Merge branch 'main' into lorenze/integration-docs-2

Pydantic 2.7.x does not support a second parameter in model validators with mode="after"

.github/security.md

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

.github/workflows/linter.yml

@@ -5,12 +5,32 @@ 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: Install Requirements
+      - name: Fetch Target Branch
+        run: git fetch origin $TARGET_BRANCH --depth=1
+
+      - name: Install Ruff
+        run: pip install ruff
+
+      - name: Get Changed Python Files
+        id: changed-files
         run: |
-          pip install ruff
+          merge_base=$(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
 
-      - name: Run Ruff Linter
-        run: ruff check
+      - name: Run Ruff on Changed Files
+        if: ${{ steps.changed-files.outputs.files != '' }}
+        run: |
+          echo "${{ steps.changed-files.outputs.files }}" \
+            | tr ' ' '\n' \
+            | grep -v 'src/crewai/cli/templates/' \
+            | xargs -I{} ruff check "{}"

.github/workflows/notify-downstream.yml

@@ -0,0 +1,33 @@
+name: Notify Downstream
+
+on:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  notify-downstream:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Generate GitHub App token
+        id: app-token
+        uses: tibdex/github-app-token@v2
+        with:
+          app_id: ${{ secrets.OSS_SYNC_APP_ID }}
+          private_key: ${{ secrets.OSS_SYNC_APP_PRIVATE_KEY }}
+
+      - name: Notify Repo B
+        uses: peter-evans/repository-dispatch@v3
+        with:
+          token: ${{ steps.app-token.outputs.token }}
+          repository: ${{ secrets.OSS_SYNC_DOWNSTREAM_REPO }}
+          event-type: upstream-commit
+          client-payload: |
+            {
+              "commit_sha": "${{ github.sha }}"
+            }
+

.github/workflows/tests.yml

@@ -12,6 +12,9 @@ jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12', '3.13']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -21,12 +24,11 @@ jobs:
         with:
           enable-cache: true
 
-
-      - name: Set up Python
-        run: uv python install 3.12.8
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
 
       - name: Install the project
         run: uv sync --dev --all-extras
 
       - name: Run tests
-        run: uv run pytest tests -vv
+        run: uv run pytest --block-network --timeout=60 -vv

.gitignore

@@ -25,4 +25,5 @@ agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
-conceptual_plan.md
+conceptual_plan.md
+build_image

.ruff.toml

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

README.md

@@ -1,32 +1,75 @@
-<div align="center">
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="docs/images/crewai_logo.png" width="600px" alt="Open source Multi-AI Agent orchestration framework">
+  </a>
+</p>
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11239" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11239" alt="crewAIInc%2FcrewAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
+</p>
 
-![Logo of CrewAI](./docs/crewai_logo.png)
+<p align="center">
+  <a href="https://crewai.com">Homepage</a>
+  ·
+  <a href="https://docs.crewai.com">Docs</a>
+  ·
+  <a href="https://app.crewai.com">Start Cloud Trial</a>
+  ·
+  <a href="https://blog.crewai.com">Blog</a>
+  ·
+  <a href="https://community.crewai.com">Forum</a>
+</p>
 
+<p align="center">
+  <a href="https://github.com/crewAIInc/crewAI">
+    <img src="https://img.shields.io/github/stars/crewAIInc/crewAI" alt="GitHub Repo stars">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/network/members">
+    <img src="https://img.shields.io/github/forks/crewAIInc/crewAI" alt="GitHub forks">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/issues">
+    <img src="https://img.shields.io/github/issues/crewAIInc/crewAI" alt="GitHub issues">
+  </a>
+  <a href="https://github.com/crewAIInc/crewAI/pulls">
+    <img src="https://img.shields.io/github/issues-pr/crewAIInc/crewAI" alt="GitHub pull requests">
+  </a>
+  <a href="https://opensource.org/licenses/MIT">
+    <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT">
+  </a>
+</p>
 
-</div>
+<p align="center">
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/v/crewai" alt="PyPI version">
+  </a>
+  <a href="https://pypi.org/project/crewai/">
+    <img src="https://img.shields.io/pypi/dm/crewai" alt="PyPI downloads">
+  </a>
+  <a href="https://twitter.com/crewAIInc">
+    <img src="https://img.shields.io/twitter/follow/crewAIInc?style=social" alt="Twitter Follow">
+  </a>
+</p>
 
 ### Fast and Flexible Multi-Agent Automation Framework
 
-CrewAI is a lean, lightning-fast Python framework built entirely from
-scratch—completely **independent of LangChain or other agent frameworks**.
-It empowers developers with both high-level simplicity and precise low-level
-control, ideal for creating autonomous AI agents tailored to any scenario.
+> CrewAI is a lean, lightning-fast Python framework built entirely from scratch—completely **independent of LangChain or other agent frameworks**.
+> It empowers developers with both high-level simplicity and precise low-level control, ideal for creating autonomous AI agents tailored to any scenario.
 
 - **CrewAI Crews**: Optimize for autonomy and collaborative intelligence.
 - **CrewAI Flows**: Enable granular, event-driven control, single LLM calls for precise task orchestration and supports Crews natively
 
-With over 100,000 developers certified through our community courses at
-[learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
+With over 100,000 developers certified through our community courses at [learn.crewai.com](https://learn.crewai.com), CrewAI is rapidly becoming the
 standard for enterprise-ready AI automation.
 
 # CrewAI Enterprise Suite
 
-CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations
-that require secure, scalable, and easy-to-manage agent-driven automation.
+CrewAI Enterprise Suite is a comprehensive bundle tailored for organizations that require secure, scalable, and easy-to-manage agent-driven automation.
 
 You can try one part of the suite the [Crew Control Plane for free](https://app.crewai.com)
 
 ## Crew Control Plane Key Features:
+
 - **Tracing & Observability**: Monitor and track your AI agents and workflows in real-time, including metrics, logs, and traces.
 - **Unified Control Plane**: A centralized platform for managing, monitoring, and scaling your AI agents and workflows.
 - **Seamless Integrations**: Easily connect with existing enterprise systems, data sources, and cloud infrastructure.
@@ -35,21 +78,9 @@ You can try one part of the suite the [Crew Control Plane for free](https://app.
 - **24/7 Support**: Dedicated enterprise support to ensure uninterrupted operation and quick resolution of issues.
 - **On-premise and Cloud Deployment Options**: Deploy CrewAI Enterprise on-premise or in the cloud, depending on your security and compliance requirements.
 
-CrewAI Enterprise is designed for enterprises seeking a powerful,
-reliable solution to transform complex business processes into efficient,
+CrewAI Enterprise is designed for enterprises seeking a powerful, reliable solution to transform complex business processes into efficient,
 intelligent automations.
 
-<h3>
-
-[Homepage](https://www.crewai.com/) | [Documentation](https://docs.crewai.com/) | [Chat with Docs](https://chatg.pt/DWjSBZn) | [Discourse](https://community.crewai.com)
-
-</h3>
-
-[![GitHub Repo stars](https://img.shields.io/github/stars/joaomdmoura/crewAI)](https://github.com/crewAIInc/crewAI)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-
-</div>
-
 ## Table of contents
 
 - [Why CrewAI?](#why-crewai)
@@ -73,7 +104,7 @@ intelligent automations.
 ## Why CrewAI?
 
 <div align="center" style="margin-bottom: 30px;">
-  <img src="docs/asset.png" alt="CrewAI Logo" width="100%">
+  <img src="docs/images/asset.png" alt="CrewAI Logo" width="100%">
 </div>
 
 CrewAI unlocks the true potential of multi-agent automation, delivering the best-in-class combination of speed, flexibility, and control with either Crews of AI Agents or Flows of Events:
@@ -88,9 +119,15 @@ CrewAI empowers developers and enterprises to confidently build intelligent auto
 
 ## Getting Started
 
-### Learning Resources
+Setup and run your first CrewAI agents by following this tutorial.
+
+[![CrewAI Getting Started Tutorial](https://img.youtube.com/vi/-kSOTtYzgEw/hqdefault.jpg)](https://www.youtube.com/watch?v=-kSOTtYzgEw "CrewAI Getting Started Tutorial")
+
+###
+ Learning Resources
 
 Learn CrewAI through our comprehensive courses:
+
 - [Multi AI Agent Systems with CrewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/) - Master the fundamentals of multi-agent systems
 - [Practical Multi AI Agents and Advanced Use Cases](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/) - Deep dive into advanced implementations
 
@@ -99,18 +136,20 @@ Learn CrewAI through our comprehensive courses:
 CrewAI offers two powerful, complementary approaches that work seamlessly together to build sophisticated AI applications:
 
 1. **Crews**: Teams of AI agents with true autonomy and agency, working together to accomplish complex tasks through role-based collaboration. Crews enable:
+
    - Natural, autonomous decision-making between agents
    - Dynamic task delegation and collaboration
    - Specialized roles with defined goals and expertise
    - Flexible problem-solving approaches
-
 2. **Flows**: Production-ready, event-driven workflows that deliver precise control over complex automations. Flows provide:
+
    - Fine-grained control over execution paths for real-world scenarios
    - Secure, consistent state management between tasks
    - Clean integration of AI agents with production Python code
    - Conditional branching for complex business logic
 
 The true power of CrewAI emerges when combining Crews and Flows. This synergy allows you to:
+
 - Build complex, production-grade applications
 - Balance autonomy with precise control
 - Handle sophisticated real-world scenarios
@@ -122,18 +161,20 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-Ensure you have Python >=3.10 <3.13 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+Ensure you have Python >=3.10 <3.14 installed on your system. CrewAI uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
 
 First, install CrewAI:
 
 ```shell
 pip install crewai
 ```
+
 If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:
 
 ```shell
 pip install 'crewai[tools]'
 ```
+
 The command above installs the basic package and also adds extra components which require more dependencies to function.
 
 ### Troubleshooting Dependencies
@@ -143,10 +184,11 @@ If you encounter issues during installation or usage, here are some common solut
 #### Common Issues
 
 1. **ModuleNotFoundError: No module named 'tiktoken'**
+
    - Install tiktoken explicitly: `pip install 'crewai[embeddings]'`
    - If using embedchain or other tools: `pip install 'crewai[tools]'`
-
 2. **Failed building wheel for tiktoken**
+
    - Ensure Rust compiler is installed (see installation steps above)
    - For Windows: Verify Visual C++ Build Tools are installed
    - Try upgrading pip: `pip install --upgrade pip`
@@ -257,10 +299,14 @@ reporting_task:
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class LatestAiDevelopmentCrew():
 	"""LatestAiDevelopment crew"""
+	agents: List[BaseAgent]
+	tasks: List[Task]
 
 	@agent
 	def researcher(self) -> Agent:
@@ -357,7 +403,7 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-CrewAI stands apart as a lean, standalone, high-performance framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
+CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent framework delivering simplicity, flexibility, and precise control—free from the complexity and limitations found in other agent frameworks.
 
 - **Standalone & Lean**: Completely independent from other frameworks like LangChain, offering faster execution and lighter resource demands.
 - **Flexible & Precise**: Easily orchestrate autonomous agents through intuitive [Crews](https://docs.crewai.com/concepts/crews) or precise [Flows](https://docs.crewai.com/concepts/flows), achieving perfect balance for your needs.
@@ -401,11 +447,17 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 ### Using Crews and Flows Together
 
-CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines. Here's how you can orchestrate multiple Crews within a Flow:
+CrewAI's power truly shines when combining Crews with Flows to create sophisticated automation pipelines.
+CrewAI flows support logical operators like `or_` and `and_` to combine multiple conditions. This can be used with `@start`, `@listen`, or `@router` decorators to create complex triggering conditions.
+
+- `or_`: Triggers when any of the specified conditions are met.
+- `and_`Triggers when all of the specified conditions are met.
+
+Here's how you can orchestrate multiple Crews within a Flow:
 
 ```python
-from crewai.flow.flow import Flow, listen, start, router
-from crewai import Crew, Agent, Task
+from crewai.flow.flow import Flow, listen, start, router, or_
+from crewai import Crew, Agent, Task, Process
 from pydantic import BaseModel
 
 # Define structured state for precise control
@@ -479,13 +531,14 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
         )
         return strategy_crew.kickoff()
 
-    @listen("medium_confidence", "low_confidence")
+    @listen(or_("medium_confidence", "low_confidence"))
     def request_additional_analysis(self):
         self.state.recommendations.append("Gather more data")
         return "Additional analysis required"
 ```
 
 This example demonstrates how to:
+
 1. Use Python code for basic data operations
 2. Create and execute Crews as steps in your workflow
 3. Use Flow decorators to manage the sequence of operations
@@ -495,7 +548,7 @@ This example demonstrates how to:
 
 CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
 
-Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
 
 ## How CrewAI Compares
 
@@ -506,7 +559,6 @@ Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 *P.S. CrewAI demonstrates significant performance advantages over LangGraph, executing 5.76x faster in certain cases like this QA task example ([see comparison](https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/QA%20Agent)) while achieving higher evaluation scores with faster completion times in certain coding tasks, like in this example ([detailed analysis](https://github.com/crewAIInc/crewAI-examples/blob/main/Notebooks/CrewAI%20Flows%20%26%20Langgraph/Coding%20Assistant/coding_assistant_eval.ipynb)).*
 
 - **Autogen**: While Autogen excels at creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
-
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
 ## Contribution
@@ -597,10 +649,10 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se
 
 CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).
 
-
 ## Frequently Asked Questions (FAQ)
 
 ### General
+
 - [What exactly is CrewAI?](#q-what-exactly-is-crewai)
 - [How do I install CrewAI?](#q-how-do-i-install-crewai)
 - [Does CrewAI depend on LangChain?](#q-does-crewai-depend-on-langchain)
@@ -608,6 +660,7 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 - [Does CrewAI collect data from users?](#q-does-crewai-collect-data-from-users)
 
 ### Features and Capabilities
+
 - [Can CrewAI handle complex use cases?](#q-can-crewai-handle-complex-use-cases)
 - [Can I use CrewAI with local AI models?](#q-can-i-use-crewai-with-local-ai-models)
 - [What makes Crews different from Flows?](#q-what-makes-crews-different-from-flows)
@@ -615,84 +668,110 @@ CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/b
 - [Does CrewAI support fine-tuning or training custom models?](#q-does-crewai-support-fine-tuning-or-training-custom-models)
 
 ### Resources and Community
+
 - [Where can I find real-world CrewAI examples?](#q-where-can-i-find-real-world-crewai-examples)
 - [How can I contribute to CrewAI?](#q-how-can-i-contribute-to-crewai)
 
 ### Enterprise Features
+
 - [What additional features does CrewAI Enterprise offer?](#q-what-additional-features-does-crewai-enterprise-offer)
 - [Is CrewAI Enterprise available for cloud and on-premise deployments?](#q-is-crewai-enterprise-available-for-cloud-and-on-premise-deployments)
 - [Can I try CrewAI Enterprise for free?](#q-can-i-try-crewai-enterprise-for-free)
 
-
-
 ### Q: What exactly is CrewAI?
+
 A: CrewAI is a standalone, lean, and fast Python framework built specifically for orchestrating autonomous AI agents. Unlike frameworks like LangChain, CrewAI does not rely on external dependencies, making it leaner, faster, and simpler.
 
 ### Q: How do I install CrewAI?
+
 A: Install CrewAI using pip:
+
 ```shell
 pip install crewai
 ```
+
 For additional tools, use:
+
 ```shell
 pip install 'crewai[tools]'
 ```
+
 ### Q: Does CrewAI depend on LangChain?
+
 A: No. CrewAI is built entirely from the ground up, with no dependencies on LangChain or other agent frameworks. This ensures a lean, fast, and flexible experience.
 
 ### Q: Can CrewAI handle complex use cases?
+
 A: Yes. CrewAI excels at both simple and highly complex real-world scenarios, offering deep customization options at both high and low levels, from internal prompts to sophisticated workflow orchestration.
 
 ### Q: Can I use CrewAI with local AI models?
+
 A: Absolutely! CrewAI supports various language models, including local ones. Tools like Ollama and LM Studio allow seamless integration. Check the [LLM Connections documentation](https://docs.crewai.com/how-to/LLM-Connections/) for more details.
 
 ### Q: What makes Crews different from Flows?
+
 A: Crews provide autonomous agent collaboration, ideal for tasks requiring flexible decision-making and dynamic interaction. Flows offer precise, event-driven control, ideal for managing detailed execution paths and secure state management. You can seamlessly combine both for maximum effectiveness.
 
 ### Q: How is CrewAI better than LangChain?
+
 A: CrewAI provides simpler, more intuitive APIs, faster execution speeds, more reliable and consistent results, robust documentation, and an active community—addressing common criticisms and limitations associated with LangChain.
 
 ### Q: Is CrewAI open-source?
+
 A: Yes, CrewAI is open-source and actively encourages community contributions and collaboration.
 
 ### Q: Does CrewAI collect data from users?
+
 A: CrewAI collects anonymous telemetry data strictly for improvement purposes. Sensitive data such as prompts, tasks, or API responses are never collected unless explicitly enabled by the user.
 
 ### Q: Where can I find real-world CrewAI examples?
+
 A: Check out practical examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), covering use cases like trip planners, stock analysis, and job postings.
 
 ### Q: How can I contribute to CrewAI?
+
 A: Contributions are warmly welcomed! Fork the repository, create your branch, implement your changes, and submit a pull request. See the Contribution section of the README for detailed guidelines.
 
 ### Q: What additional features does CrewAI Enterprise offer?
+
 A: CrewAI Enterprise provides advanced features such as a unified control plane, real-time observability, secure integrations, advanced security, actionable insights, and dedicated 24/7 enterprise support.
 
 ### Q: Is CrewAI Enterprise available for cloud and on-premise deployments?
+
 A: Yes, CrewAI Enterprise supports both cloud-based and on-premise deployment options, allowing enterprises to meet their specific security and compliance requirements.
 
 ### Q: Can I try CrewAI Enterprise for free?
+
 A: Yes, you can explore part of the CrewAI Enterprise Suite by accessing the [Crew Control Plane](https://app.crewai.com) for free.
 
 ### Q: Does CrewAI support fine-tuning or training custom models?
+
 A: Yes, CrewAI can integrate with custom-trained or fine-tuned models, allowing you to enhance your agents with domain-specific knowledge and accuracy.
 
 ### Q: Can CrewAI agents interact with external tools and APIs?
+
 A: Absolutely! CrewAI agents can easily integrate with external tools, APIs, and databases, empowering them to leverage real-world data and resources.
 
 ### Q: Is CrewAI suitable for production environments?
+
 A: Yes, CrewAI is explicitly designed with production-grade standards, ensuring reliability, stability, and scalability for enterprise deployments.
 
 ### Q: How scalable is CrewAI?
+
 A: CrewAI is highly scalable, supporting simple automations and large-scale enterprise workflows involving numerous agents and complex tasks simultaneously.
 
 ### Q: Does CrewAI offer debugging and monitoring tools?
+
 A: Yes, CrewAI Enterprise includes advanced debugging, tracing, and real-time observability features, simplifying the management and troubleshooting of your automations.
 
 ### Q: What programming languages does CrewAI support?
+
 A: CrewAI is primarily Python-based but easily integrates with services and APIs written in any programming language through its flexible API integration capabilities.
 
 ### Q: Does CrewAI offer educational resources for beginners?
+
 A: Yes, CrewAI provides extensive beginner-friendly tutorials, courses, and documentation through learn.crewai.com, supporting developers at all skill levels.
 
 ### Q: Can CrewAI automate human-in-the-loop workflows?
+
 A: Yes, CrewAI fully supports human-in-the-loop workflows, allowing seamless collaboration between human experts and AI agents for enhanced decision-making.

docs/api-reference/introduction.mdx

@@ -0,0 +1,119 @@
+---
+title: "Introduction"
+description: "Complete reference for the CrewAI Enterprise REST API"
+icon: "code"
+---
+
+# CrewAI Enterprise API
+
+Welcome to the CrewAI Enterprise API reference. This API allows you to programmatically interact with your deployed crews, enabling integration with your applications, workflows, and services.
+
+## Quick Start
+
+<Steps>
+  <Step title="Get Your API Credentials">
+    Navigate to your crew's detail page in the CrewAI Enterprise dashboard and copy your Bearer Token from the Status tab.
+  </Step>
+  
+  <Step title="Discover Required Inputs">
+    Use the `GET /inputs` endpoint to see what parameters your crew expects.
+  </Step>
+  
+  <Step title="Start a Crew Execution">
+    Call `POST /kickoff` with your inputs to start the crew execution and receive a `kickoff_id`.
+  </Step>
+  
+  <Step title="Monitor Progress">
+    Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
+  </Step>
+</Steps>
+
+## Authentication
+
+All API requests require authentication using a Bearer token. Include your token in the `Authorization` header:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+### Token Types
+
+| Token Type | Scope | Use Case |
+|:-----------|:--------|:----------|
+| **Bearer Token** | Organization-level access | Full crew operations, ideal for server-to-server integration |
+| **User Bearer Token** | User-scoped access | Limited permissions, suitable for user-specific operations |
+
+<Tip>
+You can find both token types in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+</Tip>
+
+## Base URL
+
+Each deployed crew has its own unique API endpoint:
+
+```
+https://your-crew-name.crewai.com
+```
+
+Replace `your-crew-name` with your actual crew's URL from the dashboard.
+
+## Typical Workflow
+
+1. **Discovery**: Call `GET /inputs` to understand what your crew needs
+2. **Execution**: Submit inputs via `POST /kickoff` to start processing  
+3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
+4. **Results**: Extract the final output from the completed response
+
+## Error Handling
+
+The API uses standard HTTP status codes:
+
+| Code | Meaning |
+|------|:--------|
+| `200` | Success |
+| `400` | Bad Request - Invalid input format |
+| `401` | Unauthorized - Invalid bearer token |
+| `404` | Not Found - Resource doesn't exist |
+| `422` | Validation Error - Missing required inputs |
+| `500` | Server Error - Contact support |
+
+## Interactive Testing
+
+<Info>
+**Why no "Send" button?** Since each CrewAI Enterprise user has their own unique crew URL, we use **reference mode** instead of an interactive playground to avoid confusion. This shows you exactly what the requests should look like without non-functional send buttons.
+</Info>
+
+Each endpoint page shows you:
+- ✅ **Exact request format** with all parameters
+- ✅ **Response examples** for success and error cases  
+- ✅ **Code samples** in multiple languages (cURL, Python, JavaScript, etc.)
+- ✅ **Authentication examples** with proper Bearer token format
+
+### **To Test Your Actual API:**
+
+<CardGroup cols={2}>
+  <Card title="Copy cURL Examples" icon="terminal">
+    Copy the cURL examples and replace the URL + token with your real values
+  </Card>
+  <Card title="Use Postman/Insomnia" icon="play">
+    Import the examples into your preferred API testing tool
+  </Card>
+</CardGroup>
+
+**Example workflow:**
+1. **Copy this cURL example** from any endpoint page
+2. **Replace `your-actual-crew-name.crewai.com`** with your real crew URL  
+3. **Replace the Bearer token** with your real token from the dashboard
+4. **Run the request** in your terminal or API client
+
+## Need Help?
+
+<CardGroup cols={2}>
+  <Card title="Enterprise Support" icon="headset" href="mailto:support@crewai.com">
+    Get help with API integration and troubleshooting
+  </Card>
+  <Card title="Enterprise Dashboard" icon="chart-line" href="https://app.crewai.com">
+    Manage your crews and view execution logs
+  </Card>
+</CardGroup>

docs/changelog.mdx

@@ -4,8 +4,233 @@ description: View the latest updates and changes to CrewAI
 icon: timeline
 ---
 
