telemetry_enabled_by_default():
+    """Test that telemetry is enabled by default."""
+    with patch.dict(os.environ, {}, clear=True):
+        with patch("crewai.telemetry.telemetry.TracerProvider"):
+            telemetry = Telemetry()
+            assert telemetry.ready is True
+
+
+from opentelemetry import trace
+
+
+@patch("crewai.telemetry.telemetry.logger.error")
+@patch(
+    "opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter.export",
+    side_effect=Exception("Test exception"),
+)
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_telemetry_fails_due_connect_timeout(export_mock, logger_mock):
+    error = Exception("Test exception")
+    export_mock.side_effect = error
+
+    tracer = trace.get_tracer(__name__)
+    with tracer.start_as_current_span("test-span"):
+        agent = Agent(
+            role="agent",
+            llm="gpt-4o-mini",
+            goal="Just say hi",
+            backstory="You are a helpful assistant that just says hi",
+        )
+        task = Task(
+            description="Just say hi",
+            expected_output="hi",
+            agent=agent,
+        )
+        crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
+        crew.kickoff()
+
+    trace.get_tracer_provider().force_flush()
+
+    export_mock.assert_called_once()
+    logger_mock.assert_called_once_with(error)
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+        until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer
+        of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
+        OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS,
+        WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY
+        KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF
+        TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
+        YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING
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+        of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY
+        THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY,
+        OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY
+        INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES,
+        EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
+        ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n    a. No trademark
+        licenses are granted under this Agreement, and in connection with the Llama
+        Materials, \\nneither Meta nor Licensee may use any name or mark owned by
+        or associated with the other or any of its affiliates, \\nexcept as required
+        for reasonable and customary use in describing and redistributing the Llama
+        Materials or as \\nset forth in this Section 5(a). Meta hereby grants you
+        a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+        \\nto comply with the last sentence of Section 1.b.i. You will comply with
+        Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+        All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+        of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
+        derivatives made by or for Meta, with respect to any\\n    derivative works
+        and modifications of the Llama Materials that are made by you, as between
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+        and modifications.\\n\\n    c. If you institute litigation or other proceedings
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+        in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results,
+        or any portion\\n    of any of the foregoing, constitutes infringement of
+        intellectual property or other rights owned or licensable\\n    by you, then
+        any licenses granted to you under this Agreement shall terminate as of the
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+        arising out of or related to your use or distribution of the Llama Materials.\\n\\n6.
+        Term and Termination. The term of this Agreement will commence upon your acceptance
+        of this Agreement or access\\nto the Llama Materials and will continue in
+        full force and effect until terminated in accordance with the terms\\nand
+        conditions herein. Meta may terminate this Agreement if you are in breach
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+        3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D). The
+        most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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+        Sexual solicitation\\n        6. Any other criminal activity\\n    1. Engage
+        in, promote, incite, or facilitate the harassment, abuse, threatening, or
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+        incite, or facilitate discrimination or other unlawful or harmful conduct
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+        Engage in any action, or facilitate any action, to intentionally circumvent
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+        to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
+        Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+        Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
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+        Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
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+        September 25, 2024\\n\\n\u201CAgreement\u201D means the terms and conditions
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+        that has legal authority\\nto bind your employer or such other person or entity
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+        Documentation (and \\nany portion thereof) made available under this Agreement.\\n\\n\u201CMeta\u201D
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+        agree to be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+        \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+        and royalty-free limited license under Meta\u2019s intellectual property or
+        other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+        distribute, copy, create derivative works \\nof, and make modifications to
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+        If you distribute or make available the Llama Materials (or any derivative
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+        contains any of them, you shall (A) provide\\na copy of this Agreement with
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+        a related website, user interface, blogpost, about page, or product documentation.
