test: fix llama converter tests to remove skip_external_api

.github/security.md

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

.github/workflows/linter.yml

@@ -5,29 +5,12 @@ on: [pull_request]
 jobs:
   lint:
     runs-on: ubuntu-latest
-    env:
-      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
-      - name: Fetch Target Branch
-        run: git fetch origin $TARGET_BRANCH --depth=1
-
-      - name: Install Ruff
-        run: pip install ruff
-
-      - name: Get Changed Python Files
-        id: changed-files
+      - name: Install Requirements
         run: |
-          merge_base=$(git merge-base origin/"$TARGET_BRANCH" HEAD)
-          changed_files=$(git diff --name-only --diff-filter=ACMRTUB "$merge_base" | grep '\.py$' || true)
-          echo "files<<EOF" >> $GITHUB_OUTPUT
-          echo "$changed_files" >> $GITHUB_OUTPUT
-          echo "EOF" >> $GITHUB_OUTPUT
+          pip install ruff
 
-      - name: Run Ruff on Changed Files
-        if: ${{ steps.changed-files.outputs.files != '' }}
-        run: |
-          echo "${{ steps.changed-files.outputs.files }}" | tr " " "\n" | xargs -I{} ruff check "{}"
+      - name: Run Ruff Linter
+        run: ruff check

.ruff.toml

@@ -2,3 +2,8 @@ exclude = [
     "templates",
     "__init__.py",
 ]
+
+[lint]
+select = [
+    "I", # isort rules
+]

README.md

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

docs/concepts/agents.mdx

@@ -21,7 +21,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
 CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
-![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+![Visual Agent Builder Screenshot](../images/enterprise/crew-studio-quickstart)
 
 The Visual Agent Builder enables:
 - Intuitive agent configuration with form-based interfaces
@@ -58,8 +58,6 @@ The Visual Agent Builder enables:
 | **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
-| **Inject Date** _(optional)_           | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
-| **Date Format** _(optional)_           | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                                |
 
 ## Creating Agents
 
@@ -228,18 +226,6 @@ custom_agent = Agent(
 )
 ```
 
-#### Date-Aware Agent
-```python Code
-date_aware_agent = Agent(
-    role="Market Analyst",
-    goal="Track market movements with precise date references",
-    backstory="Expert in time-sensitive financial analysis and reporting",
-    inject_date=True,  # Automatically inject current date into tasks
-    date_format="%B %d, %Y",  # Format as "May 21, 2025"
-    verbose=True
-)
-```
-
 ### Parameter Details
 
 #### Critical Parameters
@@ -346,12 +332,6 @@ When `memory` is enabled, the agent will maintain context across multiple intera
   - Main `llm` for complex reasoning
   - `function_calling_llm` for efficient tool usage
 
-### Date Awareness
-- Use `inject_date: true` to provide agents with current date awareness
-- Customize the date format with `date_format` using standard Python datetime format codes
-- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
-- Invalid date formats will be logged as warnings and will not modify the task description
-
 ### Model Compatibility
 - Set `use_system_prompt: false` for older models that don't support system messages
 - Ensure your chosen `llm` supports the features you need (like function calling)

docs/concepts/cli.mdx

@@ -4,7 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
-## Overview
+# CrewAI CLI Documentation
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 
@@ -110,8 +110,6 @@ crewai reset-memories [OPTIONS]
 - `-s, --short`: Reset SHORT TERM memory
 - `-e, --entities`: Reset ENTITIES memory
 - `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
-- `-kn, --knowledge`: Reset KNOWLEDGE storage
-- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
 - `-a, --all`: Reset ALL memories
 
 Example:

docs/concepts/collaboration.mdx

@@ -4,7 +4,7 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
 icon: screen-users
 ---
 
-## Overview    
+## Collaboration Fundamentals      
 
 Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
 

docs/concepts/crews.mdx

@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 icon: people-group
 ---
 
-## Overview
+## What is a Crew?
 
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
@@ -27,7 +27,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -117,12 +117,6 @@ class YourCrewName:
         )
 ```
 
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={"any": "input here"})
-```
-
 <Note>
 Tasks will be executed in the order they are defined.
 </Note>
@@ -190,11 +184,6 @@ class YourCrewName:
             verbose=True
         )
 ```
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={})
-```
 
 In this example:
 
@@ -257,7 +246,7 @@ print(f"Token Usage: {crew_output.token_usage}")
 You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
 In case of `True(Boolean)` will save as `logs.txt`.
 
-In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
 
 ```python Code
 # Save crew logs

docs/concepts/event-listener.mdx

@@ -4,7 +4,7 @@ description: 'Tap into CrewAI events to build custom integrations and monitoring
 icon: spinner
 ---
 
-## Overview
+# Event Listeners
 
 CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
 
@@ -21,7 +21,7 @@ When specific actions occur in CrewAI (like a Crew starting execution, an Agent
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
 CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
-![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
+![Prompt Tracing Dashboard](../images/enterprise/prompt-tracing.png)
 
 With Prompt Tracing you can:
 - View the complete history of all prompts sent to your LLM
@@ -224,15 +224,6 @@ CrewAI provides a wide range of events that you can listen for:
 - **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
 - **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
 
-### Knowledge Events
-
-- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
-- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
-- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
-- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
-- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
-- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
-
 ### Flow Events
 
 - **FlowCreatedEvent**: Emitted when a Flow is created

docs/concepts/flows.mdx

@@ -4,7 +4,7 @@ description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
 ---
 
-## Overview
+## Introduction
 
 CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
 
@@ -75,12 +75,11 @@ class ExampleFlow(Flow):
 
 
 flow = ExampleFlow()
-flow.plot()
 result = flow.kickoff()
 
 print(f"Generated fun fact: {result}")
 ```
-![Flow Visual image](/images/crewai-flow-1.png)
+
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
 
 Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
@@ -147,7 +146,6 @@ class OutputExampleFlow(Flow):
 
 
 flow = OutputExampleFlow()
-flow.plot("my_flow_plot")
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
@@ -160,10 +158,9 @@ Second method received: Output from first_method
 ```
 
 </CodeGroup>
-![Flow Visual image](/images/crewai-flow-2.png)
 
 In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
-The `kickoff()` method will return the final output, which is then printed to the console. The `plot()` method will generate the HTML file, which will help you understand the flow.
+The `kickoff()` method will return the final output, which is then printed to the console.
 
 #### Accessing and Updating State
 
@@ -195,7 +192,6 @@ class StateExampleFlow(Flow[ExampleState]):
         return self.state.message
 
 flow = StateExampleFlow()
-flow.plot("my_flow_plot")
 final_output = flow.kickoff()
 print(f"Final Output: {final_output}")
 print("Final State:")
@@ -210,8 +206,6 @@ counter=2 message='Hello from first_method - updated by second_method'
 
 </CodeGroup>
 
-![Flow Visual image](/images/crewai-flow-2.png)
-
 In this example, the state is updated by both `first_method` and `second_method`.
 After the Flow has run, you can access the final state to see the updates made by these methods.
 
@@ -255,12 +249,9 @@ class UnstructuredExampleFlow(Flow):
 
 
 flow = UnstructuredExampleFlow()
-flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
-![Flow Visual image](/images/crewai-flow-3.png)
-
 **Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
 
 **Key Points:**
@@ -311,8 +302,6 @@ flow = StructuredExampleFlow()
 flow.kickoff()
 ```
 
-![Flow Visual image](/images/crewai-flow-3.png)
-
 **Key Points:**
 
 - **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
@@ -447,7 +436,6 @@ class OrExampleFlow(Flow):
 
 
 flow = OrExampleFlow()
-flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -458,8 +446,6 @@ Logger: Hello from the second method
 
 </CodeGroup>
 
-![Flow Visual image](/images/crewai-flow-4.png)
-
 When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
 The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
 
@@ -488,7 +474,6 @@ class AndExampleFlow(Flow):
         print(self.state)
 
 flow = AndExampleFlow()
-flow.plot()
 flow.kickoff()
 ```
 
@@ -499,8 +484,6 @@ flow.kickoff()
 
 </CodeGroup>
 
-![Flow Visual image](/images/crewai-flow-5.png)
-
 When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
 The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
 
@@ -544,7 +527,6 @@ class RouterFlow(Flow[ExampleState]):
 
 
 flow = RouterFlow()
-flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -556,8 +538,6 @@ Fourth method running
 
 </CodeGroup>
 
-![Flow Visual image](/images/crewai-flow-6.png)
-
 In the above example, the `start_method` generates a random boolean value and sets it in the state.
 The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
 If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
@@ -661,7 +641,6 @@ class MarketResearchFlow(Flow[MarketResearchState]):
 # Usage example
 async def run_flow():
     flow = MarketResearchFlow()
-    flow.plot("MarketResearchFlowPlot")
     result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
     return result
 
@@ -671,8 +650,6 @@ if __name__ == "__main__":
     asyncio.run(run_flow())
 ```
 
-![Flow Visual image](/images/crewai-flow-7.png)
-
 This example demonstrates several key features of using Agents in flows:
 
 1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
@@ -769,16 +746,13 @@ def kickoff():
 
 def plot():
     poem_flow = PoemFlow()
-    poem_flow.plot("PoemFlowPlot")
+    poem_flow.plot()
 
 if __name__ == "__main__":
     kickoff()
-    plot()
 ```
 
-In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method. The PoemFlowPlot will be generated by `plot()` method.
-
-![Flow Visual image](/images/crewai-flow-8.png)
+In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
 
 ### Running the Flow
 

docs/concepts/knowledge.mdx

@@ -4,7 +4,7 @@ description: What is knowledge in CrewAI and how to use it.
 icon: book
 ---
 
-## Overview
+## What is Knowledge?
 
 Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
 Think of it as giving your agents a reference library they can consult while working.
@@ -36,15 +36,12 @@ CrewAI supports various types of knowledge sources out of the box:
 
 ## Supported Knowledge Parameters
 
-<ParamField body="sources" type="List[BaseKnowledgeSource]" required="Yes"> 
-    List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content. 
-</ParamField>
-<ParamField body="collection_name" type="str"> 
-    Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to \"knowledge\" if not provided. 
-</ParamField>
-<ParamField body="storage" type="Optional[KnowledgeStorage]"> 
-Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. 
-</ParamField>
+| Parameter                    | Type                                | Required | Description                                                                                                                                           |
+| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
+| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
+| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
+
 
 <Tip>
 Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
@@ -400,93 +397,6 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 John is 30 years old and lives in San Francisco.
 ```
 </CodeGroup>
-
-## Query Rewriting
-
-CrewAI implements an intelligent query rewriting mechanism to optimize knowledge retrieval. When an agent needs to search through knowledge sources, the raw task prompt is automatically transformed into a more effective search query.
-
-### How Query Rewriting Works
-
-1. When an agent executes a task with knowledge sources available, the `_get_knowledge_search_query` method is triggered
-2. The agent's LLM is used to transform the original task prompt into an optimized search query
-3. This optimized query is then used to retrieve relevant information from knowledge sources
-
-### Benefits of Query Rewriting
-
-<CardGroup cols={2}>
-  <Card title="Improved Retrieval Accuracy" icon="bullseye-arrow">
-    By focusing on key concepts and removing irrelevant content, query rewriting helps retrieve more relevant information.
-  </Card>
-  <Card title="Context Awareness" icon="brain">
-    The rewritten queries are designed to be more specific and context-aware for vector database retrieval.
-  </Card>
-</CardGroup>
-
-### Implementation Details
-
-Query rewriting happens transparently using a system prompt that instructs the LLM to:
-
-- Focus on key words of the intended task
-- Make the query more specific and context-aware
-- Remove irrelevant content like output format instructions
-- Generate only the rewritten query without preamble or postamble
-
-<Tip>
-  This mechanism is fully automatic and requires no configuration from users. The agent's LLM is used to perform the query rewriting, so using a more capable LLM can improve the quality of rewritten queries.
-</Tip>
-
-## Knowledge Events
-
-CrewAI emits events during the knowledge retrieval process that you can listen for using the event system. These events allow you to monitor, debug, and analyze how knowledge is being retrieved and used by your agents.
-
-### Available Knowledge Events
-
-- **KnowledgeRetrievalStartedEvent**: Emitted when an agent starts retrieving knowledge from sources
-- **KnowledgeRetrievalCompletedEvent**: Emitted when knowledge retrieval is completed, including the query used and the retrieved content
-- **KnowledgeQueryStartedEvent**: Emitted when a query to knowledge sources begins
-- **KnowledgeQueryCompletedEvent**: Emitted when a query completes successfully
-- **KnowledgeQueryFailedEvent**: Emitted when a query to knowledge sources fails
-- **KnowledgeSearchQueryFailedEvent**: Emitted when a search query fails
-
-### Example: Monitoring Knowledge Retrieval
-
-```python
-from crewai.utilities.events import (
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-)
-from crewai.utilities.events.base_event_listener import BaseEventListener
-
-class KnowledgeMonitorListener(BaseEventListener):
-    def setup_listeners(self, crewai_event_bus):
-        @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
-        def on_knowledge_retrieval_started(source, event):
-            print(f"Agent '{event.agent.role}' started retrieving knowledge")
-            
-        @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
-        def on_knowledge_retrieval_completed(source, event):
-            print(f"Agent '{event.agent.role}' completed knowledge retrieval")
-            print(f"Query: {event.query}")
-            print(f"Retrieved {len(event.retrieved_knowledge)} knowledge chunks")
-
-# Create an instance of your listener
-knowledge_monitor = KnowledgeMonitorListener()
-```
-
-For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
-
-### 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.
@@ -540,13 +450,6 @@ crew = Crew(
 result = crew.kickoff(
     inputs={"question": "What is the storage capacity of the XPS 13?"}
 )
-
-# Resetting the agent specific knowledge via crew object
-crew.reset_memories(command_type = 'agent_knowledge')
-
-# Resetting the agent specific knowledge via CLI
-crewai reset-memories --agent-knowledge 
-crewai reset-memories -akn
 ```
 
 <Info>
@@ -750,11 +653,4 @@ recent_news = SpaceNewsKnowledgeSource(
     - Configure appropriate embedding models
     - Consider using local embedding providers for faster processing
   </Accordion>
-
-  <Accordion title="One Time Knowledge">
-    - With the typical file structure provided by CrewAI, knowledge sources are embedded every time the kickoff is triggered.
-    - If the knowledge sources are large, this leads to inefficiency and increased latency, as the same data is embedded each time.
-    - To resolve this, directly initialize the knowledge parameter instead of the knowledge_sources parameter.
-    - Link to the issue to get complete idea [Github Issue](https://github.com/crewAIInc/crewAI/issues/2755)
-  </Accordion>  
 </AccordionGroup>

docs/concepts/llms.mdx

@@ -4,10 +4,9 @@ description: 'A comprehensive guide to configuring and using Large Language Mode
 icon: 'microchip-ai'
 ---
 
-## Overview
-
-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
-
+<Note>
+  CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+</Note>
 