-<Update label="2025-03-17" description="v0.108.0">
-  **Features**
+<Update label="2024-05-22" description="v0.121.0" tags={["Latest"]}>
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01210.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.121.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed encoding error when creating tools
+  - Fixed failing llama test
+  - Updated logging configuration for consistency
+  - Enhanced telemetry initialization and event handling
+
+  **New Features & Enhancements**
+  - Added **markdown attribute** to the Task class
+  - Added **reasoning attribute** to the Agent class
+  - Added **inject_date flag** to Agent for automatic date injection
+  - Implemented **HallucinationGuardrail** (no-op with test coverage)
+
+  **Documentation & Guides**
+  - Added documentation for **StagehandTool** and improved MDX structure
+  - Added documentation for **MCP integration** and updated enterprise docs
+  - Documented knowledge events and updated reasoning docs
+  - Added stop parameter documentation
+  - Fixed import references in doc examples (before_kickoff, after_kickoff)
+  - General docs updates and restructuring for clarity
+</Update>
+
+<Update label="2024-05-15" description="v0.120.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01201.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed **interpolation with hyphens**
+</Update>
+
+<Update label="2024-05-14" description="v0.120.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01200.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.120.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Enabled **full Ruff rule set** by default for stricter linting
+  - Addressed race condition in FilteredStream using context managers
+  - Fixed agent knowledge reset issue
+  - Refactored agent fetching logic into utility module
+
+  **New Features & Enhancements**
+  - Added support for **loading an Agent directly from a repository**
+  - Enabled setting an empty context for Task
+  - Enhanced Agent repository feedback and fixed Tool auto-import behavior
+  - Introduced direct initialization of knowledge (bypassing knowledge_sources)
+
+  **Documentation & Guides**
+  - Updated security.md for current security practices
+  - Cleaned up Google setup section for clarity
+  - Added link to AI Studio when entering Gemini key
+  - Updated Arize Phoenix observability guide
+  - Refreshed flow documentation
+</Update>
+
+<Update label="2024-05-08" description="v0.119.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01190.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.119.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Improved test reliability by enhancing pytest handling for flaky tests
+  - Fixed memory reset crash when embedding dimensions mismatch
+  - Enabled parent flow identification for Crew and LiteAgent
+  - Prevented telemetry-related crashes when unavailable
+  - Upgraded **LiteLLM version** for better compatibility
+  - Fixed llama converter tests by removing skip_external_api
+
+  **New Features & Enhancements**
+  - Introduced **knowledge retrieval prompt re-writing** in Agent for improved tracking and debugging
+  - Made LLM setup and quickstart guides model-agnostic
+
+  **Documentation & Guides**
+  - Added advanced configuration docs for the RAG tool
+  - Updated Windows troubleshooting guide
+  - Refined documentation examples for better clarity
+  - Fixed typos across docs and config files
+</Update>
+
+<Update label="2024-04-28" description="v0.118.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01180.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.118.0">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Fixed issues with missing prompt or system templates
+  - Removed global logging configuration to avoid unintended overrides
+  - Renamed **TaskGuardrail to LLMGuardrail** for improved clarity
+  - Downgraded litellm to version 1.167.1 for compatibility
+  - Added missing init.py files to ensure proper module initialization
+
+  **New Features & Enhancements**
+  - Added support for **no-code Guardrail creation** to simplify AI behavior controls
+
+  **Documentation & Guides**
+  - Removed CrewStructuredTool from public documentation to reflect internal usage
+  - Updated enterprise documentation and YouTube embed for improved onboarding experience
+</Update>
+
+<Update label="2024-04-20" description="v0.117.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01170.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Added `result_as_answer` parameter support in `@tool` decorator.
+  - Introduced support for new language models: GPT-4.1, Gemini-2.0, and Gemini-2.5 Pro.
+  - Enhanced knowledge management capabilities.
+  - Added Huggingface provider option in CLI.
+  - Improved compatibility and CI support for Python 3.10+.
+
+  **Core Improvements & Fixes**
+  - Fixed issues with incorrect template parameters and missing inputs.
+  - Improved asynchronous flow handling with coroutine condition checks.
+  - Enhanced memory management with isolated configuration and correct memory object copying.
+  - Fixed initialization of lite agents with correct references.
+  - Addressed Python type hint issues and removed redundant imports.
+  - Updated event placement for improved tool usage tracking.
+  - Raised explicit exceptions when flows fail.
+  - Removed unused code and redundant comments from various modules.
+  - Updated GitHub App token action to v2.
+
+  **Documentation & Guides**
+  - Enhanced documentation structure, including enterprise deployment instructions.
+  - Automatically create output folders for documentation generation.
+  - Fixed broken link in WeaviateVectorSearchTool documentation.
+  - Fixed guardrail documentation usage and import paths for JSON search tools.
+  - Updated documentation for CodeInterpreterTool.
+  - Improved SEO, contextual navigation, and error handling for documentation pages.
+</Update>
+
+<Update label="2024-04-25" description="v0.117.1">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01171.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.117.1">View on GitHub</a>
+  </div>
+
+  **Core Improvements & Fixes**
+  - Upgraded **crewai-tools** to latest version
+  - Upgraded **liteLLM** to latest version
+  - Fixed **Mem0 OSS**
+</Update>
+
+<Update label="2024-04-07" description="v0.114.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01140.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.114.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
+  - Agents as an atomic unit. (`Agent(...).kickoff()`)
+  - Support for [Custom LLM implementations](https://docs.crewai.com/guides/advanced/custom-llm).
+  - Integrated External Memory and [Opik observability](https://docs.crewai.com/how-to/opik-observability).
+  - Enhanced YAML extraction.
+  - Multimodal agent validation.
+  - Added Secure fingerprints for agents and crews.
+
+  **Core Improvements & Fixes**
+  - Improved serialization, agent copying, and Python compatibility.
+  - Added wildcard support to `emit()`
+  - Added support for additional router calls and context window adjustments.
+  - Fixed typing issues, validation, and import statements.
+  - Improved method performance.
+  - Enhanced agent task handling, event emissions, and memory management.
+  - Fixed CLI issues, conditional tasks, cloning behavior, and tool outputs.
+
+  **Documentation & Guides**
+  - Improved documentation structure, theme, and organization.
+  - Added guides for Local NVIDIA NIM with WSL2, W&B Weave, and Arize Phoenix.
+  - Updated tool configuration examples, prompts, and observability docs.
+  - Guide on using singular agents within Flows.
+</Update>
+
+<Update label="2024-03-17" description="v0.108.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01080.png" />
+  </Frame>
+
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.108.0">View on GitHub</a>
+  </div>
+
+  **New Features & Enhancements**
   - Converted tabs to spaces in `crew.py` template
   - Enhanced LLM Streaming Response Handling and Event System
   - Included `model_name`
@@ -24,7 +249,16 @@ icon: timeline
   - Added documentation for `ApifyActorsTool`
 </Update>
 
-<Update label="2025-03-10" description="v0.105.0">
+<Update label="2024-03-10" description="v0.105.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01050.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.105.0">View on GitHub</a>
+  </div>
+
   **Core Improvements & Fixes**
   - Fixed issues with missing template variables and user memory configuration
   - Improved async flow support and addressed agent response formatting
@@ -45,7 +279,16 @@ icon: timeline
   - Fixed typos in prompts and updated Amazon Bedrock model listings
 </Update>
 
-<Update label="2025-02-12" description="v0.102.0">
+<Update label="2024-02-12" description="v0.102.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01020.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.102.0">View on GitHub</a>
+  </div>
+
   **Core Improvements & Fixes**
   - Enhanced LLM Support: Improved structured LLM output, parameter handling, and formatting for Anthropic models
   - Crew & Agent Stability: Fixed issues with cloning agents/crews using knowledge sources, multiple task outputs in conditional tasks, and ignored Crew task callbacks
@@ -65,7 +308,16 @@ icon: timeline
   - Fixed Various Typos & Formatting Issues
 </Update>
 
-<Update label="2025-01-28" description="v0.100.0">
+<Update label="2024-01-28" description="v0.100.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v01000.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.100.0">View on GitHub</a>
+  </div>
+
   **Features**
   - Add Composio docs
   - Add SageMaker as a LLM provider
@@ -80,7 +332,16 @@ icon: timeline
   - Improve formatting and clarity in CLI and Composio Tool docs
 </Update>
 
-<Update label="2025-01-20" description="v0.98.0">
+<Update label="2024-01-20" description="v0.98.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0980.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.98.0">View on GitHub</a>
+  </div>
+
   **Features**
   - Conversation crew v1
   - Add unique ID to flow states
@@ -101,7 +362,16 @@ icon: timeline
   - Fixed typos, nested pydantic model issue, and docling issues
 </Update>
 
-<Update label="2025-01-04" description="v0.95.0">
+<Update label="2024-01-04" description="v0.95.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0950.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.95.0">View on GitHub</a>
+  </div>
+
   **New Features**
   - Adding Multimodal Abilities to Crew
   - Programatic Guardrails
@@ -132,6 +402,14 @@ icon: timeline
 </Update>
 
 <Update label="2024-12-05" description="v0.86.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0860.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.86.0">View on GitHub</a>
+  </div>
   **Changes**
   - Remove all references to pipeline and pipeline router
   - Add Nvidia NIM as provider in Custom LLM
@@ -142,6 +420,14 @@ icon: timeline
 </Update>
 
 <Update label="2024-12-04" description="v0.85.0">
+  ## Release Highlights
+  <Frame>
+    <img src="/images/releases/v0850.png" />
+  </Frame>
+  
+  <div style={{ textAlign: 'center', marginBottom: '1rem' }}>
+    <a href="https://github.com/crewAIInc/crewAI/releases/tag/0.85.0">View on GitHub</a>
+  </div>
   **Features**
   - Added knowledge to agent level
   - Feat/remove langchain

docs/common-room-tracking.js

@@ -0,0 +1,18 @@
+(function() {
+  if (typeof window === 'undefined') return;
+  if (typeof window.signals !== 'undefined') return;
+  var script = document.createElement('script');
+  script.src = 'https://cdn.cr-relay.com/v1/site/883520f4-c431-44be-80e7-e123a1ee7a2b/signals.js';
+  script.async = true;
+  window.signals = Object.assign(
+    [],
+    ['page', 'identify', 'form'].reduce(function (acc, method){
+      acc[method] = function () {
+        signals.push([method, arguments]);
+        return signals;
+      };
+     return acc;
+    }, {})
+  );
+  document.head.appendChild(script);
+})(); 

docs/concepts/agents.mdx

@@ -18,6 +18,18 @@ In the CrewAI framework, an `Agent` is an autonomous unit that can:
 Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a `Researcher` agent might excel at gathering and analyzing information, while a `Writer` agent might be better at creating content.
 </Tip>
 
+<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)
+
+The Visual Agent Builder enables:
+- Intuitive agent configuration with form-based interfaces
+- Real-time testing and validation
+- Template library with pre-configured agent types
+- Easy customization of agent attributes and behaviors
+</Note>
+
 ## Agent Attributes
 
 | Attribute                               | Parameter                | Type                          | Description                                                                                                          |
@@ -31,7 +43,6 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Iterations** _(optional)_         | `max_iter`               | `int`                         | Maximum iterations before the agent must provide its best answer. Default is 20.                                      |
 | **Max RPM** _(optional)_                | `max_rpm`                | `Optional[int]`               | Maximum requests per minute to avoid rate limits.                                                                     |
 | **Max Execution Time** _(optional)_     | `max_execution_time`     | `Optional[int]`               | Maximum time (in seconds) for task execution.                                                                         |
-| **Memory** _(optional)_                 | `memory`                 | `bool`                        | Whether the agent should maintain memory of interactions. Default is True.                                            |
 | **Verbose** _(optional)_                | `verbose`                | `bool`                        | Enable detailed execution logs for debugging. Default is False.                                                       |
 | **Allow Delegation** _(optional)_       | `allow_delegation`       | `bool`                        | Allow the agent to delegate tasks to other agents. Default is False.                                                  |
 | **Step Callback** _(optional)_          | `step_callback`          | `Optional[Any]`               | Function called after each agent step, overrides crew callback.                                                       |
@@ -43,6 +54,11 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
 | **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
 | **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
+| **Multimodal** _(optional)_             | `multimodal`             | `bool`                        | Whether the agent supports multimodal capabilities. Default is False.                                                  |
+| **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).                               |
+| **Reasoning** _(optional)_              | `reasoning`              | `bool`                        | Whether the agent should reflect and create a plan before executing a task. Default is False.                         |
+| **Max Reasoning Attempts** _(optional)_ | `max_reasoning_attempts` | `Optional[int]`               | Maximum number of reasoning attempts before executing the task. If None, will try until ready.                         |
 | **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.                                                 |
@@ -106,7 +122,7 @@ class LatestAiDevelopmentCrew():
   @agent
   def researcher(self) -> Agent:
     return Agent(
-      config=self.agents_config['researcher'],
+      config=self.agents_config['researcher'], # type: ignore[index]
       verbose=True,
       tools=[SerperDevTool()]
     )
@@ -114,7 +130,7 @@ class LatestAiDevelopmentCrew():
   @agent
   def reporting_analyst(self) -> Agent:
     return Agent(
-      config=self.agents_config['reporting_analyst'],
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
       verbose=True
     )
 ```
@@ -139,7 +155,6 @@ agent = Agent(
               "you excel at finding patterns in complex datasets.",
     llm="gpt-4",  # Default: OPENAI_MODEL_NAME or "gpt-4"
     function_calling_llm=None,  # Optional: Separate LLM for tool calling
-    memory=True,  # Default: True
     verbose=False,  # Default: False
     allow_delegation=False,  # Default: False
     max_iter=20,  # Default: 20 iterations
@@ -150,6 +165,11 @@ agent = Agent(
     code_execution_mode="safe",  # Default: "safe" (options: "safe", "unsafe")
     respect_context_window=True,  # Default: True
     use_system_prompt=True,  # Default: True
+    multimodal=False,  # Default: False
+    inject_date=False,  # Default: False
+    date_format="%Y-%m-%d",  # Default: ISO format
+    reasoning=False,  # Default: False
+    max_reasoning_attempts=None,  # Default: None
     tools=[SerperDevTool()],  # Optional: List of tools
     knowledge_sources=None,  # Optional: List of knowledge sources
     embedder=None,  # Optional: Custom embedder configuration
@@ -214,6 +234,44 @@ custom_agent = Agent(
 )
 ```
 
+#### Date-Aware Agent with Reasoning
+```python Code
+strategic_agent = Agent(
+    role="Market Analyst",
+    goal="Track market movements with precise date references and strategic planning",
+    backstory="Expert in time-sensitive financial analysis and strategic reporting",
+    inject_date=True,  # Automatically inject current date into tasks
+    date_format="%B %d, %Y",  # Format as "May 21, 2025"
+    reasoning=True,  # Enable strategic planning
+    max_reasoning_attempts=2,  # Limit planning iterations
+    verbose=True
+)
+```
+
+#### Reasoning Agent
+```python Code
+reasoning_agent = Agent(
+    role="Strategic Planner",
+    goal="Analyze complex problems and create detailed execution plans",
+    backstory="Expert strategic planner who methodically breaks down complex challenges",
+    reasoning=True,  # Enable reasoning and planning
+    max_reasoning_attempts=3,  # Limit reasoning attempts
+    max_iter=30,  # Allow more iterations for complex planning
+    verbose=True
+)
+```
+
+#### Multimodal Agent
+```python Code
+multimodal_agent = Agent(
+    role="Visual Content Analyst",
+    goal="Analyze and process both text and visual content",
+    backstory="Specialized in multimodal analysis combining text and image understanding",
+    multimodal=True,  # Enable multimodal capabilities
+    verbose=True
+)
+```
+
 ### Parameter Details
 
 #### Critical Parameters
@@ -233,17 +291,31 @@ custom_agent = Agent(
 
 #### Code Execution
 - `allow_code_execution`: Must be True to run code
-- `code_execution_mode`: 
+- `code_execution_mode`:
   - `"safe"`: Uses Docker (recommended for production)
   - `"unsafe"`: Direct execution (use only in trusted environments)
 
+<Note>
+  This runs a default Docker image. If you want to configure the docker image, the checkout the Code Interpreter Tool in the tools section.
+  Add the code interpreter tool as a tool in the agent as a tool parameter.
+  </Note>
+
+#### Advanced Features
+- `multimodal`: Enable multimodal capabilities for processing text and visual content
+- `reasoning`: Enable agent to reflect and create plans before executing tasks
+- `inject_date`: Automatically inject current date into task descriptions
+
 #### Templates
 - `system_template`: Defines agent's core behavior
 - `prompt_template`: Structures input format
 - `response_template`: Formats agent responses
 
 <Note>
-When using custom templates, you can use variables like `{role}`, `{goal}`, and `{input}` in your templates. These will be automatically populated during execution.
+When using custom templates, ensure that both `system_template` and `prompt_template` are defined. The `response_template` is optional but recommended for consistent output formatting.
+</Note>  
+
+<Note>
+When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
 </Note>
 
 ## Agent Tools
@@ -290,6 +362,170 @@ analyst = Agent(
 When `memory` is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
 </Note>
 
+## Context Window Management
+
+CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model's token limits. This powerful feature is controlled by the `respect_context_window` parameter.
+
+### How Context Window Management Works
+
+When an agent's conversation history grows too large for the LLM's context window, CrewAI automatically detects this situation and can either:
+
+1. **Automatically summarize content** (when `respect_context_window=True`)
+2. **Stop execution with an error** (when `respect_context_window=False`)
+
+### Automatic Context Handling (`respect_context_window=True`)
+
+This is the **default and recommended setting** for most use cases. When enabled, CrewAI will:
+
+```python Code
+# Agent with automatic context management (default)
+smart_agent = Agent(
+    role="Research Analyst",
+    goal="Analyze large documents and datasets",
+    backstory="Expert at processing extensive information",
+    respect_context_window=True,  # 🔑 Default: auto-handle context limits
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ⚠️ **Warning message**: `"Context length exceeded. Summarizing content to fit the model context window."`
+- 🔄 **Automatic summarization**: CrewAI intelligently summarizes the conversation history
+- ✅ **Continued execution**: Task execution continues seamlessly with the summarized context
+- 📝 **Preserved information**: Key information is retained while reducing token count
+
+### Strict Context Limits (`respect_context_window=False`)
+
+When you need precise control and prefer execution to stop rather than lose any information:
+
+```python Code
+# Agent with strict context limits
+strict_agent = Agent(
+    role="Legal Document Reviewer",
+    goal="Provide precise legal analysis without information loss",
+    backstory="Legal expert requiring complete context for accurate analysis",
+    respect_context_window=False,  # ❌ Stop execution on context limit
+    verbose=True
+)
+```
+
+**What happens when context limits are exceeded:**
+- ❌ **Error message**: `"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."`
+- 🛑 **Execution stops**: Task execution halts immediately
+- 🔧 **Manual intervention required**: You need to modify your approach
+
+### Choosing the Right Setting
+
+#### Use `respect_context_window=True` (Default) when:
+- **Processing large documents** that might exceed context limits
+- **Long-running conversations** where some summarization is acceptable
+- **Research tasks** where general context is more important than exact details
+- **Prototyping and development** where you want robust execution
+
+```python Code
+# Perfect for document processing
+document_processor = Agent(
+    role="Document Analyst", 
+    goal="Extract insights from large research papers",
+    backstory="Expert at analyzing extensive documentation",
+    respect_context_window=True,  # Handle large documents gracefully
+    max_iter=50,  # Allow more iterations for complex analysis
+    verbose=True
+)
+```
+
+#### Use `respect_context_window=False` when:
+- **Precision is critical** and information loss is unacceptable
+- **Legal or medical tasks** requiring complete context
+- **Code review** where missing details could introduce bugs
+- **Financial analysis** where accuracy is paramount
+
+```python Code
+# Perfect for precision tasks
+precision_agent = Agent(
+    role="Code Security Auditor",
+    goal="Identify security vulnerabilities in code",
+    backstory="Security expert requiring complete code context",
+    respect_context_window=False,  # Prefer failure over incomplete analysis
+    max_retry_limit=1,  # Fail fast on context issues
+    verbose=True
+)
+```
+
+### Alternative Approaches for Large Data
+
+When dealing with very large datasets, consider these strategies:
+
+#### 1. Use RAG Tools
+```python Code
+from crewai_tools import RagTool
+
+# Create RAG tool for large document processing
+rag_tool = RagTool()
+
+rag_agent = Agent(
+    role="Research Assistant",
+    goal="Query large knowledge bases efficiently",
+    backstory="Expert at using RAG tools for information retrieval",
+    tools=[rag_tool],  # Use RAG instead of large context windows
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+#### 2. Use Knowledge Sources
+```python Code
+# Use knowledge sources instead of large prompts
+knowledge_agent = Agent(
+    role="Knowledge Expert",
+    goal="Answer questions using curated knowledge",
+    backstory="Expert at leveraging structured knowledge sources",
+    knowledge_sources=[your_knowledge_sources],  # Pre-processed knowledge
+    respect_context_window=True,
+    verbose=True
+)
+```
+
+### Context Window Best Practices
+
+1. **Monitor Context Usage**: Enable `verbose=True` to see context management in action
+2. **Design for Efficiency**: Structure tasks to minimize context accumulation
+3. **Use Appropriate Models**: Choose LLMs with context windows suitable for your tasks
+4. **Test Both Settings**: Try both `True` and `False` to see which works better for your use case
+5. **Combine with RAG**: Use RAG tools for very large datasets instead of relying solely on context windows
+
+### Troubleshooting Context Issues
+
+**If you're getting context limit errors:**
+```python Code
+# Quick fix: Enable automatic handling
+agent.respect_context_window = True
+
+# Better solution: Use RAG tools for large data
+from crewai_tools import RagTool
+agent.tools = [RagTool()]
+
+# Alternative: Break tasks into smaller pieces
+# Or use knowledge sources instead of large prompts
+```
+
+**If automatic summarization loses important information:**
+```python Code
+# Disable auto-summarization and use RAG instead
+agent = Agent(
+    role="Detailed Analyst",
+    goal="Maintain complete information accuracy",
+    backstory="Expert requiring full context",
+    respect_context_window=False,  # No summarization
+    tools=[RagTool()],  # Use RAG for large data
+    verbose=True
+)
+```
+
+<Note>
+The context window management feature works automatically in the background. You don't need to call any special functions - just set `respect_context_window` to your preferred behavior and CrewAI handles the rest!
+</Note>
+
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution
@@ -304,11 +540,17 @@ When `memory` is enabled, the agent will maintain context across multiple intera
 - Adjust `max_iter` and `max_retry_limit` based on task complexity
 
