feat: add Confident AI observability integration documentation

- Add comprehensive Confident AI integration guide - Include setup instructions and code examples - Update observability overview with new integration - Add navigation entry for new documentation Closes #3285 Co-Authored-By: João <joao@crewai.com>
2026-04-30 23:02:50 +00:00 · 2025-08-07 13:11:06 +00:00
40 changed files with 292 additions and 1532 deletions
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -128,9 +128,7 @@
                      "en/tools/file-document/jsonsearchtool",
                      "en/tools/file-document/csvsearchtool",
                      "en/tools/file-document/directorysearchtool",
-                      "en/tools/file-document/directoryreadtool",
-                      "en/tools/file-document/ocrtool",
-                      "en/tools/file-document/pdf-text-writing-tool"
+                      "en/tools/file-document/directoryreadtool"
                    ]
                  },
                  {
@@ -148,8 +146,7 @@
                      "en/tools/web-scraping/stagehandtool",
                      "en/tools/web-scraping/firecrawlcrawlwebsitetool",
                      "en/tools/web-scraping/firecrawlscrapewebsitetool",
-                      "en/tools/web-scraping/oxylabsscraperstool",
-                      "en/tools/web-scraping/brightdata-tools"
+                      "en/tools/web-scraping/oxylabsscraperstool"
                    ]
                  },
                  {
@@ -166,11 +163,7 @@
                      "en/tools/search-research/youtubechannelsearchtool",
                      "en/tools/search-research/youtubevideosearchtool",
                      "en/tools/search-research/tavilysearchtool",
-                      "en/tools/search-research/tavilyextractortool",
-                      "en/tools/search-research/arxivpapertool",
-                      "en/tools/search-research/serpapi-googlesearchtool",
-                      "en/tools/search-research/serpapi-googleshoppingtool",
-                      "en/tools/search-research/databricks-query-tool"
+                      "en/tools/search-research/tavilyextractortool"
                    ]
                  },
                  {
@@ -182,9 +175,7 @@
                      "en/tools/database-data/snowflakesearchtool",
                      "en/tools/database-data/nl2sqltool",
                      "en/tools/database-data/qdrantvectorsearchtool",
-                      "en/tools/database-data/weaviatevectorsearchtool",
-                      "en/tools/database-data/mongodbvectorsearchtool",
-                      "en/tools/database-data/singlestoresearchtool"
+                      "en/tools/database-data/weaviatevectorsearchtool"
                    ]
                  },
                  {
@@ -216,8 +207,7 @@
                      "en/tools/automation/overview",
                      "en/tools/automation/apifyactorstool",
                      "en/tools/automation/composiotool",
-                      "en/tools/automation/multiontool",
-                      "en/tools/automation/zapieractionstool"
+                      "en/tools/automation/multiontool"
                    ]
                  }
                ]
@@ -228,6 +218,7 @@
                  "en/observability/overview",
                  "en/observability/agentops",
                  "en/observability/arize-phoenix",
+                  "en/observability/confident-ai",
                  "en/observability/langdb",
                  "en/observability/langfuse",
                  "en/observability/langtrace",
--- a/docs/en/observability/confident-ai.mdx
+++ b/docs/en/observability/confident-ai.mdx
@@ -0,0 +1,170 @@
+---
+title: Confident AI Integration
+description: Monitor and evaluate your CrewAI agents with Confident AI's comprehensive observability platform.
+icon: shield-check
+---
+
+# Introduction
+
+Confident AI provides a comprehensive observability and evaluation platform for LLM applications, including CrewAI agents. It offers detailed tracing, monitoring, and evaluation capabilities to help you understand agent performance, identify issues, and ensure reliable operation in production environments.
+
+## Confident AI
+
+[Confident AI](https://confident-ai.com) is built on top of DeepEval, an open-source evaluation framework, and provides advanced monitoring, tracing, and evaluation features for AI applications.
+
+At a high level, Confident AI gives you the ability to monitor agent execution, evaluate output quality, track performance metrics, and gain insights into your CrewAI workflows. For more information, check out the [Confident AI Documentation](https://documentation.confident-ai.com).
+
+### Overview
+
+Confident AI provides end-to-end observability for CrewAI agents in both development and production environments. It offers detailed tracing of agent interactions, LLM calls, and task execution, along with comprehensive evaluation metrics and analytics dashboards.
+
+The platform enables you to monitor agent performance, identify bottlenecks, evaluate output quality, and optimize your CrewAI workflows for better results and cost efficiency.
+
+### Features
+
+- **End-to-End Tracing**: Complete visibility into agent execution flows and LLM interactions
+- **Performance Monitoring**: Track execution times, token usage, and resource consumption
+- **Quality Evaluation**: Automated evaluation of agent outputs using various metrics
+- **Cost Tracking**: Monitor LLM API usage and associated costs
+- **Real-time Analytics**: Live dashboards for monitoring agent performance
+- **Custom Metrics**: Define and track domain-specific evaluation criteria
+- **Anomaly Detection**: Identify unusual patterns in agent behavior
+- **Compliance Monitoring**: Ensure outputs meet safety and quality standards
+- **A/B Testing**: Compare different agent configurations and prompts
+- **Historical Analysis**: Track performance trends over time
+
+### Using Confident AI
+
+<Steps>
+   <Step title="Create an Account">
+      Sign up for a Confident AI account at: [Confident AI Platform](https://app.confident-ai.com)
+   </Step>
+   <Step title="Get Your API Key">
+      Obtain your API key from the Confident AI dashboard under Settings > API Keys
+   </Step>
+   <Step title="Configure Your Environment">
+      Add your API key to your environment variables:
+      ```bash
+      CONFIDENT_API_KEY=<YOUR_CONFIDENT_API_KEY>
+      ```
+   </Step>
+   <Step title="Install DeepEval">
+      Install DeepEval with Confident AI integration:
+      ```bash
+      pip install deepeval
+      ```
+   </Step>
+   <Step title="Initialize Confident AI Tracing">
+      Before using `Crew` in your script, include these lines:
+
+      ```python
+      from deepeval.integrations.crewai import instrument_crewai
+      
+      # Initialize Confident AI tracing for CrewAI
+      instrument_crewai(api_key="<your-confident-api-key>")
+      ```
+
+      This will automatically trace all CrewAI agent interactions and send the data to your Confident AI dashboard.
+   </Step>
+   <Step title="Run Your CrewAI Application">
+      Execute your CrewAI workflows as usual. All agent interactions, LLM calls, and task executions will be automatically traced and sent to Confident AI:
+
+      ```python
+      from crewai import Agent, Task, Crew
+      from deepeval.integrations.crewai import instrument_crewai
+
+      # Initialize tracing
+      instrument_crewai(api_key="<your-confident-api-key>")
+
+      # Define your agents and tasks
+      researcher = Agent(
+          role='Researcher',
+          goal='Research and analyze market trends',
+          backstory='You are an expert market researcher.',
+          verbose=True
+      )
+
+      task = Task(
+          description='Research the latest AI trends in 2024',
+          agent=researcher,
+          expected_output='A comprehensive report on AI trends'
+      )
+
+      crew = Crew(
+          agents=[researcher],
+          tasks=[task],
+          verbose=True
+      )
+
+      # Execute the crew - all interactions will be traced
+      result = crew.kickoff()
+      ```
+   </Step>
+</Steps>
+
+### Advanced Configuration
+
+You can customize the tracing behavior with additional configuration options:
+
+```python
+from deepeval.integrations.crewai import instrument_crewai
+
+# Advanced configuration
+instrument_crewai(
+    api_key="<your-confident-api-key>",
+    project_name="my-crewai-project",
+    environment="production",
+    custom_metadata={
+        "version": "1.0.0",
+        "team": "ai-research"
+    }
+)
+```
+
+### Evaluation and Monitoring
+
+Confident AI automatically evaluates your agent outputs using various metrics. You can also define custom evaluation criteria:
+
+```python
+from deepeval import evaluate
+from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric
+
+# Define evaluation metrics
+metrics = [
+    AnswerRelevancyMetric(threshold=0.8),
+    FaithfulnessMetric(threshold=0.7)
+]
+
+# Your CrewAI execution will be automatically evaluated
+# Results will appear in your Confident AI dashboard
+```
+
+### Key Benefits
+
+- **Comprehensive Visibility**: Complete tracing of agent workflows and interactions
+- **Quality Assurance**: Automated evaluation ensures consistent output quality
+- **Performance Optimization**: Identify bottlenecks and optimize agent performance
+- **Cost Management**: Track and optimize LLM usage costs
+- **Production Monitoring**: Real-time monitoring for production deployments
+- **Continuous Improvement**: Historical data enables iterative optimization
+
+### Further Information
+
+To get started with Confident AI:
+
+- [Sign up for Confident AI](https://app.confident-ai.com)
+- [Read the Documentation](https://documentation.confident-ai.com)
+- [Explore DeepEval](https://github.com/confident-ai/deepeval)
+- [CrewAI Integration Guide](https://documentation.confident-ai.com/docs/llm-tracing/integrations/crewai)
+
+For support or questions, reach out to the Confident AI team through their documentation or support channels.
+
+#### Extra links
+
+<a href="https://confident-ai.com">🌐 Confident AI Website</a>
+<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
+<a href="https://app.confident-ai.com">📊 Confident AI Dashboard</a>
+<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
+<a href="https://documentation.confident-ai.com">📙 Documentation</a>
+<span>&nbsp;&nbsp;•&nbsp;&nbsp;</span>
+<a href="https://github.com/confident-ai/deepeval">🔧 DeepEval GitHub</a>
--- a/docs/en/observability/overview.mdx
+++ b/docs/en/observability/overview.mdx
@@ -49,6 +49,10 @@ Observability is crucial for understanding how your CrewAI agents perform, ident
    AI observability platform for monitoring and troubleshooting.
  </Card>