 ## What are LLMs?
 
@@ -170,55 +169,19 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
-  <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+  <Accordion title="Google">
+    Set the following environment variables in your `.env` file:
 
-    ```toml .env
+    ```toml Code
+    # Option 1: Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
+
+    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
+    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7,
-    )
-    ```
-
-    ### Gemini models
-
-    Google offers a range of powerful models optimized for different use cases.
-
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
-  </Accordion>
-  <Accordion title="Google (Vertex AI)">
-    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
+    Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
     ```python Code
     import json
 
@@ -242,18 +205,14 @@ In this section, you'll find detailed examples that help you select, configure,
         vertex_credentials=vertex_credentials_json
     )
     ```
-
     Google offers a range of powerful models optimized for different use cases:
 
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | Model                  | Context Window | Best For                                                          |
+    |-----------------------|----------------|------------------------------------------------------------------|
+    | gemini-2.0-flash-exp  | 1M tokens      | Higher quality at faster speed, multimodal model, good for most tasks |
+    | gemini-1.5-flash      | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B   | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro        | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
   </Accordion>
 
   <Accordion title="Azure">
@@ -678,24 +637,18 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
     CrewAI emits events for each chunk received during streaming:
 
     ```python
-    from crewai.utilities.events import (
-      LLMStreamChunkEvent
-    )
-    from crewai.utilities.events.base_event_listener import BaseEventListener
+    from crewai import LLM
+    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
 
-    class MyCustomListener(BaseEventListener):
-        def setup_listeners(self, crewai_event_bus):
-            @crewai_event_bus.on(LLMStreamChunkEvent)
-            def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
-              # Process each chunk as it arrives
-              print(f"Received chunk: {event.chunk}")
+    class MyEventHandler(EventHandler):
+        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
+            # Process each chunk as it arrives
+            print(f"Received chunk: {event.chunk}")
 
-    my_listener = MyCustomListener()
+    # Register the event handler
+    from crewai.utilities.events import crewai_event_bus
+    crewai_event_bus.register_handler(MyEventHandler())
     ```
-
-    <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
-    </Tip>
   </Tab>
 </Tabs>
 
@@ -792,24 +745,6 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
-  
-  <Accordion title="Drop Additional Parameters">
-    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
-    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
-
-    ```python
-    from crewai import LLM
-    import os
-
-    os.environ["OPENAI_API_KEY"] = "<api-key>"
-
-    o3_llm = LLM(
-        model="o3",
-        drop_params=True,
-        additional_drop_params=["stop"]
-    )
-    ```
-  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions

docs/concepts/memory.mdx

@@ -4,7 +4,7 @@ description: Leveraging memory systems in the CrewAI framework to enhance agent
 icon: database
 ---
 
-## Overview
+## Introduction to Memory Systems in CrewAI
 
 The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. 
 This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember, 
@@ -679,7 +679,6 @@ crewai reset-memories [OPTIONS]
 | `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
 | `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
 | `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False   |
-| `-akn`, `--agent-knowledge` | Reset AGENT KNOWLEDGE storage | Flag (boolean) | False   |
 | `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |
 
 Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
@@ -717,11 +716,9 @@ my_crew.reset_memories(command_type = 'all') # Resets all the memory
 | `entities`         | Reset ENTITIES memory.           | 
 | `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
 | `knowledge`        | Reset KNOWLEDGE memory.          |
-| `agent_knowledge`  | Reset AGENT KNOWLEDGE memory.    |
 | `all`              | Reset ALL memories.              |
 
 
-
 ## Benefits of Using CrewAI's Memory System
 
 - 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.

docs/concepts/planning.mdx

@@ -1,10 +1,10 @@
 ---
 title: Planning
 description: Learn how to add planning to your CrewAI Crew and improve their performance.
-icon: ruler-combined
+icon: brain
 ---
 
-## Overview
+## Introduction
 
 The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, 
 all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.

docs/concepts/processes.mdx

@@ -4,8 +4,7 @@ description: Detailed guide on workflow management through processes in CrewAI,
 icon: bars-staggered
 ---
 
-## Overview
-
+## Understanding Processes
 <Tip>
   Processes orchestrate the execution of tasks by agents, akin to project management in human teams. 
   These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.

docs/concepts/reasoning.mdx

@@ -1,147 +0,0 @@
----
-title: Reasoning
-description: "Learn how to enable and use agent reasoning to improve task execution."
-icon: brain
----
-
-## Overview
-
-Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
-
-## Usage
-
-To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze complex datasets and provide insights",
-    backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
-    reasoning=True,  # Enable reasoning
-    max_reasoning_attempts=3  # Optional: Set a maximum number of reasoning attempts
-)
-```
-
-## How It Works
-
-When reasoning is enabled, before executing a task, the agent will:
-
-1. Reflect on the task and create a detailed plan
-2. Evaluate whether it's ready to execute the task
-3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
-4. Inject the reasoning plan into the task description before execution
-
-This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
-
-## Configuration Options
-
-<ParamField body="reasoning" type="bool" default="False"> 
-    Enable or disable reasoning 
-</ParamField>
-
-<ParamField body="max_reasoning_attempts" type="int" default="None"> 
-    Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready. 
-</ParamField>
-
-## Example
-
-Here's a complete example:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Create an agent with reasoning enabled
-analyst = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    backstory="You are an expert data analyst.",
-    reasoning=True,
-    max_reasoning_attempts=3  # Optional: Set a limit on reasoning attempts
-)
-
-# Create a task
-analysis_task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=analyst
-)
-
-# Create a crew and run the task
-crew = Crew(agents=[analyst], tasks=[analysis_task])
-result = crew.kickoff()
-
-print(result)
-```
-
-## Error Handling
-
-The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
-
-Here's how to handle potential errors in your code:
-
-```python
-from crewai import Agent, Task
-import logging
-
-# Set up logging to capture any reasoning errors
-logging.basicConfig(level=logging.INFO)
-
-# Create an agent with reasoning enabled
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze data and provide insights",
-    reasoning=True,
-    max_reasoning_attempts=3
-)
-
-# Create a task
-task = Task(
-    description="Analyze the provided sales data and identify key trends.",
-    expected_output="A report highlighting the top 3 sales trends.",
-    agent=agent
-)
-
-# Execute the task
-# If an error occurs during reasoning, it will be logged and execution will continue
-result = agent.execute_task(task)
-```
-
-## Example Reasoning Output
-
-Here's an example of what a reasoning plan might look like for a data analysis task:
-
-```
-Task: Analyze the provided sales data and identify key trends.
-
-Reasoning Plan:
-I'll analyze the sales data to identify the top 3 trends.
-
-1. Understanding of the task:
-   I need to analyze sales data to identify key trends that would be valuable for business decision-making.
-
-2. Key steps I'll take:
-   - First, I'll examine the data structure to understand what fields are available
-   - Then I'll perform exploratory data analysis to identify patterns
-   - Next, I'll analyze sales by time periods to identify temporal trends
-   - I'll also analyze sales by product categories and customer segments
-   - Finally, I'll identify the top 3 most significant trends
-
-3. Approach to challenges:
-   - If the data has missing values, I'll decide whether to fill or filter them
-   - If the data has outliers, I'll investigate whether they're valid data points or errors
-   - If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
-
-4. Use of available tools:
-   - I'll use data analysis tools to explore and visualize the data
-   - I'll use statistical tools to identify significant patterns
-   - I'll use knowledge retrieval to access relevant information about sales analysis
-
-5. Expected outcome:
-   A concise report highlighting the top 3 sales trends with supporting evidence from the data.
-
-READY: I am ready to execute the task.
-```
-
-This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.

docs/concepts/tasks.mdx

@@ -4,7 +4,7 @@ description: Detailed guide on managing and creating tasks within the CrewAI fra
 icon: list-check
 ---
 
-## Overview
+## Overview of a Task
 
 In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
 
@@ -15,7 +15,7 @@ Tasks within CrewAI can be collaborative, requiring multiple agents to work toge
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
 CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
-![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+![Task Builder Screenshot](../images/enterprise/crew-studio-quickstart.png)
 
 The Visual Task Builder enables:
 - Drag-and-drop task creation

docs/concepts/testing.mdx

@@ -4,7 +4,7 @@ description: Learn how to test your CrewAI Crew and evaluate their performance.
 icon: vial
 ---
 
-## Overview
+## Introduction
 
 Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
 

docs/concepts/tools.mdx

@@ -4,7 +4,7 @@ description: Understanding and leveraging tools within the CrewAI framework for
 icon: screwdriver-wrench
 ---
 
-## Overview
+## Introduction
 
 CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
@@ -18,6 +18,8 @@ enabling everything from simple searches to complex interactions and effective t
 <Note type="info" title="Enterprise Enhancement: Tools Repository">
 CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
 
+![Tools Repository Screenshot](../images/enterprise/tools-repository.png)
+
 The Enterprise Tools Repository includes:
 - Pre-built connectors for popular enterprise systems
 - Custom tool creation interface

docs/concepts/training.mdx

@@ -4,7 +4,7 @@ description: Learn how to train your CrewAI agents by giving them feedback early
 icon: dumbbell
 ---
 
-## Overview
+## Introduction
 
 The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.

docs/docs.json

@@ -74,7 +74,6 @@
               "concepts/collaboration",
               "concepts/training",
               "concepts/memory",
-              "concepts/reasoning",
               "concepts/planning",
               "concepts/testing",
               "concepts/cli",
@@ -130,7 +129,6 @@
               "tools/seleniumscrapingtool",
               "tools/snowflakesearchtool",
               "tools/spidertool",
-              "tools/stagehandtool",
               "tools/txtsearchtool",
               "tools/visiontool",
               "tools/weaviatevectorsearchtool",
@@ -140,12 +138,6 @@
               "tools/youtubevideosearchtool"
             ]
           },
-          {
-            "group": "MCP Integration",
-            "pages": [
-              "mcp/crewai-mcp-integration"
-            ] 
-          },
           {
             "group": "Agent Monitoring & Observability",
             "pages": [

docs/enterprise/introduction.mdx

@@ -64,14 +64,14 @@ CrewAI Enterprise extends the power of the open-source framework with features d
       Sign Up
     </Card>
   </Step>
-  <Step title="Build your first crew">
-    Use code or Crew Studio to build your crew
+  <Step title="Create your first crew">
+    Use code or Crew Studio to create your crew
     <Card 
-      title="Build Crew" 
+      title="Create Crew" 
       icon="paintbrush"
-      href="/enterprise/guides/build-crew"
+      href="/enterprise/guides/create-crew"
     >
-      Build Crew
+      Create Crew
     </Card>
   </Step>
   <Step title="Deploy your crew">

docs/guides/advanced/customizing-prompts.mdx

@@ -4,6 +4,8 @@ description: Dive deeper into low-level prompt customization for CrewAI, enablin
 icon: message-pen
 ---
 
+# Customizing Prompts at a Low Level
+
 ## Why Customize Prompts?
 
 Although CrewAI's default prompts work well for many scenarios, low-level customization opens the door to significantly more flexible and powerful agent behavior. Here’s why you might want to take advantage of this deeper control:

docs/guides/advanced/fingerprinting.mdx

@@ -4,6 +4,8 @@ description: Learn how to use CrewAI's fingerprinting system to uniquely identif
 icon: fingerprint
 ---
 
+# Fingerprinting in CrewAI
+
 ## Overview
 
 Fingerprints in CrewAI provide a way to uniquely identify and track components throughout their lifecycle. Each `Agent`, `Crew`, and `Task` automatically receives a unique fingerprint when created, which cannot be manually overridden.

docs/guides/agents/crafting-effective-agents.mdx

@@ -4,6 +4,8 @@ description: Learn best practices for designing powerful, specialized AI agents
 icon: robot
 ---
 
+# Crafting Effective Agents
+
 ## The Art and Science of Agent Design
 
 At the heart of CrewAI lies the agent - a specialized AI entity designed to perform specific roles within a collaborative framework. While creating basic agents is simple, crafting truly effective agents that produce exceptional results requires understanding key design principles and best practices.

docs/guides/concepts/evaluating-use-cases.mdx

@@ -4,6 +4,8 @@ description: Learn how to assess your AI application needs and choose the right
 icon: scale-balanced
 ---
 
+# Evaluating Use Cases for CrewAI
+
 ## Understanding the Decision Framework
 
 When building AI applications with CrewAI, one of the most important decisions you'll make is choosing the right approach for your specific use case. Should you use a Crew? A Flow? A combination of both? This guide will help you evaluate your requirements and make informed architectural decisions.

docs/guides/crews/first-crew.mdx

@@ -4,6 +4,8 @@ description: Step-by-step tutorial to create a collaborative AI team that works
 icon: users-gear
 ---
 
+# Build Your First Crew
+
 ## Unleashing the Power of Collaborative AI
 
 Imagine having a team of specialized AI agents working together seamlessly to solve complex problems, each contributing their unique skills to achieve a common goal. This is the power of CrewAI - a framework that enables you to create collaborative AI systems that can accomplish tasks far beyond what a single AI could achieve alone.
@@ -33,8 +35,7 @@ Let's get started building your first crew!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+2. Set up your OpenAI API key in your environment variables
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Project
@@ -91,8 +92,7 @@ For our research crew, we'll create two agents:
 1. A **researcher** who excels at finding and organizing information
 2. An **analyst** who can interpret research findings and create insightful reports
 
-Let's modify the `agents.yaml` file to define these specialized agents. Be sure
-to set `llm` to the provider you are using.
+Let's modify the `agents.yaml` file to define these specialized agents:
 
 ```yaml
 # src/research_crew/config/agents.yaml
@@ -107,7 +107,7 @@ researcher:
     finding relevant information from various sources. You excel at
     organizing information in a clear and structured manner, making
     complex topics accessible to others.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 
 analyst:
   role: >
@@ -120,7 +120,7 @@ analyst:
     and technical writing. You have a talent for identifying patterns
     and extracting meaningful insights from research data, then
     communicating those insights effectively through well-crafted reports.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 ```
 
 Notice how each agent has a distinct role, goal, and backstory. These elements aren't just descriptive - they actively shape how the agent approaches its tasks. By crafting these carefully, you can create agents with specialized skills and perspectives that complement each other.
@@ -282,12 +282,12 @@ This script prepares the environment, specifies our research topic, and kicks of
 
 Create a `.env` file in your project root with your API keys:
 
-```sh
+```
+OPENAI_API_KEY=your_openai_api_key
 SERPER_API_KEY=your_serper_api_key
-# Add your provider's API key here too.
 ```
 
-See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
+You can get a Serper API key from [Serper.dev](https://serper.dev/).
 
 ## Step 8: Install Dependencies
 

docs/guides/flows/first-flow.mdx

@@ -4,6 +4,8 @@ description: Learn how to create structured, event-driven workflows with precise
 icon: diagram-project
 ---
 
+# Build Your First Flow
+
 ## Taking Control of AI Workflows with Flows
 
 CrewAI Flows represent the next level in AI orchestration - combining the collaborative power of AI agent crews with the precision and flexibility of procedural programming. While crews excel at agent collaboration, flows give you fine-grained control over exactly how and when different components of your AI system interact.
@@ -43,8 +45,7 @@ Let's dive in and build your first flow!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your LLM API key in your environment, following the [LLM setup
-   guide](/concepts/llms#setting-up-your-llm)
+2. Set up your OpenAI API key in your environment variables
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Flow Project
@@ -106,8 +107,6 @@ Now, let's modify the generated files for the content writer crew. We'll set up
 
 1. First, update the agents configuration file to define our content creation team:
 
-   Remember to set `llm` to the provider you are using.
-
 ```yaml
 # src/guide_creator_flow/crews/content_crew/config/agents.yaml
 content_writer:
@@ -120,7 +119,7 @@ content_writer:
     You are a talented educational writer with expertise in creating clear, engaging
     content. You have a gift for explaining complex concepts in accessible language
     and organizing information in a way that helps readers build their understanding.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 
 content_reviewer:
   role: >
@@ -133,7 +132,7 @@ content_reviewer:
     content. You have an eye for detail, clarity, and coherence. You excel at
     improving content while maintaining the original author's voice and ensuring
     consistent quality across multiple sections.
-  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+  llm: openai/gpt-4o-mini
 ```
 
 These agent definitions establish the specialized roles and perspectives that will shape how our AI agents approach content creation. Notice how each agent has a distinct purpose and expertise.
@@ -442,15 +441,10 @@ This is the power of flows - combining different types of processing (user inter
 
 ## Step 6: Set Up Your Environment Variables
 
-Create a `.env` file in your project root with your API keys. See the [LLM setup
-guide](/concepts/llms#setting-up-your-llm) for details on configuring a provider.
+Create a `.env` file in your project root with your API keys:
 
-```sh .env
+```
 OPENAI_API_KEY=your_openai_api_key
-# or
-GEMINI_API_KEY=your_gemini_api_key
-# or
-ANTHROPIC_API_KEY=your_anthropic_api_key
 ```
 
 ## Step 7: Install Dependencies
@@ -553,10 +547,7 @@ Let's break down the key components of flows to help you understand how to build
 Flows allow you to make direct calls to language models when you need simple, structured responses:
 
 ```python
-llm = LLM(
-    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
-    response_format=GuideOutline
-)
+llm = LLM(model="openai/gpt-4o-mini", response_format=GuideOutline)
 response = llm.call(messages=messages)
 ```
 

docs/guides/flows/mastering-flow-state.mdx

@@ -4,6 +4,8 @@ description: A comprehensive guide to managing, persisting, and leveraging state
 icon: diagram-project
 ---
 
+# Mastering Flow State Management
+
 ## Understanding the Power of State in Flows
 
 State management is the backbone of any sophisticated AI workflow. In CrewAI Flows, the state system allows you to maintain context, share data between steps, and build complex application logic. Mastering state management is essential for creating reliable, maintainable, and powerful AI applications.

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

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

docs/how-to/before-and-after-kickoff-hooks.mdx

@@ -12,8 +12,7 @@ The before kickoff hook is executed before the crew starts its tasks. It receive
 Here's an example of defining a before kickoff function in your `crew.py`:
 
 ```python
-from crewai import CrewBase
-from crewai.project import before_kickoff
+from crewai import CrewBase, before_kickoff
 
 @CrewBase
 class MyCrew:
@@ -35,8 +34,7 @@ The after kickoff hook is executed after the crew has completed its tasks. It re
 Here's how you can define an after kickoff function in your `crew.py`:
 
 ```python
-from crewai import CrewBase
-from crewai.project import after_kickoff
+from crewai import CrewBase, after_kickoff
 
 @CrewBase
 class MyCrew:

docs/mcp/crewai-mcp-integration.mdx

@@ -1,229 +0,0 @@
----
-title: 'MCP Servers as Tools in CrewAI'
-description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
-icon: 'plug'
----
-
-## Overview
-
-The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers. 
-The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents. 
-This gives your crews access to a vast ecosystem of functionalities. For now, we support **Standard Input/Output** (Stdio) and **Server-Sent Events** (SSE) transport mechanisms. 
-
-<Info>
-We will also be integrating **Streamable HTTP** transport in the near future. 
-Streamable HTTP is designed for efficient, bi-directional communication over a single HTTP connection.
-</Info>
-
-## Installation
-
-Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
-
-```shell
-uv pip install 'crewai-tools[mcp]'
-```
-
-### Integrating MCP Tools with `MCPServerAdapter`
-
-The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. 
-It supports different transport mechanisms, primarily **Stdio** (for local servers) and **SSE** (Server-Sent Events).You have two main options for managing the connection lifecycle:
-
-### Option 1: Fully Managed Connection (Recommended)
-
-Using a Python context manager (`with` statement) is the recommended approach. It automatically handles starting and stopping the connection to the MCP server.
-
-**For a local Stdio-based MCP server:**
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters
-import os
-
-server_params=StdioServerParameters(
-    command="uxv", # Or your python3 executable i.e. "python3"
-    args=["mock_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-with MCPServerAdapter(server_params) as tools:
-    print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
-
-    # Example: Using the tools from the Stdio MCP server in a CrewAI Agent
-    agent = Agent(
-        role="Web Information Retriever",
-        goal="Scrape content from a specified URL.",
-        backstory="An AI that can fetch and process web page data via an MCP tool.",
-        tools=tools,
-        verbose=True,
-    )
-    task = Task(
-        description="Scrape content from a specified URL.",
-        expected_output="Scraped content from the specified URL.",
-        agent=agent,
-    )
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-    result = crew.kickoff()
-    print(result)
-```
-
-**For a remote SSE-based MCP server:**
-
-```python
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew
-
-server_params = {"url": "http://localhost:8000/sse"}
-
-with MCPServerAdapter(server_params) as tools:
-    print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
-
-    # Example: Using the tools from the SSE MCP server in a CrewAI Agent
-    agent = Agent(
-        role="Web Information Retriever",
-        goal="Scrape content from a specified URL.",
-        backstory="An AI that can fetch and process web page data via an MCP tool.",
-        tools=tools,
-        verbose=True,
-    )
-    task = Task(
-        description="Scrape content from a specified URL.",
-        expected_output="Scraped content from the specified URL.",
-        agent=agent,
-    )
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-    result = crew.kickoff()
-    print(result)
-```
-
-### Option 2: More control over the MCP server connection lifecycle
-
-If you need finer-grained control over the MCP server connection lifecycle, you can instantiate `MCPServerAdapter` directly and manage its `start()` and `stop()` methods.
-
-<Info>
-You **MUST** call `mcp_server_adapter.stop()` to ensure the connection is closed and resources are released. Using a `try...finally` block is highly recommended.
-</Info>
-
-#### Stdio Transport Example (Manual)
-
-```python
-from mcp import StdioServerParameters
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew
-import os
-
-stdio_params = StdioServerParameters(
-    command="uvx", # Or your python3 executable i.e. "python3"
-    args=["--quiet", "your-mcp-server@0.1.3"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
-try:
-    mcp_server_adapter.start() # Manually start the connection
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
-
-    # Use 'tools' with your Agent, Task, Crew setup as in Option 1
-    agent = Agent(
-        role="Medical Researcher", 
-        goal="Find recent studies on a given topic using PubMed.", 
-        backstory="An AI assistant specialized in biomedical literature research.", 
-        tools=tools,
-        verbose=True 
-    )
-    
-    task = Task(
-        description="Search for recent articles on 'crispr gene editing'.",
-        expected_output="A summary of the top 3 recent articles.",
-        agent=agent
-    )
-    
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = crew.kickoff()
-    print(result)
-finally:
-    print("Stopping Stdio MCP server connection (manual)...")
-    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
-```
-
-
-#### SSE Transport Example (Manual)
-
-```python
-from crewai_tools import MCPServerAdapter
-from crewai import Agent, Task, Crew, Process
-from mcp import StdioServerParameters
-
-
-server_params = {"url": "http://localhost:8000/sse"}
-
-try:
-    mcp_server_adapter = MCPServerAdapter(server_params)
-    mcp_server_adapter.start()
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
-
-    agent = Agent(
-        role="Medical Researcher", 
-        goal="Find recent studies on a given topic using PubMed.", 
-        backstory="An AI assistant specialized in biomedical literature research.", 
-        tools=tools,
-        verbose=True 
-    )
-    
-    task = Task(
-        description="Search for recent articles on 'crispr gene editing'.",
-        expected_output="A summary of the top 3 recent articles.",
-        agent=agent
-    )
-    
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = crew.kickoff()
-    print(result)
-finally:
-    print("Stopping SSE MCP server connection (manual)...")
-    mcp_server_adapter.stop() # **Crucial: Ensure stop is called**
-```
-
-## Staying Safe with MCP
-<Warning>
-Always ensure that you trust an MCP Server before using it.
-</Warning>
-
-#### Security Warning: DNS Rebinding Attacks
-SSE transports can be vulnerable to DNS rebinding attacks if not properly secured. 
-To prevent this:
-
-1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
-2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
-3. **Implement proper authentication** for all SSE connections
-
-Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
-
-For more details, see the [MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations) documentation.
-
-### Limitations
-*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`. 
-Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
-*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/quickstart.mdx