 ### Memory and Context Management
-- Use `memory: true` for tasks requiring historical context
 - Leverage `knowledge_sources` for domain-specific information
-- Configure `embedder_config` when using custom embedding models
+- Configure `embedder` when using custom embedding models
 - Use custom templates (`system_template`, `prompt_template`, `response_template`) for fine-grained control over agent behavior
 
+### Advanced Features
+- Enable `reasoning: true` for agents that need to plan and reflect before executing complex tasks
+- Set appropriate `max_reasoning_attempts` to control planning iterations (None for unlimited attempts)
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Enable `multimodal: true` for agents that need to process both text and visual content
+
 ### Agent Collaboration
 - Enable `allow_delegation: true` when agents need to work together
 - Use `step_callback` to monitor and log agent interactions
@@ -316,6 +558,13 @@ 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 and Reasoning
+- Use `inject_date: true` to provide agents with current date awareness for time-sensitive tasks
+- 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
+- Enable `reasoning: true` for complex tasks that benefit from upfront planning and reflection
+
 ### 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)
@@ -338,7 +587,6 @@ When `memory` is enabled, the agent will maintain context across multiple intera
    - Review code sandbox settings
 
 4. **Memory Issues**: If agent responses seem inconsistent:
-   - Verify memory is enabled
    - Check knowledge source configuration
    - Review conversation history management
 

docs/concepts/cli.mdx

@@ -4,7 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
-# CrewAI CLI Documentation
+## Overview
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 
@@ -110,6 +110,8 @@ 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:
@@ -179,7 +181,109 @@ def crew(self) -> Crew:
 ```
 </Note>
 
-### 10. API Keys
+### 10. Deploy
+
+Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
+
+- **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
+    ```shell Terminal
+    crewai signup
+    ```
+    If you already have an account, you can login with:
+    ```shell Terminal
+    crewai login
+    ```
+
+- **Create a deployment**: Once you are authenticated, you can create a deployment for your crew or flow from the root of your localproject.
+    ```shell Terminal
+    crewai deploy create
+    ```
+    - Reads your local project configuration.
+    - Prompts you to confirm the environment variables (like `OPENAI_API_KEY`, `SERPER_API_KEY`) found locally. These will be securely stored with the deployment on the Enterprise platform. Ensure your sensitive keys are correctly configured locally (e.g., in a `.env` file) before running this.
+
+### 11. Organization Management
+
+Manage your CrewAI Enterprise organizations.
+
+```shell Terminal
+crewai org [COMMAND] [OPTIONS]
+```
+
+#### Commands:
+
+- `list`: List all organizations you belong to
+```shell Terminal
+crewai org list
+```
+
+- `current`: Display your currently active organization
+```shell Terminal
+crewai org current
+```
+
+- `switch`: Switch to a specific organization
+```shell Terminal
+crewai org switch <organization_id>
+```
+
+<Note>
+You must be authenticated to CrewAI Enterprise to use these organization management commands.
+</Note>
+
+- **Create a deployment** (continued):
+    - Links the deployment to the corresponding remote GitHub repository (it usually detects this automatically).
+
+- **Deploy the Crew**: Once you are authenticated, you can deploy your crew or flow to CrewAI Enterprise.
+    ```shell Terminal
+    crewai deploy push
+    ``` 
+    - Initiates the deployment process on the CrewAI Enterprise platform.
+    - Upon successful initiation, it will output the Deployment created successfully! message along with the Deployment Name and a unique Deployment ID (UUID).
+
+- **Deployment Status**: You can check the status of your deployment with:
+    ```shell Terminal
+    crewai deploy status
+    ```
+    This fetches the latest deployment status of your most recent deployment attempt (e.g., `Building Images for Crew`, `Deploy Enqueued`, `Online`).
+
+- **Deployment Logs**: You can check the logs of your deployment with:
+    ```shell Terminal
+    crewai deploy logs
+    ```
+    This streams the deployment logs to your terminal.
+
+- **List deployments**: You can list all your deployments with:
+    ```shell Terminal
+    crewai deploy list
+    ```
+    This lists all your deployments.
+
+- **Delete a deployment**: You can delete a deployment with:
+    ```shell Terminal
+    crewai deploy remove
+    ```
+    This deletes the deployment from the CrewAI Enterprise platform.
+
+- **Help Command**: You can get help with the CLI with:
+    ```shell Terminal
+    crewai deploy --help
+    ```
+    This shows the help message for the CrewAI Deploy CLI.
+
+Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI Enterprise](http://app.crewai.com) using the CLI.
+
+<iframe
+  width="100%"
+  height="400"
+  src="https://www.youtube.com/embed/3EqSV-CYDZA"
+  title="CrewAI Deployment Guide"
+  frameborder="0"
+  style={{ borderRadius: '10px' }}
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+  allowfullscreen
+></iframe>
+
+### 11. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 

docs/concepts/collaboration.mdx

@@ -1,52 +1,362 @@
 ---
 title: Collaboration
-description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
+description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
 icon: screen-users
 ---
 
-## Collaboration Fundamentals      
+## Overview
 
-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.
+Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other's expertise. When `allow_delegation=True`, agents automatically gain access to powerful collaboration tools.
 
-- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
-- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
-- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
+## Quick Start: Enable Collaboration
 
-## Enhanced Attributes for Improved Collaboration
+```python
+from crewai import Agent, Crew, Task
 
-The `Crew` class has been enriched with several attributes to support advanced functionalities:
+# Enable collaboration for agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Conduct thorough research on any topic",
+    backstory="Expert researcher with access to various sources",
+    allow_delegation=True,  # 🔑 Key setting for collaboration
+    verbose=True
+)
 
-| Feature                                    | Description                                                                                                                                                                                                                  |
-|:-------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **Language Model Management** (`manager_llm`, `function_calling_llm`) | Manages language models for executing tasks and tools. `manager_llm` is required for hierarchical processes, while `function_calling_llm` is optional with a default value for streamlined interactions.                    |
-| **Custom Manager Agent** (`manager_agent`) | Specifies a custom agent as the manager, replacing the default CrewAI manager.                                                                                                                                                |
-| **Process Flow** (`process`)           | Defines execution logic (e.g., sequential, hierarchical) for task distribution.                                                                                                                                                |
-| **Verbose Logging** (`verbose`)        | Provides detailed logging for monitoring and debugging. Accepts integer and boolean values to control verbosity level.                                                                                                       |
-| **Rate Limiting** (`max_rpm`)          | Limits requests per minute to optimize resource usage. Setting guidelines depend on task complexity and load.                                                                                                                 |
-| **Internationalization / Customization** (`language`, `prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)                                                                                      |
-| **Execution and Output Handling** (`full_output`) | Controls output granularity, distinguishing between full and final outputs.                                                                                                                                                  |
-| **Callback and Telemetry** (`step_callback`, `task_callback`) | Enables step-wise and task-level execution monitoring and telemetry for performance analytics.                                                                                                                               |
-| **Crew Sharing** (`share_crew`)        | Allows sharing crew data with CrewAI for model improvement. Privacy implications and benefits should be considered.                                                                                                          |
-| **Usage Metrics** (`usage_metrics`)    | Logs all LLM usage metrics during task execution for performance insights.                                                                                                                           |
-| **Memory Usage** (`memory`)            | Enables memory for storing execution history, aiding in agent learning and task efficiency.                                                                                                                                  |
-| **Embedder Configuration** (`embedder`) | Configures the embedder for language understanding and generation, with support for provider customization.                                                                                                                   |
-| **Cache Management** (`cache`)         | Specifies whether to cache tool execution results, enhancing performance.                                                                                                                                                   |
-| **Output Logging** (`output_log_file`) | Defines the file path for logging crew execution output.                                                                                                                                                                      |
-| **Planning Mode** (`planning`)         | Enables action planning before task execution. Set `planning=True` to activate.                                                                                                                                              |
-| **Replay Feature** (`replay`)         | Provides CLI for listing tasks from the last run and replaying from specific tasks, aiding in task management and troubleshooting.                                                                                           |
+writer = Agent(
+    role="Content Writer", 
+    goal="Create engaging content based on research",
+    backstory="Skilled writer who transforms research into compelling content",
+    allow_delegation=True,  # 🔑 Enables asking questions to other agents
+    verbose=True
+)
 
-## Delegation (Dividing to Conquer)
+# Agents can now collaborate automatically
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[...],
+    verbose=True
+)
+```
 
-Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
+## How Agent Collaboration Works
 
-## Implementing Collaboration and Delegation
+When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
 
-Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
+### 1. **Delegate Work Tool**
+Allows agents to assign tasks to teammates with specific expertise.
 
-## Example Scenario
+```python
+# Agent automatically gets this tool:
+# Delegate work to coworker(task: str, context: str, coworker: str)
+```
 
-Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
+### 2. **Ask Question Tool** 
+Enables agents to ask specific questions to gather information from colleagues.
 
-## Conclusion
+```python
+# Agent automatically gets this tool:
+# Ask question to coworker(question: str, context: str, coworker: str)
+```
 
-The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
+## Collaboration in Action
+
+Here's a complete example showing agents collaborating on a content creation task:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Create collaborative agents
+researcher = Agent(
+    role="Research Specialist",
+    goal="Find accurate, up-to-date information on any topic",
+    backstory="""You're a meticulous researcher with expertise in finding 
+    reliable sources and fact-checking information across various domains.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+writer = Agent(
+    role="Content Writer",
+    goal="Create engaging, well-structured content",
+    backstory="""You're a skilled content writer who excels at transforming 
+    research into compelling, readable content for different audiences.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+editor = Agent(
+    role="Content Editor",
+    goal="Ensure content quality and consistency",
+    backstory="""You're an experienced editor with an eye for detail, 
+    ensuring content meets high standards for clarity and accuracy.""",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Create a task that encourages collaboration
+article_task = Task(
+    description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
+    
+    The article should include:
+    - Current AI applications in healthcare
+    - Emerging trends and technologies  
+    - Potential challenges and ethical considerations
+    - Expert predictions for the next 5 years
+    
+    Collaborate with your teammates to ensure accuracy and quality.""",
+    expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
+    agent=writer  # Writer leads, but can delegate research to researcher
+)
+
+# Create collaborative crew
+crew = Crew(
+    agents=[researcher, writer, editor],
+    tasks=[article_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+result = crew.kickoff()
+```
+
+## Collaboration Patterns
+
+### Pattern 1: Research → Write → Edit
+```python
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="Comprehensive research summary with key findings and sources",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write an article based on the research findings",
+    expected_output="Engaging 800-word article about quantum computing",
+    agent=writer,
+    context=[research_task]  # Gets research output as context
+)
+
+editing_task = Task(
+    description="Edit and polish the article for publication",
+    expected_output="Publication-ready article with improved clarity and flow",
+    agent=editor,
+    context=[writing_task]  # Gets article draft as context
+)
+```
+
+### Pattern 2: Collaborative Single Task
+```python
+collaborative_task = Task(
+    description="""Create a marketing strategy for a new AI product.
+    
+    Writer: Focus on messaging and content strategy
+    Researcher: Provide market analysis and competitor insights
+    
+    Work together to create a comprehensive strategy.""",
+    expected_output="Complete marketing strategy with research backing",
+    agent=writer  # Lead agent, but can delegate to researcher
+)
+```
+
+## Hierarchical Collaboration
+
+For complex projects, use a hierarchical process with a manager agent:
+
+```python
+from crewai import Agent, Crew, Task, Process
+
+# Manager agent coordinates the team
+manager = Agent(
+    role="Project Manager",
+    goal="Coordinate team efforts and ensure project success",
+    backstory="Experienced project manager skilled at delegation and quality control",
+    allow_delegation=True,
+    verbose=True
+)
+
+# Specialist agents
+researcher = Agent(
+    role="Researcher",
+    goal="Provide accurate research and analysis",
+    backstory="Expert researcher with deep analytical skills",
+    allow_delegation=False,  # Specialists focus on their expertise
+    verbose=True
+)
+
+writer = Agent(
+    role="Writer", 
+    goal="Create compelling content",
+    backstory="Skilled writer who creates engaging content",
+    allow_delegation=False,
+    verbose=True
+)
+
+# Manager-led task
+project_task = Task(
+    description="Create a comprehensive market analysis report with recommendations",
+    expected_output="Executive summary, detailed analysis, and strategic recommendations",
+    agent=manager  # Manager will delegate to specialists
+)
+
+# Hierarchical crew
+crew = Crew(
+    agents=[manager, researcher, writer],
+    tasks=[project_task],
+    process=Process.hierarchical,  # Manager coordinates everything
+    manager_llm="gpt-4o",  # Specify LLM for manager
+    verbose=True
+)
+```
+
+## Best Practices for Collaboration
+
+### 1. **Clear Role Definition**
+```python
+# ✅ Good: Specific, complementary roles
+researcher = Agent(role="Market Research Analyst", ...)
+writer = Agent(role="Technical Content Writer", ...)
+
+# ❌ Avoid: Overlapping or vague roles  
+agent1 = Agent(role="General Assistant", ...)
+agent2 = Agent(role="Helper", ...)
+```
+
+### 2. **Strategic Delegation Enabling**
+```python
+# ✅ Enable delegation for coordinators and generalists
+lead_agent = Agent(
+    role="Content Lead",
+    allow_delegation=True,  # Can delegate to specialists
+    ...
+)
+
+# ✅ Disable for focused specialists (optional)
+specialist_agent = Agent(
+    role="Data Analyst", 
+    allow_delegation=False,  # Focuses on core expertise
+    ...
+)
+```
+
+### 3. **Context Sharing**
+```python
+# ✅ Use context parameter for task dependencies
+writing_task = Task(
+    description="Write article based on research",
+    agent=writer,
+    context=[research_task],  # Shares research results
+    ...
+)
+```
+
+### 4. **Clear Task Descriptions**
+```python
+# ✅ Specific, actionable descriptions
+Task(
+    description="""Research competitors in the AI chatbot space.
+    Focus on: pricing models, key features, target markets.
+    Provide data in a structured format.""",
+    ...
+)
+
+# ❌ Vague descriptions that don't guide collaboration
+Task(description="Do some research about chatbots", ...)
+```
+
+## Troubleshooting Collaboration
+
+### Issue: Agents Not Collaborating
+**Symptoms:** Agents work in isolation, no delegation occurs
+```python
+# ✅ Solution: Ensure delegation is enabled
+agent = Agent(
+    role="...",
+    allow_delegation=True,  # This is required!
+    ...
+)
+```
+
+### Issue: Too Much Back-and-Forth
+**Symptoms:** Agents ask excessive questions, slow progress
+```python
+# ✅ Solution: Provide better context and specific roles
+Task(
+    description="""Write a technical blog post about machine learning.
+    
+    Context: Target audience is software developers with basic ML knowledge.
+    Length: 1200 words
+    Include: code examples, practical applications, best practices
+    
+    If you need specific technical details, delegate research to the researcher.""",
+    ...
+)
+```
+
+### Issue: Delegation Loops
+**Symptoms:** Agents delegate back and forth indefinitely
+```python
+# ✅ Solution: Clear hierarchy and responsibilities
+manager = Agent(role="Manager", allow_delegation=True)
+specialist1 = Agent(role="Specialist A", allow_delegation=False)  # No re-delegation
+specialist2 = Agent(role="Specialist B", allow_delegation=False)
+```
+
+## Advanced Collaboration Features
+
+### Custom Collaboration Rules
+```python
+# Set specific collaboration guidelines in agent backstory
+agent = Agent(
+    role="Senior Developer",
+    backstory="""You lead development projects and coordinate with team members.
+    
+    Collaboration guidelines:
+    - Delegate research tasks to the Research Analyst
+    - Ask the Designer for UI/UX guidance  
+    - Consult the QA Engineer for testing strategies
+    - Only escalate blocking issues to the Project Manager""",
+    allow_delegation=True
+)
+```
+
+### Monitoring Collaboration
+```python
+def track_collaboration(output):
+    """Track collaboration patterns"""
+    if "Delegate work to coworker" in output.raw:
+        print("🤝 Delegation occurred")
+    if "Ask question to coworker" in output.raw:
+        print("❓ Question asked")
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    step_callback=track_collaboration,  # Monitor collaboration
+    verbose=True
+)
+```
+
+## Memory and Learning
+
+Enable agents to remember past collaborations:
+
+```python
+agent = Agent(
+    role="Content Lead",
+    memory=True,  # Remembers past interactions
+    allow_delegation=True,
+    verbose=True
+)
+```
+
+With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
+
+## Next Steps
+
+- **Try the examples**: Start with the basic collaboration example
+- **Experiment with roles**: Test different agent role combinations  
+- **Monitor interactions**: Use `verbose=True` to see collaboration in action
+- **Optimize task descriptions**: Clear tasks lead to better collaboration
+- **Scale up**: Try hierarchical processes for complex projects
+
+Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.

docs/concepts/crews.mdx

@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 icon: people-group
 ---
 
-## What is a Crew?
+## Overview
 
 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.
 
@@ -20,17 +20,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Function Calling LLM** _(optional)_ | `function_calling_llm` | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling.                                                                  |
 | **Config** _(optional)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
-| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
-| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
 | **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -55,12 +52,16 @@ After creating your CrewAI project as outlined in the [Installation](/installati
 ```python code
 from crewai import Agent, Crew, Task, Process
 from crewai.project import CrewBase, agent, task, crew, before_kickoff, after_kickoff
-
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class YourCrewName:
     """Description of your crew"""
 
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
     # Paths to your YAML configuration files
     # To see an example agent and task defined in YAML, checkout the following:
     # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
@@ -83,27 +84,27 @@ class YourCrewName:
     @agent
     def agent_one(self) -> Agent:
         return Agent(
-            config=self.agents_config['agent_one'],
+            config=self.agents_config['agent_one'], # type: ignore[index]
             verbose=True
         )
 
     @agent
     def agent_two(self) -> Agent:
         return Agent(
-            config=self.agents_config['agent_two'],
+            config=self.agents_config['agent_two'], # type: ignore[index]
             verbose=True
         )
 
     @task
     def task_one(self) -> Task:
         return Task(
-            config=self.tasks_config['task_one']
+            config=self.tasks_config['task_one'] # type: ignore[index]
         )
 
     @task
     def task_two(self) -> Task:
         return Task(
-            config=self.tasks_config['task_two']
+            config=self.tasks_config['task_two'] # type: ignore[index]
         )
 
     @crew
@@ -116,6 +117,12 @@ 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>
@@ -183,6 +190,11 @@ class YourCrewName:
             verbose=True
         )
 ```
+How to run the above code:
+
+```python code
+YourCrewName().crew().kickoff(inputs={})
+```
 
 In this example:
 
@@ -245,7 +257,7 @@ print(f"Token Usage: {crew_output.token_usage}")
 You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
 In case of `True(Boolean)` will save as `logs.txt`.
 
-In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
 
 ```python Code
 # Save crew logs
@@ -313,12 +325,12 @@ for result in results:
 
 # Example of using kickoff_async
 inputs = {'topic': 'AI in healthcare'}
-async_result = my_crew.kickoff_async(inputs=inputs)
+async_result = await my_crew.kickoff_async(inputs=inputs)
 print(async_result)
 
 # Example of using kickoff_for_each_async
 inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
-async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
+async_results = await my_crew.kickoff_for_each_async(inputs=inputs_array)
 for async_result in async_results:
     print(async_result)
 ```

docs/concepts/event-listener.mdx

@@ -4,7 +4,7 @@ description: 'Tap into CrewAI events to build custom integrations and monitoring
 icon: spinner
 ---
 
-# Event Listeners
+## Overview
 
 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.
 
@@ -13,11 +13,25 @@ CrewAI provides a powerful event system that allows you to listen for and react
 CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:
 
 1. **CrewAIEventsBus**: A singleton event bus that manages event registration and emission
-2. **CrewEvent**: Base class for all events in the system
+2. **BaseEvent**: Base class for all events in the system
 3. **BaseEventListener**: Abstract base class for creating custom event listeners
 
 When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.
 
+<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)
+
+With Prompt Tracing you can:
+- View the complete history of all prompts sent to your LLM
+- Track token usage and costs
+- Debug agent reasoning failures
+- Share prompt sequences with your team
+- Compare different prompt strategies
+- Export traces for compliance and auditing
+</Note>
+
 ## Creating a Custom Event Listener
 
 To create a custom event listener, you need to:
@@ -40,17 +54,17 @@ from crewai.utilities.events.base_event_listener import BaseEventListener
 class MyCustomListener(BaseEventListener):
     def __init__(self):
         super().__init__()
-    
+
     def setup_listeners(self, crewai_event_bus):
         @crewai_event_bus.on(CrewKickoffStartedEvent)
         def on_crew_started(source, event):
             print(f"Crew '{event.crew_name}' has started execution!")
-        
+
         @crewai_event_bus.on(CrewKickoffCompletedEvent)
         def on_crew_completed(source, event):
             print(f"Crew '{event.crew_name}' has completed execution!")
             print(f"Output: {event.output}")
-        
+
         @crewai_event_bus.on(AgentExecutionCompletedEvent)
         def on_agent_execution_completed(source, event):
             print(f"Agent '{event.agent.role}' completed task")
@@ -83,7 +97,7 @@ my_listener = MyCustomListener()
 
 class MyCustomCrew:
     # Your crew implementation...
-    
+
     def crew(self):
         return Crew(
             agents=[...],
@@ -106,7 +120,7 @@ my_listener = MyCustomListener()
 
 class MyCustomFlow(Flow):
     # Your flow implementation...
-    
+
     @start()
     def first_step(self):
         # ...
@@ -210,6 +224,20 @@ 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
+
+### LLM Guardrail Events
+
+- **LLMGuardrailStartedEvent**: Emitted when a guardrail validation starts. Contains details about the guardrail being applied and retry count.
+- **LLMGuardrailCompletedEvent**: Emitted when a guardrail validation completes. Contains details about validation success/failure, results, and error messages if any.
+
 ### Flow Events
 
 - **FlowCreatedEvent**: Emitted when a Flow is created
@@ -234,7 +262,7 @@ Each event handler receives two parameters:
 1. **source**: The object that emitted the event
 2. **event**: The event instance, containing event-specific data
 
-The structure of the event object depends on the event type, but all events inherit from `CrewEvent` and include:
+The structure of the event object depends on the event type, but all events inherit from `BaseEvent` and include:
 
 - **timestamp**: The time when the event was emitted
 - **type**: A string identifier for the event type
@@ -324,9 +352,9 @@ with crewai_event_bus.scoped_handlers():
     @crewai_event_bus.on(CrewKickoffStartedEvent)
     def temp_handler(source, event):
         print("This handler only exists within this context")
-    
+
     # Do something that emits events
-    
+
 # Outside the context, the temporary handler is removed
 ```
 

docs/concepts/flows.mdx

@@ -4,7 +4,7 @@ description: Learn how to create and manage AI workflows using CrewAI Flows.
 icon: arrow-progress
 ---
 
-## Introduction
+## Overview
 
 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.
 