+  <Card title="Confident AI" icon="shield-check" href="/en/observability/confident-ai">
+    Comprehensive observability and evaluation platform with end-to-end tracing and quality monitoring.
+  </Card>
+
  <Card title="Portkey" icon="key" href="/en/observability/portkey">
    AI gateway with comprehensive monitoring and reliability features.
  </Card>
--- a/docs/en/telemetry.mdx
+++ b/docs/en/telemetry.mdx
@@ -35,7 +35,7 @@ os.environ['OTEL_SDK_DISABLED'] = 'true'

 ### Data Explanation:
 | Defaulted | Data                                      | Reason and Specifics                                                                                                       |
-|:----------|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
+|-----------|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|
 | Yes       | CrewAI and Python Version                 | Tracks software versions. Example: CrewAI v1.2.3, Python 3.8.10. No personal data. |
 | Yes       | Crew Metadata | Includes: randomly generated key and ID, process type (e.g., 'sequential', 'parallel'), boolean flag for memory usage (true/false), count of tasks, count of agents. All non-personal. |
 | Yes       | Agent Data | Includes: randomly generated key and ID, role name (should not include personal info), boolean settings (verbose, delegation enabled, code execution allowed), max iterations, max RPM, max retry limit, LLM info (see LLM Attributes), list of tool names (should not include personal info). No personal data. |
--- a/docs/en/tools/automation/overview.mdx
+++ b/docs/en/tools/automation/overview.mdx
@@ -20,10 +20,6 @@ These tools enable your agents to automate workflows, integrate with external pl
  <Card title="Multion Tool" icon="window-restore" href="/en/tools/automation/multiontool">
    Automate browser interactions and web-based workflows.
  </Card>
-
-  <Card title="Zapier Actions Adapter" icon="bolt" href="/en/tools/automation/zapieractionstool">
-    Expose Zapier Actions as CrewAI tools for automation across thousands of apps.
-  </Card>
 </CardGroup>

 ## **Common Use Cases**
--- a/docs/en/tools/automation/zapieractionstool.mdx
+++ b/docs/en/tools/automation/zapieractionstool.mdx
@@ -1,58 +0,0 @@
---
-title: Zapier Actions Tool
-description: The `ZapierActionsAdapter` exposes Zapier actions as CrewAI tools for automation.
-icon: bolt
---
-
-# `ZapierActionsAdapter`
-
-## Description
-
-Use the Zapier adapter to list and call Zapier actions as CrewAI tools. This enables agents to trigger automations across thousands of apps.
-
-## Installation
-
-This adapter is included with `crewai-tools`. No extra install required.
-
-## Environment Variables
-
- `ZAPIER_API_KEY` (required): Zapier API key. Get one from the Zapier Actions dashboard at https://actions.zapier.com/ (create an account, then generate an API key). You can also pass `zapier_api_key` directly when constructing the adapter.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools.adapters.zapier_adapter import ZapierActionsAdapter
-
-adapter = ZapierActionsAdapter(api_key="your_zapier_api_key")
-tools = adapter.tools()
-
-agent = Agent(
-    role="Automator",
-    goal="Execute Zapier actions",
-    backstory="Automation specialist",
-    tools=tools,
-    verbose=True,
-)
-
-task = Task(
-    description="Create a new Google Sheet and add a row using Zapier actions",
-    expected_output="Confirmation with created resource IDs",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes & limits
-
- The adapter lists available actions for your key and creates `BaseTool` wrappers dynamically.
- Handle action‑specific required fields in your task instructions or tool call.
- Rate limits depend on your Zapier plan; see the Zapier Actions docs.
-
-## Notes
-
- The adapter fetches available actions and generates `BaseTool` wrappers dynamically.
-
-
--- a/docs/en/tools/database-data/mongodbvectorsearchtool.mdx
+++ b/docs/en/tools/database-data/mongodbvectorsearchtool.mdx
@@ -1,168 +0,0 @@
---
-title: MongoDB Vector Search Tool
-description: The `MongoDBVectorSearchTool` performs vector search on MongoDB Atlas with optional indexing helpers.
-icon: "leaf"
---
-
-# `MongoDBVectorSearchTool`
-
-## Description
-
-Perform vector similarity queries on MongoDB Atlas collections. Supports index creation helpers and bulk insert of embedded texts.
-
-MongoDB Atlas supports native vector search. Learn more:
-https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/
-
-## Installation
-
-Install with the MongoDB extra:
-
-```shell
-pip install crewai-tools[mongodb]
-```
-
-or
-
-```shell
-uv add crewai-tools --extra mongodb
-```
-
-## Parameters
-
-### Initialization
-
- `connection_string` (str, required)
- `database_name` (str, required)
- `collection_name` (str, required)
- `vector_index_name` (str, default `vector_index`)
- `text_key` (str, default `text`)
- `embedding_key` (str, default `embedding`)
- `dimensions` (int, default `1536`)
-
-### Run Parameters
-
- `query` (str, required): Natural language query to embed and search.
-
-## Quick start
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-  connection_string="mongodb+srv://...",
-  database_name="mydb",
-  collection_name="docs",
-)
-
-print(tool.run(query="how to create vector index"))
-```
-
-## Index creation helpers
-
-Use `create_vector_search_index(...)` to provision an Atlas Vector Search index with the correct dimensions and similarity.
-
-## Common issues
-
- Authentication failures: ensure your Atlas IP Access List allows your runner and the connection string includes credentials.
- Index not found: create the vector index first; name must match `vector_index_name`.
- Dimensions mismatch: align embedding model dimensions with `dimensions`.
-
-## More examples
-
-### Basic initialization
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-```
-
-### Custom query configuration
-
-```python Code
-from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool
-
-query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2)
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-    query_config=query_config,
-    vector_index_name="my_vector_index",
-)
-
-rag_agent = Agent(
-    name="rag_agent",
-    role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.",
-    goal="...",
-    backstory="...",
-    tools=[tool],
-)
-```
-
-### Preloading the database and creating the index
-
-```python Code
-import os
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-
-# Load text content from a local folder and add to MongoDB
-texts = []
-for fname in os.listdir("knowledge"):
-    path = os.path.join("knowledge", fname)
-    if os.path.isfile(path):
-        with open(path, "r", encoding="utf-8") as f:
-            texts.append(f.read())
-
-tool.add_texts(texts)
-
-# Create the Atlas Vector Search index (e.g., 3072 dims for text-embedding-3-large)
-tool.create_vector_search_index(dimensions=3072)
-```
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    connection_string="mongodb+srv://...",
-    database_name="mydb",
-    collection_name="docs",
-)
-
-agent = Agent(
-    role="RAG Agent",
-    goal="Answer using MongoDB vector search",
-    backstory="Knowledge retrieval specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Find relevant content for 'indexing guidance'",
-    expected_output="A concise answer citing the most relevant matches",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-
--- a/docs/en/tools/database-data/overview.mdx
+++ b/docs/en/tools/database-data/overview.mdx
@@ -32,14 +32,6 @@ These tools enable your agents to interact with various database systems, from t
  <Card title="Weaviate Vector Search" icon="network-wired" href="/en/tools/database-data/weaviatevectorsearchtool">
    Perform semantic search with Weaviate vector database.
  </Card>
-
-  <Card title="MongoDB Vector Search" icon="leaf" href="/en/tools/database-data/mongodbvectorsearchtool">
-    Vector similarity search on MongoDB Atlas with indexing helpers.
-  </Card>
-
-  <Card title="SingleStore Search" icon="database" href="/en/tools/database-data/singlestoresearchtool">
-    Safe SELECT/SHOW queries on SingleStore with pooling and validation.
-  </Card>
 </CardGroup>