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

docs/tools/nl2sqltool.mdx

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

docs/tools/stagehandtool.mdx

@@ -1,244 +0,0 @@
----
-title: Stagehand Tool
-description: Web automation tool that integrates Stagehand with CrewAI for browser interaction and automation
-icon: hand
----
-
-
-# Overview
-
-The `StagehandTool` integrates the [Stagehand](https://docs.stagehand.dev/get_started/introduction) framework with CrewAI, enabling agents to interact with websites and automate browser tasks using natural language instructions.
-
-## Overview
-
-Stagehand is a powerful browser automation framework built by Browserbase that allows AI agents to:
-
-- Navigate to websites
-- Click buttons, links, and other elements
-- Fill in forms
-- Extract data from web pages
-- Observe and identify elements
-- Perform complex workflows
-
-The StagehandTool wraps the Stagehand Python SDK to provide CrewAI agents with browser control capabilities through three core primitives:
-
-1. **Act**: Perform actions like clicking, typing, or navigating
-2. **Extract**: Extract structured data from web pages
-3. **Observe**: Identify and analyze elements on the page
-
-## Prerequisites
-
-Before using this tool, ensure you have:
-
-1. A [Browserbase](https://www.browserbase.com/) account with API key and project ID
-2. An API key for an LLM (OpenAI or Anthropic Claude)
-3. The Stagehand Python SDK installed
-
-Install the required dependency:
-
-```bash
-pip install stagehand-py
-```
-
-## Usage
-
-### Basic Implementation
-
-The StagehandTool can be implemented in two ways:
-
-#### 1. Using Context Manager (Recommended)
-<Tip>
-  The context manager approach is recommended as it ensures proper cleanup of resources even if exceptions occur.
-</Tip>
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import StagehandTool
-from stagehand.schemas import AvailableModel
-
-# Initialize the tool with your API keys using a context manager
-with StagehandTool(
-    api_key="your-browserbase-api-key",
-    project_id="your-browserbase-project-id",
-    model_api_key="your-llm-api-key",  # OpenAI or Anthropic API key
-    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,  # Optional: specify which model to use
-) as stagehand_tool:
-    # Create an agent with the tool
-    researcher = Agent(
-        role="Web Researcher",
-        goal="Find and summarize information from websites",
-        backstory="I'm an expert at finding information online.",
-        verbose=True,
-        tools=[stagehand_tool],
-    )
-
-    # Create a task that uses the tool
-    research_task = Task(
-        description="Go to https://www.example.com and tell me what you see on the homepage.",
-        agent=researcher,
-    )
-
-    # Run the crew
-    crew = Crew(
-        agents=[researcher],
-        tasks=[research_task],
-        verbose=True,
-    )
-
-    result = crew.kickoff()
-    print(result)
-```
-
-#### 2. Manual Resource Management
-
-```python
-from crewai import Agent, Task, Crew
-from crewai_tools import StagehandTool
-from stagehand.schemas import AvailableModel
-
-# Initialize the tool with your API keys
-stagehand_tool = StagehandTool(
-    api_key="your-browserbase-api-key",
-    project_id="your-browserbase-project-id",
-    model_api_key="your-llm-api-key",
-    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
-)
-
-try:
-    # Create an agent with the tool
-    researcher = Agent(
-        role="Web Researcher",
-        goal="Find and summarize information from websites",
-        backstory="I'm an expert at finding information online.",
-        verbose=True,
-        tools=[stagehand_tool],
-    )
-
-    # Create a task that uses the tool
-    research_task = Task(
-        description="Go to https://www.example.com and tell me what you see on the homepage.",
-        agent=researcher,
-    )
-
-    # Run the crew
-    crew = Crew(
-        agents=[researcher],
-        tasks=[research_task],
-        verbose=True,
-    )
-
-    result = crew.kickoff()
-    print(result)
-finally:
-    # Explicitly clean up resources
-    stagehand_tool.close()
-```
-
-## Command Types
-
-The StagehandTool supports three different command types for specific web automation tasks:
-
-### 1. Act Command
-
-The `act` command type (default) enables webpage interactions like clicking buttons, filling forms, and navigation.
-
-```python
-# Perform an action (default behavior)
-result = stagehand_tool.run(
-    instruction="Click the login button", 
-    url="https://example.com",
-    command_type="act"  # Default, so can be omitted
-)
-
-# Fill out a form
-result = stagehand_tool.run(
-    instruction="Fill the contact form with name 'John Doe', email 'john@example.com', and message 'Hello world'", 
-    url="https://example.com/contact"
-)
-```
-
-### 2. Extract Command
-
-The `extract` command type retrieves structured data from webpages.
-
-```python
-# Extract all product information
-result = stagehand_tool.run(
-    instruction="Extract all product names, prices, and descriptions", 
-    url="https://example.com/products",
-    command_type="extract"
-)
-
-# Extract specific information with a selector
-result = stagehand_tool.run(
-    instruction="Extract the main article title and content", 
-    url="https://example.com/blog/article",
-    command_type="extract",
-    selector=".article-container"  # Optional CSS selector
-)
-```
-
-### 3. Observe Command
-
-The `observe` command type identifies and analyzes webpage elements.
-
-```python
-# Find interactive elements
-result = stagehand_tool.run(
-    instruction="Find all interactive elements in the navigation menu", 
-    url="https://example.com",
-    command_type="observe"
-)
-
-# Identify form fields
-result = stagehand_tool.run(
-    instruction="Identify all the input fields in the registration form", 
-    url="https://example.com/register",
-    command_type="observe",
-    selector="#registration-form"
-)
-```
-
-## Configuration Options
-
-Customize the StagehandTool behavior with these parameters:
-
-```python
-stagehand_tool = StagehandTool(
-    api_key="your-browserbase-api-key",
-    project_id="your-browserbase-project-id",
-    model_api_key="your-llm-api-key",
-    model_name=AvailableModel.CLAUDE_3_7_SONNET_LATEST,
-    dom_settle_timeout_ms=5000,  # Wait longer for DOM to settle
-    headless=True,  # Run browser in headless mode
-    self_heal=True,  # Attempt to recover from errors
-    wait_for_captcha_solves=True,  # Wait for CAPTCHA solving
-    verbose=1,  # Control logging verbosity (0-3)
-)
-```
-
-## Best Practices
-
-1. **Be Specific**: Provide detailed instructions for better results
-2. **Choose Appropriate Command Type**: Select the right command type for your task
-3. **Use Selectors**: Leverage CSS selectors to improve accuracy
-4. **Break Down Complex Tasks**: Split complex workflows into multiple tool calls
-5. **Implement Error Handling**: Add error handling for potential issues
-
-## Troubleshooting
-
-
-Common issues and solutions:
-
-- **Session Issues**: Verify API keys for both Browserbase and LLM provider
-- **Element Not Found**: Increase `dom_settle_timeout_ms` for slower pages
-- **Action Failures**: Use `observe` to identify correct elements first
-- **Incomplete Data**: Refine instructions or provide specific selectors
-
-
-## Additional Resources
-
-For questions about the CrewAI integration:
-- Join Stagehand's [Slack community](https://stagehand.dev/slack)
-- Open an issue in the [Stagehand repository](https://github.com/browserbase/stagehand)
-- Visit [Stagehand documentation](https://docs.stagehand.dev/)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.121.0"
+version = "0.118.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.45.0"]
+tools = ["crewai-tools~=0.42.2"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.121.0"
+__version__ = "0.118.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -20,7 +20,6 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.agent_utils import (
     get_tool_names,
-    load_agent_from_repository,
     parse_tools,
     render_text_description_and_args,
 )
@@ -32,14 +31,6 @@ from crewai.utilities.events.agent_events import (
     AgentExecutionStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -115,26 +106,10 @@ class Agent(BaseAgent):
         default=False,
         description="Whether the agent is multimodal.",
     )
-    inject_date: bool = Field(
-        default=False,
-        description="Whether to automatically inject the current date into tasks.",
-    )
-    date_format: str = Field(
-        default="%Y-%m-%d",
-        description="Format string for date when inject_date is enabled.",
-    )
     code_execution_mode: Literal["safe", "unsafe"] = Field(
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    reasoning: bool = Field(
-        default=False,
-        description="Whether the agent should reflect and create a plan before executing a task.",
-    )
-    max_reasoning_attempts: Optional[int] = Field(
-        default=None,
-        description="Maximum number of reasoning attempts before executing the task. If None, will try until ready.",
-    )
     embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
@@ -147,20 +122,6 @@ class Agent(BaseAgent):
         default=None,
         description="Knowledge context for the crew.",
     )
-    knowledge_search_query: Optional[str] = Field(
-        default=None,
-        description="Knowledge search query for the agent dynamically generated by the agent.",
-    )
-    from_repository: Optional[str] = Field(
-        default=None,
-        description="The Agent's role to be used from your repository.",
-    )
-
-    @model_validator(mode="before")
-    def validate_from_repository(cls, v):
-        if v is not None and (from_repository := v.get("from_repository")):
-            return load_agent_from_repository(from_repository) | v
-        return v
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -224,7 +185,7 @@ class Agent(BaseAgent):
         self,
         task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[List[BaseTool]] = None
     ) -> str:
         """Execute a task with the agent.
 
@@ -241,23 +202,6 @@ class Agent(BaseAgent):
             ValueError: If the max execution time is not a positive integer.
             RuntimeError: If the agent execution fails for other reasons.
         """
-        if self.reasoning:
-            try:
-                from crewai.utilities.reasoning_handler import AgentReasoning, AgentReasoningOutput
-                
-                reasoning_handler = AgentReasoning(task=task, agent=self)
-                reasoning_output: AgentReasoningOutput = reasoning_handler.handle_agent_reasoning()
-                
-                # Add the reasoning plan to the task description
-                task.description += f"\n\nReasoning Plan:\n{reasoning_output.plan.plan}"
-            except Exception as e:
-                if hasattr(self, '_logger'):
-                    self._logger.log("error", f"Error during reasoning process: {str(e)}")
-                else:
-                    print(f"Error during reasoning process: {str(e)}")
-            
-        self._inject_date_to_task(task)
-            
         if self.tools_handler:
             self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
@@ -301,65 +245,27 @@ class Agent(BaseAgent):
         knowledge_config = (
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
-
         if self.knowledge:
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeRetrievalStartedEvent(
-                    agent=self,
-                ),
+            agent_knowledge_snippets = self.knowledge.query(
+                [task.prompt()], **knowledge_config
             )
-            try:
-                self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt
+            if agent_knowledge_snippets:
+                self.agent_knowledge_context = extract_knowledge_context(
+                    agent_knowledge_snippets
                 )
-                if self.knowledge_search_query:
-                    agent_knowledge_snippets = self.knowledge.query(
-                        [self.knowledge_search_query], **knowledge_config
-                    )
-                    if agent_knowledge_snippets:
-                        self.agent_knowledge_context = extract_knowledge_context(
-                            agent_knowledge_snippets
-                        )
-                        if self.agent_knowledge_context:
-                            task_prompt += self.agent_knowledge_context
-                    if self.crew:
-                        knowledge_snippets = self.crew.query_knowledge(
-                            [self.knowledge_search_query], **knowledge_config
-                        )
-                        if knowledge_snippets:
-                            self.crew_knowledge_context = extract_knowledge_context(
-                                knowledge_snippets
-                            )
-                            if self.crew_knowledge_context:
-                                task_prompt += self.crew_knowledge_context
+                if self.agent_knowledge_context:
+                    task_prompt += self.agent_knowledge_context
 
-                    crewai_event_bus.emit(
-                        self,
-                        event=KnowledgeRetrievalCompletedEvent(
-                            query=self.knowledge_search_query,
-                            agent=self,
-                            retrieved_knowledge=(
-                                (self.agent_knowledge_context or "")
-                                + (
-                                    "\n"
-                                    if self.agent_knowledge_context
-                                    and self.crew_knowledge_context
-                                    else ""
-                                )
-                                + (self.crew_knowledge_context or "")
-                            ),
-                        ),
-                    )
-            except Exception as e:
-                crewai_event_bus.emit(
-                    self,
-                    event=KnowledgeSearchQueryFailedEvent(
-                        query=self.knowledge_search_query or "",
-                        agent=self,
-                        error=str(e),
-                    ),
+        if self.crew:
+            knowledge_snippets = self.crew.query_knowledge(
+                [task.prompt()], **knowledge_config
+            )
+            if knowledge_snippets:
+                self.crew_knowledge_context = extract_knowledge_context(
+                    knowledge_snippets
                 )
+                if self.crew_knowledge_context:
+                    task_prompt += self.crew_knowledge_context
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
@@ -382,19 +288,12 @@ class Agent(BaseAgent):
 
             # Determine execution method based on timeout setting
             if self.max_execution_time is not None:
-                if (
-                    not isinstance(self.max_execution_time, int)
-                    or self.max_execution_time <= 0
-                ):
-                    raise ValueError(
-                        "Max Execution time must be a positive integer greater than zero"
-                    )
-                result = self._execute_with_timeout(
-                    task_prompt, task, self.max_execution_time
-                )
+                if not isinstance(self.max_execution_time, int) or self.max_execution_time <= 0:
+                    raise ValueError("Max Execution time must be a positive integer greater than zero")
+                result = self._execute_with_timeout(task_prompt, task, self.max_execution_time)
             else:
                 result = self._execute_without_timeout(task_prompt, task)
-
+                
         except TimeoutError as e:
             # Propagate TimeoutError without retry
             crewai_event_bus.emit(
@@ -446,46 +345,54 @@ class Agent(BaseAgent):
         )
         return result
 
-    def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> str:
+    def _execute_with_timeout(
+        self,
+        task_prompt: str,
+        task: Task,
+        timeout: int
+    ) -> str:
         """Execute a task with a timeout.
-
+        
         Args:
             task_prompt: The prompt to send to the agent.
             task: The task being executed.
             timeout: Maximum execution time in seconds.
-
+            
         Returns:
             The output of the agent.
-
+            
         Raises:
             TimeoutError: If execution exceeds the timeout.
             RuntimeError: If execution fails for other reasons.
         """
         import concurrent.futures
-
         with concurrent.futures.ThreadPoolExecutor() as executor:
             future = executor.submit(
-                self._execute_without_timeout, task_prompt=task_prompt, task=task
+                self._execute_without_timeout,
+                task_prompt=task_prompt,
+                task=task
             )
-
+            
             try:
                 return future.result(timeout=timeout)
             except concurrent.futures.TimeoutError:
                 future.cancel()
-                raise TimeoutError(
-                    f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task."
-                )
+                raise TimeoutError(f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task.")
             except Exception as e:
                 future.cancel()
                 raise RuntimeError(f"Task execution failed: {str(e)}")
 
-    def _execute_without_timeout(self, task_prompt: str, task: Task) -> str:
+    def _execute_without_timeout(
+        self,
+        task_prompt: str,
+        task: Task
+    ) -> str:
         """Execute a task without a timeout.
-
+        
         Args:
             task_prompt: The prompt to send to the agent.
             task: The task being executed.
-
+            
         Returns:
             The output of the agent.
         """
@@ -618,26 +525,6 @@ class Agent(BaseAgent):
 
         return description
 
-    def _inject_date_to_task(self, task):
-        """Inject the current date into the task description if inject_date is enabled."""
-        if self.inject_date:
-            from datetime import datetime
-            try:
-                valid_format_codes = ['%Y', '%m', '%d', '%H', '%M', '%S', '%B', '%b', '%A', '%a']
-                is_valid = any(code in self.date_format for code in valid_format_codes)
-                
-                if not is_valid:
-                    raise ValueError(f"Invalid date format: {self.date_format}")
-                
-                current_date: str = datetime.now().strftime(self.date_format)
-                task.description += f"\n\nCurrent Date: {current_date}"
-            except Exception as e:
-                if hasattr(self, '_logger'):
-                    self._logger.log("warning", f"Failed to inject date: {str(e)}")
-                else:
-                    print(f"Warning: Failed to inject date: {str(e)}")
-
-
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
         if not shutil.which("docker"):
@@ -673,61 +560,6 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
 
-    def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
-        """Generate a search query for the knowledge base based on the task description."""
-        crewai_event_bus.emit(
-            self,
-            event=KnowledgeQueryStartedEvent(
-                task_prompt=task_prompt,
-                agent=self,
-            ),
-        )
-        query = self.i18n.slice("knowledge_search_query").format(
-            task_prompt=task_prompt
-        )
-        rewriter_prompt = self.i18n.slice("knowledge_search_query_system_prompt")
-        if not isinstance(self.llm, BaseLLM):
-            self._logger.log(
-                "warning",
-                f"Knowledge search query failed: LLM for agent '{self.role}' is not an instance of BaseLLM",
-            )
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryFailedEvent(
-                    agent=self,
-                    error="LLM is not compatible with knowledge search queries",
-                ),
-            )
-            return None
-
-        try:
-            rewritten_query = self.llm.call(
-                [
-                    {
-                        "role": "system",
-                        "content": rewriter_prompt,
-                    },
-                    {"role": "user", "content": query},
-                ]
-            )
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryCompletedEvent(
-                    query=query,
-                    agent=self,
-                ),
-            )
-            return rewritten_query
-        except Exception as e:
-            crewai_event_bus.emit(
-                self,
-                event=KnowledgeQueryFailedEvent(
-                    agent=self,
-                    error=str(e),
-                ),
-            )
-            return None
-
     def kickoff(
         self,
         messages: Union[str, List[Dict[str, str]]],

src/crewai/cli/authentication/token.py

@@ -5,5 +5,5 @@ def get_auth_token() -> str:
     """Get the authentication token."""
     access_token = TokenManager().get_token()
     if not access_token:
-        raise Exception("No token found, make sure you are logged in")
+        raise Exception()
     return access_token

src/crewai/cli/cli.py

@@ -1,5 +1,6 @@
+import os
 from importlib.metadata import version as get_version
-from typing import Optional
+from typing import Optional, Tuple
 
 import click
 
@@ -137,8 +138,12 @@ def log_tasks_outputs() -> None:
 @click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
 @click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
 @click.option("-kn", "--knowledge", is_flag=True, help="Reset KNOWLEDGE storage")
-@click.option("-akn", "--agent-knowledge", is_flag=True, help="Reset AGENT KNOWLEDGE storage")
-@click.option("-k","--kickoff-outputs",is_flag=True,help="Reset LATEST KICKOFF TASK OUTPUTS")
+@click.option(
+    "-k",
+    "--kickoff-outputs",
+    is_flag=True,
+    help="Reset LATEST KICKOFF TASK OUTPUTS",
+)
 @click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
 def reset_memories(
     long: bool,
@@ -146,20 +151,18 @@ def reset_memories(
     entities: bool,
     knowledge: bool,
     kickoff_outputs: bool,
-    agent_knowledge: bool,
     all: bool,
 ) -> None:
     """
-    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs, knowledge, agent_knowledge). This will delete all the data saved.
+    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
     """
     try:
-        memory_types = [long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all]
-        if not any(memory_types):
+        if not all and not (long or short or entities or knowledge or kickoff_outputs):
             click.echo(
                 "Please specify at least one memory type to reset using the appropriate flags."
             )
             return
-        reset_memories_command(long, short, entities, knowledge, agent_knowledge, kickoff_outputs, all)
+        reset_memories_command(long, short, entities, knowledge, kickoff_outputs, all)
     except Exception as e:
         click.echo(f"An error occurred while resetting memories: {e}", err=True)
 

src/crewai/cli/constants.py

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

src/crewai/cli/plus_api.py

@@ -14,7 +14,6 @@ class PlusAPI:
 
     TOOLS_RESOURCE = "/crewai_plus/api/v1/tools"
     CREWS_RESOURCE = "/crewai_plus/api/v1/crews"
-    AGENTS_RESOURCE = "/crewai_plus/api/v1/agents"
 
     def __init__(self, api_key: str) -> None:
         self.api_key = api_key
@@ -38,9 +37,6 @@ class PlusAPI:
     def get_tool(self, handle: str):
         return self._make_request("GET", f"{self.TOOLS_RESOURCE}/{handle}")
 
-    def get_agent(self, handle: str):
-        return self._make_request("GET", f"{self.AGENTS_RESOURCE}/{handle}")
-
     def publish_tool(
         self,
         handle: str,

src/crewai/cli/reset_memories_command.py

@@ -10,7 +10,6 @@ def reset_memories_command(
     short,
     entity,
     knowledge,
-    agent_knowledge,
     kickoff_outputs,
     all,
 ) -> None:
@@ -24,11 +23,10 @@ def reset_memories_command(
       kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
       all (bool): Whether to reset all memories.
       knowledge (bool): Whether to reset the knowledge.
-      agent_knowledge (bool): Whether to reset the agents knowledge.
     """
 
     try:
-        if not any([long, short, entity, kickoff_outputs, knowledge, agent_knowledge, all]):
+        if not any([long, short, entity, kickoff_outputs, knowledge, all]):
             click.echo(
                 "No memory type specified. Please specify at least one type to reset."
             )
@@ -69,11 +67,6 @@ def reset_memories_command(
                 click.echo(
                     f"[Crew ({crew.name if crew.name else crew.id})] Knowledge has been reset."
                 )
-            if agent_knowledge:
-                crew.reset_memories(command_type="agent_knowledge")
-                click.echo(
-                    f"[Crew ({crew.name if crew.name else crew.id})] Agents knowledge has been reset."
-                )
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.121.0,<1.0.0"
+    "crewai[tools]>=0.118.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.121.0,<1.0.0",
+    "crewai[tools]>=0.118.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.121.0"
+    "crewai[tools]>=0.118.0"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -161,7 +161,7 @@ def tree_find_and_replace(directory, find, replace):
         for filename in files:
             filepath = os.path.join(path, filename)
 