@@ -75,11 +75,12 @@ 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.
@@ -146,6 +147,7 @@ class OutputExampleFlow(Flow):
 
 
 flow = OutputExampleFlow()
+flow.plot("my_flow_plot")
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
@@ -158,9 +160,10 @@ 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 `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.
 
 #### Accessing and Updating State
 
@@ -192,6 +195,7 @@ 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:")
@@ -206,6 +210,8 @@ 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.
 
@@ -249,9 +255,12 @@ 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:**
@@ -302,6 +311,8 @@ 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.
@@ -436,6 +447,7 @@ class OrExampleFlow(Flow):
 
 
 flow = OrExampleFlow()
+flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -446,6 +458,8 @@ 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.
 
@@ -474,6 +488,7 @@ class AndExampleFlow(Flow):
         print(self.state)
 
 flow = AndExampleFlow()
+flow.plot()
 flow.kickoff()
 ```
 
@@ -484,6 +499,8 @@ 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.
 
@@ -527,6 +544,7 @@ class RouterFlow(Flow[ExampleState]):
 
 
 flow = RouterFlow()
+flow.plot("my_flow_plot")
 flow.kickoff()
 ```
 
@@ -538,6 +556,8 @@ 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"`.
@@ -545,6 +565,122 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
+## Adding Agents to Flows
+
+Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
+
+```python
+import asyncio
+from typing import Any, Dict, List
+
+from crewai_tools import SerperDevTool
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent
+from crewai.flow.flow import Flow, listen, start
+
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str] = Field(description="List of identified market trends")
+    market_size: str = Field(description="Estimated market size")
+    competitors: List[str] = Field(description="Major competitors in the space")
+
+
+# Define flow state
+class MarketResearchState(BaseModel):
+    product: str = ""
+    analysis: MarketAnalysis | None = None
+
+
+# Create a flow class
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self) -> Dict[str, Any]:
+        print(f"Starting market research for {self.state.product}")
+        return {"product": self.state.product}
+
+    @listen(initialize_research)
+    async def analyze_market(self) -> Dict[str, Any]:
+        # Create an Agent for market research
+        analyst = Agent(
+            role="Market Research Analyst",
+            goal=f"Analyze the market for {self.state.product}",
+            backstory="You are an experienced market analyst with expertise in "
+            "identifying market trends and opportunities.",
+            tools=[SerperDevTool()],
+            verbose=True,
+        )
+
+        # Define the research query
+        query = f"""
+        Research the market for {self.state.product}. Include:
+        1. Key market trends
+        2. Market size
+        3. Major competitors
+
+        Format your response according to the specified structure.
+        """
+
+        # Execute the analysis with structured output format
+        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
+        if result.pydantic:
+            print("result", result.pydantic)
+        else:
+            print("result", result)
+
+        # Return the analysis to update the state
+        return {"analysis": result.pydantic}
+
+    @listen(analyze_market)
+    def present_results(self, analysis) -> None:
+        print("\nMarket Analysis Results")
+        print("=====================")
+
+        if isinstance(analysis, dict):
+            # If we got a dict with 'analysis' key, extract the actual analysis object
+            market_analysis = analysis.get("analysis")
+        else:
+            market_analysis = analysis
+
+        if market_analysis and isinstance(market_analysis, MarketAnalysis):
+            print("\nKey Market Trends:")
+            for trend in market_analysis.key_trends:
+                print(f"- {trend}")
+
+            print(f"\nMarket Size: {market_analysis.market_size}")
+
+            print("\nMajor Competitors:")
+            for competitor in market_analysis.competitors:
+                print(f"- {competitor}")
+        else:
+            print("No structured analysis data available.")
+            print("Raw analysis:", analysis)
+
+
+# Usage example
+async def run_flow():
+    flow = MarketResearchFlow()
+    flow.plot("MarketResearchFlowPlot")
+    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
+    return result
+
+
+# Run the flow
+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.
+
+2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
+
+3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+
 ## Adding Crews to Flows
 
 Creating a flow with multiple crews in CrewAI is straightforward.
@@ -633,13 +769,16 @@ def kickoff():
 
 def plot():
     poem_flow = PoemFlow()
-    poem_flow.plot()
+    poem_flow.plot("PoemFlowPlot")
 
 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.
+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)
 
 ### Running the Flow
 

docs/concepts/llamaindex-tools.mdx

@@ -1,71 +0,0 @@
----
-title: Using LlamaIndex Tools
-description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
-icon: toolbox
----
-
-## Using LlamaIndex Tools
-
-<Info>
-    CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more. 
-</Info>
-
-Here are the available built-in tools offered by LlamaIndex.
-
-```python Code
-from crewai import Agent
-from crewai_tools import LlamaIndexTool
-
-# Example 1: Initialize from FunctionTool
-from llama_index.core.tools import FunctionTool
-
-your_python_function = lambda ...: ...
-og_tool = FunctionTool.from_defaults(
-    your_python_function, 
-    name="<name>", 
-    description='<description>'
-)
-tool = LlamaIndexTool.from_tool(og_tool)
-
-# Example 2: Initialize from LlamaHub Tools
-from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
-wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
-wolfram_tools = wolfram_spec.to_tool_list()
-tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
-
-# Example 3: Initialize Tool from a LlamaIndex Query Engine
-query_engine = index.as_query_engine()
-query_tool = LlamaIndexTool.from_query_engine(
-    query_engine,
-    name="Uber 2019 10K Query Tool",
-    description="Use this tool to lookup the 2019 Uber 10K Annual Report"
-)
-
-# Create and assign the tools to an agent
-agent = Agent(
-    role='Research Analyst',
-    goal='Provide up-to-date market analysis',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[tool, *tools, query_tool]
-)
-
-# rest of the code ...
-```
-
-## Steps to Get Started
-
-To effectively use the LlamaIndexTool, follow these steps:
-
-<Steps>
-    <Step title="Package Installation">
-    Make sure that `crewai[tools]` package is installed in your Python environment:
-    <CodeGroup>
-        ```shell Terminal
-        pip install 'crewai[tools]'
-        ```
-    </CodeGroup>
-    </Step>
-    <Step title="Install and Use LlamaIndex">
-    Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
-    </Step>
-</Steps>

docs/concepts/llms.mdx

@@ -4,9 +4,10 @@ description: 'A comprehensive guide to configuring and using Large Language Mode
 icon: 'microchip-ai'
 ---
 
-<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>
+## 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.
+
 
 ## What are LLMs?
 
@@ -27,23 +28,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
   </Card>
 </CardGroup>
 
-## Setting Up Your LLM
+## Setting up your LLM
 
-There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
+There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
 
 <Tabs>
   <Tab title="1. Environment Variables">
-    The simplest way to get started. Set these variables in your environment:
+    The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
 
-    ```bash
-    # Required: Your API key for authentication
-    OPENAI_API_KEY=<your-api-key>
+    ```bash .env
+    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
 
-    # Optional: Default model selection
-    OPENAI_MODEL_NAME=gpt-4o-mini  # Default if not set
-
-    # Optional: Organization ID (if applicable)
-    OPENAI_ORGANIZATION_ID=<your-org-id>
+    # Be sure to set your API keys here too. See the Provider
+    # section below.
     ```
 
     <Warning>
@@ -53,13 +50,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
   <Tab title="2. YAML Configuration">
     Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
 
-    ```yaml
+    ```yaml agents.yaml {6}
     researcher:
         role: Research Specialist
         goal: Conduct comprehensive research and analysis
         backstory: A dedicated research professional with years of experience
         verbose: true
-        llm: openai/gpt-4o-mini # your model here
+        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
         # (see provider configuration examples below for more)
     ```
 
@@ -74,23 +71,23 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
   <Tab title="3. Direct Code">
     For maximum flexibility, configure LLMs directly in your Python code:
 
-    ```python
+    ```python {4,8}
     from crewai import LLM
 
     # Basic configuration
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...
 
     # Advanced configuration with detailed parameters
     llm = LLM(
-        model="gpt-4o-mini",
+        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
         temperature=0.7,        # Higher for more creative outputs
-        timeout=120,           # Seconds to wait for response
-        max_tokens=4000,       # Maximum length of response
-        top_p=0.9,            # Nucleus sampling parameter
-        frequency_penalty=0.1, # Reduce repetition
-        presence_penalty=0.1,  # Encourage topic diversity
+        timeout=120,            # Seconds to wait for response
+        max_tokens=4000,        # Maximum length of response
+        top_p=0.9,              # Nucleus sampling parameter
+        frequency_penalty=0.1 , # Reduce repetition
+        presence_penalty=0.1,   # Encourage topic diversity
         response_format={"type": "json"},  # For structured outputs
-        seed=42               # For reproducible results
+        seed=42                 # For reproducible results
     )
     ```
 
@@ -110,7 +107,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
 
 ## Provider Configuration Examples
 
-
 CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
 In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
 
@@ -156,6 +152,39 @@ In this section, you'll find detailed examples that help you select, configure,
     | o1                  | 200,000 tokens   | Fast reasoning, complex reasoning             |
   </Accordion>
 
+  <Accordion title="Meta-Llama">
+    Meta's Llama API provides access to Meta's family of large language models.
+    The API is available through the [Meta Llama API](https://llama.developer.meta.com?utm_source=partner-crewai&utm_medium=website).
+    Set the following environment variables in your `.env` file:
+
+    ```toml Code
+    # Meta Llama API Key Configuration
+    LLAMA_API_KEY=LLM|your_api_key_here
+    ```
+
+    Example usage in your CrewAI project:
+    ```python Code
+    from crewai import LLM
+
+    # Initialize Meta Llama LLM
+    llm = LLM(
+        model="meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8",
+        temperature=0.8,
+        stop=["END"],
+        seed=42
+    )
+    ```
+
+    All models listed here https://llama.developer.meta.com/docs/models/ are supported.
+
+    | Model ID | Input context length | Output context length | Input Modalities | Output Modalities |
+    | --- | --- | --- | --- | --- |
+    | `meta_llama/Llama-4-Scout-17B-16E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+    | `meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text |
+    | `meta_llama/Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text |
+    | `meta_llama/Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text |
+  </Accordion>
+
   <Accordion title="Anthropic">
     ```toml Code
     # Required
@@ -174,19 +203,55 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
-  <Accordion title="Google">
-    Set the following environment variables in your `.env` file:
+  <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).
 
-    ```toml Code
-    # Option 1: Gemini accessed with an API key.
+    ```toml .env
     # 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
     ```
 
-    Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
+    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:
     ```python Code
     import json
 
@@ -210,14 +275,18 @@ 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-2.0-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 |
+    | 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 |
   </Accordion>
 
   <Accordion title="Azure">
@@ -383,7 +452,7 @@ In this section, you'll find detailed examples that help you select, configure,
     | microsoft/phi-3-medium-4k-instruct                                  | 4,096 tokens   | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
     | microsoft/phi-3-medium-128k-instruct                                | 128K tokens    | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
     | microsoft/phi-3.5-mini-instruct                                     | 128K tokens    | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
-    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
     | microsoft/kosmos-2                                                  | 1,024 tokens   | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
     | microsoft/phi-3-vision-128k-instruct                               | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
     | microsoft/phi-3.5-vision-instruct                                  | 128k tokens    | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
@@ -407,19 +476,19 @@ In this section, you'll find detailed examples that help you select, configure,
   </Accordion>
 
   <Accordion title="Local NVIDIA NIM Deployed using WSL2">
-    
-    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux). 
-    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services. 
+
+    NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
+    This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
     Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
-    
+
     Here is a step-by-step guide to setting up a local NVIDIA NIM model:
-    
+
     1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
 
     2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
 
     3. Configure your crewai local models:
-  
+
     ```python Code
     from crewai.llm import LLM
 
@@ -438,10 +507,10 @@ In this section, you'll find detailed examples that help you select, configure,
         @agent
         def researcher(self) -> Agent:
             return Agent(
-                config=self.agents_config['researcher'],
+                config=self.agents_config['researcher'], # type: ignore[index]
                 llm=local_nvidia_nim_llm
             )
-        
+
         # ...
     ```
   </Accordion>
@@ -535,14 +604,13 @@ In this section, you'll find detailed examples that help you select, configure,
   <Accordion title="Hugging Face">
     Set the following environment variables in your `.env` file:
     ```toml Code
-    HUGGINGFACE_API_KEY=<your-api-key>
+    HF_TOKEN=<your-api-key>
     ```
 
     Example usage in your CrewAI project:
     ```python Code
     llm = LLM(
-        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
-        base_url="your_api_endpoint"
+        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct"
     )
     ```
   </Accordion>
@@ -638,23 +706,29 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
 
     When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
   </Tab>
-  
+
   <Tab title="Event Handling">
     CrewAI emits events for each chunk received during streaming:
-    
+
     ```python
-    from crewai import LLM
-    from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
-    
-    class MyEventHandler(EventHandler):
-        def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
-            # Process each chunk as it arrives
-            print(f"Received chunk: {event.chunk}")
-    
-    # Register the event handler
-    from crewai.utilities.events import crewai_event_bus
-    crewai_event_bus.register_handler(MyEventHandler())
+    from crewai.utilities.events import (
+      LLMStreamChunkEvent
+    )
+    from crewai.utilities.events.base_event_listener import BaseEventListener
+
+    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}")
+
+    my_listener = MyCustomListener()
     ```
+
+    <Tip>
+      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details 
+    </Tip>
   </Tab>
 </Tabs>
 
@@ -751,6 +825,24 @@ 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
@@ -786,7 +878,7 @@ Learn how to get the most out of your LLM configuration:
     <Tip>
       Use larger context models for extensive tasks
     </Tip>
-    
+
     ```python
     # Large context model
     llm = LLM(model="openai/gpt-4o")  # 128K tokens

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: brain
+icon: ruler-combined
 ---
 
-## Introduction
+## Overview
 
 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.
@@ -29,6 +29,10 @@ my_crew = Crew(
 
 From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
 
+<Warning>
+When planning is enabled, crewAI will use `gpt-4o-mini` as the default LLM for planning, which requires a valid OpenAI API key. Since your agents might be using different LLMs, this could cause confusion if you don't have an OpenAI API key configured or if you're experiencing unexpected behavior related to LLM API calls.
+</Warning>
+
 #### Planning LLM
 
 Now you can define the LLM that will be used to plan the tasks. 

docs/concepts/processes.mdx

@@ -4,7 +4,8 @@ description: Detailed guide on workflow management through processes in CrewAI,
 icon: bars-staggered
 ---
 
-## Understanding Processes
+## Overview
+
 <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

@@ -0,0 +1,147 @@
+---
+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 of a Task
+## Overview
 
 In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
 
@@ -12,6 +12,18 @@ Tasks provide all necessary details for execution, such as a description, the ag
 
 Tasks within CrewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
+<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)
+
+The Visual Task Builder enables:
+- Drag-and-drop task creation
+- Visual task dependencies and flow
+- Real-time testing and validation
+- Easy sharing and collaboration
+</Note>
+
 ### Task Execution Flow
 
 Tasks can be executed in two ways:
@@ -39,6 +51,7 @@ crew = Crew(
 | **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
 | **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
 | **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Whether the task should have a human review the final answer of the agent. Defaults to False.                        |
+| **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
@@ -82,6 +95,7 @@ reporting_task:
     A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
   agent: reporting_analyst
+  markdown: true
   output_file: report.md
 ```
 
@@ -101,7 +115,7 @@ class LatestAiDevelopmentCrew():
   @agent
   def researcher(self) -> Agent:
     return Agent(
-      config=self.agents_config['researcher'],
+      config=self.agents_config['researcher'], # type: ignore[index]
       verbose=True,
       tools=[SerperDevTool()]
     )
@@ -109,20 +123,20 @@ class LatestAiDevelopmentCrew():
   @agent
   def reporting_analyst(self) -> Agent:
     return Agent(
-      config=self.agents_config['reporting_analyst'],
+      config=self.agents_config['reporting_analyst'], # type: ignore[index]
       verbose=True
     )
 
   @task
   def research_task(self) -> Task:
     return Task(
-      config=self.tasks_config['research_task']
+      config=self.tasks_config['research_task'] # type: ignore[index]
     )
 
   @task
   def reporting_task(self) -> Task:
     return Task(
-      config=self.tasks_config['reporting_task']
+      config=self.tasks_config['reporting_task'] # type: ignore[index]
     )
 
   @crew
@@ -170,9 +184,9 @@ reporting_task = Task(
     """,
     expected_output="""
         A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
     """,
     agent=reporting_analyst,
+    markdown=True,  # Enable markdown formatting for the final output
     output_file="report.md"
 )
 ```
@@ -245,6 +259,54 @@ if task_output.pydantic:
     print(f"Pydantic Output: {task_output.pydantic}")
 ```
 
+## Markdown Output Formatting
+
+The `markdown` parameter enables automatic markdown formatting for task outputs. When set to `True`, the task will instruct the agent to format the final answer using proper Markdown syntax.
+
+### Using Markdown Formatting
+
+```python Code
+# Example task with markdown formatting enabled
+formatted_task = Task(
+    description="Create a comprehensive report on AI trends",
+    expected_output="A well-structured report with headers, sections, and bullet points",
+    agent=reporter_agent,
+    markdown=True  # Enable automatic markdown formatting
+)
+```
+
+When `markdown=True`, the agent will receive additional instructions to format the output using:
+- `#` for headers
+- `**text**` for bold text  
+- `*text*` for italic text
+- `-` or `*` for bullet points
+- `` `code` `` for inline code
+- ``` ```language ``` for code blocks
+
+### YAML Configuration with Markdown
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Analyze the market data and create a detailed report
+  expected_output: >
+    A comprehensive analysis with charts and key findings
+  agent: analyst
+  markdown: true  # Enable markdown formatting
+  output_file: analysis.md
+```
+
+### Benefits of Markdown Output
+
+- **Consistent Formatting**: Ensures all outputs follow proper markdown conventions
+- **Better Readability**: Structured content with headers, lists, and emphasis
+- **Documentation Ready**: Output can be directly used in documentation systems
+- **Cross-Platform Compatibility**: Markdown is universally supported
+
+<Note>
+The markdown formatting instructions are automatically added to the task prompt when `markdown=True`, so you don't need to specify formatting requirements in your task description.
+</Note>
+
 ## Task Dependencies and Context
 
 Tasks can depend on the output of other tasks using the `context` attribute. For example:
@@ -276,26 +338,20 @@ To add a guardrail to a task, provide a validation function through the `guardra
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
+from crewai import TaskOutput
 
-def validate_blog_content(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
         word_count = len(result.split())
         if word_count > 200:
-            return (False, {
-                "error": "Blog content exceeds 200 words",
-                "code": "WORD_COUNT_ERROR",
-                "context": {"word_count": word_count}
-            })
+            return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
         return (True, result.strip())
     except Exception as e:
-        return (False, {
-            "error": "Unexpected error during validation",
-            "code": "SYSTEM_ERROR"
-        })
+        return (False, "Unexpected error during validation")
 
 blog_task = Task(
     description="Write a blog post about AI",
@@ -313,29 +369,28 @@ blog_task = Task(
    - Type hints are recommended but optional
 
 2. **Return Values**:
-   - Success: Return `(True, validated_result)`
-   - Failure: Return `(False, error_details)`
+   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
+   - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
+
+### LLMGuardrail
+
+The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
 
 ### Error Handling Best Practices
 
 1. **Structured Error Responses**:
 ```python Code
-def validate_with_context(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+from crewai import TaskOutput, LLMGuardrail
+
+def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
     try:
         # Main validation logic
         validated_data = perform_validation(result)
         return (True, validated_data)
     except ValidationError as e:
-        return (False, {
-            "error": str(e),
-            "code": "VALIDATION_ERROR",
-            "context": {"input": result}
-        })
+        return (False, f"VALIDATION_ERROR: {str(e)}")
     except Exception as e:
-        return (False, {
-            "error": "Unexpected error",
-            "code": "SYSTEM_ERROR"
-        })
+        return (False, str(e))
 ```
 
 2. **Error Categories**:
@@ -346,28 +401,25 @@ def validate_with_context(result: str) -> Tuple[bool, Union[Dict[str, Any], str]
 3. **Validation Chain**:
 ```python Code
 from typing import Any, Dict, List, Tuple, Union
+from crewai import TaskOutput
 
-def complex_validation(result: str) -> Tuple[bool, Union[str, Dict[str, Any]]]:
+def complex_validation(result: TaskOutput) -> Tuple[bool, Any]:
     """Chain multiple validation steps."""
     # Step 1: Basic validation
     if not result:
-        return (False, {"error": "Empty result", "code": "EMPTY_INPUT"})
+        return (False, "Empty result")
 
     # Step 2: Content validation
     try:
         validated = validate_content(result)
         if not validated:
-            return (False, {"error": "Invalid content", "code": "CONTENT_ERROR"})
+            return (False, "Invalid content")
 
         # Step 3: Format validation
         formatted = format_output(validated)
         return (True, formatted)
     except Exception as e:
-        return (False, {
-            "error": str(e),
-            "code": "VALIDATION_ERROR",
-            "context": {"step": "content_validation"}
-        })
+        return (False, str(e))
 ```
 
 ### Handling Guardrail Results
@@ -382,19 +434,16 @@ When a guardrail returns `(False, error)`:
 Example with retry handling:
 ```python Code
 from typing import Optional, Tuple, Union
+from crewai import TaskOutput, Task
 
-def validate_json_output(result: str) -> Tuple[bool, Union[Dict[str, Any], str]]:
+def validate_json_output(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate and parse JSON output."""
     try:
         # Try to parse as JSON
         data = json.loads(result)
         return (True, data)
     except json.JSONDecodeError as e:
-        return (False, {
-            "error": "Invalid JSON format",
-            "code": "JSON_ERROR",
-            "context": {"line": e.lineno, "column": e.colno}
-        })
+        return (False, "Invalid JSON format")
 
 task = Task(
     description="Generate a JSON report",
@@ -414,7 +463,7 @@ It's also important to note that the output of the final task of a crew becomes
 ### Using `output_pydantic`
 The `output_pydantic` property allows you to define a Pydantic model that the task output should conform to. This ensures that the output is not only structured but also validated according to the Pydantic model.
 
-Here’s an example demonstrating how to use output_pydantic:
+Here's an example demonstrating how to use output_pydantic:
 
 ```python Code
 import json
@@ -495,7 +544,7 @@ In this example:
 ### Using `output_json`
 The `output_json` property allows you to define the expected output in JSON format. This ensures that the task's output is a valid JSON structure that can be easily parsed and used in your application.
 