 ## **Common Use Cases**
--- a/docs/en/tools/database-data/singlestoresearchtool.mdx
+++ b/docs/en/tools/database-data/singlestoresearchtool.mdx
@@ -1,61 +0,0 @@
---
-title: SingleStore Search Tool
-description: The `SingleStoreSearchTool` safely executes SELECT/SHOW queries on SingleStore with pooling.
-icon: circle
---
-
-# `SingleStoreSearchTool`
-
-## Description
-
-Execute read‑only queries (`SELECT`/`SHOW`) against SingleStore with connection pooling and input validation.
-
-## Installation
-
-```shell
-uv add crewai-tools[singlestore]
-```
-
-## Environment Variables
-
-Variables like `SINGLESTOREDB_HOST`, `SINGLESTOREDB_USER`, `SINGLESTOREDB_PASSWORD`, etc., can be used, or `SINGLESTOREDB_URL` as a single DSN.
-
-Generate the API key from the SingleStore dashboard, [docs here](https://docs.singlestore.com/cloud/reference/management-api/#generate-an-api-key).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SingleStoreSearchTool
-
-tool = SingleStoreSearchTool(
-    tables=["products"], 
-    host="host", 
-    user="user", 
-    password="pass", 
-    database="db",
-)
-
-agent = Agent(
-    role="Analyst", 
-    goal="Query SingleStore", 
-    tools=[tool], 
-    verbose=True,
-)
-
-task = Task(
-    description="List 5 products", 
-    expected_output="5 rows as JSON/text", 
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-
--- a/docs/en/tools/file-document/ocrtool.mdx
+++ b/docs/en/tools/file-document/ocrtool.mdx
@@ -1,89 +0,0 @@
---
-title: OCR Tool
-description: The `OCRTool` extracts text from local images or image URLs using an LLM with vision.
-icon: image
---
-
-# `OCRTool`
-
-## Description
-
-Extract text from images (local path or URL). Uses a vision‑capable LLM via CrewAI’s LLM interface.
-
-## Installation
-
-No extra install beyond `crewai-tools`. Ensure your selected LLM supports vision.
-
-## Parameters
-
-### Run Parameters
-
- `image_path_url` (str, required): Local image path or HTTP(S) URL.
-
-## Examples
-
-### Direct usage
-
-```python Code
-from crewai_tools import OCRTool
-
-print(OCRTool().run(image_path_url="/tmp/receipt.png"))
-```
-
-### With an agent
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-ocr = OCRTool()
-
-agent = Agent(
-    role="OCR", 
-    goal="Extract text", 
-    tools=[ocr],
-)
-
-task = Task(
-    description="Extract text from https://example.com/invoice.jpg", 
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
- Ensure the selected LLM supports image inputs.
- For large images, consider downscaling to reduce token usage.
- - You can pass a specific LLM instance to the tool (e.g., `LLM(model="gpt-4o")`) if needed, matching the README guidance.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-tool = OCRTool()
-
-agent = Agent(
-    role="OCR Specialist",
-    goal="Extract text from images",
-    backstory="Vision‑enabled analyst",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Extract text from https://example.com/receipt.png",
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-
--- a/docs/en/tools/file-document/overview.mdx
+++ b/docs/en/tools/file-document/overview.mdx
@@ -52,14 +52,6 @@ These tools enable your agents to work with various file formats and document ty
  <Card title="Directory Read Tool" icon="folder" href="/en/tools/file-document/directoryreadtool">
    Read and list directory contents, file structures, and metadata.
  </Card>
-
-  <Card title="OCR Tool" icon="image" href="/en/tools/file-document/ocrtool">
-    Extract text from images (local files or URLs) using a vision‑capable LLM.
-  </Card>
-
-  <Card title="PDF Text Writing Tool" icon="file-pdf" href="/en/tools/file-document/pdf-text-writing-tool">
-    Write text at specific coordinates in PDFs, with optional custom fonts.
-  </Card>
 </CardGroup>

 ## **Common Use Cases**
--- a/docs/en/tools/file-document/pdf-text-writing-tool.mdx
+++ b/docs/en/tools/file-document/pdf-text-writing-tool.mdx
@@ -1,76 +0,0 @@
---
-title: PDF Text Writing Tool
-description: The `PDFTextWritingTool` writes text to specific positions in a PDF, supporting custom fonts.
-icon: file-pdf
---
-
-# `PDFTextWritingTool`
-
-## Description
-
-Write text at precise coordinates on a PDF page, optionally embedding a custom TrueType font.
-
-## Parameters
-
-### Run Parameters
-
- `pdf_path` (str, required): Path to the input PDF.
- `text` (str, required): Text to add.
- `position` (tuple[int, int], required): `(x, y)` coordinates.
- `font_size` (int, default `12`)
- `font_color` (str, default `"0 0 0 rg"`)
- `font_name` (str, default `"F1"`)
- `font_file` (str, optional): Path to `.ttf` file.
- `page_number` (int, default `0`)
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import PDFTextWritingTool
-
-tool = PDFTextWritingTool()
-
-agent = Agent(
-    role="PDF Editor",
-    goal="Annotate PDFs",
-    backstory="Documentation specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Write 'CONFIDENTIAL' at (72, 720) on page 1 of ./sample.pdf",
-    expected_output="Confirmation message",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-### Direct usage
-
-```python Code
-from crewai_tools import PDFTextWritingTool
-
-PDFTextWritingTool().run(
-  pdf_path="./input.pdf",
-  text="CONFIDENTIAL",
-  position=(72, 720),
-  font_size=18,
-  page_number=0,
-)
-```
-
-## Tips
-
- Coordinate origin is the bottom‑left corner.
- If using a custom font (`font_file`), ensure it is a valid `.ttf`.
-
-
--- a/docs/en/tools/search-research/arxivpapertool.mdx
+++ b/docs/en/tools/search-research/arxivpapertool.mdx
@@ -1,112 +0,0 @@
---
-title: Arxiv Paper Tool
-description: The `ArxivPaperTool` searches arXiv for papers matching a query and optionally downloads PDFs.
-icon: box-archive
---
-
-# `ArxivPaperTool`
-
-## Description
-
-The `ArxivPaperTool` queries the arXiv API for academic papers and returns compact, readable results. It can also optionally download PDFs to disk.
-
-## Installation
-
-This tool has no special installation beyond `crewai-tools`.
-
-```shell
-uv add crewai-tools
-```
-
-No API key is required. This tool uses the public arXiv Atom API.
-
-## Steps to Get Started
-
-1. Initialize the tool.
-2. Provide a `search_query` (e.g., "transformer neural network").
-3. Optionally set `max_results` (1–100) and enable PDF downloads in the constructor.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import ArxivPaperTool
-
-tool = ArxivPaperTool(
-    download_pdfs=False,
-    save_dir="./arxiv_pdfs",
-    use_title_as_filename=True,
-)
-
-agent = Agent(
-    role="Researcher",
-    goal="Find relevant arXiv papers",
-    backstory="Expert at literature discovery",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search arXiv for 'transformer neural network' and list top 5 results.",
-    expected_output="A concise list of 5 relevant papers with titles, links, and summaries.",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-### Direct usage (without Agent)
-
-```python Code
-from crewai_tools import ArxivPaperTool
-
-tool = ArxivPaperTool(
-    download_pdfs=True, 
-    save_dir="./arxiv_pdfs",
-)
-print(tool.run(search_query="mixture of experts", max_results=3))
-```
-
-## Parameters
-
-### Initialization Parameters
-
- `download_pdfs` (bool, default `False`): Whether to download PDFs.
- `save_dir` (str, default `./arxiv_pdfs`): Directory to save PDFs.
- `use_title_as_filename` (bool, default `False`): Use paper titles for filenames.
-
-### Run Parameters
-
- `search_query` (str, required): The arXiv search query.
- `max_results` (int, default `5`, range 1–100): Number of results.
-
-## Output format
-
-The tool returns a human‑readable list of papers with:
- Title
- Link (abs page)
- Snippet/summary (truncated)
-
-When `download_pdfs=True`, PDFs are saved to disk and the summary mentions saved files.
-
-## Usage Notes
-
- The tool returns formatted text with key metadata and links.
- When `download_pdfs=True`, PDFs will be stored in `save_dir`.
-
-## Troubleshooting
-
- If you receive a network timeout, re‑try or reduce `max_results`.
- Invalid XML errors indicate an arXiv response parse issue; try a simpler query.
- File system errors (e.g., permission denied) may occur when saving PDFs; ensure `save_dir` is writable.
-
-## Related links
-
- arXiv API docs: https://info.arxiv.org/help/api/index.html
-
-## Error Handling
-
- Network issues, invalid XML, and OS errors are handled with informative messages.
-
-
--- a/docs/en/tools/search-research/bravesearchtool.mdx
+++ b/docs/en/tools/search-research/bravesearchtool.mdx
@@ -23,7 +23,7 @@ pip install 'crewai[tools]'
 To effectively use the `BraveSearchTool`, follow these steps:

 1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a Brave Search API key at https://api.search.brave.com/app/keys (sign in to generate a key).
+2. **API Key Acquisition**: Acquire a Brave Search API key by registering at [Brave Search API](https://api.search.brave.com/app/keys).
 3. **Environment Configuration**: Store your obtained API key in an environment variable named `BRAVE_API_KEY` to facilitate its use by the tool.