-            with open(filepath, "r", encoding="utf-8", errors="ignore") as file:
+            with open(filepath, "r") as file:
                 contents = file.read()
             with open(filepath, "w") as file:
                 file.write(contents.replace(find, replace))

src/crewai/crew.py

@@ -52,7 +52,7 @@ from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import BaseTool, Tool
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import NOT_SPECIFIED, TRAINING_DATA_FILE
+from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.events.crew_events import (
@@ -315,7 +315,9 @@ class Crew(FlowTrackable, BaseModel):
         """Initialize private memory attributes."""
         self._external_memory = (
             # External memory doesn’t support a default value since it was designed to be managed entirely externally
-            self.external_memory.set_crew(self) if self.external_memory else None
+            self.external_memory.set_crew(self)
+            if self.external_memory
+            else None
         )
 
         self._long_term_memory = self.long_term_memory
@@ -476,7 +478,7 @@ class Crew(FlowTrackable, BaseModel):
         separated by a synchronous task.
         """
         for i, task in enumerate(self.tasks):
-            if task.async_execution and isinstance(task.context, list):
+            if task.async_execution and task.context:
                 for context_task in task.context:
                     if context_task.async_execution:
                         for j in range(i - 1, -1, -1):
@@ -494,7 +496,7 @@ class Crew(FlowTrackable, BaseModel):
         task_indices = {id(task): i for i, task in enumerate(self.tasks)}
 
         for task in self.tasks:
-            if isinstance(task.context, list):
+            if task.context:
                 for context_task in task.context:
                     if id(context_task) not in task_indices:
                         continue  # Skip context tasks not in the main tasks list
@@ -1032,14 +1034,11 @@ class Crew(FlowTrackable, BaseModel):
                 )
         return cast(List[BaseTool], tools)
 
-    def _get_context(self, task: Task, task_outputs: List[TaskOutput]) -> str:
-        if not task.context:
-            return ""
-
+    def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
         context = (
-            aggregate_raw_outputs_from_task_outputs(task_outputs)
-            if task.context is NOT_SPECIFIED
-            else aggregate_raw_outputs_from_tasks(task.context)
+            aggregate_raw_outputs_from_tasks(task.context)
+            if task.context
+            else aggregate_raw_outputs_from_task_outputs(task_outputs)
         )
         return context
 
@@ -1202,6 +1201,7 @@ class Crew(FlowTrackable, BaseModel):
             "_long_term_memory",
             "_entity_memory",
             "_external_memory",
+            "_telemetry",
             "agents",
             "tasks",
             "knowledge_sources",
@@ -1226,7 +1226,7 @@ class Crew(FlowTrackable, BaseModel):
             task_mapping[task.key] = cloned_task
 
         for cloned_task, original_task in zip(cloned_tasks, self.tasks):
-            if isinstance(original_task.context, list):
+            if original_task.context:
                 cloned_context = [
                     task_mapping[context_task.key]
                     for context_task in original_task.context
@@ -1353,7 +1353,7 @@ class Crew(FlowTrackable, BaseModel):
 
         Args:
             command_type: Type of memory to reset.
-                Valid options: 'long', 'short', 'entity', 'knowledge', 'agent_knowledge'
+                Valid options: 'long', 'short', 'entity', 'knowledge',
                 'kickoff_outputs', or 'all'
 
         Raises:
@@ -1366,7 +1366,6 @@ class Crew(FlowTrackable, BaseModel):
                 "short",
                 "entity",
                 "knowledge",
-                "agent_knowledge",
                 "kickoff_outputs",
                 "all",
                 "external",
@@ -1391,14 +1390,19 @@ class Crew(FlowTrackable, BaseModel):
 
     def _reset_all_memories(self) -> None:
         """Reset all available memory systems."""
-        memory_systems = self._get_memory_systems()
+        memory_systems = [
+            ("short term", getattr(self, "_short_term_memory", None)),
+            ("entity", getattr(self, "_entity_memory", None)),
+            ("external", getattr(self, "_external_memory", None)),
+            ("long term", getattr(self, "_long_term_memory", None)),
+            ("task output", getattr(self, "_task_output_handler", None)),
+            ("knowledge", getattr(self, "knowledge", None)),
+        ]
 
-        for memory_type, config in memory_systems.items():
-            if (system := config.get("system")) is not None:
-                name = config.get("name")
+        for name, system in memory_systems:
+            if system is not None:
                 try:
-                    reset_fn: Callable = cast(Callable, config.get("reset"))
-                    reset_fn(system)
+                    system.reset()
                     self._logger.log(
                         "info",
                         f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
@@ -1417,17 +1421,24 @@ class Crew(FlowTrackable, BaseModel):
         Raises:
             RuntimeError: If the specified memory system fails to reset
         """
-        memory_systems = self._get_memory_systems()
-        config = memory_systems[memory_type]
-        system = config.get("system")
-        name = config.get("name")
+        reset_functions = {
+            "long": (getattr(self, "_long_term_memory", None), "long term"),
+            "short": (getattr(self, "_short_term_memory", None), "short term"),
+            "entity": (getattr(self, "_entity_memory", None), "entity"),
+            "knowledge": (getattr(self, "knowledge", None), "knowledge"),
+            "kickoff_outputs": (
+                getattr(self, "_task_output_handler", None),
+                "task output",
+            ),
+            "external": (getattr(self, "_external_memory", None), "external"),
+        }
 
-        if system is None:
+        memory_system, name = reset_functions[memory_type]
+        if memory_system is None:
             raise RuntimeError(f"{name} memory system is not initialized")
 
         try:
-            reset_fn: Callable = cast(Callable, config.get("reset"))
-            reset_fn(system)
+            memory_system.reset()
             self._logger.log(
                 "info",
                 f"[Crew ({self.name if self.name else self.id})] {name} memory has been reset",
@@ -1436,73 +1447,3 @@ class Crew(FlowTrackable, BaseModel):
             raise RuntimeError(
                 f"[Crew ({self.name if self.name else self.id})] Failed to reset {name} memory: {str(e)}"
             ) from e
-
-    def _get_memory_systems(self):
-        """Get all available memory systems with their configuration.
-
-        Returns:
-            Dict containing all memory systems with their reset functions and display names.
-        """
-
-        def default_reset(memory):
-            return memory.reset()
-
-        def knowledge_reset(memory):
-            return self.reset_knowledge(memory)
-
-        # Get knowledge for agents
-        agent_knowledges = [
-            getattr(agent, "knowledge", None)
-            for agent in self.agents
-            if getattr(agent, "knowledge", None) is not None
-        ]
-        # Get knowledge for crew and agents
-        crew_knowledge = getattr(self, "knowledge", None)
-        crew_and_agent_knowledges = (
-            [crew_knowledge] if crew_knowledge is not None else []
-        ) + agent_knowledges
-
-        return {
-            "short": {
-                "system": getattr(self, "_short_term_memory", None),
-                "reset": default_reset,
-                "name": "Short Term",
-            },
-            "entity": {
-                "system": getattr(self, "_entity_memory", None),
-                "reset": default_reset,
-                "name": "Entity",
-            },
-            "external": {
-                "system": getattr(self, "_external_memory", None),
-                "reset": default_reset,
-                "name": "External",
-            },
-            "long": {
-                "system": getattr(self, "_long_term_memory", None),
-                "reset": default_reset,
-                "name": "Long Term",
-            },
-            "kickoff_outputs": {
-                "system": getattr(self, "_task_output_handler", None),
-                "reset": default_reset,
-                "name": "Task Output",
-            },
-            "knowledge": {
-                "system": crew_and_agent_knowledges
-                if crew_and_agent_knowledges
-                else None,
-                "reset": knowledge_reset,
-                "name": "Crew Knowledge and Agent Knowledge",
-            },
-            "agent_knowledge": {
-                "system": agent_knowledges if agent_knowledges else None,
-                "reset": knowledge_reset,
-                "name": "Agent Knowledge",
-            },
-        }
-
-    def reset_knowledge(self, knowledges: List[Knowledge]) -> None:
-        """Reset crew and agent knowledge storage."""
-        for ks in knowledges:
-            ks.reset()

src/crewai/llm.py

@@ -5,7 +5,8 @@ import sys
 import threading
 import warnings
 from collections import defaultdict
-from contextlib import contextmanager, redirect_stderr, redirect_stdout
+from contextlib import contextmanager
+from types import SimpleNamespace
 from typing import (
     Any,
     DefaultDict,
@@ -30,6 +31,7 @@ from crewai.utilities.events.llm_events import (
     LLMCallType,
     LLMStreamChunkEvent,
 )
+from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
@@ -43,9 +45,6 @@ with warnings.catch_warnings():
     from litellm.utils import supports_response_schema
 
 
-import io
-from typing import TextIO
-
 from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -55,17 +54,12 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 load_dotenv()
 
 
-class FilteredStream(io.TextIOBase):
-    _lock = None
-
-    def __init__(self, original_stream: TextIO):
+class FilteredStream:
+    def __init__(self, original_stream):
         self._original_stream = original_stream
         self._lock = threading.Lock()
 
-    def write(self, s: str) -> int:
-        if not self._lock:
-            self._lock = threading.Lock()
-
+    def write(self, s) -> int:
         with self._lock:
             # Filter out extraneous messages from LiteLLM
             if (
@@ -220,11 +214,15 @@ def suppress_warnings():
         )
 
         # Redirect stdout and stderr
-        with (
-            redirect_stdout(FilteredStream(sys.stdout)),
-            redirect_stderr(FilteredStream(sys.stderr)),
-        ):
+        old_stdout = sys.stdout
+        old_stderr = sys.stderr
+        sys.stdout = FilteredStream(old_stdout)
+        sys.stderr = FilteredStream(old_stderr)
+        try:
             yield
+        finally:
+            sys.stdout = old_stdout
+            sys.stderr = old_stderr
 
 
 class Delta(TypedDict):

src/crewai/task.py

@@ -2,6 +2,7 @@ import datetime
 import inspect
 import json
 import logging
+import re
 import threading
 import uuid
 from concurrent.futures import Future
@@ -40,7 +41,6 @@ from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
-from crewai.utilities.constants import NOT_SPECIFIED
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.events import (
     TaskCompletedEvent,
@@ -97,7 +97,7 @@ class Task(BaseModel):
     )
     context: Optional[List["Task"]] = Field(
         description="Other tasks that will have their output used as context for this task.",
-        default=NOT_SPECIFIED,
+        default=None,
     )
     async_execution: Optional[bool] = Field(
         description="Whether the task should be executed asynchronously or not.",
@@ -135,10 +135,6 @@ class Task(BaseModel):
         description="Whether the task should have a human review the final answer of the agent",
         default=False,
     )
-    markdown: Optional[bool] = Field(
-        description="Whether the task should instruct the agent to return the final answer formatted in Markdown",
-        default=False,
-    )
     converter_cls: Optional[Type[Converter]] = Field(
         description="A converter class used to export structured output",
         default=None,
@@ -526,14 +522,10 @@ class Task(BaseModel):
         return guardrail_result
 
     def prompt(self) -> str:
-        """Generates the task prompt with optional markdown formatting.
-        
-        When the markdown attribute is True, instructions for formatting the
-        response in Markdown syntax will be added to the prompt.
-        
+        """Prompt the task.
+
         Returns:
-            str: The formatted prompt string containing the task description,
-                 expected output, and optional markdown formatting instructions.
+            Prompt of the task.
         """
         tasks_slices = [self.description]
 
@@ -541,17 +533,6 @@ class Task(BaseModel):
             expected_output=self.expected_output
         )
         tasks_slices = [self.description, output]
-        
-        if self.markdown:
-            markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.
-Follow these guidelines:
-- Use # for headers
-- Use ** for bold text
-- Use * for italic text
-- Use - or * for bullet points
-- Use `code` for inline code
-- Use ```language for code blocks"""
-            tasks_slices.append(markdown_instruction)
         return "\n".join(tasks_slices)
 
     def interpolate_inputs_and_add_conversation_history(
@@ -662,7 +643,7 @@ Follow these guidelines:
 
         cloned_context = (
             [task_mapping[context_task.key] for context_task in self.context]
-            if isinstance(self.context, list)
+            if self.context
             else None
         )
 

src/crewai/tasks/hallucination_guardrail.py

@@ -1,96 +0,0 @@
-"""Hallucination Guardrail Placeholder for CrewAI.
-
-This is a no-op version of the HallucinationGuardrail for the open-source repository.
-
-Classes:
-    HallucinationGuardrail: Placeholder guardrail that validates task outputs.
-"""
-
-from typing import Any, Optional, Tuple
-
-from crewai.llm import LLM
-from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.logger import Logger
-
-
-class HallucinationGuardrail:
-    """Placeholder for the HallucinationGuardrail feature.
-
-    Attributes:
-        context: The reference context that outputs would be checked against.
-        llm: The language model that would be used for evaluation.
-        threshold: Optional minimum faithfulness score that would be required to pass.
-        tool_response: Optional tool response information that would be used in evaluation.
-
-    Examples:
-        >>> # Basic usage with default verdict logic
-        >>> guardrail = HallucinationGuardrail(
-        ...     context="AI helps with various tasks including analysis and generation.",
-        ...     llm=agent.llm
-        ... )
-
-        >>> # With custom threshold for stricter validation
-        >>> strict_guardrail = HallucinationGuardrail(
-        ...     context="Quantum computing uses qubits in superposition.",
-        ...     llm=agent.llm,
-        ...     threshold=8.0  # Would require score >= 8 to pass in enterprise version
-        ... )
-
-        >>> # With tool response for additional context
-        >>> guardrail_with_tools = HallucinationGuardrail(
-        ...     context="The current weather data",
-        ...     llm=agent.llm,
-        ...     tool_response="Weather API returned: Temperature 22°C, Humidity 65%"
-        ... )
-    """
-
-    def __init__(
-        self,
-        context: str,
-        llm: LLM,
-        threshold: Optional[float] = None,
-        tool_response: str = "",
-    ):
-        """Initialize the HallucinationGuardrail placeholder.
-
-        Args:
-            context: The reference context that outputs would be checked against.
-            llm: The language model that would be used for evaluation.
-            threshold: Optional minimum faithfulness score that would be required to pass.
-            tool_response: Optional tool response information that would be used in evaluation.
-        """
-        self.context = context
-        self.llm: LLM = llm
-        self.threshold = threshold
-        self.tool_response = tool_response
-        self._logger = Logger(verbose=True)
-        self._logger.log(
-            "warning",
-            """Hallucination detection is a no-op in open source, use it for free at https://app.crewai.com\n""",
-            color="red",
-        )
-
-    @property
-    def description(self) -> str:
-        """Generate a description of this guardrail for event logging."""
-        return "HallucinationGuardrail (no-op)"
-
-    def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
-        """Validate a task output against hallucination criteria.
-
-        In the open source, this method always returns that the output is valid.
-
-        Args:
-            task_output: The output to be validated.
-
-        Returns:
-            A tuple containing:
-                - True
-                - The raw task output
-        """
-        self._logger.log(
-            "warning",
-            "Premium hallucination detection skipped (use for free at https://app.crewai.com)\n",
-            color="red",
-        )
-        return True, task_output.raw

src/crewai/telemetry/telemetry.py

@@ -9,19 +9,6 @@ import warnings
 from contextlib import contextmanager
 from importlib.metadata import version
 from typing import TYPE_CHECKING, Any, Optional
-import threading
-
-from opentelemetry import trace
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
-    OTLPSpanExporter,
-)
-from opentelemetry.sdk.resources import SERVICE_NAME, Resource
-from opentelemetry.sdk.trace import TracerProvider
-from opentelemetry.sdk.trace.export import (
-    BatchSpanProcessor,
-    SpanExportResult,
-)
-from opentelemetry.trace import Span, Status, StatusCode
 
 from crewai.telemetry.constants import (
     CREWAI_TELEMETRY_BASE_URL,
@@ -38,6 +25,18 @@ def suppress_warnings():
         yield
 
 
+from opentelemetry import trace  # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
+    OTLPSpanExporter,  # noqa: E402
+)
+from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
+from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
+from opentelemetry.sdk.trace.export import (  # noqa: E402
+    BatchSpanProcessor,
+    SpanExportResult,
+)
+from opentelemetry.trace import Span, Status, StatusCode  # noqa: E402
+
 if TYPE_CHECKING:
     from crewai.crew import Crew
     from crewai.task import Task
@@ -65,17 +64,7 @@ class Telemetry:
     attribute in the Crew class.
     """
 
-    _instance = None
-    _lock = threading.Lock()
-
-    def __new__(cls):
-        if cls._instance is None:
-            with cls._lock:
-                if cls._instance is None:
-                    cls._instance = super(Telemetry, cls).__new__(cls)
-        return cls._instance
-
-    def __init__(self) -> None:
+    def __init__(self):
         self.ready: bool = False
         self.trace_set: bool = False
 
@@ -243,7 +232,7 @@ class Telemetry:
                                 "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
-                                    if isinstance(task.context, list)
+                                    if task.context
                                     else None
                                 ),
                                 "tools_names": [
@@ -759,7 +748,7 @@ class Telemetry:
                             "agent_key": task.agent.key if task.agent else None,
                             "context": (
                                 [task.description for task in task.context]
-                                if isinstance(task.context, list)
+                                if task.context
                                 else None
                             ),
                             "tools_names": [

src/crewai/translations/en.json

@@ -27,9 +27,7 @@
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary.",
     "lite_agent_system_prompt_with_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
     "lite_agent_system_prompt_without_tools": "You are {role}. {backstory}\nYour personal goal is: {goal}\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python.",
-    "knowledge_search_query": "The original query is: {task_prompt}.",
-    "knowledge_search_query_system_prompt": "Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."
+    "lite_agent_response_format": "\nIMPORTANT: Your final answer MUST contain all the information requested in the following format: {response_format}\n\nIMPORTANT: Ensure the final output does not include any code block markers like ```json or ```python."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
@@ -51,11 +49,5 @@
       "description": "See image to understand its content, you can optionally ask a question about the image",
       "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe."
     }
-  },
-  "reasoning": {
-    "initial_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are creating a strategic plan for a task that requires your expertise and unique perspective.",
-    "refine_plan": "You are {role}, a professional with the following background: {backstory}\n\nYour primary goal is: {goal}\n\nAs {role}, you are refining a strategic plan for a task that requires your expertise and unique perspective.",
-    "create_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou have been assigned the following task:\n{description}\n\nExpected output:\n{expected_output}\n\nAvailable tools: {tools}\n\nBefore executing this task, create a detailed plan that leverages your expertise as {role} and outlines:\n1. Your understanding of the task from your professional perspective\n2. The key steps you'll take to complete it, drawing on your background and skills\n3. How you'll approach any challenges that might arise, considering your expertise\n4. How you'll strategically use the available tools based on your experience\n5. The expected outcome and how it aligns with your goal\n\nAfter creating your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan because [specific reason].\"",
-    "refine_plan_prompt": "You are {role} with this background: {backstory}\n\nYour primary goal is: {goal}\n\nYou created the following plan for this task:\n{current_plan}\n\nHowever, you indicated that you're not ready to execute the task yet.\n\nPlease refine your plan further, drawing on your expertise as {role} to address any gaps or uncertainties.\n\nAfter refining your plan, assess whether you feel ready to execute the task.\nConclude with one of these statements:\n- \"READY: I am ready to execute the task.\"\n- \"NOT READY: I need to refine my plan further because [specific reason].\""
   }
 }

src/crewai/utilities/agent_utils.py

@@ -16,7 +16,6 @@ from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_types import ToolResult
 from crewai.utilities import I18N, Printer
-from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -429,41 +428,3 @@ def show_agent_logs(
             printer.print(
                 content=f"\033[95m## Final Answer:\033[00m \033[92m\n{formatted_answer.output}\033[00m\n\n"
             )
-
-
-def load_agent_from_repository(from_repository: str) -> Dict[str, Any]:
-    attributes: Dict[str, Any] = {}
-    if from_repository:
-        import importlib
-
-        from crewai.cli.authentication.token import get_auth_token
-        from crewai.cli.plus_api import PlusAPI
-
-        client = PlusAPI(api_key=get_auth_token())
-        response = client.get_agent(from_repository)
-        if response.status_code == 404:
-            raise AgentRepositoryError(
-                f"Agent {from_repository} does not exist, make sure the name is correct or the agent is available on your organization"
-            )
-
-        if response.status_code != 200:
-            raise AgentRepositoryError(
-                f"Agent {from_repository} could not be loaded: {response.text}"
-            )
-
-        agent = response.json()
-        for key, value in agent.items():
-            if key == "tools":
-                attributes[key] = []
-                for tool in value:
-                    try:
-                        module = importlib.import_module("crewai_tools")
-                        tool_class = getattr(module, tool["name"])
-                        attributes[key].append(tool_class())
-                    except Exception as e:
-                        raise AgentRepositoryError(
-                            f"Tool {tool['name']} could not be loaded: {e}"
-                        ) from e
-            else:
-                attributes[key] = value
-    return attributes

src/crewai/utilities/constants.py

@@ -5,14 +5,3 @@ KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
 MAX_FILE_NAME_LENGTH = 255
 EMITTER_COLOR = "bold_blue"
-
-
-class _NotSpecified:
-    def __repr__(self):
-        return "NOT_SPECIFIED"
-
-
-# Sentinel value used to detect when no value has been explicitly provided.
-# Unlike `None`, which might be a valid value from the user, `NOT_SPECIFIED` allows
-# us to distinguish between "not passed at all" and "explicitly passed None" or "[]".
-NOT_SPECIFIED = _NotSpecified()

src/crewai/utilities/errors.py

@@ -1,5 +1,4 @@
 """Error message definitions for CrewAI database operations."""
-
 from typing import Optional
 
 
@@ -38,9 +37,3 @@ class DatabaseError:
             The formatted error message
         """
         return template.format(str(error))
-
-
-class AgentRepositoryError(Exception):
-    """Exception raised when an agent repository is not found."""
-
-    ...