-Here’s an example demonstrating how to use `output_json`:
+Here's an example demonstrating how to use `output_json`:
 
 ```python Code
 import json
@@ -755,6 +804,8 @@ Task guardrails provide a powerful way to validate, transform, or filter task ou
 
 ### Basic Usage
 
+#### Define your own logic to validate
+
 ```python Code
 from typing import Tuple, Union
 from crewai import Task
@@ -774,6 +825,57 @@ task = Task(
 )
 ```
 
+#### Leverage a no-code approach for validation
+
+```python Code
+from crewai import Task
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail="Ensure the response is a valid JSON object"
+)
+```
+
+#### Using YAML
+
+```yaml
+research_task:
+  ...
+  guardrail: make sure each bullet contains a minimum of 100 words
+  ...
+```
+
+```python Code
+@CrewBase
+class InternalCrew:
+    agents_config = "config/agents.yaml"
+    tasks_config = "config/tasks.yaml"
+
+    ...
+    @task
+    def research_task(self):
+        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
+    ...
+```
+
+
+#### Use custom models for code generation
+
+```python Code
+from crewai import Task
+from crewai.llm import LLM
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=LLMGuardrail(
+        description="Ensure the response is a valid JSON object",
+        llm=LLM(model="gpt-4o-mini"),
+    )
+)
+```
+
 ### How Guardrails Work
 
 1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.

docs/concepts/testing.mdx

@@ -4,7 +4,7 @@ description: Learn how to test your CrewAI Crew and evaluate their performance.
 icon: vial
 ---
 
-## Introduction
+## Overview
 
 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
 ---
 
-## Introduction
+## Overview
 
 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.
@@ -15,6 +15,16 @@ A tool in CrewAI is a skill or function that agents can utilize to perform vario
 This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
+<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.
+
+The Enterprise Tools Repository includes:
+- Pre-built connectors for popular enterprise systems
+- Custom tool creation interface
+- Version control and sharing capabilities
+- Security and compliance features
+</Note>
+
 ## Key Characteristics of Tools
 
 - **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
@@ -22,6 +32,7 @@ enabling everything from simple searches to complex interactions and effective t
 - **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
 - **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
 - **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
+- **Asynchronous Support**: Handles both synchronous and asynchronous tools, enabling non-blocking operations.
 
 ## Using CrewAI Tools
 
@@ -79,7 +90,7 @@ research = Task(
 )
 
 write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
+    description='Write an engaging blog post about the AI industry, based on the research analyst's summary. Draw inspiration from the latest blog posts in the directory.',
     expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
     agent=writer,
     output_file='blog-posts/new_post.md'  # The final blog post will be saved here
@@ -141,7 +152,7 @@ Here is a list of the available tools and their descriptions:
 ## Creating your own Tools
 
 <Tip>
-  Developers can craft `custom tools` tailored for their agent’s needs or
+  Developers can craft `custom tools` tailored for their agent's needs or
   utilize pre-built options.
 </Tip>
 
@@ -167,6 +178,62 @@ class MyCustomTool(BaseTool):
         return "Tool's result"
 ```
 
+## Asynchronous Tool Support
+
+CrewAI supports asynchronous tools, allowing you to implement tools that perform non-blocking operations like network requests, file I/O, or other async operations without blocking the main execution thread.
+
+### Creating Async Tools
+
+You can create async tools in two ways:
+
+#### 1. Using the `tool` Decorator with Async Functions
+
+```python Code
+from crewai.tools import tool
+
+@tool("fetch_data_async")
+async def fetch_data_async(query: str) -> str:
+    """Asynchronously fetch data based on the query."""
+    # Simulate async operation
+    await asyncio.sleep(1)
+    return f"Data retrieved for {query}"
+```
+
+#### 2. Implementing Async Methods in Custom Tool Classes
+
+```python Code
+from crewai.tools import BaseTool
+
+class AsyncCustomTool(BaseTool):
+    name: str = "async_custom_tool"
+    description: str = "An asynchronous custom tool"
+    
+    async def _run(self, query: str = "") -> str:
+        """Asynchronously run the tool"""
+        # Your async implementation here
+        await asyncio.sleep(1)
+        return f"Processed {query} asynchronously"
+```
+
+### Using Async Tools
+
+Async tools work seamlessly in both standard Crew workflows and Flow-based workflows:
+
+```python Code
+# In standard Crew
+agent = Agent(role="researcher", tools=[async_custom_tool])
+
+# In Flow
+class MyFlow(Flow):
+    @start()
+    async def begin(self):
+        crew = Crew(agents=[agent])
+        result = await crew.kickoff_async()
+        return result
+```
+
+The CrewAI framework automatically handles the execution of both synchronous and asynchronous tools, so you don't need to worry about how to call them differently.
+
 ### Utilizing the `tool` Decorator
 
 ```python Code
@@ -178,48 +245,6 @@ def my_tool(question: str) -> str:
     return "Result from your custom tool"
 ```
 
-### Structured Tools
-
-The `StructuredTool` class wraps functions as tools, providing flexibility and validation while reducing boilerplate. It supports custom schemas and dynamic logic for seamless integration of complex functionalities.
-
-#### Example:
-Using `StructuredTool.from_function`, you can wrap a function that interacts with an external API or system, providing a structured interface. This enables robust validation and consistent execution, making it easier to integrate complex functionalities into your applications as demonstrated in the following example:
-
-```python
-from crewai.tools.structured_tool import CrewStructuredTool
-from pydantic import BaseModel
-
-# Define the schema for the tool's input using Pydantic
-class APICallInput(BaseModel):
-    endpoint: str
-    parameters: dict
-
-# Wrapper function to execute the API call
-def tool_wrapper(*args, **kwargs):
-    # Here, you would typically call the API using the parameters
-    # For demonstration, we'll return a placeholder string
-    return f"Call the API at {kwargs['endpoint']} with parameters {kwargs['parameters']}"
-
-# Create and return the structured tool
-def create_structured_tool():
-    return CrewStructuredTool.from_function(
-        name='Wrapper API',
-        description="A tool to wrap API calls with structured input.",
-        args_schema=APICallInput,
-        func=tool_wrapper,
-    )
-
-# Example usage
-structured_tool = create_structured_tool()
-
-# Execute the tool with structured input
-result = structured_tool._run(**{
-    "endpoint": "https://example.com/api",
-    "parameters": {"key1": "value1", "key2": "value2"}
-})
-print(result)  # Output: Call the API at https://example.com/api with parameters {'key1': 'value1', 'key2': 'value2'}
-```
-
 ### Custom Caching Mechanism
 
 <Tip>

docs/concepts/training.mdx

@@ -4,7 +4,7 @@ description: Learn how to train your CrewAI agents by giving them feedback early
 icon: dumbbell
 ---
 
-## Introduction
+## Overview
 
 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/custom_llm.md

@@ -1,642 +0,0 @@
-# Custom LLM Implementations
-
-CrewAI now supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to create your own LLM implementations that don't rely on litellm's authentication mechanism.
-
-## Using Custom LLM Implementations
-
-To create a custom LLM implementation, you need to:
-
-1. Inherit from the `BaseLLM` abstract base class
-2. Implement the required methods:
-   - `call()`: The main method to call the LLM with messages
-   - `supports_function_calling()`: Whether the LLM supports function calling
-   - `supports_stop_words()`: Whether the LLM supports stop words
-   - `get_context_window_size()`: The context window size of the LLM
-
-## Example: Basic Custom LLM
-
-```python
-from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
-
-class CustomLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not api_key or not isinstance(api_key, str):
-            raise ValueError("Invalid API key: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with the given messages.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM
-        # For example, using requests:
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint, 
-                headers=headers, 
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        # Return True if your LLM supports function calling
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        # Return True if your LLM supports stop words
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        # Return the context window size of your LLM
-        return 8192
-```
-
-## Error Handling Best Practices
-
-When implementing custom LLMs, it's important to handle errors properly to ensure robustness and reliability. Here are some best practices:
-
-### 1. Implement Try-Except Blocks for API Calls
-
-Always wrap API calls in try-except blocks to handle different types of errors:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        # API call implementation
-        response = requests.post(
-            self.endpoint,
-            headers=self.headers,
-            json=self.prepare_payload(messages),
-            timeout=30  # Set a reasonable timeout
-        )
-        response.raise_for_status()  # Raise an exception for HTTP errors
-        return response.json()["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-### 2. Implement Retry Logic for Transient Failures
-
-For transient failures like network issues or rate limiting, implement retry logic with exponential backoff:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import time
-    
-    max_retries = 3
-    retry_delay = 1  # seconds
-    
-    for attempt in range(max_retries):
-        try:
-            response = requests.post(
-                self.endpoint,
-                headers=self.headers,
-                json=self.prepare_payload(messages),
-                timeout=30
-            )
-            response.raise_for_status()
-            return response.json()["choices"][0]["message"]["content"]
-        except (requests.Timeout, requests.ConnectionError) as e:
-            if attempt < max_retries - 1:
-                time.sleep(retry_delay * (2 ** attempt))  # Exponential backoff
-                continue
-            raise TimeoutError(f"LLM request failed after {max_retries} attempts: {str(e)}")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-```
-
-### 3. Validate Input Parameters
-
-Always validate input parameters to prevent runtime errors:
-
-```python
-def __init__(self, api_key: str, endpoint: str):
-    super().__init__()
-    if not api_key or not isinstance(api_key, str):
-        raise ValueError("Invalid API key: must be a non-empty string")
-    if not endpoint or not isinstance(endpoint, str):
-        raise ValueError("Invalid endpoint URL: must be a non-empty string")
-    self.api_key = api_key
-    self.endpoint = endpoint
-```
-
-### 4. Handle Authentication Errors Gracefully
-
-Provide clear error messages for authentication failures:
-
-```python
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    try:
-        response = requests.post(self.endpoint, headers=self.headers, json=data)
-        if response.status_code == 401:
-            raise ValueError("Authentication failed: Invalid API key or token")
-        elif response.status_code == 403:
-            raise ValueError("Authorization failed: Insufficient permissions")
-        response.raise_for_status()
-        # Process response
-    except Exception as e:
-        # Handle error
-        raise
-```
-
-## Example: JWT-based Authentication
-
-For services that use JWT-based authentication instead of API keys, you can implement a custom LLM like this:
-
-```python
-from crewai import BaseLLM, Agent, Task
-from typing import Any, Dict, List, Optional, Union
-
-class JWTAuthLLM(BaseLLM):
-    def __init__(self, jwt_token: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
-        if not jwt_token or not isinstance(jwt_token, str):
-            raise ValueError("Invalid JWT token: must be a non-empty string")
-        if not endpoint or not isinstance(endpoint, str):
-            raise ValueError("Invalid endpoint URL: must be a non-empty string")
-        self.jwt_token = jwt_token
-        self.endpoint = endpoint
-        self.stop = []  # You can customize stop words if needed
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """Call the LLM with JWT authentication.
-        
-        Args:
-            messages: Input messages for the LLM.
-            tools: Optional list of tool schemas for function calling.
-            callbacks: Optional list of callback functions.
-            available_functions: Optional dict mapping function names to callables.
-            
-        Returns:
-            Either a text response from the LLM or the result of a tool function call.
-            
-        Raises:
-            TimeoutError: If the LLM request times out.
-            RuntimeError: If the LLM request fails for other reasons.
-            ValueError: If the response format is invalid.
-        """
-        # Implement your own logic to call the LLM with JWT authentication
-        import requests
-        
-        try:
-            headers = {
-                "Authorization": f"Bearer {self.jwt_token}",
-                "Content-Type": "application/json"
-            }
-            
-            # Convert string message to proper format if needed
-            if isinstance(messages, str):
-                messages = [{"role": "user", "content": messages}]
-            
-            data = {
-                "messages": messages,
-                "tools": tools
-            }
-            
-            response = requests.post(
-                self.endpoint,
-                headers=headers,
-                json=data,
-                timeout=30  # Set a reasonable timeout
-            )
-            
-            if response.status_code == 401:
-                raise ValueError("Authentication failed: Invalid JWT token")
-            elif response.status_code == 403:
-                raise ValueError("Authorization failed: Insufficient permissions")
-                
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            return response.json()["choices"][0]["message"]["content"]
-        except requests.Timeout:
-            raise TimeoutError("LLM request timed out")
-        except requests.RequestException as e:
-            raise RuntimeError(f"LLM request failed: {str(e)}")
-        except (KeyError, IndexError, ValueError) as e:
-            raise ValueError(f"Invalid response format: {str(e)}")
-        
-    def supports_function_calling(self) -> bool:
-        """Check if the LLM supports function calling.
-        
-        Returns:
-            True if the LLM supports function calling, False otherwise.
-        """
-        return True
-        
-    def supports_stop_words(self) -> bool:
-        """Check if the LLM supports stop words.
-        
-        Returns:
-            True if the LLM supports stop words, False otherwise.
-        """
-        return True
-        
-    def get_context_window_size(self) -> int:
-        """Get the context window size of the LLM.
-        
-        Returns:
-            The context window size as an integer.
-        """
-        return 8192
-```
-
-## Troubleshooting
-
-Here are some common issues you might encounter when implementing custom LLMs and how to resolve them:
-
-### 1. Authentication Failures
-
-**Symptoms**: 401 Unauthorized or 403 Forbidden errors
-
-**Solutions**:
-- Verify that your API key or JWT token is valid and not expired
-- Check that you're using the correct authentication header format
-- Ensure that your token has the necessary permissions
-
-### 2. Timeout Issues
-
-**Symptoms**: Requests taking too long or timing out
-
-**Solutions**:
-- Implement timeout handling as shown in the examples
-- Use retry logic with exponential backoff
-- Consider using a more reliable network connection
-
-### 3. Response Parsing Errors
-
-**Symptoms**: KeyError, IndexError, or ValueError when processing responses
-
-**Solutions**:
-- Validate the response format before accessing nested fields
-- Implement proper error handling for malformed responses
-- Check the API documentation for the expected response format
-
-### 4. Rate Limiting
-
-**Symptoms**: 429 Too Many Requests errors
-
-**Solutions**:
-- Implement rate limiting in your custom LLM
-- Add exponential backoff for retries
-- Consider using a token bucket algorithm for more precise rate control
-
-## Advanced Features
-
-### Logging
-
-Adding logging to your custom LLM can help with debugging and monitoring:
-
-```python
-import logging
-from typing import Any, Dict, List, Optional, Union
-
-class LoggingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.logger = logging.getLogger("crewai.llm.custom")
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self.logger.info(f"Calling LLM with {len(messages) if isinstance(messages, list) else 1} messages")
-        try:
-            # API call implementation
-            response = self._make_api_call(messages, tools)
-            self.logger.debug(f"LLM response received: {response[:100]}...")
-            return response
-        except Exception as e:
-            self.logger.error(f"LLM call failed: {str(e)}")
-            raise
-```
-
-### Rate Limiting
-
-Implementing rate limiting can help avoid overwhelming the LLM API:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class RateLimitedLLM(BaseLLM):
-    def __init__(
-        self, 
-        api_key: str, 
-        endpoint: str, 
-        requests_per_minute: int = 60
-    ):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.requests_per_minute = requests_per_minute
-        self.request_times: List[float] = []
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        self._enforce_rate_limit()
-        # Record this request time
-        self.request_times.append(time.time())
-        # Make the actual API call
-        return self._make_api_call(messages, tools)
-        
-    def _enforce_rate_limit(self) -> None:
-        """Enforce the rate limit by waiting if necessary."""
-        now = time.time()
-        # Remove request times older than 1 minute
-        self.request_times = [t for t in self.request_times if now - t < 60]
-        
-        if len(self.request_times) >= self.requests_per_minute:
-            # Calculate how long to wait
-            oldest_request = min(self.request_times)
-            wait_time = 60 - (now - oldest_request)
-            if wait_time > 0:
-                time.sleep(wait_time)
-```
-
-### Metrics Collection
-
-Collecting metrics can help you monitor your LLM usage:
-
-```python
-import time
-from typing import Any, Dict, List, Optional, Union
-
-class MetricsCollectingLLM(BaseLLM):
-    def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
-        self.api_key = api_key
-        self.endpoint = endpoint
-        self.metrics: Dict[str, Any] = {
-            "total_calls": 0,
-            "total_tokens": 0,
-            "errors": 0,
-            "latency": []
-        }
-        
-    def call(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        tools: Optional[List[dict]] = None,
-        callbacks: Optional[List[Any]] = None,
-        available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        start_time = time.time()
-        self.metrics["total_calls"] += 1
-        
-        try:
-            response = self._make_api_call(messages, tools)
-            # Estimate tokens (simplified)
-            if isinstance(messages, str):
-                token_estimate = len(messages) // 4
-            else:
-                token_estimate = sum(len(m.get("content", "")) // 4 for m in messages)
-            self.metrics["total_tokens"] += token_estimate
-            return response
-        except Exception as e:
-            self.metrics["errors"] += 1
-            raise
-        finally:
-            latency = time.time() - start_time
-            self.metrics["latency"].append(latency)
-            
-    def get_metrics(self) -> Dict[str, Any]:
-        """Return the collected metrics."""
-        avg_latency = sum(self.metrics["latency"]) / len(self.metrics["latency"]) if self.metrics["latency"] else 0
-        return {
-            **self.metrics,
-            "avg_latency": avg_latency
-        }
-```
-
-## Advanced Usage: Function Calling
-
-If your LLM supports function calling, you can implement the function calling logic in your custom LLM:
-
-```python
-import json
-from typing import Any, Dict, List, Optional, Union
-
-def call(
-    self,
-    messages: Union[str, List[Dict[str, str]]],
-    tools: Optional[List[dict]] = None,
-    callbacks: Optional[List[Any]] = None,
-    available_functions: Optional[Dict[str, Any]] = None,
-) -> Union[str, Any]:
-    import requests
-    
-    try:
-        headers = {
-            "Authorization": f"Bearer {self.jwt_token}",
-            "Content-Type": "application/json"
-        }
-        
-        # Convert string message to proper format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        
-        data = {
-            "messages": messages,
-            "tools": tools
-        }
-        
-        response = requests.post(
-            self.endpoint,
-            headers=headers,
-            json=data,
-            timeout=30
-        )
-        response.raise_for_status()
-        response_data = response.json()
-        
-        # Check if the LLM wants to call a function
-        if response_data["choices"][0]["message"].get("tool_calls"):
-            tool_calls = response_data["choices"][0]["message"]["tool_calls"]
-            
-            # Process each tool call
-            for tool_call in tool_calls:
-                function_name = tool_call["function"]["name"]
-                function_args = json.loads(tool_call["function"]["arguments"])
-                
-                if available_functions and function_name in available_functions:
-                    function_to_call = available_functions[function_name]
-                    function_response = function_to_call(**function_args)
-                    
-                    # Add the function response to the messages
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "name": function_name,
-                        "content": str(function_response)
-                    })
-            
-            # Call the LLM again with the updated messages
-            return self.call(messages, tools, callbacks, available_functions)
-        
-        # Return the text response if no function call
-        return response_data["choices"][0]["message"]["content"]
-    except requests.Timeout:
-        raise TimeoutError("LLM request timed out")
-    except requests.RequestException as e:
-        raise RuntimeError(f"LLM request failed: {str(e)}")
-    except (KeyError, IndexError, ValueError) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-## Using Your Custom LLM with CrewAI
-
-Once you've implemented your custom LLM, you can use it with CrewAI agents and crews:
-
-```python
-from crewai import Agent, Task, Crew
-from typing import Dict, Any
-
-# Create your custom LLM instance
-jwt_llm = JWTAuthLLM(
-    jwt_token="your.jwt.token", 
-    endpoint="https://your-llm-endpoint.com/v1/chat/completions"
-)
-
-# Use it with an agent
-agent = Agent(
-    role="Research Assistant",
-    goal="Find information on a topic",
-    backstory="You are a research assistant tasked with finding information.",
-    llm=jwt_llm,
-)
-
-# Create a task for the agent
-task = Task(
-    description="Research the benefits of exercise",
-    agent=agent,
-    expected_output="A summary of the benefits of exercise",
-)
-
-# Execute the task
-result = agent.execute_task(task)
-print(result)
-
-# Or use it with a crew
-crew = Crew(
-    agents=[agent],
-    tasks=[task],
-    manager_llm=jwt_llm,  # Use your custom LLM for the manager
-)
-
-# Run the crew
-result = crew.kickoff()
-print(result)
-```
-
-## Implementing Your Own Authentication Mechanism
-
-The `BaseLLM` class allows you to implement any authentication mechanism you need, not just JWT or API keys. You can use:
-
-- OAuth tokens
-- Client certificates
-- Custom headers
-- Session-based authentication
-- Any other authentication method required by your LLM provider
-
-Simply implement the appropriate authentication logic in your custom LLM class.