 ## Example
--- a/docs/en/tools/search-research/databricks-query-tool.mdx
+++ b/docs/en/tools/search-research/databricks-query-tool.mdx
@@ -1,80 +0,0 @@
---
-title: Databricks SQL Query Tool
-description: The `DatabricksQueryTool` executes SQL queries against Databricks workspace tables.
-icon: trowel-bricks
---
-
-# `DatabricksQueryTool`
-
-## Description
-
-Run SQL against Databricks workspace tables with either CLI profile or direct host/token authentication.
-
-## Installation
-
-```shell
-uv add crewai-tools[databricks-sdk]
-```
-
-## Environment Variables
-
- `DATABRICKS_CONFIG_PROFILE` or (`DATABRICKS_HOST` + `DATABRICKS_TOKEN`)
-
-Create a personal access token and find host details in the Databricks workspace under User Settings → Developer. 
-Docs: https://docs.databricks.com/en/dev-tools/auth/pat.html
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import DatabricksQueryTool
-
-tool = DatabricksQueryTool(
-    default_catalog="main", 
-    default_schema="default",
-)
-
-agent = Agent(
-    role="Data Analyst",
-    goal="Query Databricks",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="SELECT * FROM my_table LIMIT 10",
-    expected_output="10 rows", 
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-result = crew.kickoff()
-
-print(result)
-```
-
-## Parameters
-
- `query` (required): SQL query to execute
- `catalog` (optional): Override default catalog
- `db_schema` (optional): Override default schema
- `warehouse_id` (optional): Override default SQL warehouse
- `row_limit` (optional): Maximum rows to return (default: 1000)
-
-## Defaults on initialization
-
- `default_catalog`
- `default_schema`
- `default_warehouse_id`
-
-### Error handling & tips
-
- Authentication errors: verify `DATABRICKS_HOST` begins with `https://` and token is valid.
- Permissions: ensure your SQL warehouse and schema are accessible by your token.
- Limits: long‑running queries should be avoided in agent loops; add filters/limits.
-
-
--- a/docs/en/tools/search-research/githubsearchtool.mdx
+++ b/docs/en/tools/search-research/githubsearchtool.mdx
@@ -24,8 +24,6 @@ pip install 'crewai[tools]'

 This command installs the necessary package to run the GithubSearchTool along with any other tools included in the crewai_tools package.

-Get a GitHub Personal Access Token at https://github.com/settings/tokens (Developer settings → Fine‑grained tokens or classic tokens).
-
 ## Example

 Here’s how you can use the GithubSearchTool to perform semantic searches within a GitHub repository:
--- a/docs/en/tools/search-research/overview.mdx
+++ b/docs/en/tools/search-research/overview.mdx
@@ -52,18 +52,6 @@ These tools enable your agents to search the web, research topics, and find info
  <Card title="Tavily Extractor Tool" icon="file-text" href="/en/tools/search-research/tavilyextractortool">
    Extract structured content from web pages using the Tavily API.
  </Card>
-
-  <Card title="Arxiv Paper Tool" icon="box-archive" href="/en/tools/search-research/arxivpapertool">
-    Search arXiv and optionally download PDFs.
-  </Card>
-
-  <Card title="SerpApi Google Search" icon="search" href="/en/tools/search-research/serpapi-googlesearchtool">
-    Google search via SerpApi with structured results.
-  </Card>
-
-  <Card title="SerpApi Google Shopping" icon="cart-shopping" href="/en/tools/search-research/serpapi-googleshoppingtool">
-    Google Shopping queries via SerpApi.
-  </Card>
 </CardGroup>

 ## **Common Use Cases**
--- a/docs/en/tools/search-research/serpapi-googlesearchtool.mdx
+++ b/docs/en/tools/search-research/serpapi-googlesearchtool.mdx
@@ -1,65 +0,0 @@
---
-title: SerpApi Google Search Tool
-description: The `SerpApiGoogleSearchTool` performs Google searches using the SerpApi service.
-icon: google
---
-
-# `SerpApiGoogleSearchTool`
-
-## Description
-
-Use the `SerpApiGoogleSearchTool` to run Google searches with SerpApi and retrieve structured results. Requires a SerpApi API key.
-
-## Installation
-
-```shell
-uv add crewai-tools[serpapi]
-```
-
-## Environment Variables
-
- `SERPAPI_API_KEY` (required): API key for SerpApi. Create one at https://serpapi.com/ (free tier available).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SerpApiGoogleSearchTool
-
-tool = SerpApiGoogleSearchTool()
-
-agent = Agent(
-    role="Researcher",
-    goal="Answer questions using Google search",
-    backstory="Search specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search for the latest CrewAI releases",
-    expected_output="A concise list of relevant results with titles and links",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
- Set `SERPAPI_API_KEY` in the environment. Create a key at https://serpapi.com/
- See also Google Shopping via SerpApi: `/en/tools/search-research/serpapi-googleshoppingtool`
-
-## Parameters
-
-### Run Parameters
-
- `search_query` (str, required): The Google query.
- `location` (str, optional): Geographic location parameter.
-
-## Notes
-
- This tool wraps SerpApi and returns structured search results.
-
-
--- a/docs/en/tools/search-research/serpapi-googleshoppingtool.mdx
+++ b/docs/en/tools/search-research/serpapi-googleshoppingtool.mdx
@@ -1,61 +0,0 @@
---
-title: SerpApi Google Shopping Tool
-description: The `SerpApiGoogleShoppingTool` searches Google Shopping results using SerpApi.
-icon: cart-shopping
---
-
-# `SerpApiGoogleShoppingTool`
-
-## Description
-
-Leverage `SerpApiGoogleShoppingTool` to query Google Shopping via SerpApi and retrieve product-oriented results.
-
-## Installation
-
-```shell
-uv add crewai-tools[serpapi]
-```
-
-## Environment Variables
-
- `SERPAPI_API_KEY` (required): API key for SerpApi. Create one at https://serpapi.com/ (free tier available).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SerpApiGoogleShoppingTool
-
-tool = SerpApiGoogleShoppingTool()
-
-agent = Agent(
-    role="Shopping Researcher",
-    goal="Find relevant products",
-    backstory="Expert in product search",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search Google Shopping for 'wireless noise-canceling headphones'",
-    expected_output="Top relevant products with titles and links",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
- Set `SERPAPI_API_KEY` in the environment. Create a key at https://serpapi.com/
- See also Google Web Search via SerpApi: `/en/tools/search-research/serpapi-googlesearchtool`
-
-## Parameters
-
-### Run Parameters
-
- `search_query` (str, required): Product search query.
- `location` (str, optional): Geographic location parameter.
-
-
--- a/docs/en/tools/search-research/serperdevtool.mdx
+++ b/docs/en/tools/search-research/serperdevtool.mdx
@@ -16,7 +16,7 @@ to fetch and display the most relevant search results based on the query provide
 To effectively use the `SerperDevTool`, follow these steps:

 1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a `serper.dev` API key at https://serper.dev/ (free tier available).
+2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
 3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.

 To incorporate this tool into your project, follow the installation instructions below:
--- a/docs/en/tools/search-research/tavilyextractortool.mdx
+++ b/docs/en/tools/search-research/tavilyextractortool.mdx
@@ -1,7 +1,7 @@
 ---
 title: "Tavily Extractor Tool"
 description: "Extract structured content from web pages using the Tavily API"
-icon: square-poll-horizontal
+icon: "file-text"
 ---