+        If you use the\\nLlama Materials or any outputs or results of the Llama Materials
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+        Materials, or any derivative works thereof, from a Licensee as part\\nof an
+        integrated end user product, then Section 2 of this Agreement will not apply
+        to you. \\n\\n        iii. You must retain in all copies of the Llama Materials
+        that you distribute the \\nfollowing attribution notice within a \u201CNotice\u201D
+        text file distributed as a part of such copies: \\n\u201CLlama 3.2 is licensed
+        under the Llama 3.2 Community License, Copyright \xA9 Meta Platforms,\\nInc.
+        All Rights Reserved.\u201D\\n\\n        iv. Your use of the Llama Materials
+        must comply with applicable laws and regulations\\n(including trade compliance
+        laws and regulations) and adhere to the Acceptable Use Policy for\\nthe Llama
+        Materials (available at https://www.llama.com/llama3_2/use-policy), which
+        is hereby \\nincorporated by reference into this Agreement.\\n  \\n2. Additional
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+        users in the preceding calendar month, you must request \\na license from
+        Meta, which Meta may grant to you in its sole discretion, and you are not
+        authorized to\\nexercise any of the rights under this Agreement unless or
+        until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer
+        of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
+        OUTPUT AND \\nRESULTS THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS,
+        WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY
+        KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF
+        TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
+        YOU ARE SOLELY RESPONSIBLE\\nFOR DETERMINING THE APPROPRIATENESS OF USING
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+        of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY
+        THEORY OF LIABILITY, \\nWHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY,
+        OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, \\nFOR ANY LOST PROFITS OR ANY
+        INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES,
+        EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF
+        ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n    a. No trademark
+        licenses are granted under this Agreement, and in connection with the Llama
+        Materials, \\nneither Meta nor Licensee may use any name or mark owned by
+        or associated with the other or any of its affiliates, \\nexcept as required
+        for reasonable and customary use in describing and redistributing the Llama
+        Materials or as \\nset forth in this Section 5(a). Meta hereby grants you
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+        \\nto comply with the last sentence of Section 1.b.i. You will comply with
+        Meta\u2019s brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+        All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+        of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
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+        \\\"**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed to promoting
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+        The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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+        \       2. Exploitation or harm to children, including the solicitation, creation,
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+        and sexual violence\\n        4. The illegal distribution of information or
+        materials to minors, including obscene materials, or failure to employ legally
+        required age-gating in connection with such information or materials.\\n        5.
+        Sexual solicitation\\n        6. Any other criminal activity\\n    1. Engage
+        in, promote, incite, or facilitate the harassment, abuse, threatening, or
+        bullying of individuals or groups of individuals\\n    2. Engage in, promote,
+        incite, or facilitate discrimination or other unlawful or harmful conduct
+        in the provision of employment, employment benefits, credit, housing, other
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+        the unauthorized or unlicensed practice of any profession including, but not
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+        \   4. Collect, process, disclose, generate, or infer private or sensitive
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+        identity, health, or demographic information, unless you have obtained the
+        right to do so in accordance with applicable law\\n    5. Engage in or facilitate
+        any action or generate any content that infringes, misappropriates, or otherwise
+        violates any third-party rights, including the outputs or results of any products
+        or services using the Llama Materials\\n    6. Create, generate, or facilitate
+        the creation of malicious code, malware, computer viruses or do anything else
+        that could disable, overburden, interfere with or impair the proper working,
+        integrity, operation or appearance of a website or computer system\\n    7.
+        Engage in any action, or facilitate any action, to intentionally circumvent
+        or remove usage restrictions or other safety measures, or to enable functionality
+        disabled by Meta\\n2. Engage in, promote, incite, facilitate, or assist in
+        the planning or development of activities that present a risk of death or
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+        \   8. Military, warfare, nuclear industries or applications, espionage, use
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+        or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical
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+        technologies, or heavy machinery\\n    12. Self-harm or harm to others, including
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+        or furthering defamatory content, including the creation of defamatory statements,
+        images, or other content\\n    16. Generating, promoting, or further distributing
+        spam\\n    17. Impersonating another individual without consent, authorization,
+        or legal right\\n    18. Representing that the use of Llama 3.2 or outputs
+        are human-generated\\n    19. Generating or facilitating false online engagement,
+        including fake reviews and other means of fake online engagement\\n4. Fail
+        to appropriately disclose to end users any known dangers of your AI system\\n5.