docs/docs.json

@@ -1,32 +1,39 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "palm",
+  "theme": "mint",
   "name": "CrewAI",
   "colors": {
     "primary": "#EB6658",
     "light": "#F3A78B",
     "dark": "#C94C3C"
   },
-  "favicon": "favicon.svg",
+  "favicon": "images/favicon.svg",
+  "contextual": {
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude"
+    ]
+  },
   "navigation": {
     "tabs": [
       {
-        "tab": "Get Started",
+        "tab": "Documentation",
         "groups": [
           {
             "group": "Get Started",
             "pages": [
               "introduction",
               "installation",
-              "quickstart",
-              "changelog"
+              "quickstart"
             ]
           },
           {
             "group": "Guides",
             "pages": [
               {
-                "group": "Concepts",
+                "group": "Strategy",
                 "pages": [
                   "guides/concepts/evaluating-use-cases"
                 ]
@@ -72,100 +79,164 @@
               "concepts/collaboration",
               "concepts/training",
               "concepts/memory",
+              "concepts/reasoning",
               "concepts/planning",
               "concepts/testing",
               "concepts/cli",
               "concepts/tools",
-              "concepts/event-listener",
-              "concepts/langchain-tools",
-              "concepts/llamaindex-tools"
+              "concepts/event-listener"
             ]
           },
           {
-            "group": "How to Guides",
+            "group": "MCP Integration",
             "pages": [
-              "how-to/create-custom-tools",
-              "how-to/sequential-process",
-              "how-to/hierarchical-process",
-              "how-to/custom-manager-agent",
-              "how-to/llm-connections",
-              "how-to/customizing-agents",
-              "how-to/multimodal-agents",
-              "how-to/coding-agents",
-              "how-to/force-tool-output-as-result",
-              "how-to/human-input-on-execution",
-              "how-to/kickoff-async",
-              "how-to/kickoff-for-each",
-              "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks"
-            ]
-          },
-          {
-            "group": "Agent Monitoring & Observability",
-            "pages": [
-              "how-to/weave-integration",
-              "how-to/agentops-observability",
-              "how-to/langfuse-observability",
-              "how-to/langtrace-observability",
-              "how-to/mlflow-observability",
-              "how-to/openlit-observability",
-              "how-to/portkey-observability"
+              "mcp/overview",
+              "mcp/stdio",
+              "mcp/sse",
+              "mcp/streamable-http",
+              "mcp/multiple-servers",
+              "mcp/security"
             ]
           },
           {
             "group": "Tools",
             "pages": [
-              "tools/aimindtool",
-              "tools/apifyactorstool",
-              "tools/bedrockinvokeagenttool",
-              "tools/bedrockkbretriever",
-              "tools/bravesearchtool",
-              "tools/browserbaseloadtool",
-              "tools/codedocssearchtool",
-              "tools/codeinterpretertool",
-              "tools/composiotool",
-              "tools/csvsearchtool",
-              "tools/dalletool",
-              "tools/directorysearchtool",
-              "tools/directoryreadtool",
-              "tools/docxsearchtool",
-              "tools/exasearchtool",
-              "tools/filereadtool",
-              "tools/filewritetool",
-              "tools/firecrawlcrawlwebsitetool",
-              "tools/firecrawlscrapewebsitetool",
-              "tools/firecrawlsearchtool",
-              "tools/githubsearchtool",
-              "tools/hyperbrowserloadtool",
-              "tools/linkupsearchtool",
-              "tools/llamaindextool",
-              "tools/serperdevtool",
-              "tools/s3readertool",
-              "tools/s3writertool",
-              "tools/scrapegraphscrapetool",
-              "tools/scrapeelementfromwebsitetool",
-              "tools/jsonsearchtool",
-              "tools/mdxsearchtool",
-              "tools/mysqltool",
-              "tools/multiontool",
-              "tools/nl2sqltool",
-              "tools/patronustools",
-              "tools/pdfsearchtool",
-              "tools/pgsearchtool",
-              "tools/qdrantvectorsearchtool",
-              "tools/ragtool",
-              "tools/scrapewebsitetool",
-              "tools/scrapflyscrapetool",
-              "tools/seleniumscrapingtool",
-              "tools/snowflakesearchtool",
-              "tools/spidertool",
-              "tools/txtsearchtool",
-              "tools/visiontool",
-              "tools/weaviatevectorsearchtool",
-              "tools/websitesearchtool",
-              "tools/xmlsearchtool",
-              "tools/youtubechannelsearchtool",
-              "tools/youtubevideosearchtool"
+              "tools/overview",
+              {
+                "group": "File & Document",
+                "pages": [
+                  "tools/file-document/overview",
+                  "tools/file-document/filereadtool",
+                  "tools/file-document/filewritetool",
+                  "tools/file-document/pdfsearchtool",
+                  "tools/file-document/docxsearchtool",
+                  "tools/file-document/mdxsearchtool",
+                  "tools/file-document/xmlsearchtool",
+                  "tools/file-document/txtsearchtool",
+                  "tools/file-document/jsonsearchtool",
+                  "tools/file-document/csvsearchtool",
+                  "tools/file-document/directorysearchtool",
+                  "tools/file-document/directoryreadtool"
+                ]
+              },
+              {
+                "group": "Web Scraping & Browsing",
+                "pages": [
+                  "tools/web-scraping/overview",
+                  "tools/web-scraping/scrapewebsitetool",
+                  "tools/web-scraping/scrapeelementfromwebsitetool",
+                  "tools/web-scraping/scrapflyscrapetool",
+                  "tools/web-scraping/seleniumscrapingtool",
+                  "tools/web-scraping/scrapegraphscrapetool",
+                  "tools/web-scraping/spidertool",
+                  "tools/web-scraping/browserbaseloadtool",
+                  "tools/web-scraping/hyperbrowserloadtool",
+                  "tools/web-scraping/stagehandtool",
+                  "tools/web-scraping/firecrawlcrawlwebsitetool",
+                  "tools/web-scraping/firecrawlscrapewebsitetool",
+                  "tools/web-scraping/firecrawlsearchtool"
+                ]
+              },
+              {
+                "group": "Search & Research",
+                "pages": [
+                  "tools/search-research/overview",
+                  "tools/search-research/serperdevtool",
+                  "tools/search-research/bravesearchtool",
+                  "tools/search-research/exasearchtool",
+                  "tools/search-research/linkupsearchtool",
+                  "tools/search-research/githubsearchtool",
+                  "tools/search-research/websitesearchtool",
+                  "tools/search-research/codedocssearchtool",
+                  "tools/search-research/youtubechannelsearchtool",
+                  "tools/search-research/youtubevideosearchtool"
+                ]
+              },
+              {
+                "group": "Database & Data",
+                "pages": [
+                  "tools/database-data/overview",
+                  "tools/database-data/mysqltool",
+                  "tools/database-data/pgsearchtool",
+                  "tools/database-data/snowflakesearchtool",
+                  "tools/database-data/nl2sqltool",
+                  "tools/database-data/qdrantvectorsearchtool",
+                  "tools/database-data/weaviatevectorsearchtool"
+                ]
+              },
+              {
+                "group": "AI & Machine Learning",
+                "pages": [
+                  "tools/ai-ml/overview",
+                  "tools/ai-ml/dalletool",
+                  "tools/ai-ml/visiontool",
+                  "tools/ai-ml/aimindtool",
+                  "tools/ai-ml/llamaindextool",
+                  "tools/ai-ml/langchaintool",
+                  "tools/ai-ml/ragtool",
+                  "tools/ai-ml/codeinterpretertool"
+                ]
+              },
+              {
+                "group": "Cloud & Storage",
+                "pages": [
+                  "tools/cloud-storage/overview",
+                  "tools/cloud-storage/s3readertool",
+                  "tools/cloud-storage/s3writertool",
+                  "tools/cloud-storage/bedrockinvokeagenttool",
+                  "tools/cloud-storage/bedrockkbretriever"
+                ]
+              },
+              {
+                "group": "Automation & Integration",
+                "pages": [
+                  "tools/automation/overview",
+                  "tools/automation/apifyactorstool",
+                  "tools/automation/composiotool",
+                  "tools/automation/multiontool"
+                ]
+              }
+            ]
+          },
+          {
+            "group": "Observability",
+            "pages": [
+              "observability/overview",
+              "observability/agentops",
+              "observability/arize-phoenix",
+              "observability/langfuse",
+              "observability/langtrace",
+              "observability/maxim",
+              "observability/mlflow",
+              "observability/openlit",
+              "observability/opik",
+              "observability/patronus-evaluation",
+              "observability/portkey",
+              "observability/weave"
+            ]
+          },
+          {
+            "group": "Learn",
+            "pages": [
+              "learn/overview",
+              "learn/llm-selection-guide",
+              "learn/conditional-tasks",
+              "learn/coding-agents",
+              "learn/create-custom-tools",
+              "learn/custom-llm",
+              "learn/custom-manager-agent",
+              "learn/customizing-agents",
+              "learn/dalle-image-generation",
+              "learn/force-tool-output-as-result",
+              "learn/hierarchical-process",
+              "learn/human-input-on-execution",
+              "learn/kickoff-async",
+              "learn/kickoff-for-each",
+              "learn/llm-connections",
+              "learn/multimodal-agents",
+              "learn/replay-tasks-from-latest-crew-kickoff",
+              "learn/sequential-process",
+              "learn/using-annotations"
             ]
           },
           {
@@ -176,6 +247,88 @@
           }
         ]
       },
+      {
+        "tab": "Enterprise",
+        "groups": [
+          {
+            "group": "Getting Started",
+            "pages": [
+              "enterprise/introduction"
+            ]
+          },
+          {
+            "group": "Features",
+            "pages": [
+              "enterprise/features/tool-repository",
+              "enterprise/features/webhook-streaming",
+              "enterprise/features/traces",
+              "enterprise/features/hallucination-guardrail",
+              "enterprise/features/integrations"
+            ]
+          },
+          {
+            "group": "Integration Docs",
+            "pages": [
+              "enterprise/integrations/asana",
+              "enterprise/integrations/box",
+              "enterprise/integrations/clickup",
+              "enterprise/integrations/github",
+              "enterprise/integrations/gmail",
+              "enterprise/integrations/google_calendar",
+              "enterprise/integrations/google_sheets",
+              "enterprise/integrations/hubspot",
+              "enterprise/integrations/jira",
+              "enterprise/integrations/linear",
+              "enterprise/integrations/notion",
+              "enterprise/integrations/salesforce",
+              "enterprise/integrations/shopify",
+              "enterprise/integrations/slack",
+              "enterprise/integrations/stripe",
+              "enterprise/integrations/zendesk"
+            ]
+          },
+          {
+            "group": "How-To Guides",
+            "pages": [
+              "enterprise/guides/build-crew",
+              "enterprise/guides/deploy-crew",
+              "enterprise/guides/kickoff-crew",
+              "enterprise/guides/update-crew",
+              "enterprise/guides/enable-crew-studio",
+              "enterprise/guides/azure-openai-setup",
+              "enterprise/guides/hubspot-trigger",
+              "enterprise/guides/react-component-export",
+              "enterprise/guides/salesforce-trigger",
+              "enterprise/guides/slack-trigger",
+              "enterprise/guides/team-management",
+              "enterprise/guides/webhook-automation",
+              "enterprise/guides/human-in-the-loop",
+              "enterprise/guides/zapier-trigger"
+            ]
+          },
+          {
+            "group": "Resources",
+            "pages": [
+              "enterprise/resources/frequently-asked-questions"
+            ]
+          }
+        ]
+      },
+      {
+        "tab": "API Reference",
+        "groups": [
+          {
+            "group": "Getting Started",
+            "pages": [
+              "api-reference/introduction"
+            ]
+          },
+          {
+            "group": "Endpoints",
+            "openapi": "enterprise-api.yaml"
+          }
+        ]
+      },
       {
         "tab": "Examples",
         "groups": [
@@ -186,27 +339,59 @@
             ]
           }
         ]
+      },
+      {
+        "tab": "Releases",
+        "groups": [
+          {
+            "group": "Releases",
+            "pages": [
+              "changelog"
+            ]
+          }
+        ]
       }
     ],
     "global": {
       "anchors": [
         {
-          "anchor": "Community",
+          "anchor": "Website",
+          "href": "https://crewai.com",
+          "icon": "globe"
+        },
+        {
+          "anchor": "Forum",
           "href": "https://community.crewai.com",
           "icon": "discourse"
+        },
+        {
+          "anchor": "Crew GPT",
+          "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
+          "icon": "robot"
+        },
+        {
+          "anchor": "Get Help",
+          "href": "mailto:support@crewai.com",
+          "icon": "headset"
         }
       ]
     }
   },
   "logo": {
-    "light": "crew_only_logo.png",
-    "dark": "crew_only_logo.png"
+    "light": "images/crew_only_logo.png",
+    "dark": "images/crew_only_logo.png"
   },
   "appearance": {
     "default": "dark",
     "strict": false
   },
   "navbar": {
+    "links": [
+      {
+        "label": "Start Cloud Trial",
+        "href": "https://app.crewai.com"
+      }
+    ],
     "primary": {
       "type": "github",
       "href": "https://github.com/crewAIInc/crewAI"
@@ -215,8 +400,23 @@
   "search": {
     "prompt": "Search CrewAI docs"
   },
+  "api": {
+    "baseUrl": "https://your-actual-crew-name.crewai.com",
+    "auth": {
+      "method": "bearer",
+      "name": "Authorization"
+    },
+    "playground": {
+      "mode": "simple"
+    }
+  },
   "seo": {
-    "indexing": "navigable"
+    "indexing": "all"
+  },
+  "errors": {
+    "404": {
+      "redirect": true
+    }
   },
   "footer": {
     "socials": {
@@ -228,4 +428,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/enterprise-api.yaml

@@ -0,0 +1,434 @@
+openapi: 3.0.3
+info:
+  title: CrewAI Enterprise API
+  description: |
+    REST API for interacting with your deployed CrewAI crews on CrewAI Enterprise.
+    
+    ## Getting Started
+    
+    1. **Find your crew URL**: Get your unique crew URL from the CrewAI Enterprise dashboard
+    2. **Copy examples**: Use the code examples from each endpoint page as templates
+    3. **Replace placeholders**: Update URLs and tokens with your actual values
+    4. **Test with your tools**: Use cURL, Postman, or your preferred API client
+    
+    ## Authentication
+    
+    All API requests require a bearer token for authentication. There are two types of tokens:
+    
+    - **Bearer Token**: Organization-level token for full crew operations
+    - **User Bearer Token**: User-scoped token for individual access with limited permissions
+    
+    You can find your bearer tokens in the Status tab of your crew's detail page in the CrewAI Enterprise dashboard.
+    
+    ## Reference Documentation
+    
+    This documentation provides comprehensive examples for each endpoint:
+    
+    - **Request formats** with all required and optional parameters
+    - **Response examples** for success and error scenarios
+    - **Code samples** in multiple programming languages
+    - **Authentication patterns** with proper Bearer token usage
+    
+    Copy the examples and customize them with your actual crew URL and authentication tokens.
+    
+    ## Workflow
+    
+    1. **Discover inputs** using `GET /inputs`
+    2. **Start execution** using `POST /kickoff`  
+    3. **Monitor progress** using `GET /status/{kickoff_id}`
+  version: 1.0.0
+  contact:
+    name: CrewAI Support
+    email: support@crewai.com
+    url: https://crewai.com
+servers:
+  - url: https://your-actual-crew-name.crewai.com
+    description: Replace with your actual deployed crew URL from the CrewAI Enterprise dashboard
+  - url: https://my-travel-crew.crewai.com
+    description: Example travel planning crew (replace with your URL)
+  - url: https://content-creation-crew.crewai.com
+    description: Example content creation crew (replace with your URL)
+  - url: https://research-assistant-crew.crewai.com
+    description: Example research assistant crew (replace with your URL)
+security:
+  - BearerAuth: []
+paths:
+  /inputs:
+    get:
+      summary: Get Required Inputs
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Retrieves the list of all required input parameters that your crew expects for execution.
+        Use this endpoint to discover what inputs you need to provide when starting a crew execution.
+      operationId: getRequiredInputs
+      responses:
+        '200':
+          description: Successfully retrieved required inputs
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  inputs:
+                    type: array
+                    items:
+                      type: string
+                    description: Array of required input parameter names
+                    example: ["budget", "interests", "duration", "age"]
+              examples:
+                travel_crew:
+                  summary: Travel planning crew inputs
+                  value:
+                    inputs: ["budget", "interests", "duration", "age"]
+                outreach_crew:
+                  summary: Outreach crew inputs  
+                  value:
+                    inputs: ["name", "title", "company", "industry", "our_product", "linkedin_url"]
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          $ref: '#/components/responses/NotFoundError'
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+  /kickoff:
+    post:
+      summary: Start Crew Execution
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Initiates a new crew execution with the provided inputs. Returns a kickoff ID that can be used 
+        to track the execution progress and retrieve results.
+        
+        Crew executions can take anywhere from seconds to minutes depending on their complexity.
+        Consider using webhooks for real-time notifications or implement polling with the status endpoint.
+      operationId: startCrewExecution
+      requestBody:
+        required: true
+        content:
+          application/json:
+            schema:
+              type: object
+              required:
+                - inputs
+              properties:
+                inputs:
+                  type: object
+                  description: Key-value pairs of all required inputs for your crew
+                  additionalProperties:
+                    type: string
+                  example:
+                    budget: "1000 USD"
+                    interests: "games, tech, ai, relaxing hikes, amazing food"
+                    duration: "7 days"
+                    age: "35"
+                meta:
+                  type: object
+                  description: Additional metadata to pass to the crew
+                  additionalProperties: true
+                  example:
+                    requestId: "user-request-12345"
+                    source: "mobile-app"
+                taskWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each task completion
+                  example: "https://your-server.com/webhooks/task"
+                stepWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed after each agent thought/action
+                  example: "https://your-server.com/webhooks/step"
+                crewWebhookUrl:
+                  type: string
+                  format: uri
+                  description: Callback URL executed when the crew execution completes
+                  example: "https://your-server.com/webhooks/crew"
+            examples:
+              travel_planning:
+                summary: Travel planning crew
+                value:
+                  inputs:
+                    budget: "1000 USD"
+                    interests: "games, tech, ai, relaxing hikes, amazing food"
+                    duration: "7 days" 
+                    age: "35"
+                  meta:
+                    requestId: "travel-req-123"
+                    source: "web-app"
+              outreach_campaign:
+                summary: Outreach crew with webhooks
+                value:
+                  inputs:
+                    name: "John Smith"
+                    title: "CTO"
+                    company: "TechCorp"
+                    industry: "Software"
+                    our_product: "AI Development Platform"
+                    linkedin_url: "https://linkedin.com/in/johnsmith"
+                  taskWebhookUrl: "https://api.example.com/webhooks/task"
+                  crewWebhookUrl: "https://api.example.com/webhooks/crew"
+      responses:
+        '200':
+          description: Crew execution started successfully
+          content:
+            application/json:
+              schema:
+                type: object
+                properties:
+                  kickoff_id:
+                    type: string
+                    format: uuid
+                    description: Unique identifier for tracking this execution
+                    example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+        '400':
+          description: Invalid request body or missing required inputs
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '422':
+          description: Validation error - ensure all required inputs are provided
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/ValidationError'
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+  /status/{kickoff_id}:
+    get:
+      summary: Get Execution Status
+      description: |
+        **📋 Reference Example Only** - *This shows the request format. To test with your actual crew, copy the cURL example and replace the URL + token with your real values.*
+        
+        Retrieves the current status and results of a crew execution using its kickoff ID.
+        
+        The response structure varies depending on the execution state:
+        - **running**: Execution in progress with current task info
+        - **completed**: Execution finished with full results
+        - **error**: Execution failed with error details
+      operationId: getExecutionStatus
+      parameters:
+        - name: kickoff_id
+          in: path
+          required: true
+          description: The kickoff ID returned from the /kickoff endpoint
+          schema:
+            type: string
+            format: uuid
+            example: "abcd1234-5678-90ef-ghij-klmnopqrstuv"
+      responses:
+        '200':
+          description: Successfully retrieved execution status
+          content:
+            application/json:
+              schema:
+                oneOf:
+                  - $ref: '#/components/schemas/ExecutionRunning'
+                  - $ref: '#/components/schemas/ExecutionCompleted'
+                  - $ref: '#/components/schemas/ExecutionError'
+              examples:
+                running:
+                  summary: Execution in progress
+                  value:
+                    status: "running"
+                    current_task: "research_task"
+                    progress:
+                      completed_tasks: 1
+                      total_tasks: 3
+                completed:
+                  summary: Execution completed successfully
+                  value:
+                    status: "completed"
+                    result:
+                      output: "Comprehensive travel itinerary for 7 days in Japan focusing on tech culture..."
+                      tasks:
+                        - task_id: "research_task"
+                          output: "Research findings on tech destinations in Japan..."
+                          agent: "Travel Researcher"
+                          execution_time: 45.2
+                        - task_id: "planning_task"
+                          output: "7-day detailed itinerary with activities and recommendations..."
+                          agent: "Trip Planner"
+                          execution_time: 62.8
+                    execution_time: 108.5
+                error:
+                  summary: Execution failed
+                  value:
+                    status: "error"
+                    error: "Task execution failed: Invalid API key for external service"
+                    execution_time: 23.1
+        '401':
+          $ref: '#/components/responses/UnauthorizedError'
+        '404':
+          description: Kickoff ID not found
+          content:
+            application/json:
+              schema:
+                $ref: '#/components/schemas/Error'
+              example:
+                error: "Execution not found"
+                message: "No execution found with ID: abcd1234-5678-90ef-ghij-klmnopqrstuv"
+        '500':
+          $ref: '#/components/responses/ServerError'
+
+components:
+  securitySchemes:
+    BearerAuth:
+      type: http
+      scheme: bearer
+      description: |
+        **📋 Reference Documentation** - *The tokens shown in examples are placeholders for reference only.*
+        
+        Use your actual Bearer Token or User Bearer Token from the CrewAI Enterprise dashboard for real API calls.
+        
+        **Bearer Token**: Organization-level access for full crew operations
+        **User Bearer Token**: User-scoped access with limited permissions
+
+  schemas:
+    ExecutionRunning:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["running"]
+          example: "running"
+        current_task:
+          type: string
+          description: Name of the currently executing task
+          example: "research_task"
+        progress:
+          type: object
+          properties:
+            completed_tasks:
+              type: integer
+              description: Number of completed tasks
+              example: 1
+            total_tasks:
+              type: integer  
+              description: Total number of tasks in the crew
+              example: 3
+
+    ExecutionCompleted:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["completed"]
+          example: "completed"
+        result:
+          type: object
+          properties:
+            output:
+              type: string
+              description: Final output from the crew execution
+              example: "Comprehensive travel itinerary..."
+            tasks:
+              type: array
+              items:
+                $ref: '#/components/schemas/TaskResult'
+        execution_time:
+          type: number
+          description: Total execution time in seconds
+          example: 108.5
+
+    ExecutionError:
+      type: object
+      properties:
+        status:
+          type: string
+          enum: ["error"]
+          example: "error"
+        error:
+          type: string
+          description: Error message describing what went wrong
+          example: "Task execution failed: Invalid API key"
+        execution_time:
+          type: number
+          description: Time until error occurred in seconds
+          example: 23.1
+
+    TaskResult:
+      type: object
+      properties:
+        task_id:
+          type: string
+          description: Unique identifier for the task
+          example: "research_task"
+        output:
+          type: string
+          description: Output generated by this task
+          example: "Research findings..."
+        agent:
+          type: string
+          description: Name of the agent that executed this task
+          example: "Travel Researcher"
+        execution_time:
+          type: number
+          description: Time taken to execute this task in seconds
+          example: 45.2
+
+    Error:
+      type: object
+      properties:
+        error:
+          type: string
+          description: Error type or title
+          example: "Authentication Error"
+        message:
+          type: string
+          description: Detailed error message
+          example: "Invalid bearer token provided"
+
+    ValidationError:
+      type: object
+      properties:
+        error:
+          type: string
+          example: "Validation Error"
+        message:
+          type: string
+          example: "Missing required inputs"
+        details:
+          type: object
+          properties:
+            missing_inputs:
+              type: array
+              items:
+                type: string
+              example: ["budget", "interests"]
+
+  responses:
+    UnauthorizedError:
+      description: Authentication failed - check your bearer token
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Unauthorized"
+            message: "Invalid or missing bearer token"
+
+    NotFoundError:
+      description: Resource not found
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Not Found"
+            message: "The requested resource was not found"
+
+    ServerError:
+      description: Internal server error
+      content:
+        application/json:
+          schema:
+            $ref: '#/components/schemas/Error'
+          example:
+            error: "Internal Server Error" 
+            message: "An unexpected error occurred" 