 The `TavilyExtractorTool` allows CrewAI agents to extract structured content from web pages using the Tavily API. It can process single URLs or lists of URLs and provides options for controlling the extraction depth and including images.
--- a/docs/en/tools/search-research/tavilysearchtool.mdx
+++ b/docs/en/tools/search-research/tavilysearchtool.mdx
@@ -22,8 +22,6 @@ Ensure your Tavily API key is set as an environment variable:
 export TAVILY_API_KEY='your_tavily_api_key'
 ```

-Get an API key at https://app.tavily.com/ (sign up, then create a key).
-
 ## Example Usage

 Here's how to initialize and use the `TavilySearchTool` within a CrewAI agent:
--- a/docs/en/tools/web-scraping/brightdata-tools.mdx
+++ b/docs/en/tools/web-scraping/brightdata-tools.mdx
@@ -1,111 +0,0 @@
---
-title: Bright Data Tools
-description: Bright Data integrations for SERP search, Web Unlocker scraping, and Dataset API.
-icon: spider
---
-
-# Bright Data Tools
-
-This set of tools integrates Bright Data services for web extraction.
-
-## Installation
-
-```shell
-uv add crewai-tools requests aiohttp
-```
-
-## Environment Variables
-
- `BRIGHT_DATA_API_KEY` (required)
- `BRIGHT_DATA_ZONE` (for SERP/Web Unlocker)
-
-Create credentials at https://brightdata.com/ (sign up, then create an API token and zone). 
-See their docs: https://developers.brightdata.com/
-
-## Included Tools
-
- `BrightDataSearchTool`: SERP search (Google/Bing/Yandex) with geo/language/device options.
- `BrightDataWebUnlockerTool`: Scrape pages with anti-bot bypass and rendering.
- `BrightDataDatasetTool`: Run Dataset API jobs and fetch results.
-
-## Examples
-
-### SERP Search
-
-```python Code
-from crewai_tools import BrightDataSearchTool
-
-tool = BrightDataSearchTool(
-    query="CrewAI", 
-    country="us",
-)
-
-print(tool.run())
-```
-
-### Web Unlocker
-
-```python Code
-from crewai_tools import BrightDataWebUnlockerTool
-
-tool = BrightDataWebUnlockerTool(
-    url="https://example.com", 
-    format="markdown",
-)
-
-print(tool.run(url="https://example.com"))
-```
-
-### Dataset API
-
-```python Code
-from crewai_tools import BrightDataDatasetTool
-
-tool = BrightDataDatasetTool(
-    dataset_type="ecommerce", 
-    url="https://example.com/product",
-)
-
-print(tool.run())
-```
-
-## Troubleshooting
-
- 401/403: verify `BRIGHT_DATA_API_KEY` and `BRIGHT_DATA_ZONE`.
- Empty/blocked content: enable rendering or try a different zone.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import BrightDataSearchTool
-
-tool = BrightDataSearchTool(
-    query="CrewAI", 
-    country="us",
-)
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Search with Bright Data",
-    backstory="Finds reliable results",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Search for CrewAI and summarize top results",
-    expected_output="Short summary with links",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-
--- a/docs/en/tools/web-scraping/overview.mdx
+++ b/docs/en/tools/web-scraping/overview.mdx
@@ -60,10 +60,6 @@ These tools enable your agents to interact with the web, extract data from websi
  <Card title="Oxylabs Scraper Tool" icon="globe" href="/en/tools/web-scraping/oxylabsscraperstool">
    Access web data at scale with Oxylabs.
  </Card>
-
-  <Card title="Bright Data Tools" icon="spider" href="/en/tools/web-scraping/brightdata-tools">
-    SERP search, Web Unlocker, and Dataset API integrations.
-  </Card>
 </CardGroup>

 ## **Common Use Cases**
--- a/src/crewai/cli/cli.py
+++ b/src/crewai/cli/cli.py
@@ -14,7 +14,6 @@ from crewai.memory.storage.kickoff_task_outputs_storage import (

 from .authentication.main import AuthenticationCommand
 from .deploy.main import DeployCommand
-from .enterprise.main import EnterpriseConfigureCommand
 from .evaluate_crew import evaluate_crew
 from .install_crew import install_crew
 from .kickoff_flow import kickoff_flow
@@ -393,20 +392,6 @@ def current():
    org_command.current()


-@crewai.group()
-def enterprise():
-    """Enterprise Configuration commands."""
-    pass
-
-
-@enterprise.command("configure")
-@click.argument("enterprise_url")
-def enterprise_configure(enterprise_url: str):
-    """Configure CrewAI Enterprise OAuth2 settings from the provided Enterprise URL."""
-    enterprise_command = EnterpriseConfigureCommand()
-    enterprise_command.configure(enterprise_url)
-
-
@crewai.group()
 def config():
    """CLI Configuration commands."""
--- a/src/crewai/cli/enterprise/init.py
+++ b/src/crewai/cli/enterprise/init.py
--- a/src/crewai/cli/enterprise/main.py
+++ b/src/crewai/cli/enterprise/main.py
@@ -1,84 +0,0 @@
-import requests
-from typing import Dict, Any
-from rich.console import Console
-from requests.exceptions import RequestException, JSONDecodeError
-
-from crewai.cli.command import BaseCommand
-from crewai.cli.settings.main import SettingsCommand
-from crewai.cli.version import get_crewai_version
-
-console = Console()
-
-
-class EnterpriseConfigureCommand(BaseCommand):
-    def __init__(self):
-        super().__init__()
-        self.settings_command = SettingsCommand()
-
-    def configure(self, enterprise_url: str) -> None:
-        try:
-            enterprise_url = enterprise_url.rstrip('/')
-
-            oauth_config = self._fetch_oauth_config(enterprise_url)
-
-            self._update_oauth_settings(enterprise_url, oauth_config)
-
-            console.print(
-                f"✅ Successfully configured CrewAI Enterprise with OAuth2 settings from {enterprise_url}",
-                style="bold green"
-            )
-
-        except Exception as e:
-            console.print(f"❌ Failed to configure Enterprise settings: {str(e)}", style="bold red")
-            raise SystemExit(1)
-
-    def _fetch_oauth_config(self, enterprise_url: str) -> Dict[str, Any]:
-        oauth_endpoint = f"{enterprise_url}/auth/parameters"
-
-        try:
-            console.print(f"🔄 Fetching OAuth2 configuration from {oauth_endpoint}...")
-            headers = {
-                "Content-Type": "application/json",
-                "User-Agent": f"CrewAI-CLI/{get_crewai_version()}",
-                "X-Crewai-Version": get_crewai_version(),
-            }
-            response = requests.get(oauth_endpoint, timeout=30, headers=headers)
-            response.raise_for_status()
-
-            try:
-                oauth_config = response.json()
-            except JSONDecodeError:
-                raise ValueError(f"Invalid JSON response from {oauth_endpoint}")
-
-            required_fields = ['audience', 'domain', 'device_authorization_client_id', 'provider']
-            missing_fields = [field for field in required_fields if field not in oauth_config]
-
-            if missing_fields:
-                raise ValueError(f"Missing required fields in OAuth2 configuration: {', '.join(missing_fields)}")
-
-            console.print("✅ Successfully retrieved OAuth2 configuration", style="green")
-            return oauth_config
-
-        except RequestException as e:
-            raise ValueError(f"Failed to connect to enterprise URL: {str(e)}")
-        except Exception as e:
-            raise ValueError(f"Error fetching OAuth2 configuration: {str(e)}")
-
-    def _update_oauth_settings(self, enterprise_url: str, oauth_config: Dict[str, Any]) -> None:
-        try:
-            config_mapping = {
-                'enterprise_base_url': enterprise_url,
-                'oauth2_provider': oauth_config['provider'],
-                'oauth2_audience': oauth_config['audience'],
-                'oauth2_client_id': oauth_config['device_authorization_client_id'],
-                'oauth2_domain': oauth_config['domain']
-            }
-
-            console.print("🔄 Updating local OAuth2 configuration...")
-
-            for key, value in config_mapping.items():
-                self.settings_command.set(key, value)
-                console.print(f"  ✓ Set {key}: {value}", style="dim")
-
-        except Exception as e:
-            raise ValueError(f"Failed to update OAuth2 settings: {str(e)}")
--- a/src/crewai/crew.py
+++ b/src/crewai/crew.py
@@ -1,3 +1,4 @@
+import os
 import asyncio
 import json
 import re
@@ -77,7 +78,6 @@ from crewai.utilities.events.listeners.tracing.trace_listener import (
 )


-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
 from crewai.utilities.formatter import (
    aggregate_raw_outputs_from_task_outputs,
    aggregate_raw_outputs_from_tasks,
@@ -248,10 +248,6 @@ class Crew(FlowTrackable, BaseModel):
        default=None,
        description="Metrics for the LLM usage during all tasks execution.",
    )
-    tracing: Optional[bool] = Field(
-        default=False,
-        description="Whether to enable tracing for the crew.",
-    )

    @field_validator("id", mode="before")
    @classmethod
@@ -283,8 +279,8 @@ class Crew(FlowTrackable, BaseModel):

        self._cache_handler = CacheHandler()
        event_listener = EventListener()
-        if is_tracing_enabled() or self.tracing:
-            trace_listener = TraceCollectionListener(tracing=self.tracing)
+        if os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true":
+            trace_listener = TraceCollectionListener()
            trace_listener.setup_listeners(crewai_event_bus)
        event_listener.verbose = self.verbose
        event_listener.formatter.verbose = self.verbose
--- a/src/crewai/flow/flow.py
+++ b/src/crewai/flow/flow.py
@@ -2,6 +2,7 @@ import asyncio
 import copy
 import inspect
 import logging
+import os
 from typing import (
    Any,
    Callable,
@@ -35,7 +36,6 @@ from crewai.utilities.events.flow_events import (
 from crewai.utilities.events.listeners.tracing.trace_listener import (
    TraceCollectionListener,
 )
-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
 from crewai.utilities.printer import Printer

 logger = logging.getLogger(__name__)
@@ -441,7 +441,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
    _router_paths: Dict[str, List[str]] = {}
    initial_state: Union[Type[T], T, None] = None
    name: Optional[str] = None
-    tracing: Optional[bool] = False

    def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