+        Interact with third party tools, models, or software designed to generate
+        unlawful content or engage in unlawful or harmful conduct and/or represent
+        that the outputs of such tools, models, or software are associated with Meta
+        or Llama 3.2\\n\\nWith respect to any multimodal models included in Llama
+        3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License
+        Agreement are not being granted to you if you are an individual domiciled
+        in, or a company with a principal place of business in, the European Union.
+        This restriction does not apply to end users of a product or service that
+        incorporates any such multimodal models.\\n\\nPlease report any violation
+        of this Policy, software \u201Cbug,\u201D or other problems that could lead
+        to a violation of this Policy through one of the following means:\\n\\n\\n\\n*
+        Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues\\u0026h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\\n*
+        Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\\n*
+        Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\\n*
+        Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
+        3.2: LlamaUseReport@meta.com\\\"\\n\",\"parameters\":\"stop                           \\\"\\u003c|start_header_id|\\u003e\\\"\\nstop
+        \                          \\\"\\u003c|end_header_id|\\u003e\\\"\\nstop                           \\\"\\u003c|eot_id|\\u003e\\\"\",\"template\":\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
+        Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end
+        }}\\n{{- if .Tools }}When you receive a tool call response, use the output
+        to format an answer to the orginal user question.\\n\\nYou are a helpful assistant
+        with tool calling capabilities.\\n{{- end }}\\u003c|eot_id|\\u003e\\n{{- range
+        $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{-
+        if eq .Role \\\"user\\\" }}\\u003c|start_header_id|\\u003euser\\u003c|end_header_id|\\u003e\\n{{-
+        if and $.Tools $last }}\\n\\nGiven the following functions, please respond
+        with a JSON for a function call with its proper arguments that best answers
+        the given prompt.\\n\\nRespond in the format {\\\"name\\\": function name,
+        \\\"parameters\\\": dictionary of argument name and its value}. Do not use
+        variables.\\n\\n{{ range $.Tools }}\\n{{- . }}\\n{{ end }}\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{-
+        else }}\\n\\n{{ .Content }}\\u003c|eot_id|\\u003e\\n{{- end }}{{ if $last
+        }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
+        end }}\\n{{- else if eq .Role \\\"assistant\\\" }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n{{-
+        if .ToolCalls }}\\n{{ range .ToolCalls }}\\n{\\\"name\\\": \\\"{{ .Function.Name
+        }}\\\", \\\"parameters\\\": {{ .Function.Arguments }}}{{ end }}\\n{{- else
+        }}\\n\\n{{ .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{
+        end }}\\n{{- else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{
+        .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
+        end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"tensors\":[{\"name\":\"rope_freqs.weight\",\"type\":\"F32\",\"shape\":[64]},{\"name\":\"token_embd.weight\",\"type\":\"Q4_K_S\",\"shape\":[3072,128256]},{\"name\":\"blk.0.attn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.0.ffn_down.weight\",\"type\":\"Q4_K_S\",\"shape\":[8192,3072]},{\"name\":\"blk.0.ffn_gate.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.0.ffn_up.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.0.ffn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.0.attn_k.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.0.attn_output.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.0.attn_q.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.0.attn_v.weight\",\"type\":\"Q4_K_S\",\"shape\":[3072,1024]},{\"name\":\"blk.1.attn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.1.ffn_down.weight\",\"type\":\"Q4_K_S\",\"shape\":[8192,3072]},{\"name\":\"blk.1.ffn_gate.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.1.ffn_up.