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -9,8 +9,7 @@ from crewai.agent import Agent
 from crewai.llm import BaseLLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.crew_events import CrewTestResultEvent
+from crewai.telemetry import Telemetry
 
 
 class TaskEvaluationPydanticOutput(BaseModel):
@@ -37,6 +36,7 @@ class CrewEvaluator:
     def __init__(self, crew, eval_llm: InstanceOf[BaseLLM]):
         self.crew = crew
         self.llm = eval_llm
+        self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
     def _setup_for_evaluating(self) -> None:
@@ -178,15 +178,11 @@ class CrewEvaluator:
         evaluation_result = evaluation_task.execute_sync()
 
         if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
-            crewai_event_bus.emit(
+            self._test_result_span = self._telemetry.individual_test_result_span(
                 self.crew,
-                CrewTestResultEvent(
-                    quality=evaluation_result.pydantic.quality,
-                    execution_duration=current_task.execution_duration,
-                    model=self.llm.model,
-                    crew_name=self.crew.name,
-                    crew=self.crew,
-                ),
+                evaluation_result.pydantic.quality,
+                current_task.execution_duration,
+                self.llm.model,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

src/crewai/utilities/events/crew_events.py

@@ -100,12 +100,3 @@ class CrewTestFailedEvent(CrewBaseEvent):
 
     error: str
     type: str = "crew_test_failed"
-
-
-class CrewTestResultEvent(CrewBaseEvent):
-    """Event emitted when a crew test result is available"""
-
-    quality: float
-    execution_duration: float
-    model: str
-    type: str = "crew_test_result"

src/crewai/utilities/events/event_listener.py

@@ -8,14 +8,6 @@ from crewai.telemetry.telemetry import Telemetry
 from crewai.utilities import Logger
 from crewai.utilities.constants import EMITTER_COLOR
 from crewai.utilities.events.base_event_listener import BaseEventListener
-from crewai.utilities.events.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 from crewai.utilities.events.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
@@ -37,7 +29,6 @@ from .crew_events import (
     CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
     CrewTestFailedEvent,
-    CrewTestResultEvent,
     CrewTestStartedEvent,
     CrewTrainCompletedEvent,
     CrewTrainFailedEvent,
@@ -57,11 +48,6 @@ from .tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from .reasoning_events import (
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-)
 
 
 class EventListener(BaseEventListener):
@@ -71,8 +57,6 @@ class EventListener(BaseEventListener):
     execution_spans: Dict[Task, Any] = Field(default_factory=dict)
     next_chunk = 0
     text_stream = StringIO()
-    knowledge_retrieval_in_progress = False
-    knowledge_query_in_progress = False
 
     def __new__(cls):
         if cls._instance is None:
@@ -135,15 +119,6 @@ class EventListener(BaseEventListener):
         def on_crew_train_failed(source, event: CrewTrainFailedEvent):
             self.formatter.handle_crew_train_failed(event.crew_name or "Crew")
 
-        @crewai_event_bus.on(CrewTestResultEvent)
-        def on_crew_test_result(source, event: CrewTestResultEvent):
-            self._telemetry.individual_test_result_span(
-                source.crew,
-                event.quality,
-                int(event.execution_duration),
-                event.model,
-            )
-
         # ----------- TASK EVENTS -----------
 
         @crewai_event_bus.on(TaskStartedEvent)
@@ -367,84 +342,5 @@ class EventListener(BaseEventListener):
         def on_crew_test_failed(source, event: CrewTestFailedEvent):
             self.formatter.handle_crew_test_failed(event.crew_name or "Crew")
 
-        @crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
-        def on_knowledge_retrieval_started(
-            source, event: KnowledgeRetrievalStartedEvent
-        ):
-            if self.knowledge_retrieval_in_progress:
-                return
-
-            self.knowledge_retrieval_in_progress = True
-
-            self.formatter.handle_knowledge_retrieval_started(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
-        def on_knowledge_retrieval_completed(
-            source, event: KnowledgeRetrievalCompletedEvent
-        ):
-            if not self.knowledge_retrieval_in_progress:
-                return
-
-            self.knowledge_retrieval_in_progress = False
-            self.formatter.handle_knowledge_retrieval_completed(
-                self.formatter.current_agent_branch,
-                self.formatter.current_crew_tree,
-                event.retrieved_knowledge,
-            )
-
-        @crewai_event_bus.on(KnowledgeQueryStartedEvent)
-        def on_knowledge_query_started(source, event: KnowledgeQueryStartedEvent):
-            pass
-
-        @crewai_event_bus.on(KnowledgeQueryFailedEvent)
-        def on_knowledge_query_failed(source, event: KnowledgeQueryFailedEvent):
-            self.formatter.handle_knowledge_query_failed(
-                self.formatter.current_agent_branch,
-                event.error,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(KnowledgeQueryCompletedEvent)
-        def on_knowledge_query_completed(source, event: KnowledgeQueryCompletedEvent):
-            pass
-
-        @crewai_event_bus.on(KnowledgeSearchQueryFailedEvent)
-        def on_knowledge_search_query_failed(
-            source, event: KnowledgeSearchQueryFailedEvent
-        ):
-            self.formatter.handle_knowledge_search_query_failed(
-                self.formatter.current_agent_branch,
-                event.error,
-                self.formatter.current_crew_tree,
-            )
-
-        # ----------- REASONING EVENTS -----------
-
-        @crewai_event_bus.on(AgentReasoningStartedEvent)
-        def on_agent_reasoning_started(source, event: AgentReasoningStartedEvent):
-            self.formatter.handle_reasoning_started(
-                self.formatter.current_agent_branch,
-                event.attempt,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(AgentReasoningCompletedEvent)
-        def on_agent_reasoning_completed(source, event: AgentReasoningCompletedEvent):
-            self.formatter.handle_reasoning_completed(
-                event.plan,
-                event.ready,
-                self.formatter.current_crew_tree,
-            )
-
-        @crewai_event_bus.on(AgentReasoningFailedEvent)
-        def on_agent_reasoning_failed(source, event: AgentReasoningFailedEvent):
-            self.formatter.handle_reasoning_failed(
-                event.error,
-                self.formatter.current_crew_tree,
-            )
-
 
 event_listener = EventListener()

src/crewai/utilities/events/event_types.py

@@ -43,19 +43,6 @@ from .tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from .reasoning_events import (
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-)
-from .knowledge_events import (
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
 
 EventTypes = Union[
     CrewKickoffStartedEvent,
@@ -87,13 +74,4 @@ EventTypes = Union[
     LLMStreamChunkEvent,
     LLMGuardrailStartedEvent,
     LLMGuardrailCompletedEvent,
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeSearchQueryFailedEvent,
 ]

src/crewai/utilities/events/knowledge_events.py

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

src/crewai/utilities/events/llm_guardrail_events.py

@@ -19,13 +19,10 @@ class LLMGuardrailStartedEvent(BaseEvent):
         from inspect import getsource
 
         from crewai.tasks.llm_guardrail import LLMGuardrail
-        from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
-        if isinstance(self.guardrail, LLMGuardrail) or isinstance(
-            self.guardrail, HallucinationGuardrail
-        ):
+        if isinstance(self.guardrail, LLMGuardrail):
             self.guardrail = self.guardrail.description.strip()
         elif isinstance(self.guardrail, Callable):
             self.guardrail = getsource(self.guardrail).strip()

src/crewai/utilities/events/reasoning_events.py

@@ -1,31 +0,0 @@
-from crewai.utilities.events.base_events import BaseEvent
-
-
-class AgentReasoningStartedEvent(BaseEvent):
-    """Event emitted when an agent starts reasoning about a task."""
-
-    type: str = "agent_reasoning_started"
-    agent_role: str
-    task_id: str
-    attempt: int = 1  # The current reasoning/refinement attempt
-
-
-class AgentReasoningCompletedEvent(BaseEvent):
-    """Event emitted when an agent finishes its reasoning process."""
-
-    type: str = "agent_reasoning_completed"
-    agent_role: str
-    task_id: str
-    plan: str
-    ready: bool
-    attempt: int = 1
-
-
-class AgentReasoningFailedEvent(BaseEvent):
-    """Event emitted when the reasoning process fails."""
-
-    type: str = "agent_reasoning_failed"
-    agent_role: str
-    task_id: str
-    error: str
-    attempt: int = 1

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

@@ -4,7 +4,6 @@ from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text
 from rich.tree import Tree
-from rich.live import Live
 
 
 class ConsoleFormatter:
@@ -16,17 +15,10 @@ class ConsoleFormatter:
     current_method_branch: Optional[Tree] = None
     current_lite_agent_branch: Optional[Tree] = None
     tool_usage_counts: Dict[str, int] = {}
-    current_reasoning_branch: Optional[Tree] = None  # Track reasoning status
 
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
         self.verbose = verbose
-        # Live instance to dynamically update a Tree renderable (e.g. the Crew tree)
-        # When multiple Tree objects are printed sequentially we reuse this Live
-        # instance so the previous render is replaced instead of writing a new one.
-        # Once any non-Tree renderable is printed we stop the Live session so the
-        # final Tree persists on the terminal.
-        self._live: Optional[Live] = None
 
     def create_panel(self, content: Text, title: str, style: str = "blue") -> Panel:
         """Create a standardized panel with consistent styling."""
@@ -67,7 +59,7 @@ class ConsoleFormatter:
         label.append(f"{prefix} ", style=f"{style} bold")
         label.append(name, style=style)
         if status:
-            label.append("\nStatus: ", style="white")
+            label.append("\n    Status: ", style="white")
             label.append(status, style=f"{style} bold")
         tree.label = label
 
@@ -76,46 +68,7 @@ class ConsoleFormatter:
         return parent.add(Text(text, style=style))
 
     def print(self, *args, **kwargs) -> None:
-        """Custom print that replaces consecutive Tree renders.
-
-        * If the argument is a single ``Tree`` instance, we either start a
-          ``Live`` session (first tree) or update the existing one (subsequent
-          trees). This results in the tree being rendered in-place instead of
-          being appended repeatedly to the log.
-
-        * A blank call (no positional arguments) is ignored while a Live
-          session is active so it does not prematurely terminate the tree
-          rendering.
-
-        * Any other renderable will terminate the Live session (if one is
-          active) so the last tree stays on screen and the new content is
-          printed normally.
-        """
-
-        # Case 1: updating / starting live Tree rendering
-        if len(args) == 1 and isinstance(args[0], Tree):
-            tree = args[0]
-
-            if not self._live:
-                # Start a new Live session for the first tree
-                self._live = Live(tree, console=self.console, refresh_per_second=4)
-                self._live.start()
-            else:
-                # Update existing Live session
-                self._live.update(tree, refresh=True)
-            return  # Nothing else to do
-
-        # Case 2: blank line while a live session is running – ignore so we
-        # don't break the in-place rendering behaviour
-        if len(args) == 0 and self._live:
-            return
-
-        # Case 3: printing something other than a Tree → terminate live session
-        if self._live:
-            self._live.stop()
-            self._live = None
-
-        # Finally, pass through to the regular Console.print implementation
+        """Print to console with consistent formatting if verbose is enabled."""
         self.console.print(*args, **kwargs)
 
     def print_panel(
@@ -203,7 +156,7 @@ class ConsoleFormatter:
 
         task_content = Text()
         task_content.append(f"📋 Task: {task_id}", style="yellow bold")
-        task_content.append("\nStatus: ", style="white")
+        task_content.append("\n   Status: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 
         task_branch = None
@@ -243,17 +196,11 @@ class ConsoleFormatter:
         # Update tree label
         for branch in crew_tree.children:
             if str(task_id) in str(branch.label):
-                # Build label without introducing stray blank lines
                 task_content = Text()
-                # First line: Task ID
                 task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
-
-                # Second line: Assigned to
-                task_content.append("\nAssigned to: ", style="white")
+                task_content.append("\n   Assigned to: ", style="white")
                 task_content.append(agent_role, style=style)
-
-                # Third line: Status
-                task_content.append("Status: ", style="white")
+                task_content.append("\n   Status: ", style="white")
                 task_content.append(status_text, style=f"{style} bold")
                 branch.label = task_content
                 self.print(crew_tree)
@@ -272,16 +219,18 @@ class ConsoleFormatter:
         if not self.verbose or not task_branch or not crew_tree:
             return None
 
-        # Instead of creating a separate Agent node, we treat the task branch
-        # itself as the logical agent branch so that Reasoning/Tool nodes are
-        # nested under the task without an extra visual level.
+        agent_branch = task_branch.add("")
+        self.update_tree_label(
+            agent_branch, "🤖 Agent:", agent_role, "green", "In Progress"
+        )
 
-        # Store the task branch as the current_agent_branch for future nesting.
-        self.current_agent_branch = task_branch
+        self.print(crew_tree)
+        self.print()
 
-        # No additional tree modification needed; return the task branch so
-        # caller logic remains unchanged.
-        return task_branch
+        # Set the current_agent_branch attribute directly
+        self.current_agent_branch = agent_branch
+
+        return agent_branch
 
     def update_agent_status(
         self,
@@ -291,10 +240,19 @@ class ConsoleFormatter:
         status: str = "completed",
     ) -> None:
         """Update agent status in the tree."""
-        # We no longer render a separate agent branch, so this method simply
-        # updates the stored branch reference (already the task branch) without
-        # altering the tree. Keeping it a no-op avoids duplicate status lines.
-        return
+        if not self.verbose or agent_branch is None or crew_tree is None:
+            return
+
+        self.update_tree_label(
+            agent_branch,
+            "🤖 Agent:",
+            agent_role,
+            "green",
+            "✅ Completed" if status == "completed" else "❌ Failed",
+        )
+
+        self.print(crew_tree)
+        self.print()
 
     def create_flow_tree(self, flow_name: str, flow_id: str) -> Optional[Tree]:
         """Create and initialize a flow tree."""
@@ -307,7 +265,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\nID: ", style="white")
+        flow_label.append("\n    ID: ", style="white")
         flow_label.append(flow_id, style="blue")
 
         flow_tree = Tree(flow_label)
@@ -322,7 +280,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\nID: ", style="white")
+        flow_label.append("\n    ID: ", style="white")
         flow_label.append(flow_id, style="blue")
         flow_tree.label = flow_label
 
@@ -436,22 +394,12 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Parent for tool usage: LiteAgent > Agent > Task
-        branch_to_use = (
-            self.current_lite_agent_branch
-            or agent_branch
-            or self.current_task_branch
-        )
-
-        # Render full crew tree when available for consistent live updates
-        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        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:
-            # If we don't have a valid branch, default to crew_tree if provided
-            if crew_tree is not None:
-                branch_to_use = tree_to_use = crew_tree
-            else:
-                return None
+            return None
 
         # Update tool usage count
         self.tool_usage_counts[tool_name] = self.tool_usage_counts.get(tool_name, 0) + 1
@@ -470,9 +418,10 @@ class ConsoleFormatter:
             "yellow",
         )
 
-        # Print updated tree immediately
-        self.print(tree_to_use)
-        self.print()
+        # Only print if this is a new tool usage
+        if tool_branch not in branch_to_use.children:
+            self.print(tree_to_use)
+            self.print()
 
         return tool_branch
 
@@ -486,8 +435,8 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Decide which tree to render: prefer full crew tree, else parent branch
-        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
         if tree_to_use is None:
             return
 
@@ -518,8 +467,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Decide which tree to render: prefer full crew tree, else parent branch
-        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
 
         if tool_branch:
             self.update_tree_label(
@@ -547,22 +496,12 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Parent for tool usage: LiteAgent > Agent > Task
-        branch_to_use = (
-            self.current_lite_agent_branch
-            or agent_branch
-            or self.current_task_branch
-        )
-
-        # Render full crew tree when available for consistent live updates
-        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        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:
-            # If we don't have a valid branch, default to crew_tree if provided
-            if crew_tree is not None:
-                branch_to_use = tree_to_use = crew_tree
-            else:
-                return None
+            return None
 
         # Only add thinking status if we don't have a current tool branch
         if self.current_tool_branch is None:
@@ -584,33 +523,24 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Decide which tree to render: prefer full crew tree, else parent branch
-        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
-        if tree_to_use is None:
+        # Use LiteAgent branch if available, otherwise use regular agent branch
+        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
 
-        # Remove the thinking status node when complete
+        # Remove the thinking status node when complete, but only if it exists
         if "Thinking" in str(tool_branch.label):
-            parents = [
-                self.current_lite_agent_branch,
-                self.current_agent_branch,
-                self.current_task_branch,
-                tree_to_use,
-            ]
-            removed = False
-            for parent in parents:
-                if isinstance(parent, Tree) and tool_branch in parent.children:
-                    parent.children.remove(tool_branch)
-                    removed = True
-                    break
-
-            # Clear pointer if we just removed the current_tool_branch
-            if self.current_tool_branch is tool_branch:
-                self.current_tool_branch = None
-
-            if removed:
-                self.print(tree_to_use)
-                self.print()
+            try:
+                # Check if the node is actually in the children list
+                if tool_branch in branch_to_use.children:
+                    branch_to_use.children.remove(tool_branch)
+                    self.print(tree_to_use)
+                    self.print()
+            except Exception:
+                # If any error occurs during removal, just continue without removing
+                pass
 
     def handle_llm_call_failed(
         self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -619,8 +549,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Decide which tree to render: prefer full crew tree, else parent branch
-        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
+        # Use LiteAgent branch if available, otherwise use crew tree
+        tree_to_use = self.current_lite_agent_branch or crew_tree
 