        class _FlowGeneric(cls):  # type: ignore
@@ -453,7 +452,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
    def __init__(
        self,
        persistence: Optional[FlowPersistence] = None,
-        tracing: Optional[bool] = False,
        **kwargs: Any,
    ) -> None:
        """Initialize a new Flow instance.
@@ -471,9 +469,8 @@ class Flow(Generic[T], metaclass=FlowMeta):

        # Initialize state with initial values
        self._state = self._create_initial_state()
-        self.tracing = tracing
-        if is_tracing_enabled() or tracing:
-            trace_listener = TraceCollectionListener(tracing=tracing)
+        if os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true":
+            trace_listener = TraceCollectionListener()
            trace_listener.setup_listeners(crewai_event_bus)
        # Apply any additional kwargs
        if kwargs:
--- a/src/crewai/lite_agent.py
+++ b/src/crewai/lite_agent.py
@@ -525,16 +525,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                    self,
                    event=LLMCallStartedEvent(
                        messages=self._messages,
-                        tools=[
-                            {
-                                "name": tool.name,
-                                "description": tool.description,
-                                "args": tool.args,
-                            }
-                            for tool in self._parsed_tools
-                        ]
-                        if self._parsed_tools
-                        else None,
+                        tools=None,
                        callbacks=self._callbacks,
                        from_agent=self,
                        model=model,
--- a/src/crewai/utilities/events/listeners/tracing/init.py
+++ b/src/crewai/utilities/events/listeners/tracing/init.py
--- a/src/crewai/utilities/events/listeners/tracing/interfaces.py
+++ b/src/crewai/utilities/events/listeners/tracing/interfaces.py
@@ -0,0 +1,33 @@
+import json
+from datetime import datetime
+
+from crewai.cli.plus_api import PlusAPI
+from crewai.cli.authentication.token import get_auth_token
+from pydantic import BaseModel
+from .trace_batch_manager import TraceBatch
+from logging import getLogger
+
+logger = getLogger(__name__)
+
+
+class TraceSender(BaseModel):
+    """Trace sender for sending trace batches to the backend"""
+
+    def send_batch(self, batch: TraceBatch) -> bool:
+        """Print trace batch to console"""
+        try:
+            payload = batch.to_dict()
+
+            def datetime_handler(obj):
+                if isinstance(obj, datetime):
+                    return obj.isoformat()
+
+            serialized_payload = json.loads(
+                json.dumps(payload, default=datetime_handler)
+            )
+
+            PlusAPI(api_key=get_auth_token()).send_trace_batch(serialized_payload)
+            return True
+        except Exception as e:
+            logger.error(f"Error sending trace batch: {e}")
+            return False
--- a/src/crewai/utilities/events/listeners/tracing/trace_batch_manager.py
+++ b/src/crewai/utilities/events/listeners/tracing/trace_batch_manager.py
@@ -66,7 +66,6 @@ class TraceBatchManager:
        self, user_context: Dict[str, str], execution_metadata: Dict[str, Any]
    ):
        """Send batch initialization to backend"""
-
        if not self.plus_api or not self.current_batch:
            return

@@ -76,8 +75,8 @@ class TraceBatchManager:
                "execution_type": execution_metadata.get("execution_type", "crew"),
                "execution_context": {
                    "crew_fingerprint": execution_metadata.get("crew_fingerprint"),
-                    "crew_name": execution_metadata.get("crew_name", None),
-                    "flow_name": execution_metadata.get("flow_name", None),
+                    "crew_name": execution_metadata.get("crew_name", "Unknown Crew"),
+                    "flow_name": execution_metadata.get("flow_name", "Unknown Flow"),
                    "crewai_version": self.current_batch.version,
                    "privacy_level": user_context.get("privacy_level", "standard"),
                },
--- a/src/crewai/utilities/events/listeners/tracing/trace_listener.py
+++ b/src/crewai/utilities/events/listeners/tracing/trace_listener.py
@@ -13,7 +13,6 @@ from crewai.utilities.events.agent_events import (
    AgentExecutionErrorEvent,
 )
 from crewai.utilities.events.listeners.tracing.types import TraceEvent
-from crewai.utilities.events.listeners.tracing.utils import is_tracing_enabled
 from crewai.utilities.events.reasoning_events import (
    AgentReasoningStartedEvent,
    AgentReasoningCompletedEvent,
@@ -66,7 +65,7 @@ from crewai.utilities.events.memory_events import (
    MemorySaveCompletedEvent,
    MemorySaveFailedEvent,
 )
-
+from .interfaces import TraceSender
 from crewai.cli.authentication.token import get_auth_token
 from crewai.cli.version import get_crewai_version

@@ -76,13 +75,13 @@ class TraceCollectionListener(BaseEventListener):
    Trace collection listener that orchestrates trace collection
    """

-    trace_enabled: Optional[bool] = False
+    trace_enabled: bool = False
    complex_events = ["task_started", "llm_call_started", "llm_call_completed"]

    _instance = None
    _initialized = False

-    def __new__(cls, batch_manager=None, tracing: Optional[bool] = False):
+    def __new__(cls, batch_manager=None, trace_sender=None):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance
@@ -90,14 +89,14 @@ class TraceCollectionListener(BaseEventListener):
    def __init__(
        self,
        batch_manager: Optional[TraceBatchManager] = None,
-        tracing: Optional[bool] = False,
+        trace_sender: Optional[TraceSender] = None,
    ):
        if self._initialized:
            return

        super().__init__()
        self.batch_manager = batch_manager or TraceBatchManager()
-        self.tracing = tracing or False
+        self.trace_sender = trace_sender or TraceSender()
        self.trace_enabled = self._check_trace_enabled()
        self._initialized = True

@@ -107,7 +106,9 @@ class TraceCollectionListener(BaseEventListener):
        if not auth_token:
            return False

-        return is_tracing_enabled() or self.tracing
+        return os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true" or bool(
+            os.getenv("CREWAI_USER_TOKEN")
+        )

    def _get_user_context(self) -> Dict[str, str]:
        """Extract user context for tracing"""
@@ -155,7 +156,7 @@ class TraceCollectionListener(BaseEventListener):
        @event_bus.on(FlowFinishedEvent)
        def on_flow_finished(source, event):
            self._handle_trace_event("flow_finished", source, event)
-            self.batch_manager.finalize_batch()
+            self._send_batch()

        @event_bus.on(FlowPlotEvent)
        def on_flow_plot(source, event):
@@ -173,12 +174,12 @@ class TraceCollectionListener(BaseEventListener):
        @event_bus.on(CrewKickoffCompletedEvent)
        def on_crew_completed(source, event):
            self._handle_trace_event("crew_kickoff_completed", source, event)
-            self.batch_manager.finalize_batch()
+            self._send_batch()

        @event_bus.on(CrewKickoffFailedEvent)
        def on_crew_failed(source, event):
            self._handle_trace_event("crew_kickoff_failed", source, event)
-            self.batch_manager.finalize_batch()
+            self._send_batch()

        @event_bus.on(TaskStartedEvent)
        def on_task_started(source, event):
@@ -302,7 +303,7 @@ class TraceCollectionListener(BaseEventListener):
        """Initialize trace batch for Flow execution"""
        user_context = self._get_user_context()
        execution_metadata = {
-            "flow_name": getattr(event, "flow_name", "Unknown Flow"),
+            "flow_name": getattr(source, "__class__.__name__", "Unknown Flow"),
            "execution_start": event.timestamp if hasattr(event, "timestamp") else None,
            "crewai_version": get_crewai_version(),
            "execution_type": "flow",
@@ -331,6 +332,14 @@ class TraceCollectionListener(BaseEventListener):
        trace_event = self._create_trace_event(event_type, source, event)
        self.batch_manager.add_event(trace_event)

+    def _send_batch(self):
+        """Send finalized batch using the configured sender"""
+        batch = self.batch_manager.finalize_batch()
+        if batch:
+            success = self.trace_sender.send_batch(batch)
+            if not success:
+                print("⚠️  Failed to send trace batch")
+
    def _create_trace_event(
        self, event_type: str, source: Any, event: Any
    ) -> TraceEvent:
@@ -351,15 +360,20 @@ class TraceCollectionListener(BaseEventListener):
        elif event_type == "task_started":
            return {
                "task_description": event.task.description,
-                "expected_output": event.task.expected_output,
                "task_name": event.task.name,
                "context": event.context,
                "agent": source.agent.role,
            }
        elif event_type == "llm_call_started":
-            return self._safe_serialize_to_dict(event)
+            return {
+                **self._safe_serialize_to_dict(event),
+                "messages": self._truncate_messages(event.messages),
+            }
        elif event_type == "llm_call_completed":
-            return self._safe_serialize_to_dict(event)
+            return {
+                **self._safe_serialize_to_dict(event),
+                "messages": self._truncate_messages(event.messages),
+            }
        else:
            return {
                "event_type": event_type,
@@ -382,7 +396,7 @@ class TraceCollectionListener(BaseEventListener):
            return {"serialization_error": str(e), "object_type": type(obj).__name__}

    # TODO: move to utils
-    def _truncate_messages(self, messages, max_content_length=500, max_messages=5):
+    def _truncate_messages(self, messages, max_content_length=200, max_messages=5):
        """Truncate message content and limit number of messages"""