docs/enterprise/features/hallucination-guardrail.mdx

@@ -0,0 +1,250 @@
+---
+title: Hallucination Guardrail
+description: "Prevent and detect AI hallucinations in your CrewAI tasks"
+icon: "shield-check"
+---
+
+## Overview
+
+The Hallucination Guardrail is an enterprise feature that validates AI-generated content to ensure it's grounded in facts and doesn't contain hallucinations. It analyzes task outputs against reference context and provides detailed feedback when potentially hallucinated content is detected.
+
+## What are Hallucinations?
+
+AI hallucinations occur when language models generate content that appears plausible but is factually incorrect or not supported by the provided context. The Hallucination Guardrail helps prevent these issues by:
+
+- Comparing outputs against reference context
+- Evaluating faithfulness to source material
+- Providing detailed feedback on problematic content
+- Supporting custom thresholds for validation strictness
+
+## Basic Usage
+
+### Setting Up the Guardrail
+
+```python
+from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
+from crewai import LLM
+
+# Basic usage - will use task's expected_output as context
+guardrail = HallucinationGuardrail(
+    llm=LLM(model="gpt-4o-mini")
+)
+
+# With explicit reference context
+context_guardrail = HallucinationGuardrail(
+    context="AI helps with various tasks including analysis and generation.",
+    llm=LLM(model="gpt-4o-mini")
+)
+```
+
+### Adding to Tasks
+
+```python
+from crewai import Task
+
+# Create your task with the guardrail
+task = Task(
+    description="Write a summary about AI capabilities",
+    expected_output="A factual summary based on the provided context",
+    agent=my_agent,
+    guardrail=guardrail  # Add the guardrail to validate output
+)
+```
+
+## Advanced Configuration
+
+### Custom Threshold Validation
+
+For stricter validation, you can set a custom faithfulness threshold (0-10 scale):
+
+```python
+# Strict guardrail requiring high faithfulness score
+strict_guardrail = HallucinationGuardrail(
+    context="Quantum computing uses qubits that exist in superposition states.",
+    llm=LLM(model="gpt-4o-mini"),
+    threshold=8.0  # Requires score >= 8 to pass validation
+)
+```
+
+### Including Tool Response Context
+
+When your task uses tools, you can include tool responses for more accurate validation:
+
+```python
+# Guardrail with tool response context
+weather_guardrail = HallucinationGuardrail(
+    context="Current weather information for the requested location",
+    llm=LLM(model="gpt-4o-mini"),
+    tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
+)
+```
+
+## How It Works
+
+### Validation Process
+
+1. **Context Analysis**: The guardrail compares task output against the provided reference context
+2. **Faithfulness Scoring**: Uses an internal evaluator to assign a faithfulness score (0-10)
+3. **Verdict Determination**: Determines if content is faithful or contains hallucinations
+4. **Threshold Checking**: If a custom threshold is set, validates against that score
+5. **Feedback Generation**: Provides detailed reasons when validation fails
+
+### Validation Logic
+
+- **Default Mode**: Uses verdict-based validation (FAITHFUL vs HALLUCINATED)
+- **Threshold Mode**: Requires faithfulness score to meet or exceed the specified threshold
+- **Error Handling**: Gracefully handles evaluation errors and provides informative feedback
+
+## Guardrail Results
+
+The guardrail returns structured results indicating validation status:
+
+```python
+# Example of guardrail result structure
+{
+    "valid": False,
+    "feedback": "Content appears to be hallucinated (score: 4.2/10, verdict: HALLUCINATED). The output contains information not supported by the provided context."
+}
+```
+
+### Result Properties
+
+- **valid**: Boolean indicating whether the output passed validation
+- **feedback**: Detailed explanation when validation fails, including:
+  - Faithfulness score
+  - Verdict classification
+  - Specific reasons for failure
+
+## Integration with Task System
+
+### Automatic Validation
+
+When a guardrail is added to a task, it automatically validates the output before the task is marked as complete:
+
+```python
+# Task output validation flow
+task_output = agent.execute_task(task)
+validation_result = guardrail(task_output)
+
+if validation_result.valid:
+    # Task completes successfully
+    return task_output
+else:
+    # Task fails with validation feedback
+    raise ValidationError(validation_result.feedback)
+```
+
+### Event Tracking
+
+The guardrail integrates with CrewAI's event system to provide observability:
+
+- **Validation Started**: When guardrail evaluation begins
+- **Validation Completed**: When evaluation finishes with results
+- **Validation Failed**: When technical errors occur during evaluation
+
+## Best Practices
+
+### Context Guidelines
+
+<Steps>
+  <Step title="Provide Comprehensive Context">
+    Include all relevant factual information that the AI should base its output on:
+
+    ```python
+    context = """
+    Company XYZ was founded in 2020 and specializes in renewable energy solutions.
+    They have 150 employees and generated $50M revenue in 2023.
+    Their main products include solar panels and wind turbines.
+    """
+    ```
+  </Step>
+
+  <Step title="Keep Context Relevant">
+    Only include information directly related to the task to avoid confusion:
+
+    ```python
+    # Good: Focused context
+    context = "The current weather in New York is 18°C with light rain."
+
+    # Avoid: Unrelated information
+    context = "The weather is 18°C. The city has 8 million people. Traffic is heavy."
+    ```
+  </Step>
+
+  <Step title="Update Context Regularly">
+    Ensure your reference context reflects current, accurate information.
+  </Step>
+</Steps>
+
+### Threshold Selection
+
+<Steps>
+  <Step title="Start with Default Validation">
+    Begin without custom thresholds to understand baseline performance.
+  </Step>
+
+  <Step title="Adjust Based on Requirements">
+    - **High-stakes content**: Use threshold 8-10 for maximum accuracy
+    - **General content**: Use threshold 6-7 for balanced validation
+    - **Creative content**: Use threshold 4-5 or default verdict-based validation
+  </Step>
+
+  <Step title="Monitor and Iterate">
+    Track validation results and adjust thresholds based on false positives/negatives.
+  </Step>
+</Steps>
+
+## Performance Considerations
+
+### Impact on Execution Time
+
+- **Validation Overhead**: Each guardrail adds ~1-3 seconds per task
+- **LLM Efficiency**: Choose efficient models for evaluation (e.g., gpt-4o-mini)
+
+### Cost Optimization
+
+- **Model Selection**: Use smaller, efficient models for guardrail evaluation
+- **Context Size**: Keep reference context concise but comprehensive
+- **Caching**: Consider caching validation results for repeated content
+
+## Troubleshooting
+
+<Accordion title="Validation Always Fails">
+  **Possible Causes:**
+  - Context is too restrictive or unrelated to task output
+  - Threshold is set too high for the content type
+  - Reference context contains outdated information
+
+  **Solutions:**
+  - Review and update context to match task requirements
+  - Lower threshold or use default verdict-based validation
+  - Ensure context is current and accurate
+</Accordion>
+
+<Accordion title="False Positives (Valid Content Marked Invalid)">
+  **Possible Causes:**
+  - Threshold too high for creative or interpretive tasks
+  - Context doesn't cover all valid aspects of the output
+  - Evaluation model being overly conservative
+
+  **Solutions:**
+  - Lower threshold or use default validation
+  - Expand context to include broader acceptable content
+  - Test with different evaluation models
+</Accordion>
+
+<Accordion title="Evaluation Errors">
+  **Possible Causes:**
+  - Network connectivity issues
+  - LLM model unavailable or rate limited
+  - Malformed task output or context
+
+  **Solutions:**
+  - Check network connectivity and LLM service status
+  - Implement retry logic for transient failures
+  - Validate task output format before guardrail evaluation
+</Accordion>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with hallucination guardrail configuration or troubleshooting.
+</Card>

docs/enterprise/features/integrations.mdx

@@ -0,0 +1,185 @@
+---
+title: Integrations
+description: "Connected applications for your agents to take actions."
+icon: "plug"
+---
+
+## Overview
+
+Enable your agents to authenticate with any OAuth enabled provider and take actions. From Salesforce and HubSpot to Google and GitHub, we've got you covered with 16+ integrated services.
+
+<Frame>
+  ![Integrations](/images/enterprise/crew_connectors.png)
+</Frame>
+
+## Supported Integrations
+
+### **Communication & Collaboration**
+- **Gmail** - Manage emails and drafts
+- **Slack** - Workspace notifications and alerts
+- **Microsoft** - Office 365 and Teams integration
+
+### **Project Management**
+- **Jira** - Issue tracking and project management
+- **ClickUp** - Task and productivity management
+- **Asana** - Team task and project coordination
+- **Notion** - Page and database management
+- **Linear** - Software project and bug tracking
+- **GitHub** - Repository and issue management
+
+### **Customer Relationship Management**
+- **Salesforce** - CRM account and opportunity management
+- **HubSpot** - Sales pipeline and contact management
+- **Zendesk** - Customer support ticket management
+
+### **Business & Finance**
+- **Stripe** - Payment processing and customer management
+- **Shopify** - E-commerce store and product management
+
+### **Productivity & Storage**
+- **Google Sheets** - Spreadsheet data synchronization
+- **Google Calendar** - Event and schedule management
+- **Box** - File storage and document management
+
+and more to come!
+
+## Prerequisites
+
+Before using Authentication Integrations, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account. You can get started with a free trial.
+
+
+## Setting Up Integrations
+
+### 1. Connect Your Account
+
+1. Navigate to [CrewAI Enterprise](https://app.crewai.com)
+2. Go to **Integrations** tab - https://app.crewai.com/crewai_plus/connectors
+3. Click **Connect** on your desired service from the Authentication Integrations section
+4. Complete the OAuth authentication flow
+5. Grant necessary permissions for your use case
+6. Get your Enterprise Token from your [CrewAI Enterprise](https://app.crewai.com) account page - https://app.crewai.com/crewai_plus/settings/account
+
+<Frame>
+  ![Integrations](/images/enterprise/enterprise_action_auth_token.png)
+</Frame>
+
+### 2. Install Integration Tools
+
+All you need is the latest version of `crewai-tools` package.
+
+```bash
+uv add crewai-tools
+```
+
+## Usage Examples
+
+### Basic Usage
+<Tip>
+  All the services you are authenticated into will be available as tools. So all you need to do is add the `CrewaiEnterpriseTools` to your agent and you are good to go.
+</Tip>
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Gmail tool will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+# print the tools
+print(enterprise_tools)
+
+# Create an agent with Gmail capabilities
+email_agent = Agent(
+    role="Email Manager",
+    goal="Manage and organize email communications",
+    backstory="An AI assistant specialized in email management and communication.",
+    tools=enterprise_tools
+)
+
+# Task to send an email
+email_task = Task(
+    description="Draft and send a follow-up email to john@example.com about the project update",
+    agent=email_agent,
+    expected_output="Confirmation that email was sent successfully"
+)
+
+# Run the task
+crew = Crew(
+    agents=[email_agent],
+    tasks=[email_task]
+)
+
+# Run the crew
+crew.kickoff()
+```
+
+### Filtering Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    actions_list=["gmail_find_email"] # only gmail_find_email tool will be available
+)
+gmail_tool = enterprise_tools[0]
+
+gmail_agent = Agent(
+    role="Gmail Manager",
+    goal="Manage gmail communications and notifications",
+    backstory="An AI assistant that helps coordinate gmail communications.",
+    tools=[gmail_tool]
+)
+
+notification_task = Task(
+    description="Find the email from john@example.com",
+    agent=gmail_agent,
+    expected_output="Email found from john@example.com"
+)
+
+# Run the task
+crew = Crew(
+    agents=[slack_agent],
+    tasks=[notification_task]
+)
+```
+
+## Best Practices
+
+### Security
+- **Principle of Least Privilege**: Only grant the minimum permissions required for your agents' tasks
+- **Regular Audits**: Periodically review connected integrations and their permissions
+- **Secure Credentials**: Never hardcode credentials; use CrewAI's secure authentication flow
+
+
+### Filtering Tools
+On a deployed crew, you can specify which actions are avialbel for each integration from the settings page of the service you connected to.
+
+<Frame>
+  ![Integrations](/images/enterprise/filtering_enterprise_action_tools.png)
+</Frame>
+
+
+### Scoped Deployments for multi user organizations
+You can deploy your crew and scope each integration to a specific user. For example, a crew that connects to google can use a specific user's gmail account.
+
+<Tip>
+  This is useful for multi user organizations where you want to scope the integration to a specific user.
+</Tip>
+
+
+Use the `user_bearer_token` to scope the integration to a specific user so that when the crew is kicked off, it will use the user's bearer token to authenticate with the integration. If user is not logged in, then the crew will not use any connected integrations. Use the default bearer token to authenticate with the integrations thats deployed with the crew.
+
+<Frame>
+  ![Integrations](/images/enterprise/user_bearer_token.png)
+</Frame>
+
+
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with integration setup or troubleshooting.
+</Card>

docs/enterprise/features/tool-repository.mdx

@@ -0,0 +1,107 @@
+---
+title: Tool Repository
+description: "Using the Tool Repository to manage your tools"
+icon: "toolbox"
+---
+
+## Overview
+
+The Tool Repository is a package manager for CrewAI tools. It allows users to publish, install, and manage tools that integrate with CrewAI crews and flows.
+
+Tools can be:
+
+- **Private**: accessible only within your organization (default)
+- **Public**: accessible to all CrewAI users if published with the `--public` flag
+
+The repository is not a version control system. Use Git to track code changes and enable collaboration.
+
+## Prerequisites
+
+Before using the Tool Repository, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account
+- [CrewAI CLI](https://docs.crewai.com/concepts/cli#cli) installed
+- uv>=0.5.0 installed. Check out [how to upgrade](https://docs.astral.sh/uv/getting-started/installation/#upgrading-uv)
+- [Git](https://git-scm.com) installed and configured
+- Access permissions to publish or install tools in your CrewAI Enterprise organization
+
+## Installing Tools
+
+To install a tool:
+
+```bash
+crewai tool install <tool-name>
+```
+
+This installs the tool and adds it to `pyproject.toml`.
+
+## Creating and Publishing Tools
+
+To create a new tool project:
+
+```bash
+crewai tool create <tool-name>
+```
+
+This generates a scaffolded tool project locally.
+
+After making changes, initialize a Git repository and commit the code:
+
+```bash
+git init
+git add .
+git commit -m "Initial version"
+```
+
+To publish the tool:
+
+```bash
+crewai tool publish
+```
+
+By default, tools are published as private. To make a tool public:
+
+```bash
+crewai tool publish --public
+```
+
+For more details on how to build tools, see [Creating your own tools](https://docs.crewai.com/concepts/tools#creating-your-own-tools).
+
+## Updating Tools
+
+To update a published tool:
+
+1. Modify the tool locally
+2. Update the version in `pyproject.toml` (e.g., from `0.1.0` to `0.1.1`)
+3. Commit the changes and publish
+
+```bash
+git commit -m "Update version to 0.1.1"
+crewai tool publish
+```
+
+## Deleting Tools
+
+To delete a tool:
+
+1. Go to [CrewAI Enterprise](https://app.crewai.com)
+2. Navigate to **Tools**
+3. Select the tool
+4. Click **Delete**
+
+<Warning>
+Deletion is permanent. Deleted tools cannot be restored or re-installed.
+</Warning>
+
+## Security Checks
+
+Every published version undergoes automated security checks, and are only available to install after they pass. 
+
+You can check the security check status of a tool at:
+
+`CrewAI Enterprise > Tools > Your Tool > Versions`
+
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with API integration or troubleshooting.
+</Card>

docs/enterprise/features/traces.mdx

@@ -0,0 +1,146 @@
+---
+title: Traces
+description: "Using Traces to monitor your Crews"
+icon: "timeline"
+---
+
+## Overview
+
+Traces provide comprehensive visibility into your crew executions, helping you monitor performance, debug issues, and optimize your AI agent workflows.
+
+## What are Traces?
+
+Traces in CrewAI Enterprise are detailed execution records that capture every aspect of your crew's operation, from initial inputs to final outputs. They record:
+
+- Agent thoughts and reasoning
+- Task execution details
+- Tool usage and outputs
+- Token consumption metrics
+- Execution times
+- Cost estimates
+
+<Frame>
+  ![Traces Overview](/images/enterprise/traces-overview.png)
+</Frame>
+
+## Accessing Traces
+
+<Steps>
+  <Step title="Navigate to the Traces Tab">
+    Once in your CrewAI Enterprise dashboard, click on the **Traces** to view all execution records.
+  </Step>
+  
+  <Step title="Select an Execution">
+    You'll see a list of all crew executions, sorted by date. Click on any execution to view its detailed trace.
+  </Step>
+</Steps>
+
+## Understanding the Trace Interface
+
+The trace interface is divided into several sections, each providing different insights into your crew's execution:
+
+### 1. Execution Summary
+
+The top section displays high-level metrics about the execution:
+
+- **Total Tokens**: Number of tokens consumed across all tasks
+- **Prompt Tokens**: Tokens used in prompts to the LLM
+- **Completion Tokens**: Tokens generated in LLM responses
+- **Requests**: Number of API calls made
+- **Execution Time**: Total duration of the crew run
+- **Estimated Cost**: Approximate cost based on token usage
+
+<Frame>
+  ![Execution Summary](/images/enterprise/trace-summary.png)
+</Frame>
+
+### 2. Tasks & Agents
+
+This section shows all tasks and agents that were part of the crew execution:
+
+- Task name and agent assignment
+- Agents and LLMs used for each task
+- Status (completed/failed)
+- Individual execution time of the task
+
+<Frame>
+  ![Task List](/images/enterprise/trace-tasks.png)
+</Frame>
+
+### 3. Final Output
+
+Displays the final result produced by the crew after all tasks are completed.
+
+<Frame>
+  ![Final Output](/images/enterprise/final-output.png)
+</Frame>
+
+### 4. Execution Timeline
+
+A visual representation of when each task started and ended, helping you identify bottlenecks or parallel execution patterns.
+
+<Frame>
+  ![Execution Timeline](/images/enterprise/trace-timeline.png)
+</Frame>
+
+### 5. Detailed Task View
+
+When you click on a specific task in the timeline or task list, you'll see:
+
+<Frame>
+  ![Detailed Task View](/images/enterprise/trace-detailed-task.png)
+</Frame>
+
+- **Task Key**: Unique identifier for the task
+- **Task ID**: Technical identifier in the system
+- **Status**: Current state (completed/running/failed)
+- **Agent**: Which agent performed the task
+- **LLM**: Language model used for this task
+- **Start/End Time**: When the task began and completed
+- **Execution Time**: Duration of this specific task
+- **Task Description**: What the agent was instructed to do
+- **Expected Output**: What output format was requested
+- **Input**: Any input provided to this task from previous tasks
+- **Output**: The actual result produced by the agent
+
+
+## Using Traces for Debugging
+
+Traces are invaluable for troubleshooting issues with your crews:
+
+<Steps>
+  <Step title="Identify Failure Points">
+    When a crew execution doesn't produce the expected results, examine the trace to find where things went wrong. Look for:
+    
+    - Failed tasks
+    - Unexpected agent decisions
+    - Tool usage errors
+    - Misinterpreted instructions
+
+    <Frame>
+      ![Failure Points](/images/enterprise/failure.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Optimize Performance">
+    Use execution metrics to identify performance bottlenecks:
+    
+    - Tasks that took longer than expected
+    - Excessive token usage
+    - Redundant tool operations
+    - Unnecessary API calls
+  </Step>
+  
+  <Step title="Improve Cost Efficiency">
+    Analyze token usage and cost estimates to optimize your crew's efficiency:
+    
+    - Consider using smaller models for simpler tasks
+    - Refine prompts to be more concise
+    - Cache frequently accessed information
+    - Structure tasks to minimize redundant operations
+  </Step>
+</Steps>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with trace analysis or any other CrewAI Enterprise features.
+</Card>

docs/enterprise/features/webhook-streaming.mdx

@@ -0,0 +1,82 @@
+---
+title: Webhook Streaming
+description: "Using Webhook Streaming to stream events to your webhook"
+icon: "webhook"
+---
+
+## Overview
+
+Enterprise Event Streaming lets you receive real-time webhook updates about your crews and flows deployed to 
+CrewAI Enterprise, such as model calls, tool usage, and flow steps.
+
+## Usage
+
+When using the Kickoff API, include a `webhooks` object to your request, for example:
+
+```json
+{
+  "inputs": {"foo": "bar"},
+  "webhooks": {
+    "events": ["crew_kickoff_started", "llm_call_started"],
+    "url": "https://your.endpoint/webhook",
+    "realtime": false,
+    "authentication": {
+      "strategy": "bearer",
+      "token": "my-secret-token"
+    }
+  }
+}
+```
+
+If `realtime` is set to `true`, each event is delivered individually and immediately, at the cost of crew/flow performance.
+
+## Webhook Format
+
+Each webhook sends a list of events:
+
+```json
+{
+  "events": [
+    {
+      "id": "event-id",
+      "execution_id": "crew-run-id",
+      "timestamp": "2025-02-16T10:58:44.965Z",
+      "type": "llm_call_started",
+      "data": {
+        "model": "gpt-4",
+        "messages": [
+          {"role": "system", "content": "You are an assistant."},
+          {"role": "user", "content": "Summarize this article."}
+        ]
+      }
+    }
+  ]
+}
+```
+
+The `data` object structure varies by event type. Refer to the [event list](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) on GitHub.
+
+As requests are sent over HTTP, the order of events can't be guaranteed. If you need ordering, use the `timestamp` field.
+
+## Supported Events
+
+CrewAI supports both system events and custom events in Enterprise Event Streaming. These events are sent to your configured webhook endpoint during crew and flow execution.
+
+- `crew_kickoff_started`
+- `crew_step_started`
+- `crew_step_completed`
+- `crew_execution_completed`
+- `llm_call_started`
+- `llm_call_completed`
+- `tool_usage_started`
+- `tool_usage_completed`
+- `crew_test_failed`
+- *...and others*
+
+Event names match the internal event bus. See [GitHub source](https://github.com/crewAIInc/crewAI/tree/main/src/crewai/utilities/events) for the full list.
+
+You can emit your own custom events, and they will be delivered through the webhook stream alongside system events.
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with webhook integration or troubleshooting.
+</Card>