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.1.ffn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.1.attn_k.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.1.attn_output.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.1.attn_q.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.1.attn_v.weight\",\"type\":\"Q4_K_S\",\"shape\":[3072,1024]},{\"name\":\"blk.10.attn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.10.ffn_down.weight\",\"type\":\"Q4_K_S\",\"shape\":[8192,3072]},{\"name\":\"blk.10.ffn_gate.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.10.ffn_up.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.10.ffn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.10.attn_k.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.10.attn_output.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.10.attn_q.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.10.attn_v.weight\",\"type\":\"Q4_K_S\",\"shape\":[3072,1024]},{\"name\":\"blk.11.attn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.11.ffn_down.weight\",\"type\":\"Q3_K_M\",\"shape\":[8192,3072]},{\"name\":\"blk.11.ffn_gate.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.11.ffn_up.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.11.ffn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.11.attn_k.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.11.attn_output.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.11.attn_q.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.11.attn_v.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.12.attn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.12.ffn_down.weight\",\"type\":\"Q3_K_M\",\"shape\":[8192,3072]},{\"name\":\"blk.12.ffn_gate.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.12.ffn_up.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,8192]},{\"name\":\"blk.12.ffn_norm.weight\",\"type\":\"F32\",\"shape\":[3072]},{\"name\":\"blk.12.attn_k.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.12.attn_output.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.12.attn_q.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,3072]},{\"name\":\"blk.12.attn_v.weight\",\"type\":\"Q3_K_M\",\"shape\":[3072,1024]},{\"name\":\"blk.13.attn_norm.weight\",\"type\Line truncated
+    headers:
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Wed, 07 May 2025 01:18:39 GMT
+      Transfer-Encoding:
+      - chunked
+    status:
+      code: 200
+      message: OK
 version: 1
@@ -357,14 +357,7 @@ def test_convert_with_instructions():
    assert output.age == 30


-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_2_model():
    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
    sample_text = "Name: Alice Llama, Age: 30"
@@ -381,8 +374,7 @@ def test_converter_with_llama3_2_model():
    assert output.age == 30


-@skip_external_api
-@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_llama3_1_model():
    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
    sample_text = "Name: Alice Llama, Age: 30"
@@ -399,13 +391,6 @@ def test_converter_with_llama3_1_model():
    assert output.age == 30


-# Skip tests that call external APIs when running in CI/CD
-skip_external_api = pytest.mark.skipif(
-    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
-)
-
-
-@skip_external_api
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_nested_model():
    llm = LLM(model="gpt-4o-mini")
@@ -738,7 +738,7 @@ wheels = [

 [[package]]
 name = "crewai"
-version = "0.118.0"
+version = "0.119.0"
 source = { editable = "." }
 dependencies = [
    { name = "appdirs" },
@@ -828,14 +828,14 @@ requires-dist = [
    { name = "blinker", specifier = ">=1.9.0" },
    { name = "chromadb", specifier = ">=0.5.23" },
    { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.42.2" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.44.0" },
    { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
    { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
    { name = "instructor", specifier = ">=1.3.3" },
    { name = "json-repair", specifier = ">=0.25.2" },
    { name = "json5", specifier = ">=0.10.0" },
    { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.67.1" },
+    { name = "litellm", specifier = "==1.68.0" },
    { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
    { name = "openai", specifier = ">=1.13.3" },
    { name = "openpyxl", specifier = ">=3.1.5" },
@@ -879,7 +879,7 @@ dev = [

 [[package]]
 name = "crewai-tools"
-version = "0.42.2"
+version = "0.44.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "chromadb" },
@@ -894,9 +894,9 @@ dependencies = [
    { name = "pytube" },
    { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/17/34/9e63e2db53d8f5c30353f271a3240687a48e55204bbd176a057c0b7658c8/crewai_tools-0.42.2.tar.gz", hash = "sha256:69365ffb168cccfea970e09b308905aa5007cfec60024d731ffac1362a0153c0", size = 754967 }
+sdist = { url = "https://files.pythonhosted.org/packages/b8/1f/2977dc72628c1225bf5788ae22a65e5a53df384d19b197646d2c4760684e/crewai_tools-0.44.0.tar.gz", hash = "sha256:44e0c26079396503a326efdd9ff34bf369d410cbf95c362cc523db65b18f3c3a", size = 892004 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4e/43/0f70b95350084e5cb1e1d74e9acb9e18a89ba675b1d579c787c2662baba7/crewai_tools-0.42.2-py3-none-any.whl", hash = "sha256:13727fb68f0efefd21edeb281be3d66ff2f5a3b5029d4e6adef388b11fd5846a", size = 583933 },
+    { url = "https://files.pythonhosted.org/packages/ba/80/b91aa837d06edbb472445ea3c92d7619518894fd3049d480e5fffbf0c21b/crewai_tools-0.44.0-py3-none-any.whl", hash = "sha256:119e2365fe66ee16e18a5e8e222994b19f76bafcc8c1bb87f61609c1e39b2463", size = 583462 },
 ]

 [[package]]
@@ -2387,7 +2387,7 @@ wheels = [

 [[package]]
 name = "litellm"
-version = "1.67.1"
+version = "1.68.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohttp" },
@@ -2402,9 +2402,9 @@ dependencies = [
    { name = "tiktoken" },
    { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/54/a4/bb3e9ae59e5a9857443448de7c04752630dc84cddcbd8cee037c0976f44f/litellm-1.67.1.tar.gz", hash = "sha256:78eab1bd3d759ec13aa4a05864356a4a4725634e78501db609d451bf72150ee7", size = 7242044 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/22/138545b646303ca3f4841b69613c697b9d696322a1386083bb70bcbba60b/litellm-1.68.0.tar.gz", hash = "sha256:9fb24643db84dfda339b64bafca505a2eef857477afbc6e98fb56512c24dbbfa", size = 7314051 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/86/c14d3c24ae13c08296d068e6f79fd4bd17a0a07bddbda94990b87c35d20e/litellm-1.67.1-py3-none-any.whl", hash = "sha256:8fff5b2a16b63bb594b94d6c071ad0f27d3d8cd4348bd5acea2fd40c8e0c11e8", size = 7607266 },
+    { url = "https://files.pythonhosted.org/packages/10/af/1e344bc8aee41445272e677d802b774b1f8b34bdc3bb5697ba30f0fb5d52/litellm-1.68.0-py3-none-any.whl", hash = "sha256:3bca38848b1a5236b11aa6b70afa4393b60880198c939e582273f51a542d4759", size = 7684460 },
 ]

 [[package]]
Author	SHA1	Message	Date
Rip&Tear	80837b145f	Update security.md update policy for better readability	2025-05-08 12:20:03 +08:00
Lorenze Jay	516d45deaa	chore: bump version to 0.119.0 and update dependencies (#2778 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details This commit updates the project version to 0.119.0 and modifies the required version of the `crewai-tools` dependency to 0.44.0 across various configuration files. Additionally, the version number is reflected in the `__init__.py` file and the CLI templates for crew, flow, and tool projects.	2025-05-07 17:29:41 -07:00
Lorenze Jay	7ad51d9d05	feat: implement knowledge retrieval events in Agent (#2727 ) Notify Downstream / notify-downstream (push) Has been cancelled Details * feat: implement knowledge retrieval events in Agent This commit introduces a series of knowledge retrieval events in the Agent class, enhancing its ability to handle knowledge queries. New events include KnowledgeRetrievalStartedEvent, KnowledgeRetrievalCompletedEvent, KnowledgeQueryGeneratedEvent, KnowledgeQueryFailedEvent, and KnowledgeSearchQueryCompletedEvent. The Agent now emits these events during knowledge retrieval processes, allowing for better tracking and handling of knowledge queries. Additionally, the console formatter has been updated to handle these new events, providing visual feedback during knowledge retrieval operations. * refactor: update knowledge query handling in Agent This commit refines the knowledge query processing in the Agent class by renaming variables for clarity and optimizing