        if not messages or not isinstance(messages, list):
            return messages
--- a/src/crewai/utilities/events/listeners/tracing/utils.py
+++ b/src/crewai/utilities/events/listeners/tracing/utils.py
@@ -1,5 +0,0 @@
-import os
-
-
-def is_tracing_enabled() -> bool:
-    return os.getenv("CREWAI_TRACING_ENABLED", "false").lower() == "true"
--- a/tests/cassettes/TestTraceListenerSetup.test_trace_listener_setup_correctly_with_tracing_flag.yaml
+++ b/tests/cassettes/TestTraceListenerSetup.test_trace_listener_setup_correctly_with_tracing_flag.yaml
@@ -1,125 +0,0 @@
-interactions:
- request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour
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-      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!"}, {"role": "user", "content": "\nCurrent Task: Say hello to
-      the world\n\nThis is the expected criteria for your final answer: hello world\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
-      This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '820'
-      content-type:
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-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.93.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
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-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
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-      - 'true'
-      x-stainless-read-timeout:
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-    status:
-      code: 200
-      message: OK
-version: 1
--- a/tests/cli/enterprise/init.py
+++ b/tests/cli/enterprise/init.py
--- a/tests/cli/enterprise/test_main.py
+++ b/tests/cli/enterprise/test_main.py
@@ -1,151 +0,0 @@
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import Mock, patch
-
-import requests
-from requests.exceptions import JSONDecodeError
-
-from crewai.cli.enterprise.main import EnterpriseConfigureCommand
-from crewai.cli.settings.main import SettingsCommand
-import shutil
-
-
-class TestEnterpriseConfigureCommand(unittest.TestCase):
-    def setUp(self):
-        self.test_dir = Path(tempfile.mkdtemp())
-        self.config_path = self.test_dir / "settings.json"
-
-        with patch('crewai.cli.enterprise.main.SettingsCommand') as mock_settings_command_class:
-            self.mock_settings_command = Mock(spec=SettingsCommand)
-            mock_settings_command_class.return_value = self.mock_settings_command
-
-            self.enterprise_command = EnterpriseConfigureCommand()
-
-    def tearDown(self):
-        shutil.rmtree(self.test_dir)
-
-    @patch('crewai.cli.enterprise.main.requests.get')
-    @patch('crewai.cli.enterprise.main.get_crewai_version')
-    def test_successful_configuration(self, mock_get_version, mock_requests_get):
-        mock_get_version.return_value = "1.0.0"
-
-        mock_response = Mock()
-        mock_response.status_code = 200
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = {
-            'audience': 'test_audience',
-            'domain': 'test.domain.com',
-            'device_authorization_client_id': 'test_client_id',
-            'provider': 'workos'
-        }
-        mock_requests_get.return_value = mock_response
-
-        enterprise_url = "https://enterprise.example.com"
-        self.enterprise_command.configure(enterprise_url)
-
-        expected_headers = {
-            "Content-Type": "application/json",
-            "User-Agent": "CrewAI-CLI/1.0.0",
-            "X-Crewai-Version": "1.0.0",
-        }
-        mock_requests_get.assert_called_once_with(
-            "https://enterprise.example.com/auth/parameters",
-            timeout=30,
-            headers=expected_headers
-        )
-
-        expected_calls = [
-            ('enterprise_base_url', 'https://enterprise.example.com'),
-            ('oauth2_provider', 'workos'),
-            ('oauth2_audience', 'test_audience'),
-            ('oauth2_client_id', 'test_client_id'),
-            ('oauth2_domain', 'test.domain.com')
-        ]
-
-        actual_calls = self.mock_settings_command.set.call_args_list
-        self.assertEqual(len(actual_calls), 5)
-
-        for i, (key, value) in enumerate(expected_calls):
-            call_args = actual_calls[i][0]
-            self.assertEqual(call_args[0], key)
-            self.assertEqual(call_args[1], value)
-
-    @patch('crewai.cli.enterprise.main.requests.get')
-    @patch('crewai.cli.enterprise.main.get_crewai_version')
-    def test_http_error_handling(self, mock_get_version, mock_requests_get):
-        mock_get_version.return_value = "1.0.0"
-
-        mock_response = Mock()
-        mock_response.raise_for_status.side_effect = requests.HTTPError("404 Not Found")
-        mock_requests_get.return_value = mock_response
-
-        with self.assertRaises(SystemExit):
-            self.enterprise_command.configure("https://enterprise.example.com")
-
-    @patch('crewai.cli.enterprise.main.requests.get')
-    @patch('crewai.cli.enterprise.main.get_crewai_version')
-    def test_invalid_json_response(self, mock_get_version, mock_requests_get):
-        mock_get_version.return_value = "1.0.0"
-
-        mock_response = Mock()
-        mock_response.status_code = 200
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.side_effect = JSONDecodeError("Invalid JSON", "", 0)
-        mock_requests_get.return_value = mock_response
-
-        with self.assertRaises(SystemExit):
-            self.enterprise_command.configure("https://enterprise.example.com")
-
-    @patch('crewai.cli.enterprise.main.requests.get')
-    @patch('crewai.cli.enterprise.main.get_crewai_version')
-    def test_missing_required_fields(self, mock_get_version, mock_requests_get):
-        mock_get_version.return_value = "1.0.0"
-
-        mock_response = Mock()
-        mock_response.status_code = 200
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = {
-            'audience': 'test_audience',
-        }
-        mock_requests_get.return_value = mock_response
-
-        with self.assertRaises(SystemExit):
-            self.enterprise_command.configure("https://enterprise.example.com")
-
-    @patch('crewai.cli.enterprise.main.requests.get')
-    @patch('crewai.cli.enterprise.main.get_crewai_version')
-    def test_settings_update_error(self, mock_get_version, mock_requests_get):
-        mock_get_version.return_value = "1.0.0"
-
-        mock_response = Mock()
-        mock_response.status_code = 200
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = {
-            'audience': 'test_audience',
-            'domain': 'test.domain.com',
-            'device_authorization_client_id': 'test_client_id',
-            'provider': 'workos'
-        }
-        mock_requests_get.return_value = mock_response
-
-        self.mock_settings_command.set.side_effect = Exception("Settings update failed")
-
-        with self.assertRaises(SystemExit):
-            self.enterprise_command.configure("https://enterprise.example.com")
-
-    def test_url_trailing_slash_removal(self):
-        with patch.object(self.enterprise_command, '_fetch_oauth_config') as mock_fetch, \
-             patch.object(self.enterprise_command, '_update_oauth_settings') as mock_update:
-
-            mock_fetch.return_value = {
-                'audience': 'test_audience',
-                'domain': 'test.domain.com',
-                'device_authorization_client_id': 'test_client_id',
-                'provider': 'workos'
-            }
-
-            self.enterprise_command.configure("https://enterprise.example.com/")
-
-            mock_fetch.assert_called_once_with("https://enterprise.example.com")
-            mock_update.assert_called_once()
--- a/tests/test_lite_agent.py
+++ b/tests/test_lite_agent.py
@@ -10,7 +10,6 @@ from crewai.flow import Flow, start
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.tools import BaseTool
 from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.llm_events import LLMCallStartedEvent
 from crewai.utilities.events.agent_events import LiteAgentExecutionStartedEvent
 from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
 from crewai.llms.base_llm import BaseLLM
@@ -149,12 +148,12 @@ def test_lite_agent_with_tools():
        "What is the population of Tokyo and how many people would that be per square kilometer if Tokyo's area is 2,194 square kilometers?"
    )

-    assert "21 million" in result.raw or "37 million" in result.raw, (
-        "Agent should find Tokyo's population"
-    )
-    assert "per square kilometer" in result.raw, (
-        "Agent should calculate population density"
-    )
+    assert (
+        "21 million" in result.raw or "37 million" in result.raw
+    ), "Agent should find Tokyo's population"
+    assert (
+        "per square kilometer" in result.raw
+    ), "Agent should calculate population density"

    received_events = []

@@ -319,17 +318,11 @@ def test_sets_parent_flow_when_inside_flow():
        flow.kickoff()
        assert captured_agent.parent_flow is flow

-
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_is_called_using_string():
    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import (
-        LLMGuardrailCompletedEvent,
-        LLMGuardrailStartedEvent,
-    )
-
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
    with crewai_event_bus.scoped_handlers():
-
        @crewai_event_bus.on(LLMGuardrailStartedEvent)
        def capture_guardrail_started(source, event):
            guardrail_events["started"].append(event)
@@ -347,26 +340,17 @@ def test_guardrail_is_called_using_string():

        result = agent.kickoff(messages="Top 10 best players in the world?")

-        assert len(guardrail_events["started"]) == 2
-        assert len(guardrail_events["completed"]) == 2
-        assert not guardrail_events["completed"][0].success
-        assert guardrail_events["completed"][1].success
-        assert (
-            "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
-            in result.raw
-        )
-
+        assert len(guardrail_events['started']) == 2
+        assert len(guardrail_events['completed']) == 2
+        assert not guardrail_events['completed'][0].success