         # Update tool branch if it exists
         if tool_branch:
@@ -662,7 +592,7 @@ class ConsoleFormatter:
         test_label = Text()
         test_label.append("🧪 Test: ", style="blue bold")
         test_label.append(crew_name or "Crew", style="blue")
-        test_label.append("\nStatus: ", style="white")
+        test_label.append("\n    Status: ", style="white")
         test_label.append("In Progress", style="yellow")
 
         test_tree = Tree(test_label)
@@ -684,7 +614,7 @@ class ConsoleFormatter:
             test_label = Text()
             test_label.append("✅ Test: ", style="green bold")
             test_label.append(crew_name or "Crew", style="green")
-            test_label.append("\nStatus: ", style="white")
+            test_label.append("\n    Status: ", style="white")
             test_label.append("Completed", style="green bold")
             flow_tree.label = test_label
 
@@ -773,7 +703,7 @@ class ConsoleFormatter:
             lite_agent_label = Text()
             lite_agent_label.append("🤖 LiteAgent: ", style="cyan bold")
             lite_agent_label.append(lite_agent_role, style="cyan")
-            lite_agent_label.append("\nStatus: ", style="white")
+            lite_agent_label.append("\n    Status: ", style="white")
             lite_agent_label.append("In Progress", style="yellow")
 
             lite_agent_tree = Tree(lite_agent_label)
@@ -812,7 +742,7 @@ class ConsoleFormatter:
         lite_agent_label = Text()
         lite_agent_label.append(f"{prefix} ", style=f"{style} bold")
         lite_agent_label.append(lite_agent_role, style=style)
-        lite_agent_label.append("Status: ", style="white")
+        lite_agent_label.append("\n    Status: ", style="white")
         lite_agent_label.append(status_text, style=f"{style} bold")
         lite_agent_branch.label = lite_agent_label
 
@@ -853,323 +783,3 @@ class ConsoleFormatter:
             self.update_lite_agent_status(
                 self.current_lite_agent_branch, lite_agent_role, status, **fields
             )
-
-    def handle_knowledge_retrieval_started(
-        self,
-        agent_branch: Optional[Tree],
-        crew_tree: Optional[Tree],
-    ) -> Optional[Tree]:
-        """Handle knowledge retrieval started event."""
-        if not self.verbose:
-            return None
-
-        branch_to_use = agent_branch or self.current_lite_agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None or tree_to_use is None:
-            # If we don't have a valid branch, default to crew_tree if provided
-            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")
-
-    # ----------- AGENT REASONING EVENTS -----------
-
-    def handle_reasoning_started(
-        self,
-        agent_branch: Optional[Tree],
-        attempt: int,
-        crew_tree: Optional[Tree],
-    ) -> Optional[Tree]:
-        """Handle agent reasoning started (or refinement) event."""
-        if not self.verbose:
-            return None
-
-        # Prefer LiteAgent > Agent > Task branch as the parent for reasoning
-        branch_to_use = (
-            self.current_lite_agent_branch
-            or agent_branch
-            or self.current_task_branch
-        )
-
-        # We always want to render the full crew tree when possible so the
-        # Live view updates coherently. Fallbacks: crew tree → branch itself.
-        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
-
-        if branch_to_use is None:
-            # Nothing to attach to, abort
-            return None
-
-        # Reuse existing reasoning branch if present
-        reasoning_branch = self.current_reasoning_branch
-        if reasoning_branch is None:
-            reasoning_branch = branch_to_use.add("")
-            self.current_reasoning_branch = reasoning_branch
-
-        # Build label text depending on attempt
-        status_text = (
-            f"Reasoning (Attempt {attempt})" if attempt > 1 else "Reasoning..."
-        )
-        self.update_tree_label(reasoning_branch, "🧠", status_text, "blue")
-
-        self.print(tree_to_use)
-        self.print()
-
-        return reasoning_branch
-
-    def handle_reasoning_completed(
-        self,
-        plan: str,
-        ready: bool,
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle agent reasoning completed event."""
-        if not self.verbose:
-            return
-
-        reasoning_branch = self.current_reasoning_branch
-        tree_to_use = (
-            self.current_crew_tree
-            or self.current_lite_agent_branch
-            or self.current_task_branch
-            or crew_tree
-        )
-
-        style = "green" if ready else "yellow"
-        status_text = "Reasoning Completed" if ready else "Reasoning Completed (Not Ready)"
-
-        if reasoning_branch is not None:
-            self.update_tree_label(reasoning_branch, "✅", status_text, style)
-
-        if tree_to_use is not None:
-            self.print(tree_to_use)
-
-        # Show plan in a panel (trim very long plans)
-        if plan:
-            plan_panel = Panel(
-                Text(plan, style="white"),
-                title="🧠 Reasoning Plan",
-                border_style=style,
-                padding=(1, 2),
-            )
-            self.print(plan_panel)
-
-        self.print()
-
-        # Clear stored branch after completion
-        self.current_reasoning_branch = None
-
-    def handle_reasoning_failed(
-        self,
-        error: str,
-        crew_tree: Optional[Tree],
-    ) -> None:
-        """Handle agent reasoning failure event."""
-        if not self.verbose:
-            return
-
-        reasoning_branch = self.current_reasoning_branch
-        tree_to_use = (
-            self.current_crew_tree
-            or self.current_lite_agent_branch
-            or self.current_task_branch
-            or crew_tree
-        )
-
-        if reasoning_branch is not None:
-            self.update_tree_label(reasoning_branch, "❌", "Reasoning Failed", "red")
-
-        if tree_to_use is not None:
-            self.print(tree_to_use)
-
-        # Error panel
-        error_content = self.create_status_content(
-            "Reasoning Failed",
-            "Error",
-            "red",
-            Error=error,
-        )
-        self.print_panel(error_content, "Reasoning Error", "red")
-
-        # Clear stored branch after failure
-        self.current_reasoning_branch = None

src/crewai/utilities/formatter.py

@@ -1,6 +1,6 @@
+import re
 from typing import TYPE_CHECKING, List
 
-
 if TYPE_CHECKING:
     from crewai.task import Task
     from crewai.tasks.task_output import TaskOutput
@@ -17,11 +17,6 @@ def aggregate_raw_outputs_from_task_outputs(task_outputs: List["TaskOutput"]) ->
 
 def aggregate_raw_outputs_from_tasks(tasks: List["Task"]) -> str:
     """Generate string context from the tasks."""
-
-    task_outputs = (
-        [task.output for task in tasks if task.output is not None]
-        if isinstance(tasks, list)
-        else []
-    )
+    task_outputs = [task.output for task in tasks if task.output is not None]
 
     return aggregate_raw_outputs_from_task_outputs(task_outputs)

src/crewai/utilities/reasoning_handler.py

@@ -1,387 +0,0 @@
-import logging
-import json
-from typing import Tuple, cast
-
-from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
-from crewai.task import Task
-from crewai.utilities import I18N
-from crewai.llm import LLM
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.reasoning_events import (
-    AgentReasoningStartedEvent,
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-)
-
-
-class ReasoningPlan(BaseModel):
-    """Model representing a reasoning plan for a task."""
-    plan: str = Field(description="The detailed reasoning plan for the task.")
-    ready: bool = Field(description="Whether the agent is ready to execute the task.")
-
-
-class AgentReasoningOutput(BaseModel):
-    """Model representing the output of the agent reasoning process."""
-    plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
-
-
-class ReasoningFunction(BaseModel):
-    """Model for function calling with reasoning."""
-    plan: str = Field(description="The detailed reasoning plan for the task.")
-    ready: bool = Field(description="Whether the agent is ready to execute the task.")
-
-
-class AgentReasoning:
-    """
-    Handles the agent reasoning process, enabling an agent to reflect and create a plan
-    before executing a task.
-    """
-    def __init__(self, task: Task, agent: Agent):
-        if not task or not agent:
-            raise ValueError("Both task and agent must be provided.")
-        self.task = task
-        self.agent = agent
-        self.llm = cast(LLM, agent.llm)
-        self.logger = logging.getLogger(__name__)
-        self.i18n = I18N()
-
-    def handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """
-        Public method for the reasoning process that creates and refines a plan
-        for the task until the agent is ready to execute it.
-
-        Returns:
-            AgentReasoningOutput: The output of the agent reasoning process.
-        """
-        # Emit a reasoning started event (attempt 1)
-        try:
-            crewai_event_bus.emit(
-                self.agent,
-                AgentReasoningStartedEvent(
-                    agent_role=self.agent.role,
-                    task_id=str(self.task.id),
-                    attempt=1,
-                ),
-            )
-        except Exception:
-            # Ignore event bus errors to avoid breaking execution
-            pass
-
-        try:
-            output = self.__handle_agent_reasoning()
-
-            # Emit reasoning completed event
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningCompletedEvent(
-                        agent_role=self.agent.role,
-                        task_id=str(self.task.id),
-                        plan=output.plan.plan,
-                        ready=output.plan.ready,
-                        attempt=1,
-                    ),
-                )
-            except Exception:
-                pass
-
-            return output
-        except Exception as e:
-            # Emit reasoning failed event
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningFailedEvent(
-                        agent_role=self.agent.role,
-                        task_id=str(self.task.id),
-                        error=str(e),
-                        attempt=1,
-                    ),
-                )
-            except Exception:
-                pass
-
-            raise
-
-    def __handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """
-        Private method that handles the agent reasoning process.
-
-        Returns:
-            AgentReasoningOutput: The output of the agent reasoning process.
-        """
-        plan, ready = self.__create_initial_plan()
-
-        plan, ready = self.__refine_plan_if_needed(plan, ready)
-
-        reasoning_plan = ReasoningPlan(plan=plan, ready=ready)
-        return AgentReasoningOutput(plan=reasoning_plan)
-
-    def __create_initial_plan(self) -> Tuple[str, bool]:
-        """
-        Creates the initial reasoning plan for the task.
-
-        Returns:
-            Tuple[str, bool]: The initial plan and whether the agent is ready to execute the task.
-        """
-        reasoning_prompt = self.__create_reasoning_prompt()
-
-        if self.llm.supports_function_calling():
-            plan, ready = self.__call_with_function(reasoning_prompt, "initial_plan")
-            return plan, ready
-        else:
-            system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self.__get_agent_backstory()
-            )
-
-            response = self.llm.call(
-                [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": reasoning_prompt}
-                ]
-            )
-
-            return self.__parse_reasoning_response(str(response))
-
-    def __refine_plan_if_needed(self, plan: str, ready: bool) -> Tuple[str, bool]:
-        """
-        Refines the reasoning plan if the agent is not ready to execute the task.
-
-        Args:
-            plan: The current reasoning plan.
-            ready: Whether the agent is ready to execute the task.
-
-        Returns:
-            Tuple[str, bool]: The refined plan and whether the agent is ready to execute the task.
-        """
-        attempt = 1
-        max_attempts = self.agent.max_reasoning_attempts
-
-        while not ready and (max_attempts is None or attempt < max_attempts):
-            # Emit event for each refinement attempt
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningStartedEvent(
-                        agent_role=self.agent.role,
-                        task_id=str(self.task.id),
-                        attempt=attempt + 1,
-                    ),
-                )
-            except Exception:
-                pass
-
-            refine_prompt = self.__create_refine_prompt(plan)
-
-            if self.llm.supports_function_calling():
-                plan, ready = self.__call_with_function(refine_prompt, "refine_plan")
-            else:
-                system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
-                    role=self.agent.role,
-                    goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory()
-                )
-
-                response = self.llm.call(
-                    [
-                        {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": refine_prompt}
-                    ]
-                )
-                plan, ready = self.__parse_reasoning_response(str(response))
-
-            attempt += 1
-
-            if max_attempts is not None and attempt >= max_attempts:
-                self.logger.warning(
-                    f"Agent reasoning reached maximum attempts ({max_attempts}) without being ready. Proceeding with current plan."
-                )
-                break
-
-        return plan, ready
-
-    def __call_with_function(self, prompt: str, prompt_type: str) -> Tuple[str, bool]:
-        """
-        Calls the LLM with function calling to get a reasoning plan.
-
-        Args:
-            prompt: The prompt to send to the LLM.
-            prompt_type: The type of prompt (initial_plan or refine_plan).
-
-        Returns:
-            Tuple[str, bool]: A tuple containing the plan and whether the agent is ready.
-        """
-        self.logger.debug(f"Using function calling for {prompt_type} reasoning")
-
-        function_schema = {
-            "type": "function",
-            "function": {
-                "name": "create_reasoning_plan",
-                "description": "Create or refine a reasoning plan for a task",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "plan": {
-                            "type": "string",
-                            "description": "The detailed reasoning plan for the task."
-                        },
-                        "ready": {
-                            "type": "boolean",
-                            "description": "Whether the agent is ready to execute the task."
-                        }
-                    },
-                    "required": ["plan", "ready"]
-                }
-            }
-        }
-
-        try:
-            system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
-                role=self.agent.role,
-                goal=self.agent.goal,
-                backstory=self.__get_agent_backstory()
-            )
-
-            # Prepare a simple callable that just returns the tool arguments as JSON
-            def _create_reasoning_plan(plan: str, ready: bool):  # noqa: N802
-                """Return the reasoning plan result in JSON string form."""
-                return json.dumps({"plan": plan, "ready": ready})
-
-            response = self.llm.call(
-                [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt}
-                ],
-                tools=[function_schema],
-                available_functions={"create_reasoning_plan": _create_reasoning_plan},
-            )
-
-            self.logger.debug(f"Function calling response: {response[:100]}...")
-
-            try:
-                result = json.loads(response)
-                if "plan" in result and "ready" in result:
-                    return result["plan"], result["ready"]
-            except (json.JSONDecodeError, KeyError):
-                pass
-
-            response_str = str(response)
-            return response_str, "READY: I am ready to execute the task." in response_str
-
-        except Exception as e:
-            self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
-
-            try:
-                system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
-                    role=self.agent.role,
-                    goal=self.agent.goal,
-                    backstory=self.__get_agent_backstory()
-                )
-
-                fallback_response = self.llm.call(
-                    [
-                        {"role": "system", "content": system_prompt},
-                        {"role": "user", "content": prompt}
-                    ]
-                )
-
-                fallback_str = str(fallback_response)
-                return fallback_str, "READY: I am ready to execute the task." in fallback_str
-            except Exception as inner_e:
-                self.logger.error(f"Error during fallback text parsing: {str(inner_e)}")
-                return "Failed to generate a plan due to an error.", True  # Default to ready to avoid getting stuck
-
-    def __get_agent_backstory(self) -> str:
-        """
-        Safely gets the agent's backstory, providing a default if not available.
-
-        Returns:
-            str: The agent's backstory or a default value.
-        """
-        return getattr(self.agent, "backstory", "No backstory provided")
-
-    def __create_reasoning_prompt(self) -> str:
-        """
-        Creates a prompt for the agent to reason about the task.
-
-        Returns:
-            str: The reasoning prompt.
-        """
-        available_tools = self.__format_available_tools()
-
-        return self.i18n.retrieve("reasoning", "create_plan_prompt").format(
-            role=self.agent.role,
-            goal=self.agent.goal,
-            backstory=self.__get_agent_backstory(),
-            description=self.task.description,
-            expected_output=self.task.expected_output,
-            tools=available_tools
-        )
-
-    def __format_available_tools(self) -> str:
-        """
-        Formats the available tools for inclusion in the prompt.
-
-        Returns:
-            str: Comma-separated list of tool names.
-        """
-        try:
-            return ', '.join([tool.name for tool in (self.task.tools or [])])
-        except (AttributeError, TypeError):
-            return "No tools available"
-
-    def __create_refine_prompt(self, current_plan: str) -> str:
-        """
-        Creates a prompt for the agent to refine its reasoning plan.
-
-        Args:
-            current_plan: The current reasoning plan.
-
-        Returns:
-            str: The refine prompt.
-        """
-        return self.i18n.retrieve("reasoning", "refine_plan_prompt").format(
-            role=self.agent.role,
-            goal=self.agent.goal,
-            backstory=self.__get_agent_backstory(),
-            current_plan=current_plan
-        )
-
-    def __parse_reasoning_response(self, response: str) -> Tuple[str, bool]:
-        """
-        Parses the reasoning response to extract the plan and whether
-        the agent is ready to execute the task.
-
-        Args:
-            response: The LLM response.
-
-        Returns:
-            Tuple[str, bool]: The plan and whether the agent is ready to execute the task.
-        """
-        if not response:
-            return "No plan was generated.", False
-
-        plan = response
-        ready = False
-
-        if "READY: I am ready to execute the task." in response:
-            ready = True
-
-        return plan, ready
-
-    def _handle_agent_reasoning(self) -> AgentReasoningOutput:
-        """
-        Deprecated method for backward compatibility.
-        Use handle_agent_reasoning() instead.
-
-        Returns:
-            AgentReasoningOutput: The output of the agent reasoning process.
-        """
-        self.logger.warning(
-            "The _handle_agent_reasoning method is deprecated. Use handle_agent_reasoning instead."
-        )
-        return self.handle_agent_reasoning()

src/crewai/utilities/string_utils.py

@@ -59,7 +59,7 @@ def interpolate_only(
     # The regex pattern to find valid variable placeholders
     # Matches {variable_name} where variable_name starts with a letter/underscore
     # and contains only letters, numbers, and underscores
-    pattern = r"\{([A-Za-z_][A-Za-z0-9_\-]*)\}"
+    pattern = r"\{([A-Za-z_][A-Za-z0-9_]*)\}"
 