the query rewriting logic. The system prompt has been updated in the translation file to enhance clarity and context for the query rewriting process. These changes aim to improve the overall readability and maintainability of the code. * fix: add missing newline at end of en.json file * fix broken tests * refactor: rename knowledge query events and enhance retrieval handling This commit renames the KnowledgeQueryGeneratedEvent to KnowledgeQueryStartedEvent to better reflect its purpose. It also updates the event handling in the EventListener and ConsoleFormatter classes to accommodate the new event structure. Additionally, the retrieval knowledge is now included in the KnowledgeRetrievalCompletedEvent, improving the overall knowledge retrieval process. * docs for transparancy * refactor: improve error handling in knowledge query processing This commit refactors the knowledge query handling in the Agent class by changing the order of checks for LLM compatibility. It now logs a warning and emits a failure event if the LLM is not an instance of BaseLLM before attempting to call the LLM. Additionally, the task_prompt attribute has been removed from the KnowledgeQueryFailedEvent, simplifying the event structure. * test: add unit test for knowledge search query and VCR cassette This commit introduces a new test, `test_get_knowledge_search_query`, to verify that the `_get_knowledge_search_query` method in the Agent class correctly interacts with the LLM using the appropriate prompts. Additionally, a VCR cassette is added to record the interactions with the OpenAI API for this test, ensuring consistent and reliable test results.	2025-05-07 11:55:42 -07:00
Mark McDonaldandTony Kipkemboi	e3887ae36e	Used model-agnostic examples in quickstart/firsts. (#2773 ) Updated prereqs and setup steps to point to the now-more-model-agnostic LLM setup guide, and updated the relevant text to go with it. Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-07 11:30:27 -04:00
omahsandTony Kipkemboi	e23bc2aaa7	Fix typos (#2774 ) * fix typos * fix typo * fix typos --------- Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-07 11:11:57 -04:00
Lucas Gomide	7fc405408e	test: fix llama converter tests to remove skip_external_api (#2770 )	2025-05-07 08:33:41 -04:00
Tony Kipkemboi	cac06adc6c	docs: update docxsearchtool.mdx (#2767 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details - add `docx2txt` as a dependency requirement for the tool	2025-05-06 17:14:05 -04:00
leopardracerandLucas Gomide	c8ec03424a	Fix typos in documentation and configuration files (#2712 ) Notify Downstream / notify-downstream (push) Has been cancelled Details * Update test_lite_agent_structured_output.yaml * Update install_crew.py * Update llms.mdx --------- Co-authored-by: Lucas Gomide <lucaslg200@gmail.com>	2025-05-06 15:07:57 -04:00
Henrique BrancoandTony Kipkemboi	bfea85d22c	docs: added Windows bug solving to docs (#2764 ) Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-06 09:55:05 -04:00
Mark McDonaldandTony Kipkemboi	836e9fc545	Removes model provider defaults from LLM Setup (#2766 ) This removes any specific model from the "Setting up your LLM" guide, but provides examples for the top-3 providers. This section also conflated "model selection" with "model configuration", where configuration is provider-specific, so I've focused this first section on just model selection, deferring the config to the "provider" section that follows. Co-authored-by: Tony Kipkemboi <iamtonykipkemboi@gmail.com>	2025-05-06 09:27:14 -04:00
Vidit Ostwal Lucas Gomide Lorenze Jay	c3726092fd	Added Advance Configuration Docs for Rag Tool (#2713 ) * Added Advance Configuration Docs for Rag Tool * Re-run test cases * Change doc * prepping new version (#2733) --------- Co-authored-by: Lucas Gomide <lucaslg200@gmail.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>	2025-05-06 09:07:52 -04:00
Lucas Gomide	dabf02a90d	build(LiteLLM): upgrade LiteLLM version (#2757 ) Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details	2025-05-05 17:07:29 -04:00
Lucas Gomide	2912c93d77	feat: prevent crash once Telemetry is not available (#2758 ) * feat: prevent crash once Telemetry is not available * tests: adding missing cassettes	2025-05-05 15:22:32 -04:00