+        assert guardrail_events['completed'][1].success
+        assert "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" in result.raw

@pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_is_called_using_callable():
    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import (
-        LLMGuardrailCompletedEvent,
-        LLMGuardrailStartedEvent,
-    )
-
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
    with crewai_event_bus.scoped_handlers():
-
        @crewai_event_bus.on(LLMGuardrailStartedEvent)
        def capture_guardrail_started(source, event):
            guardrail_events["started"].append(event)
@@ -384,22 +368,16 @@ def test_guardrail_is_called_using_callable():

        result = agent.kickoff(messages="Top 1 best players in the world?")

-        assert len(guardrail_events["started"]) == 1
-        assert len(guardrail_events["completed"]) == 1
-        assert guardrail_events["completed"][0].success
+        assert len(guardrail_events['started']) == 1
+        assert len(guardrail_events['completed']) == 1
+        assert guardrail_events['completed'][0].success
        assert "Pelé - Santos, 1958" in result.raw

-
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_guardrail_reached_attempt_limit():
    guardrail_events = defaultdict(list)
-    from crewai.utilities.events import (
-        LLMGuardrailCompletedEvent,
-        LLMGuardrailStartedEvent,
-    )
-
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
    with crewai_event_bus.scoped_handlers():
-
        @crewai_event_bus.on(LLMGuardrailStartedEvent)
        def capture_guardrail_started(source, event):
            guardrail_events["started"].append(event)
@@ -412,23 +390,18 @@ def test_guardrail_reached_attempt_limit():
            role="Sports Analyst",
            goal="Gather information about the best soccer players",
            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-            guardrail=lambda output: (
-                False,
-                "You are not allowed to include Brazilian players",
-            ),
+            guardrail=lambda output: (False, "You are not allowed to include Brazilian players"),
            guardrail_max_retries=2,
        )

-        with pytest.raises(
-            Exception, match="Agent's guardrail failed validation after 2 retries"
-        ):
+        with pytest.raises(Exception, match="Agent's guardrail failed validation after 2 retries"):
            agent.kickoff(messages="Top 10 best players in the world?")

-        assert len(guardrail_events["started"]) == 3  # 2 retries + 1 initial call
-        assert len(guardrail_events["completed"]) == 3  # 2 retries + 1 initial call
-        assert not guardrail_events["completed"][0].success
-        assert not guardrail_events["completed"][1].success
-        assert not guardrail_events["completed"][2].success
+        assert len(guardrail_events['started']) == 3 # 2 retries + 1 initial call
+        assert len(guardrail_events['completed']) == 3 # 2 retries + 1 initial call
+        assert not guardrail_events['completed'][0].success
+        assert not guardrail_events['completed'][1].success
+        assert not guardrail_events['completed'][2].success


@pytest.mark.vcr(filter_headers=["authorization"])
@@ -441,35 +414,22 @@ def test_agent_output_when_guardrail_returns_base_model():
        role="Sports Analyst",
        goal="Gather information about the best soccer players",
        backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
-        guardrail=lambda output: (
-            True,
-            Player(name="Lionel Messi", country="Argentina"),
-        ),
+        guardrail=lambda output: (True, Player(name="Lionel Messi", country="Argentina")),
    )

    result = agent.kickoff(messages="Top 10 best players in the world?")

    assert result.pydantic == Player(name="Lionel Messi", country="Argentina")

-
 def test_lite_agent_with_custom_llm_and_guardrails():
    """Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
-
    class CustomLLM(BaseLLM):
        def __init__(self, response: str = "Custom response"):
            super().__init__(model="custom-model")
            self.response = response
            self.call_count = 0

-        def call(
-            self,
-            messages,
-            tools=None,
-            callbacks=None,
-            available_functions=None,
-            from_task=None,
-            from_agent=None,
-        ) -> str:
+        def call(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None) -> str:
            self.call_count += 1

            if "valid" in str(messages) and "feedback" in str(messages):
@@ -496,7 +456,7 @@ def test_lite_agent_with_custom_llm_and_guardrails():
        goal="Analyze soccer players",
        backstory="You analyze soccer players and their performance.",
        llm=custom_llm,
-        guardrail="Only include Brazilian players",
+        guardrail="Only include Brazilian players"
    )

    result = agent.kickoff("Tell me about the best soccer players")
@@ -514,7 +474,7 @@ def test_lite_agent_with_custom_llm_and_guardrails():
        goal="Test goal",
        backstory="Test backstory",
        llm=custom_llm2,
-        guardrail=test_guardrail,
+        guardrail=test_guardrail
    )

    result2 = agent2.kickoff("Test message")
@@ -524,96 +484,12 @@ def test_lite_agent_with_custom_llm_and_guardrails():
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_with_invalid_llm():
    """Test that LiteAgent raises proper error when create_llm returns None."""
-    with patch("crewai.lite_agent.create_llm", return_value=None):
+    with patch('crewai.lite_agent.create_llm', return_value=None):
        with pytest.raises(ValueError) as exc_info:
            LiteAgent(
                role="Test Agent",
-                goal="Test goal",
+                goal="Test goal", 
                backstory="Test backstory",
-                llm="invalid-model",
+                llm="invalid-model"
            )
-        assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)
-
-
-def test_lite_agent_emits_llm_call_started_event_with_tools():
-    """Test that LiteAgent emits LLMCallStartedEvent with correct tools information."""
-    captured_events = []
-
-    @crewai_event_bus.on(LLMCallStartedEvent)
-    def capture_llm_event(source, event):
-        captured_events.append(event)
-
-    agent = LiteAgent(
-        role="Test Agent",
-        goal="Test Goal",
-        backstory="Test Backstory",
-        tools=[WebSearchTool(), CalculatorTool()],
-        llm=Mock(spec=LLM),
-    )
-
-    agent.llm.call.return_value = "Final Answer: Test response"
-
-    agent.kickoff("Test query")
-
-    # Verify event was emitted with tools
-    assert len(captured_events) > 0
-    event = captured_events[0]
-    assert event.tools is not None
-    assert len(event.tools) == 2
-
-    # Verify tool structure
-    tool_names = [tool["name"] for tool in event.tools]
-    assert "search_web" in tool_names
-    assert "calculate" in tool_names
-
-    for tool in event.tools:
-        assert "name" in tool
-        assert "description" in tool
-        assert "args" in tool
-
-
-def test_lite_agent_emits_llm_call_started_event_without_tools():
-    """Test that LiteAgent emits LLMCallStartedEvent with tools=None when no tools provided."""
-    captured_events = []
-
-    @crewai_event_bus.on(LLMCallStartedEvent)
-    def capture_llm_event(source, event):
-        captured_events.append(event)
-
-    agent = LiteAgent(
-        role="Test Agent",
-        goal="Test Goal",
-        backstory="Test Backstory",
-        tools=[],  # No tools
-        llm=Mock(spec=LLM),
-    )
-
-    agent.llm.call.return_value = "Final Answer: Test response"
-
-    agent.kickoff("Test query")
-
-    assert len(captured_events) > 0
-    event = captured_events[0]
-    assert event.tools is None
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_lite_agent_tool_calling_reproduction():
-    """Test reproduction of GitHub issue #3302 - LiteAgent tool calling."""
-
-    agent = LiteAgent(
-        role="Research Assistant",
-        goal="Help with research tasks",
-        backstory="You are a helpful research assistant.",
-        tools=[WebSearchTool()],
-        llm=LLM(model="gpt-4o-mini"),
-    )
-
-    result = agent.kickoff("Search for information about Python programming")
-
-    # Verify the agent produced a meaningful result
-    assert result.raw is not None
-    assert len(result.raw) > 0
-
-    # Verify tools were actually available during execution
-    assert len(agent._parsed_tools) > 0
+        assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)
--- a/tests/tracing/test_tracing.py
+++ b/tests/tracing/test_tracing.py
@@ -33,6 +33,10 @@ class TestTraceListenerSetup:
                "crewai.utilities.events.listeners.tracing.trace_batch_manager.get_auth_token",
                return_value="mock_token_12345",
            ),
+            patch(
+                "crewai.utilities.events.listeners.tracing.interfaces.get_auth_token",
+                return_value="mock_token_12345",
+            ),
        ):
            yield

@@ -292,21 +296,7 @@ class TestTraceListenerSetup:

            assert trace_listener.trace_enabled is True
            assert trace_listener.batch_manager is not None
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_trace_listener_setup_correctly_with_tracing_flag(self):
-        """Test that trace listener is set up correctly when enabled"""
-        agent = Agent(role="Test Agent", goal="Test goal", backstory="Test backstory")
-        task = Task(
-            description="Say hello to the world",
-            expected_output="hello world",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task], verbose=True, tracing=True)
-        crew.kickoff()
-        trace_listener = TraceCollectionListener(tracing=True)
-        assert trace_listener.trace_enabled is True
-        assert trace_listener.batch_manager is not None
+            assert trace_listener.trace_sender is not None

    # Helper method to ensure cleanup
    def teardown_method(self):