     # Find all matching variables in the input string
     variables = re.findall(pattern, input_string)

tests/agent_reasoning_test.py

@@ -1,261 +0,0 @@
-"""Tests for reasoning in agents."""
-
-import json
-import pytest
-
-from crewai import Agent, Task
-from crewai.llm import LLM
-from crewai.utilities.reasoning_handler import AgentReasoning
-
-
-@pytest.fixture
-def mock_llm_responses():
-    """Fixture for mock LLM responses."""
-    return {
-        "ready": "I'll solve this simple math problem.\n\nREADY: I am ready to execute the task.\n\n",
-        "not_ready": "I need to think about derivatives.\n\nNOT READY: I need to refine my plan because I'm not sure about the derivative rules.",
-        "ready_after_refine": "I'll use the power rule for derivatives where d/dx(x^n) = n*x^(n-1).\n\nREADY: I am ready to execute the task.",
-        "execution": "4"
-    }
-
-
-def test_agent_with_reasoning(mock_llm_responses):
-    """Test agent with reasoning."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Simple math task: What's 2+2?",
-        expected_output="The answer should be a number.",
-        agent=agent
-    )
-    
-    agent.llm.call = lambda messages, *args, **kwargs: (
-        mock_llm_responses["ready"]
-        if any("create a detailed plan" in msg.get("content", "") for msg in messages)
-        else mock_llm_responses["execution"]
-    )
-    
-    result = agent.execute_task(task)
-    
-    assert result == mock_llm_responses["execution"]
-    assert "Reasoning Plan:" in task.description
-
-
-def test_agent_with_reasoning_not_ready_initially(mock_llm_responses):
-    """Test agent with reasoning that requires refinement."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True,
-        max_reasoning_attempts=2,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Complex math task: What's the derivative of x²?",
-        expected_output="The answer should be a mathematical expression.",
-        agent=agent
-    )
-    
-    call_count = [0]
-    
-    def mock_llm_call(messages, *args, **kwargs):
-        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
-            call_count[0] += 1
-            if call_count[0] == 1:
-                return mock_llm_responses["not_ready"]
-            else:
-                return mock_llm_responses["ready_after_refine"]
-        else:
-            return "2x"
-    
-    agent.llm.call = mock_llm_call
-    
-    result = agent.execute_task(task)
-    
-    assert result == "2x"
-    assert call_count[0] == 2  # Should have made 2 reasoning calls
-    assert "Reasoning Plan:" in task.description
-
-
-def test_agent_with_reasoning_max_attempts_reached():
-    """Test agent with reasoning that reaches max attempts without being ready."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True,
-        max_reasoning_attempts=2,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Complex math task: Solve the Riemann hypothesis.",
-        expected_output="A proof or disproof of the hypothesis.",
-        agent=agent
-    )
-    
-    call_count = [0]
-    
-    def mock_llm_call(messages, *args, **kwargs):
-        if any("create a detailed plan" in msg.get("content", "") for msg in messages) or any("refine your plan" in msg.get("content", "") for msg in messages):
-            call_count[0] += 1
-            return f"Attempt {call_count[0]}: I need more time to think.\n\nNOT READY: I need to refine my plan further."
-        else:
-            return "This is an unsolved problem in mathematics."
-    
-    agent.llm.call = mock_llm_call
-    
-    result = agent.execute_task(task)
-    
-    assert result == "This is an unsolved problem in mathematics."
-    assert call_count[0] == 2  # Should have made exactly 2 reasoning calls (max_attempts)
-    assert "Reasoning Plan:" in task.description
-
-
-def test_agent_reasoning_input_validation():
-    """Test input validation in AgentReasoning."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True
-    )
-    
-    with pytest.raises(ValueError, match="Both task and agent must be provided"):
-        AgentReasoning(task=None, agent=agent)
-    
-    task = Task(
-        description="Simple task",
-        expected_output="Simple output"
-    )
-    with pytest.raises(ValueError, match="Both task and agent must be provided"):
-        AgentReasoning(task=task, agent=None)
-
-
-def test_agent_reasoning_error_handling():
-    """Test error handling during the reasoning process."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True
-    )
-    
-    task = Task(
-        description="Task that will cause an error",
-        expected_output="Output that will never be generated",
-        agent=agent
-    )
-    
-    call_count = [0]
-    
-    def mock_llm_call_error(*args, **kwargs):
-        call_count[0] += 1
-        if call_count[0] <= 2:  # First calls are for reasoning
-            raise Exception("LLM error during reasoning")
-        return "Fallback execution result"  # Return a value for task execution
-    
-    agent.llm.call = mock_llm_call_error
-    
-    result = agent.execute_task(task)
-    
-    assert result == "Fallback execution result"
-    assert call_count[0] > 2  # Ensure we called the mock multiple times
-
-
-def test_agent_with_function_calling():
-    """Test agent with reasoning using function calling."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Simple math task: What's 2+2?",
-        expected_output="The answer should be a number.",
-        agent=agent
-    )
-    
-    agent.llm.supports_function_calling = lambda: True
-    
-    def mock_function_call(messages, *args, **kwargs):
-        if "tools" in kwargs:
-            return json.dumps({
-                "plan": "I'll solve this simple math problem: 2+2=4.",
-                "ready": True
-            })
-        else:
-            return "4"
-    
-    agent.llm.call = mock_function_call
-    
-    result = agent.execute_task(task)
-    
-    assert result == "4"
-    assert "Reasoning Plan:" in task.description
-    assert "I'll solve this simple math problem: 2+2=4." in task.description
-
-
-def test_agent_with_function_calling_fallback():
-    """Test agent with reasoning using function calling that falls back to text parsing."""
-    llm = LLM("gpt-3.5-turbo")
-    
-    agent = Agent(
-        role="Test Agent",
-        goal="To test the reasoning feature",
-        backstory="I am a test agent created to verify the reasoning feature works correctly.",
-        llm=llm,
-        reasoning=True,
-        verbose=True
-    )
-    
-    task = Task(
-        description="Simple math task: What's 2+2?",
-        expected_output="The answer should be a number.",
-        agent=agent
-    )
-    
-    agent.llm.supports_function_calling = lambda: True
-    
-    def mock_function_call(messages, *args, **kwargs):
-        if "tools" in kwargs:
-            return "Invalid JSON that will trigger fallback. READY: I am ready to execute the task."
-        else:
-            return "4"
-    
-    agent.llm.call = mock_function_call
-    
-    result = agent.execute_task(task)
-    
-    assert result == "4"
-    assert "Reasoning Plan:" in task.description
-    assert "Invalid JSON that will trigger fallback" in task.description

tests/agent_test.py

@@ -2,13 +2,14 @@
 
 import os
 from unittest import mock
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 
 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
@@ -18,7 +19,6 @@ from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
-from crewai.utilities.errors import AgentRepositoryError
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
 
@@ -73,7 +73,6 @@ def test_agent_creation():
     assert agent.goal == "test goal"
     assert agent.backstory == "test backstory"
 
-
 def test_agent_with_only_system_template():
     """Test that an agent with only system_template works without errors."""
     agent = Agent(
@@ -89,7 +88,6 @@ def test_agent_with_only_system_template():
     assert agent.goal == "Test Goal"
     assert agent.backstory == "Test Backstory"
 
-
 def test_agent_with_only_prompt_template():
     """Test that an agent with only system_template works without errors."""
     agent = Agent(
@@ -121,8 +119,7 @@ def test_agent_with_missing_response_template():
     assert agent.role == "Test Role"
     assert agent.goal == "Test Goal"
     assert agent.backstory == "Test Backstory"
-
-
+    
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
     assert agent.llm.model == "gpt-4o-mini"
@@ -309,7 +306,9 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (patch.object(CacheHandler, "read") as read,):
+    with (
+        patch.object(CacheHandler, "read") as read,
+    ):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1039,7 +1038,7 @@ def test_agent_human_input():
             CrewAgentExecutor,
             "_invoke_loop",
             return_value=AgentFinish(output="Hello", thought="", text=""),
-        ),
+        ) as mock_invoke_loop,
     ):
         # Execute the task
         output = agent.execute_task(task)
@@ -1631,10 +1630,13 @@ def test_agent_with_knowledge_sources():
     # Create a knowledge source with some content
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
-    with patch("crewai.knowledge") as MockKnowledge:
+
+    with patch(
+        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.search.return_value = [{"content": content}]
+        mock_knowledge_instance.query.return_value = [{"content": content}]
 
         agent = Agent(
             role="Information Agent",
@@ -1688,7 +1690,7 @@ def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold():
 
             assert agent.knowledge is not None
             mock_knowledge_query.assert_called_once_with(
-                ["Brandon's favorite color"],
+                [task.prompt()],
                 **knowledge_config.model_dump(),
             )
 
@@ -1725,7 +1727,7 @@ def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_defau
 
             assert agent.knowledge is not None
             mock_knowledge_query.assert_called_once_with(
-                ["Brandon's favorite color"],
+                [task.prompt()],
                 **knowledge_config.model_dump(),
             )
 
@@ -1735,7 +1737,9 @@ def test_agent_with_knowledge_sources_extensive_role():
     content = "Brandon's favorite color is red and he likes Mexican food."
     string_source = StringKnowledgeSource(content=content)
 
-    with patch("crewai.knowledge") as MockKnowledge:
+    with patch(
+        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+    ) as MockKnowledge:
         mock_knowledge_instance = MockKnowledge.return_value
         mock_knowledge_instance.sources = [string_source]
         mock_knowledge_instance.query.return_value = [{"content": content}]
@@ -1799,40 +1803,6 @@ def test_agent_with_knowledge_sources_works_with_copy():
             assert isinstance(agent_copy.llm, LLM)
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_generate_search_query():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-
-    with patch("crewai.knowledge") as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-
-        agent = Agent(
-            role="Information Agent with extensive role description that is longer than 80 characters",
-            goal="Provide information based on knowledge sources",
-            backstory="You have access to specific knowledge sources.",
-            llm=LLM(model="gpt-4o-mini"),
-            knowledge_sources=[string_source],
-        )
-
-        task = Task(
-            description="What is Brandon's favorite color?",
-            expected_output="The answer to the question, in a format like this: `{{name: str, favorite_color: str}}`",
-            agent=agent,
-        )
-
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-
-        # Updated assertion to check the JSON content
-        assert "Brandon" in str(agent.knowledge_search_query)
-        assert "favorite color" in str(agent.knowledge_search_query)
-
-        assert "red" in result.raw.lower()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_litellm_auth_error_handling():
     """Test that LiteLLM authentication errors are handled correctly and not retried."""
@@ -1970,153 +1940,3 @@ def test_litellm_anthropic_error_handling():
 
     # Verify the LLM call was only made once (no retries)
     mock_llm_call.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_get_knowledge_search_query():
-    """Test that _get_knowledge_search_query calls the LLM with the correct prompts."""
-    from crewai.utilities.i18n import I18N
-
-    content = "The capital of France is Paris."
-    string_source = StringKnowledgeSource(content=content)
-
-    agent = Agent(
-        role="Information Agent",
-        goal="Provide information based on knowledge sources",
-        backstory="I have access to knowledge sources",
-        llm=LLM(model="gpt-4"),
-        knowledge_sources=[string_source],
-    )
-
-    task = Task(
-        description="What is the capital of France?",
-        expected_output="The capital of France is Paris.",
-        agent=agent,
-    )
-
-    i18n = I18N()
-    task_prompt = task.prompt()
-
-    with patch.object(agent, "_get_knowledge_search_query") as mock_get_query:
-        mock_get_query.return_value = "Capital of France"
-
-        crew = Crew(agents=[agent], tasks=[task])
-        crew.kickoff()
-
-        mock_get_query.assert_called_once_with(task_prompt)
-
-    with patch.object(agent.llm, "call") as mock_llm_call:
-        agent._get_knowledge_search_query(task_prompt)
-
-        mock_llm_call.assert_called_once_with(
-            [
-                {
-                    "role": "system",
-                    "content": i18n.slice(
-                        "knowledge_search_query_system_prompt"
-                    ).format(task_prompt=task.description),
-                },
-                {
-                    "role": "user",
-                    "content": i18n.slice("knowledge_search_query").format(
-                        task_prompt=task_prompt
-                    ),
-                },
-            ]
-        )
-
-
-@pytest.fixture
-def mock_get_auth_token():
-    with patch(
-        "crewai.cli.authentication.token.get_auth_token", return_value="test_token"
-    ):
-        yield
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository(mock_get_agent, mock_get_auth_token):
-    from crewai_tools import SerperDevTool
-
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 200
-    mock_get_response.json.return_value = {
-        "role": "test role",
-        "goal": "test goal",
-        "backstory": "test backstory",
-        "tools": [{"name": "SerperDevTool"}],
-    }
-    mock_get_agent.return_value = mock_get_response
-    agent = Agent(from_repository="test_agent")
-
-    assert agent.role == "test role"
-    assert agent.goal == "test goal"
-    assert agent.backstory == "test backstory"
-    assert len(agent.tools) == 1
-    assert isinstance(agent.tools[0], SerperDevTool)
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository_override_attributes(mock_get_agent, mock_get_auth_token):
-    from crewai_tools import SerperDevTool
-
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 200
-    mock_get_response.json.return_value = {
-        "role": "test role",
-        "goal": "test goal",
-        "backstory": "test backstory",
-        "tools": [{"name": "SerperDevTool"}],
-    }
-    mock_get_agent.return_value = mock_get_response
-    agent = Agent(from_repository="test_agent", role="Custom Role")
-
-    assert agent.role == "Custom Role"
-    assert agent.goal == "test goal"
-    assert agent.backstory == "test backstory"
-    assert len(agent.tools) == 1
-    assert isinstance(agent.tools[0], SerperDevTool)
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository_with_invalid_tools(mock_get_agent, mock_get_auth_token):
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 200
-    mock_get_response.json.return_value = {
-        "role": "test role",
-        "goal": "test goal",
-        "backstory": "test backstory",
-        "tools": [{"name": "DoesNotExist"}],
-    }
-    mock_get_agent.return_value = mock_get_response
-    with pytest.raises(
-        AgentRepositoryError,
-        match="Tool DoesNotExist could not be loaded: module 'crewai_tools' has no attribute 'DoesNotExist'",
-    ):
-        Agent(from_repository="test_agent")
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository_internal_error(mock_get_agent, mock_get_auth_token):
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 500
-    mock_get_response.text = "Internal server error"
-    mock_get_agent.return_value = mock_get_response
-    with pytest.raises(
-        AgentRepositoryError,
-        match="Agent test_agent could not be loaded: Internal server error",
-    ):
-        Agent(from_repository="test_agent")
-
-
-@patch("crewai.cli.plus_api.PlusAPI.get_agent")
-def test_agent_from_repository_agent_not_found(mock_get_agent, mock_get_auth_token):
-    mock_get_response = MagicMock()
-    mock_get_response.status_code = 404
-    mock_get_response.text = "Agent not found"
-    mock_get_agent.return_value = mock_get_response
-    with pytest.raises(
-        AgentRepositoryError,
-        match="Agent test_agent does not exist, make sure the name is correct or the agent is available on your organization",
-    ):
-        Agent(from_repository="test_agent")

tests/cassettes/test_agent_with_knowledge_sources.yaml

@@ -16,7 +16,7 @@ interactions:
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.68.2
+      - OpenAI/Python 1.52.1
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -26,132 +26,25 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.68.2
-      x-stainless-read-timeout:
-      - '600'
+      - 1.52.1
       x-stainless-retry-count:
       - '0'
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
-      - 3.12.9
+      - 3.11.9
     method: POST
     uri: https://api.openai.com/v1/embeddings
   response:
-    body:
-      string: !!binary |
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-      - '2020-10-01'
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 version: 1

tests/cli/cli_test.py

@@ -162,18 +162,8 @@ def test_reset_knowledge(mock_get_crews, runner):
     assert call_count == 1, "reset_memories should have been called once"
 
 
-def test_reset_agent_knowledge(mock_get_crews, runner):
-    result = runner.invoke(reset_memories, ["--agent-knowledge"])
-    call_count = 0
-    for crew in mock_get_crews.return_value:
-        crew.reset_memories.assert_called_once_with(command_type="agent_knowledge")
-        assert f"[Crew ({crew.name})] Agents knowledge has been reset." in result.output
-        call_count += 1
-
-    assert call_count == 1, "reset_memories should have been called once"
-
-
 def test_reset_memory_from_many_crews(mock_get_crews, runner):
+
     crews = []
     for crew_id in ["id-1234", "id-5678"]:
         mock_crew = mock.Mock(spec=Crew)

tests/conftest.py

@@ -46,6 +46,4 @@ def setup_test_environment():
 def vcr_config(request) -> dict:
     return {
         "cassette_library_dir": "tests/cassettes",
-        "record_mode": "new_episodes",
-        "filter_headers": [("authorization", "AUTHORIZATION-XXX")],
     }

tests/crew_test.py

@@ -2,19 +2,22 @@
 
 import hashlib
 import json
+import os
+import tempfile
 from concurrent.futures import Future
 from unittest import mock
-from unittest.mock import ANY, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 import pydantic_core
 import pytest
 
 from crewai.agent import Agent
 from crewai.agents import CacheHandler
+from crewai.agents.cache import CacheHandler
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
-from crewai.flow import Flow, start
-from crewai.knowledge.knowledge import Knowledge
+from crewai.flow import Flow, listen, start
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
@@ -3138,30 +3141,6 @@ def test_replay_with_context():
         assert crew.tasks[1].context[0].output.raw == "context raw output"
 
 
-def test_replay_with_context_set_to_nullable():
-    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
-    task1 = Task(
-        description="Context Task", expected_output="Say Task Output", agent=agent
-    )
-    task2 = Task(
-        description="Test Task", expected_output="Say Hi", agent=agent, context=[]
-    )
-    task3 = Task(
-        description="Test Task 3", expected_output="Say Hi", agent=agent, context=None
-    )
-
-    crew = Crew(agents=[agent], tasks=[task1, task2, task3], process=Process.sequential)
-    with patch("crewai.task.Task.execute_sync") as mock_execute_task:
-        mock_execute_task.return_value = TaskOutput(
-            description="Test Task Output",
-            raw="test raw output",
-            agent="test_agent",
-        )
-        crew.kickoff()
-
-    mock_execute_task.assert_called_with(agent=ANY, context="", tools=ANY)
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_replay_with_invalid_task_id():
     agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
@@ -4404,165 +4383,3 @@ def test_sets_parent_flow_when_inside_flow(researcher, writer):
     flow = MyFlow()
     result = flow.kickoff()
     assert result.parent_flow is flow
-
-
-def test_reset_knowledge_with_no_crew_knowledge(researcher,writer):
-    crew = Crew(
-        agents=[researcher, writer],
-        process=Process.sequential,
-        tasks=[
-            Task(description="Task 1", expected_output="output", agent=researcher),
-            Task(description="Task 2", expected_output="output", agent=writer),
-        ]
-    )
-
-    with pytest.raises(RuntimeError) as excinfo:
-        crew.reset_memories(command_type='knowledge')
-        
-        # Optionally, you can also check the error message
-    assert "Crew Knowledge and Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
-
-
-def test_reset_knowledge_with_only_crew_knowledge(researcher,writer):
-    mock_ks = MagicMock(spec=Knowledge)
-
-    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
-        crew = Crew(
-            agents=[researcher, writer],
-            process=Process.sequential,
-            tasks=[
-                Task(description="Task 1", expected_output="output", agent=researcher),
-                Task(description="Task 2", expected_output="output", agent=writer),
-            ],
-            knowledge=mock_ks
-        )
-
-        crew.reset_memories(command_type='knowledge')
-        mock_reset_agent_knowledge.assert_called_once_with([mock_ks])
-
-
-def test_reset_knowledge_with_crew_and_agent_knowledge(researcher,writer):
-    mock_ks_crew = MagicMock(spec=Knowledge)
-    mock_ks_research = MagicMock(spec=Knowledge)
-    mock_ks_writer = MagicMock(spec=Knowledge)
-
-    researcher.knowledge = mock_ks_research
-    writer.knowledge = mock_ks_writer
-
-    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
-        crew = Crew(
-            agents=[researcher, writer],
-            process=Process.sequential,
-            tasks=[
-                Task(description="Task 1", expected_output="output", agent=researcher),
-                Task(description="Task 2", expected_output="output", agent=writer),
-            ],
-            knowledge=mock_ks_crew
-        )
-
-        crew.reset_memories(command_type='knowledge')
-        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_crew,mock_ks_research,mock_ks_writer])
-
-
-def test_reset_knowledge_with_only_agent_knowledge(researcher,writer):
-    mock_ks_research = MagicMock(spec=Knowledge)
-    mock_ks_writer = MagicMock(spec=Knowledge)
-
-    researcher.knowledge = mock_ks_research
-    writer.knowledge = mock_ks_writer
-
-    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
-        crew = Crew(
-            agents=[researcher, writer],
-            process=Process.sequential,
-            tasks=[
-                Task(description="Task 1", expected_output="output", agent=researcher),
-                Task(description="Task 2", expected_output="output", agent=writer),
-            ],
-        )
-
-        crew.reset_memories(command_type='knowledge')
-        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
-
-
-def test_reset_agent_knowledge_with_no_agent_knowledge(researcher,writer):
-    crew = Crew(
-        agents=[researcher, writer],
-        process=Process.sequential,
-        tasks=[
-            Task(description="Task 1", expected_output="output", agent=researcher),
-            Task(description="Task 2", expected_output="output", agent=writer),
-        ],
-    )
-
-    with pytest.raises(RuntimeError) as excinfo:
-        crew.reset_memories(command_type='agent_knowledge')
-    
-    # Optionally, you can also check the error message
-    assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
-
-
-def test_reset_agent_knowledge_with_only_crew_knowledge(researcher,writer):
-    mock_ks = MagicMock(spec=Knowledge)
-
-    crew = Crew(
-        agents=[researcher, writer],
-        process=Process.sequential,
-        tasks=[
-            Task(description="Task 1", expected_output="output", agent=researcher),
-            Task(description="Task 2", expected_output="output", agent=writer),
-        ],
-        knowledge=mock_ks
-    )
-
-    with pytest.raises(RuntimeError) as excinfo:
-        crew.reset_memories(command_type='agent_knowledge')
-    
-    # Optionally, you can also check the error message
-    assert "Agent Knowledge memory system is not initialized" in str(excinfo.value)  # Replace with the expected message
-
-
-def test_reset_agent_knowledge_with_crew_and_agent_knowledge(researcher,writer):
-    mock_ks_crew = MagicMock(spec=Knowledge)
-    mock_ks_research = MagicMock(spec=Knowledge)
-    mock_ks_writer = MagicMock(spec=Knowledge)
-
-    researcher.knowledge = mock_ks_research
-    writer.knowledge = mock_ks_writer
-
-    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
-        crew = Crew(
-            agents=[researcher, writer],
-            process=Process.sequential,
-            tasks=[
-                Task(description="Task 1", expected_output="output", agent=researcher),
-                Task(description="Task 2", expected_output="output", agent=writer),
-            ],
-            knowledge=mock_ks_crew
-        )
-
-        crew.reset_memories(command_type='agent_knowledge')
-        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
-
-
-def test_reset_agent_knowledge_with_only_agent_knowledge(researcher,writer):
-    mock_ks_research = MagicMock(spec=Knowledge)
-    mock_ks_writer = MagicMock(spec=Knowledge)
-
-    researcher.knowledge = mock_ks_research
-    writer.knowledge = mock_ks_writer
-
-    with patch.object(Crew,'reset_knowledge') as mock_reset_agent_knowledge:
-        crew = Crew(
-            agents=[researcher, writer],
-            process=Process.sequential,
-            tasks=[
-                Task(description="Task 1", expected_output="output", agent=researcher),
-                Task(description="Task 2", expected_output="output", agent=writer),
-            ],
-        )
-
-        crew.reset_memories(command_type='agent_knowledge')
-        mock_reset_agent_knowledge.assert_called_once_with([mock_ks_research,mock_ks_writer])
-
-

