updating docs

docs/core-concepts/Collaboration.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: How Agents Collaborate in CrewAI
 description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.

docs/core-concepts/Crews.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Crews
 description: Understanding and utilizing crews in the crewAI framework.

docs/core-concepts/Tasks.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Tasks
 description: Overview and management of tasks within the crewAI framework.

docs/core-concepts/Tools.md

@@ -1,4 +1,3 @@
-```
 ---
 title: crewAI Tools
 description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
@@ -39,7 +38,7 @@ from crewai_tools import (
 )
 
 # Set up API keys
-os.environ["SERPER_API_KEY"] = "Your Key"
+os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 os.environ["OPENAI_API_KEY"] = "Your Key"
 
 # Instantiate tools

docs/telemetry/Telemetry.md

@@ -1,4 +1,7 @@
-```markdown
+---
+title: Telemetry
+description: Understanding the telemetry data collected by CrewAI and how it contributes to the enhancement of the library.
+---
 ## Telemetry
 
 CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
@@ -9,24 +12,16 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
 - **Version of CrewAI**: Assessing the adoption rate of our latest version helps us understand user needs and guide our updates.
 - **Python Version**: Identifying the Python versions our users operate with assists in prioritizing our support efforts for these versions.
 - **General OS Information**: Details like the number of CPUs and the operating system type (macOS, Windows, Linux) enable us to focus our development on the most used operating systems and explore the potential for OS-specific features.
-- **Platform-Specific Information**: Collection of `platform`, `platform_release`, `platform_system`, and `platform_version` provides a comprehensive overview of the users' operating systems.
 - **Number of Agents and Tasks in a Crew**: Ensures our internal testing mirrors real-world scenarios, helping us guide users towards best practices.
-- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus. Attributes such as `crew_process` including execution details are collected for this purpose.
+- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus.
 - **Memory and Delegation Use by Agents**: Insights into how these features are used help evaluate their effectiveness and future.
 - **Task Execution Mode**: Knowing whether tasks are executed in parallel or sequentially influences our emphasis on enhancing parallel execution capabilities.
-- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users. This includes the collection of `crew_language` to indicate the default language model being used.
+- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users.
 - **Roles of Agents within a Crew**: Understanding the various roles agents play aids in crafting better tools, integrations, and examples.
-- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas. This includes the collection of tool names used by agents and tasks.
-- **Crew and Agent Specific Attributes**: Information such as `crew_id`, `verbose?`, `max_iter`, `max_rpm`, `i18n.language`, and `llm` attributes (like `name`, `model_name`, `base_url`, `model`, `top_k`, `temperature`) are collected to enhance our understanding and support of the ecosystem.
+- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
 
 ### Opt-In Further Telemetry Sharing
 Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed `crew` and `task` execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
 
 ### Updates and Revisions
 We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.
-
-### Transparency and Review
-To foster greater transparency, users can inquire about the telemetry data being collected by contacting our support team. This initiative aims to provide users with the opportunity to understand the scope of our data collection practices better and aligns with our commitment to transparency and trust.
-
-By ensuring the documentation is up-to-date with these changes, we aim to maintain transparency with our users, fostering trust, and promoting an understanding of how telemetry data is used to enhance CrewAI.
-```