tests/task_test.py

@@ -398,79 +398,6 @@ def test_output_json_hierarchical():
     assert result.json == '{"score": 4}'
     assert result.to_dict() == {"score": 4}
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_inject_date():
-    reporter = Agent(
-        role="Reporter",
-        goal="Report the date",
-        backstory="You're an expert reporter, specialized in reporting the date.",
-        allow_delegation=False,
-        inject_date=True,
-    )
-
-    task = Task(
-        description="What is the date today?",
-        expected_output="The date today as you were told, same format as the date you were told.",
-        agent=reporter,
-    )
-
-    crew = Crew(
-        agents=[reporter],
-        tasks=[task],
-        process=Process.sequential,
-    )
-    result = crew.kickoff()
-    assert "2025-05-21" in result.raw
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_inject_date_custom_format():
-    reporter = Agent(
-        role="Reporter",
-        goal="Report the date",
-        backstory="You're an expert reporter, specialized in reporting the date.",
-        allow_delegation=False,
-        inject_date=True,
-        date_format="%B %d, %Y",
-    )
-
-    task = Task(
-        description="What is the date today?",
-        expected_output="The date today.",
-        agent=reporter,
-    )
-
-    crew = Crew(
-        agents=[reporter],
-        tasks=[task],
-        process=Process.sequential,
-    )
-    result = crew.kickoff()
-    assert "May 21, 2025" in result.raw
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_no_inject_date():
-    reporter = Agent(
-        role="Reporter",
-        goal="Report the date",
-        backstory="You're an expert reporter, specialized in reporting the date.",
-        allow_delegation=False,
-        inject_date=False,
-    )
-
-    task = Task(
-        description="What is the date today?",
-        expected_output="The date today.",
-        agent=reporter,
-    )
-
-    crew = Crew(
-        agents=[reporter],
-        tasks=[task],
-        process=Process.sequential,
-    )
-    result = crew.kickoff()
-    assert "2025-05-21" not in result.raw
-
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_json_property_without_output_json():
@@ -910,6 +837,9 @@ def test_interpolate_inputs():
 
 def test_interpolate_only():
     """Test the interpolate_only method for various scenarios including JSON structure preservation."""
+    task = Task(
+        description="Unused in this test", expected_output="Unused in this test"
+    )
 
     # Test JSON structure preservation
     json_string = '{"info": "Look at {placeholder}", "nested": {"val": "{nestedVal}"}}'
@@ -941,6 +871,10 @@ def test_interpolate_only():
 
 def test_interpolate_only_with_dict_inside_expected_output():
     """Test the interpolate_only method for various scenarios including JSON structure preservation."""
+    task = Task(
+        description="Unused in this test",
+        expected_output="Unused in this test: {questions}",
+    )
 
     json_string = '{"questions": {"main_question": "What is the user\'s name?", "secondary_question": "What is the user\'s age?"}}'
     result = interpolate_only(
@@ -1160,6 +1094,11 @@ def test_task_execution_times():
 
 
 def test_interpolate_with_list_of_strings():
+    task = Task(
+        description="Test list interpolation",
+        expected_output="List: {items}",
+    )
+
     # Test simple list of strings
     input_str = "Available items: {items}"
     inputs = {"items": ["apple", "banana", "cherry"]}
@@ -1173,6 +1112,11 @@ def test_interpolate_with_list_of_strings():
 
 
 def test_interpolate_with_list_of_dicts():
+    task = Task(
+        description="Test list of dicts interpolation",
+        expected_output="People: {people}",
+    )
+
     input_data = {
         "people": [
             {"name": "Alice", "age": 30, "skills": ["Python", "AI"]},
@@ -1193,6 +1137,11 @@ def test_interpolate_with_list_of_dicts():
 
 
 def test_interpolate_with_nested_structures():
+    task = Task(
+        description="Test nested structures",
+        expected_output="Company: {company}",
+    )
+
     input_data = {
         "company": {
             "name": "TechCorp",
@@ -1216,6 +1165,11 @@ def test_interpolate_with_nested_structures():
 
 
 def test_interpolate_with_special_characters():
+    task = Task(
+        description="Test special characters in dicts",
+        expected_output="Data: {special_data}",
+    )
+
     input_data = {
         "special_data": {
             "quotes": """This has "double" and 'single' quotes""",
@@ -1234,6 +1188,11 @@ def test_interpolate_with_special_characters():
 
 
 def test_interpolate_mixed_types():
+    task = Task(
+        description="Test mixed type interpolation",
+        expected_output="Mixed: {data}",
+    )
+
     input_data = {
         "data": {
             "name": "Test Dataset",
@@ -1255,6 +1214,11 @@ def test_interpolate_mixed_types():
 
 
 def test_interpolate_complex_combination():
+    task = Task(
+        description="Test complex combination",
+        expected_output="Report: {report}",
+    )
+
     input_data = {
         "report": [
             {
@@ -1279,6 +1243,11 @@ def test_interpolate_complex_combination():
 
 
 def test_interpolate_invalid_type_validation():
+    task = Task(
+        description="Test invalid type validation",
+        expected_output="Should never reach here",
+    )
+
     # Test with invalid top-level type
     with pytest.raises(ValueError) as excinfo:
         interpolate_only("{data}", {"data": set()})  # type: ignore we are purposely testing this failure
@@ -1299,6 +1268,11 @@ def test_interpolate_invalid_type_validation():
 
 
 def test_interpolate_custom_object_validation():
+    task = Task(
+        description="Test custom object rejection",
+        expected_output="Should never reach here",
+    )
+
     class CustomObject:
         def __init__(self, value):
             self.value = value
@@ -1330,6 +1304,11 @@ def test_interpolate_custom_object_validation():
 
 
 def test_interpolate_valid_complex_types():
+    task = Task(
+        description="Test valid complex types",
+        expected_output="Validation should pass",
+    )
+
     # Valid complex structure
     valid_data = {
         "name": "Valid Dataset",
@@ -1349,6 +1328,11 @@ def test_interpolate_valid_complex_types():
 
 
 def test_interpolate_edge_cases():
+    task = Task(
+        description="Test edge cases",
+        expected_output="Edge case handling",
+    )
+
     # Test empty dict and list
     assert interpolate_only("{}", {"data": {}}) == "{}"
     assert interpolate_only("[]", {"data": []}) == "[]"
@@ -1363,6 +1347,11 @@ def test_interpolate_edge_cases():
 
 
 def test_interpolate_valid_types():
+    task = Task(
+        description="Test valid types including null and boolean",
+        expected_output="Should pass validation",
+    )
+
     # Test with boolean and null values (valid JSON types)
     valid_data = {
         "name": "Test",
@@ -1384,11 +1373,11 @@ def test_interpolate_valid_types():
 
 def test_task_with_no_max_execution_time():
     researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-        max_execution_time=None,
+    role="Researcher",
+    goal="Make the best research and analysis on content about AI and AI agents",
+    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+    allow_delegation=False,
+    max_execution_time=None
     )
 
     task = Task(
@@ -1397,7 +1386,7 @@ def test_task_with_no_max_execution_time():
         agent=researcher,
     )
 
-    with patch.object(Agent, "_execute_without_timeout", return_value="ok") as execute:
+    with patch.object(Agent, "_execute_without_timeout", return_value = "ok") as execute:
         result = task.execute_sync(agent=researcher)
         assert result.raw == "ok"
         execute.assert_called_once()
@@ -1406,7 +1395,6 @@ def test_task_with_no_max_execution_time():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_with_max_execution_time():
     from crewai.tools import tool
-
     """Test that execution raises TimeoutError when max_execution_time is exceeded."""
 
     @tool("what amazing tool", result_as_answer=True)
@@ -1424,7 +1412,7 @@ def test_task_with_max_execution_time():
         ),
         allow_delegation=False,
         tools=[my_tool],
-        max_execution_time=4,
+        max_execution_time=4
     )
 
     task = Task(
@@ -1440,7 +1428,6 @@ def test_task_with_max_execution_time():
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_with_max_execution_time_exceeded():
     from crewai.tools import tool
-
     """Test that execution raises TimeoutError when max_execution_time is exceeded."""
 
     @tool("what amazing tool", result_as_answer=True)
@@ -1458,7 +1445,7 @@ def test_task_with_max_execution_time_exceeded():
         ),
         allow_delegation=False,
         tools=[my_tool],
-        max_execution_time=1,
+        max_execution_time=1
     )
 
     task = Task(
@@ -1468,28 +1455,4 @@ def test_task_with_max_execution_time_exceeded():
     )
 
     with pytest.raises(TimeoutError):
-        task.execute_sync(agent=researcher)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_interpolation_with_hyphens():
-    agent = Agent(
-        role="Researcher",
-        goal="be an assistant that responds with {interpolation-with-hyphens}",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-    )
-    task = Task(
-        description="be an assistant that responds with {interpolation-with-hyphens}",
-        expected_output="The response should be addressing: {interpolation-with-hyphens}",
-        agent=agent,
-    )
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        verbose=True,
-    )
-    result = crew.kickoff(inputs={"interpolation-with-hyphens": "say hello world"})
-    assert "say hello world" in task.prompt()
-
-    assert result.raw == "Hello, World!"
+        task.execute_sync(agent=researcher)

tests/telemetry/test_telemetry.py

@@ -6,8 +6,6 @@ import pytest
 from crewai import Agent, Crew, Task
 from crewai.telemetry import Telemetry
 
-from opentelemetry import trace
-
 
 @pytest.mark.parametrize(
     "env_var,value,expected_ready",
@@ -36,6 +34,9 @@ def test_telemetry_enabled_by_default():
             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",
@@ -66,32 +67,3 @@ def test_telemetry_fails_due_connect_timeout(export_mock, logger_mock):
 
     export_mock.assert_called_once()
     logger_mock.assert_called_once_with(error)
-
-
-def test_telemetry_singleton_pattern():
-    """Test that Telemetry uses the singleton pattern correctly."""
-    Telemetry._instance = None
-
-    telemetry1 = Telemetry()
-    telemetry2 = Telemetry()
-
-    assert telemetry1 is telemetry2
-
-    setattr(telemetry1, "test_attribute", "test_value")
-    assert hasattr(telemetry2, "test_attribute")
-    assert getattr(telemetry2, "test_attribute") == "test_value"
-
-    import threading
-
-    instances = []
-
-    def create_instance():
-        instances.append(Telemetry())
-
-    threads = [threading.Thread(target=create_instance) for _ in range(5)]
-    for thread in threads:
-        thread.start()
-    for thread in threads:
-        thread.join()
-
-    assert all(instance is telemetry1 for instance in instances)

tests/test_agent_inject_date.py

@@ -1,117 +0,0 @@
-from datetime import datetime
-from unittest.mock import patch
-
-from crewai.agent import Agent
-from crewai.task import Task
-
-
-def test_agent_inject_date():
-    """Test that the inject_date flag injects the current date into the task.
-    
-    Tests that when inject_date=True, the current date is added to the task description.
-    """
-    with patch('datetime.datetime') as mock_datetime:
-        mock_datetime.now.return_value = datetime(2025, 1, 1)
-        
-        agent = Agent(
-            role="test_agent",
-            goal="test_goal",
-            backstory="test_backstory",
-            inject_date=True,
-        )
-        
-        task = Task(
-            description="Test task",
-            expected_output="Test output",
-            agent=agent,
-        )
-        
-        # Store original description
-        original_description = task.description
-        
-        agent._inject_date_to_task(task)
-        
-        assert "Current Date: 2025-01-01" in task.description
-        assert task.description != original_description
-
-
-def test_agent_without_inject_date():
-    """Test that without inject_date flag, no date is injected.
-    
-    Tests that when inject_date=False (default), no date is added to the task description.
-    """
-    agent = Agent(
-        role="test_agent",
-        goal="test_goal",
-        backstory="test_backstory",
-        # inject_date is False by default
-    )
-    
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-    
-    original_description = task.description
-    
-    agent._inject_date_to_task(task)
-    
-    assert task.description == original_description
-
-
-def test_agent_inject_date_custom_format():
-    """Test that the inject_date flag with custom date_format works correctly.
-    
-    Tests that when inject_date=True with a custom date_format, the date is formatted correctly.
-    """
-    with patch('datetime.datetime') as mock_datetime:
-        mock_datetime.now.return_value = datetime(2025, 1, 1)
-        
-        agent = Agent(
-            role="test_agent",
-            goal="test_goal",
-            backstory="test_backstory",
-            inject_date=True,
-            date_format="%d/%m/%Y",
-        )
-        
-        task = Task(
-            description="Test task",
-            expected_output="Test output",
-            agent=agent,
-        )
-        
-        # Store original description
-        original_description = task.description
-        
-        agent._inject_date_to_task(task)
-        
-        assert "Current Date: 01/01/2025" in task.description
-        assert task.description != original_description
-
-
-def test_agent_inject_date_invalid_format():
-    """Test error handling with invalid date format.
-    
-    Tests that when an invalid date_format is provided, the task description remains unchanged.
-    """
-    agent = Agent(
-        role="test_agent",
-        goal="test_goal",
-        backstory="test_backstory",
-        inject_date=True,
-        date_format="invalid",
-    )
-    
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-    
-    original_description = task.description
-    
-    agent._inject_date_to_task(task)
-    
-    assert task.description == original_description

tests/test_hallucination_guardrail.py

@@ -1,108 +0,0 @@
-from unittest.mock import Mock
-
-import pytest
-
-from crewai.llm import LLM
-from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
-from crewai.tasks.task_output import TaskOutput
-
-
-def test_hallucination_guardrail_initialization():
-    """Test that the hallucination guardrail initializes correctly with all parameters."""
-    mock_llm = Mock(spec=LLM)
-
-    guardrail = HallucinationGuardrail(context="Test reference context", llm=mock_llm)
-
-    assert guardrail.context == "Test reference context"
-    assert guardrail.llm == mock_llm
-    assert guardrail.threshold is None
-    assert guardrail.tool_response == ""
-
-    guardrail = HallucinationGuardrail(
-        context="Test reference context",
-        llm=mock_llm,
-        threshold=8.5,
-        tool_response="Sample tool response",
-    )
-
-    assert guardrail.context == "Test reference context"
-    assert guardrail.llm == mock_llm
-    assert guardrail.threshold == 8.5
-    assert guardrail.tool_response == "Sample tool response"
-
-
-def test_hallucination_guardrail_no_op_behavior():
-    """Test that the guardrail always returns True in the open-source version."""
-    mock_llm = Mock(spec=LLM)
-    guardrail = HallucinationGuardrail(
-        context="Test reference context",
-        llm=mock_llm,
-        threshold=9.0,
-    )
-
-    task_output = TaskOutput(
-        raw="Sample task output",
-        description="Test task",
-        expected_output="Expected output",
-        agent="Test Agent",
-    )
-
-    result, output = guardrail(task_output)
-
-    assert result is True
-    assert output == "Sample task output"
-
-
-def test_hallucination_guardrail_description():
-    """Test that the guardrail provides the correct description for event logging."""
-    guardrail = HallucinationGuardrail(
-        context="Test reference context", llm=Mock(spec=LLM)
-    )
-
-    assert guardrail.description == "HallucinationGuardrail (no-op)"
-
-
-@pytest.mark.parametrize(
-    "context,task_output_text,threshold,tool_response",
-    [
-        (
-            "Earth orbits the Sun once every 365.25 days.",
-            "It takes Earth approximately one year to go around the Sun.",
-            None,
-            "",
-        ),
-        (
-            "Python was created by Guido van Rossum in 1991.",
-            "Python is a programming language developed by Guido van Rossum.",
-            7.5,
-            "",
-        ),
-        (
-            "The capital of France is Paris.",
-            "Paris is the largest city and capital of France.",
-            9.0,
-            "Geographic API returned: France capital is Paris",
-        ),
-    ],
-)
-def test_hallucination_guardrail_always_passes(
-    context, task_output_text, threshold, tool_response
-):
-    """Test that the guardrail always passes regardless of configuration in open-source version."""
-    mock_llm = Mock(spec=LLM)
-
-    guardrail = HallucinationGuardrail(
-        context=context, llm=mock_llm, threshold=threshold, tool_response=tool_response
-    )
-
-    task_output = TaskOutput(
-        raw=task_output_text,
-        description="Test task",
-        expected_output="Expected output",
-        agent="Test Agent",
-    )
-
-    result, output = guardrail(task_output)
-
-    assert result is True
-    assert output == task_output_text

tests/test_markdown_task.py

@@ -1,96 +0,0 @@
-"""Test the markdown attribute in Task class."""
-
-import pytest
-from pydantic import BaseModel
-
-from crewai import Agent, Task
-
-
-@pytest.mark.parametrize(
-    "markdown_enabled,should_contain_instructions",
-    [
-        (True, True),
-        (False, False),
-    ],
-)
-def test_markdown_option_in_task_prompt(markdown_enabled, should_contain_instructions):
-    """Test that markdown flag correctly controls the inclusion of markdown formatting instructions."""
-    
-    researcher = Agent(
-        role="Researcher",
-        goal="Research a topic",
-        backstory="You're a researcher specialized in providing well-formatted content.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Research advances in AI in 2023",
-        expected_output="A summary of key AI advances in 2023",
-        markdown=markdown_enabled,
-        agent=researcher,
-    )
-
-    prompt = task.prompt()
-    
-    assert "Research advances in AI in 2023" in prompt
-    assert "A summary of key AI advances in 2023" in prompt
-    
-    if should_contain_instructions:
-        assert "Your final answer MUST be formatted in Markdown syntax." in prompt
-        assert "Use # for headers" in prompt
-        assert "Use ** for bold text" in prompt
-    else:
-        assert "Your final answer MUST be formatted in Markdown syntax." not in prompt
-
-
-def test_markdown_with_empty_description():
-    """Test markdown formatting with empty description."""
-    
-    researcher = Agent(
-        role="Researcher",
-        goal="Research a topic",
-        backstory="You're a researcher.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="",
-        expected_output="A summary",
-        markdown=True,
-        agent=researcher,
-    )
-
-    prompt = task.prompt()
-    
-    assert prompt.strip() != ""
-    assert "A summary" in prompt
-    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
-
-
-def test_markdown_with_complex_output_format():
-    """Test markdown with JSON output format to ensure compatibility."""
-    
-    class ResearchOutput(BaseModel):
-        title: str
-        findings: list[str]
-    
-    researcher = Agent(
-        role="Researcher",
-        goal="Research a topic",
-        backstory="You're a researcher.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Research topic",
-        expected_output="Research results",
-        markdown=True,
-        output_json=ResearchOutput,
-        agent=researcher,
-    )
-
-    prompt = task.prompt()
-    
-    assert "Your final answer MUST be formatted in Markdown syntax." in prompt
-    assert "Research topic" in prompt
-    assert "Research results" in prompt