docs/enterprise/guides/azure-openai-setup.mdx

@@ -0,0 +1,51 @@
+---
+title: "Azure OpenAI Setup"
+description: "Configure Azure OpenAI with Crew Studio for enterprise LLM connections"
+icon: "microsoft"
+---
+
+This guide walks you through connecting Azure OpenAI with Crew Studio for seamless enterprise AI operations.
+
+## Setup Process
+
+<Steps>
+    <Step title="Access Azure OpenAI Studio">
+        1. In Azure, go to `Azure AI Services > select your deployment > open Azure OpenAI Studio`.
+        2. On the left menu, click `Deployments`. If you don't have one, create a deployment with your desired model.
+        3. Once created, select your deployment and locate the `Target URI` and `Key` on the right side of the page. Keep this page open, as you'll need this information.
+            <Frame>
+                <img src="/images/enterprise/azure-openai-studio.png" alt="Azure OpenAI Studio" />
+            </Frame>
+    </Step>
+
+    <Step title="Configure CrewAI Enterprise Connection">
+        4. In another tab, open `CrewAI Enterprise > LLM Connections`. Name your LLM Connection, select Azure as the provider, and choose the same model you selected in Azure.
+        5. On the same page, add environment variables from step 3:
+            - One named `AZURE_DEPLOYMENT_TARGET_URL` (using the Target URI). The URL should look like this: https://your-deployment.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview
+            - Another named `AZURE_API_KEY` (using the Key).
+        6. Click `Add Connection` to save your LLM Connection.
+    </Step>
+
+    <Step title="Set Default Configuration">
+        7. In `CrewAI Enterprise > Settings > Defaults > Crew Studio LLM Settings`, set the new LLM Connection and model as defaults.
+    </Step>
+
+    <Step title="Configure Network Access">
+        8. Ensure network access settings:
+            - In Azure, go to `Azure OpenAI > select your deployment`.
+            - Navigate to `Resource Management > Networking`.
+            - Ensure that `Allow access from all networks` is enabled. If this setting is restricted, CrewAI may be blocked from accessing your Azure OpenAI endpoint.
+    </Step>
+</Steps>
+
+## Verification
+
+You're all set! Crew Studio will now use your Azure OpenAI connection. Test the connection by creating a simple crew or task to ensure everything is working properly.
+
+## Troubleshooting
+
+If you encounter issues:
+- Verify the Target URI format matches the expected pattern
+- Check that the API key is correct and has proper permissions
+- Ensure network access is configured to allow CrewAI connections
+- Confirm the deployment model matches what you've configured in CrewAI 

docs/enterprise/guides/build-crew.mdx

@@ -0,0 +1,43 @@
+---
+title: "Build Crew"
+description: "A Crew is a group of agents that work together to complete a task."
+icon: "people-arrows"
+---
+
+## Overview
+
+[CrewAI Enterprise](https://app.crewai.com) streamlines the process of **creating**, **deploying**, and **managing** your AI agents in production environments.
+
+## Getting Started
+
+<iframe 
+    width="100%"
+    height="400"
+    src="https://www.youtube.com/embed/-kSOTtYzgEw"
+    title="Building Crews with CrewAI CLI" 
+    frameborder="0"
+    style={{ borderRadius: '10px' }}
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowfullscreen
+></iframe>
+
+### Installation and Setup
+
+<Card title="Follow Standard Installation" icon="wrench" href="/installation">
+  Follow our standard installation guide to set up CrewAI CLI and create your first project.
+</Card>
+
+### Building Your Crew
+
+<Card title="Quickstart Tutorial" icon="rocket" href="/quickstart">
+  Follow our quickstart guide to create your first agent crew using YAML configuration.
+</Card>
+
+## Support and Resources
+
+For Enterprise-specific support or questions, contact our dedicated support team at [support@crewai.com](mailto:support@crewai.com).
+
+
+<Card title="Schedule a Demo" icon="calendar" href="mailto:support@crewai.com">
+  Book time with our team to learn more about Enterprise features and how they can benefit your organization.
+</Card>

docs/enterprise/guides/deploy-crew.mdx

@@ -0,0 +1,293 @@
+---
+title: "Deploy Crew"
+description: "Deploying a Crew on CrewAI Enterprise"
+icon: "rocket"
+---
+
+<Note>
+After creating a crew locally or through Crew Studio, the next step is deploying it to the CrewAI Enterprise platform. This guide covers multiple deployment methods to help you choose the best approach for your workflow.
+</Note>
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="Crew Ready for Deployment" icon="users">
+    You should have a working crew either built locally or created through Crew Studio
+  </Card>
+  <Card title="GitHub Repository" icon="github">
+    Your crew code should be in a GitHub repository (for GitHub integration method)
+  </Card>
+</CardGroup>
+
+## Option 1: Deploy Using CrewAI CLI
+
+The CLI provides the fastest way to deploy locally developed crews to the Enterprise platform.
+
+<Steps>
+  <Step title="Install CrewAI CLI">
+    If you haven't already, install the CrewAI CLI:
+
+    ```bash
+    pip install crewai[tools]
+    ```
+
+    <Tip>
+    The CLI comes with the main CrewAI package, but the `[tools]` extra ensures you have all deployment dependencies.
+    </Tip>
+
+  </Step>
+
+  <Step title="Authenticate with the Enterprise Platform">
+    First, you need to authenticate your CLI with the CrewAI Enterprise platform:
+
+    ```bash
+    # If you already have a CrewAI Enterprise account
+    crewai login
+
+    # If you're creating a new account
+    crewai signup
+    ```
+
+    When you run either command, the CLI will:
+    1. Display a URL and a unique device code
+    2. Open your browser to the authentication page
+    3. Prompt you to confirm the device
+    4. Complete the authentication process
+
+    Upon successful authentication, you'll see a confirmation message in your terminal!
+
+  </Step>
+
+  <Step title="Create a Deployment">
+
+    From your project directory, run:
+
+    ```bash
+    crewai deploy create
+    ```
+
+    This command will:
+    1. Detect your GitHub repository information
+    2. Identify environment variables in your local `.env` file
+    3. Securely transfer these variables to the Enterprise platform
+    4. Create a new deployment with a unique identifier
+
+    On successful creation, you'll see a message like:
+    ```shell
+    Deployment created successfully!
+    Name: your_project_name
+    Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
+    Current Status: Deploy Enqueued
+    ```
+
+  </Step>
+
+  <Step title="Monitor Deployment Progress">
+
+    Track the deployment status with:
+
+    ```bash
+    crewai deploy status
+    ```
+
+    For detailed logs of the build process:
+
+    ```bash
+    crewai deploy logs
+    ```
+
+    <Tip>
+    The first deployment typically takes 10-15 minutes as it builds the container images. Subsequent deployments are much faster.
+    </Tip>
+
+  </Step>
+</Steps>
+
+## Additional CLI Commands
+
+The CrewAI CLI offers several commands to manage your deployments:
+
+  ```bash
+  # List all your deployments
+  crewai deploy list
+
+  # Get the status of your deployment
+  crewai deploy status
+
+  # View the logs of your deployment
+  crewai deploy logs
+
+  # Push updates after code changes
+  crewai deploy push
+
+  # Remove a deployment
+  crewai deploy remove <deployment_id>
+  ``` 
+
+## Option 2: Deploy Directly via Web Interface
+
+You can also deploy your crews directly through the CrewAI Enterprise web interface by connecting your GitHub account. This approach doesn't require using the CLI on your local machine.
+
+<Steps>
+
+  <Step title="Pushing to GitHub">
+
+  You need to push your crew to a GitHub repository. If you haven't created a crew yet, you can [follow this tutorial](/quickstart).
+
+  </Step>
+
+  <Step title="Connecting GitHub to CrewAI Enterprise">
+
+    1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+    2. Click on the button "Connect GitHub" 
+
+    <Frame>
+      ![Connect GitHub Button](/images/enterprise/connect-github.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Select the Repository">
+
+    After connecting your GitHub account, you'll be able to select which repository to deploy:
+
+    <Frame>
+      ![Select Repository](/images/enterprise/select-repo.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Set Environment Variables">
+
+    Before deploying, you'll need to set up your environment variables to connect to your LLM provider or other services:
+
+    1. You can add variables individually or in bulk
+    2. Enter your environment variables in `KEY=VALUE` format (one per line)
+
+    <Frame>
+      ![Set Environment Variables](/images/enterprise/set-env-variables.png)
+    </Frame>
+
+  </Step>
+
+  <Step title="Deploy Your Crew">
+
+    1. Click the "Deploy" button to start the deployment process
+    2. You can monitor the progress through the progress bar
+    3. The first deployment typically takes around 10-15 minutes; subsequent deployments will be faster
+
+    <Frame>
+      ![Deploy Progress](/images/enterprise/deploy-progress.png)
+    </Frame>
+
+    Once deployment is complete, you'll see:
+    - Your crew's unique URL
+    - A Bearer token to protect your crew API
+    - A "Delete" button if you need to remove the deployment
+
+  </Step>
+
+</Steps>
+
+## ⚠️ Environment Variable Security Requirements
+
+<Warning>
+**Important**: CrewAI Enterprise has security restrictions on environment variable names that can cause deployment failures if not followed.
+</Warning>
+
+### Blocked Environment Variable Patterns
+
+For security reasons, the following environment variable naming patterns are **automatically filtered** and will cause deployment issues:
+
+**Blocked Patterns:**
+- Variables ending with `_TOKEN` (e.g., `MY_API_TOKEN`)
+- Variables ending with `_PASSWORD` (e.g., `DB_PASSWORD`) 
+- Variables ending with `_SECRET` (e.g., `API_SECRET`)
+- Variables ending with `_KEY` in certain contexts
+
+**Specific Blocked Variables:**
+- `GITHUB_USER`, `GITHUB_TOKEN`
+- `AWS_REGION`, `AWS_DEFAULT_REGION` 
+- Various internal CrewAI system variables
+
+### Allowed Exceptions
+
+Some variables are explicitly allowed despite matching blocked patterns:
+- `AZURE_AD_TOKEN`
+- `AZURE_OPENAI_AD_TOKEN` 
+- `ENTERPRISE_ACTION_TOKEN`
+- `CREWAI_ENTEPRISE_TOOLS_TOKEN`
+
+### How to Fix Naming Issues
+
+If your deployment fails due to environment variable restrictions:
+
+```bash
+# ❌ These will cause deployment failures
+OPENAI_TOKEN=sk-...
+DATABASE_PASSWORD=mypassword
+API_SECRET=secret123
+
+# ✅ Use these naming patterns instead  
+OPENAI_API_KEY=sk-...
+DATABASE_CREDENTIALS=mypassword
+API_CONFIG=secret123
+```
+
+### Best Practices
+
+1. **Use standard naming conventions**: `PROVIDER_API_KEY` instead of `PROVIDER_TOKEN`
+2. **Test locally first**: Ensure your crew works with the renamed variables
+3. **Update your code**: Change any references to the old variable names
+4. **Document changes**: Keep track of renamed variables for your team
+
+<Tip>
+If you encounter deployment failures with cryptic environment variable errors, check your variable names against these patterns first.
+</Tip>
+
+### Interact with Your Deployed Crew
+
+Once deployment is complete, you can access your crew through:
+
+1. **REST API**: The platform generates a unique HTTPS endpoint with these key routes:
+   - `/inputs`: Lists the required input parameters
+   - `/kickoff`: Initiates an execution with provided inputs
+   - `/status/{kickoff_id}`: Checks the execution status
+
+2. **Web Interface**: Visit [app.crewai.com](https://app.crewai.com) to access:
+   - **Status tab**: View deployment information, API endpoint details, and authentication token
+   - **Run tab**: Visual representation of your crew's structure
+   - **Executions tab**: History of all executions
+   - **Metrics tab**: Performance analytics
+   - **Traces tab**: Detailed execution insights
+
+### Trigger an Execution
+
+From the Enterprise dashboard, you can:
+
+1. Click on your crew's name to open its details
+2. Select "Trigger Crew" from the management interface
+3. Enter the required inputs in the modal that appears
+4. Monitor progress as the execution moves through the pipeline
+
+### Monitoring and Analytics
+
+The Enterprise platform provides comprehensive observability features:
+
+- **Execution Management**: Track active and completed runs
+- **Traces**: Detailed breakdowns of each execution
+- **Metrics**: Token usage, execution times, and costs
+- **Timeline View**: Visual representation of task sequences
+
+### Advanced Features
+
+The Enterprise platform also offers:
+
+- **Environment Variables Management**: Securely store and manage API keys
+- **LLM Connections**: Configure integrations with various LLM providers
+- **Custom Tools Repository**: Create, share, and install tools
+- **Crew Studio**: Build crews through a chat interface without writing code
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with deployment issues or questions about the Enterprise platform.
+</Card>

docs/enterprise/guides/enable-crew-studio.mdx

@@ -0,0 +1,166 @@
+---
+title: "Enable Crew Studio"
+description: "Enabling Crew Studio on CrewAI Enterprise"
+icon: "comments"
+---
+
+<Tip>
+Crew Studio is a powerful **no-code/low-code** tool that allows you to quickly scaffold or build Crews through a conversational interface. 
+</Tip>
+
+## What is Crew Studio?
+
+Crew Studio is an innovative way to create AI agent crews without writing code. 
+
+<Frame>
+  ![Crew Studio Interface](/images/enterprise/crew-studio-interface.png)
+</Frame>
+
+With Crew Studio, you can:
+
+- Chat with the Crew Assistant to describe your problem
+- Automatically generate agents and tasks
+- Select appropriate tools
+- Configure necessary inputs
+- Generate downloadable code for customization
+- Deploy directly to the CrewAI Enterprise platform
+
+## Configuration Steps
+
+Before you can start using Crew Studio, you need to configure your LLM connections:
+
+<Steps>
+  <Step title="Set Up LLM Connection">
+    Go to the **LLM Connections** tab in your CrewAI Enterprise dashboard and create a new LLM connection.
+
+    <Note>
+      Feel free to use any LLM provider you want that is supported by CrewAI.
+    </Note>
+    
+    Configure your LLM connection:
+    
+    - Enter a `Connection Name` (e.g., `OpenAI`)
+    - Select your model provider: `openai` or `azure`
+    - Select models you'd like to use in your Studio-generated Crews
+      - We recommend at least `gpt-4o`, `o1-mini`, and `gpt-4o-mini`
+    - Add your API key as an environment variable:
+      - For OpenAI: Add `OPENAI_API_KEY` with your API key
+      - For Azure OpenAI: Refer to [this article](https://blog.crewai.com/configuring-azure-openai-with-crewai-a-comprehensive-guide/) for configuration details
+    - Click `Add Connection` to save your configuration
+    
+    <Frame>
+      ![LLM Connection Configuration](/images/enterprise/llm-connection-config.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Verify Connection Added">
+    Once you complete the setup, you'll see your new connection added to the list of available connections.
+    
+    <Frame>
+      ![Connection Added](/images/enterprise/connection-added.png)
+    </Frame>
+  </Step>
+  
+  <Step title="Configure LLM Defaults">
+    In the main menu, go to **Settings → Defaults** and configure the LLM Defaults settings:
+    
+    - Select default models for agents and other components
+    - Set default configurations for Crew Studio
+    
+    Click `Save Settings` to apply your changes.
+    
+    <Frame>
+      ![LLM Defaults Configuration](/images/enterprise/llm-defaults.png)
+    </Frame>
+  </Step>
+</Steps>
+
+## Using Crew Studio
+
+Now that you've configured your LLM connection and default settings, you're ready to start using Crew Studio!
+
+<Steps>
+  <Step title="Access Studio">
+    Navigate to the **Studio** section in your CrewAI Enterprise dashboard.
+  </Step>
+  
+  <Step title="Start a Conversation">
+    Start a conversation with the Crew Assistant by describing the problem you want to solve:
+    
+    ```md
+    I need a crew that can research the latest AI developments and create a summary report.
+    ```
+    
+    The Crew Assistant will ask clarifying questions to better understand your requirements.
+  </Step>
+  
+  <Step title="Review Generated Crew">
+    Review the generated crew configuration, including:
+    
+    - Agents and their roles
+    - Tasks to be performed
+    - Required inputs
+    - Tools to be used
+    
+    This is your opportunity to refine the configuration before proceeding.
+  </Step>
+  
+  <Step title="Deploy or Download">
+    Once you're satisfied with the configuration, you can:
+    
+    - Download the generated code for local customization
+    - Deploy the crew directly to the CrewAI Enterprise platform
+    - Modify the configuration and regenerate the crew
+  </Step>
+  
+  <Step title="Test Your Crew">
+    After deployment, test your crew with sample inputs to ensure it performs as expected.
+  </Step>
+</Steps>
+
+<Tip>
+For best results, provide clear, detailed descriptions of what you want your crew to accomplish. Include specific inputs and expected outputs in your description.
+</Tip>
+
+## Example Workflow
+
+Here's a typical workflow for creating a crew with Crew Studio:
+
+<Steps>
+  <Step title="Describe Your Problem">
+    Start by describing your problem:
+    
+    ```md
+    I need a crew that can analyze financial news and provide investment recommendations
+    ```
+  </Step>
+  
+  <Step title="Answer Questions">
+    Respond to clarifying questions from the Crew Assistant to refine your requirements.
+  </Step>
+  
+  <Step title="Review the Plan">
+    Review the generated crew plan, which might include:
+    
+    - A Research Agent to gather financial news
+    - An Analysis Agent to interpret the data
+    - A Recommendations Agent to provide investment advice
+  </Step>
+  
+  <Step title="Approve or Modify">
+    Approve the plan or request changes if necessary.
+  </Step>
+  
+  <Step title="Download or Deploy">
+    Download the code for customization or deploy directly to the platform.
+  </Step>
+  
+  <Step title="Test and Refine">
+    Test your crew with sample inputs and refine as needed.
+  </Step>
+</Steps>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Crew Studio or any other CrewAI Enterprise features.
+</Card>
+

docs/enterprise/guides/hubspot-trigger.mdx

@@ -0,0 +1,53 @@
+---
+title: "HubSpot Trigger"
+description: "Trigger CrewAI crews directly from HubSpot Workflows"
+icon: "hubspot"
+---
+
+This guide provides a step-by-step process to set up HubSpot triggers for CrewAI Enterprise, enabling you to initiate crews directly from HubSpot Workflows.
+
+## Prerequisites
+
+- A CrewAI Enterprise account
+- A HubSpot account with the [HubSpot Workflows](https://knowledge.hubspot.com/workflows/create-workflows) feature
+
+## Setup Steps
+
+<Steps>
+    <Step title="Connect your HubSpot account with CrewAI Enterprise">
+        - Log in to your `CrewAI Enterprise account > Triggers`
+        - Select `HubSpot` from the list of available triggers
+        - Choose the HubSpot account you want to connect with CrewAI Enterprise
+        - Follow the on-screen prompts to authorize CrewAI Enterprise access to your HubSpot account
+        - A confirmation message will appear once HubSpot is successfully connected with CrewAI Enterprise
+    </Step>
+    <Step title="Create a HubSpot Workflow">
+        - Log in to your `HubSpot account > Automations > Workflows > New workflow`
+        - Select the workflow type that fits your needs (e.g., Start from scratch)
+        - In the workflow builder, click the Plus (+) icon to add a new action.
+        - Choose `Integrated apps > CrewAI > Kickoff a Crew`.
+        - Select the Crew you want to initiate.
+        - Click `Save` to add the action to your workflow
+        <Frame>
+            <img src="/images/enterprise/hubspot-workflow-1.png" alt="HubSpot Workflow 1" />
+        </Frame>
+    </Step>
+    <Step title="Use Crew results with other actions">
+        - After the Kickoff a Crew step, click the Plus (+) icon to add a new action.
+        - For example, to send an internal email notification, choose `Communications > Send internal email notification`
+        - In the Body field, click `Insert data`, select `View properties or action outputs from > Action outputs > Crew Result` to include Crew data in the email
+            <Frame>
+                <img src="/images/enterprise/hubspot-workflow-2.png" alt="HubSpot Workflow 2" />
+            </Frame>
+        - Configure any additional actions as needed
+        - Review your workflow steps to ensure everything is set up correctly
+        - Activate the workflow
+            <Frame>
+                <img src="/images/enterprise/hubspot-workflow-3.png" alt="HubSpot Workflow 3" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Additional Resources
+
+For more detailed information on available actions and customization options, refer to the [HubSpot Workflows Documentation](https://knowledge.hubspot.com/workflows/create-workflows). 

docs/enterprise/guides/human-in-the-loop.mdx

@@ -0,0 +1,78 @@
+---
+title: "HITL Workflows"
+description: "Learn how to implement Human-In-The-Loop workflows in CrewAI for enhanced decision-making"
+icon: "user-check"
+---
+
+Human-In-The-Loop (HITL) is a powerful approach that combines artificial intelligence with human expertise to enhance decision-making and improve task outcomes. This guide shows you how to implement HITL within CrewAI.
+
+## Setting Up HITL Workflows
+
+<Steps>
+    <Step title="Configure Your Task">
+        Set up your task with human input enabled:
+        <Frame>
+            <img src="/images/enterprise/crew-human-input.png" alt="Crew Human Input" />
+        </Frame>
+    </Step>
+
+    <Step title="Provide Webhook URL">
+        When kicking off your crew, include a webhook URL for human input:
+        <Frame>
+            <img src="/images/enterprise/crew-webhook-url.png" alt="Crew Webhook URL" />
+        </Frame>
+    </Step>
+
+    <Step title="Receive Webhook Notification">
+        Once the crew completes the task requiring human input, you'll receive a webhook notification containing:
+            - **Execution ID**
+            - **Task ID**
+            - **Task output**
+    </Step>
+
+    <Step title="Review Task Output">
+        The system will pause in the `Pending Human Input` state. Review the task output carefully.
+    </Step>
+
+    <Step title="Submit Human Feedback">
+        Call the resume endpoint of your crew with the following information:
+        <Frame>
+            <img src="/images/enterprise/crew-resume-endpoint.png" alt="Crew Resume Endpoint" />
+        </Frame>
+        <Warning>
+            **Feedback Impact on Task Execution**:
+            It's crucial to exercise care when providing feedback, as the entire feedback content will be incorporated as additional context for further task executions.
+        </Warning>
+        This means:
+        - All information in your feedback becomes part of the task's context.
+        - Irrelevant details may negatively influence it.
+        - Concise, relevant feedback helps maintain task focus and efficiency.
+        - Always review your feedback carefully before submission to ensure it contains only pertinent information that will positively guide the task's execution.
+    </Step>
+    <Step title="Handle Negative Feedback">
+        If you provide negative feedback:
+        - The crew will retry the task with added context from your feedback.
+        - You'll receive another webhook notification for further review.
+        - Repeat steps 4-6 until satisfied.
+    </Step>
+
+    <Step title="Execution Continuation">
+        When you submit positive feedback, the execution will proceed to the next steps.
+    </Step>
+</Steps>
+
+## Best Practices
+
+- **Be Specific**: Provide clear, actionable feedback that directly addresses the task at hand
+- **Stay Relevant**: Only include information that will help improve the task execution
+- **Be Timely**: Respond to HITL prompts promptly to avoid workflow delays
+- **Review Carefully**: Double-check your feedback before submitting to ensure accuracy
+
+## Common Use Cases
+
+HITL workflows are particularly valuable for:
+- Quality assurance and validation
+- Complex decision-making scenarios
+- Sensitive or high-stakes operations
+- Creative tasks requiring human judgment
+- Compliance and regulatory reviews 

docs/enterprise/guides/kickoff-crew.mdx

@@ -0,0 +1,186 @@
+---
+title: "Kickoff Crew"
+description: "Kickoff a Crew on CrewAI Enterprise"
+icon: "flag-checkered"
+---
+
+## Overview
+
+Once you've deployed your crew to the CrewAI Enterprise platform, you can kickoff executions through the web interface or the API. This guide covers both approaches.
+
+## Method 1: Using the Web Interface
+
+### Step 1: Navigate to Your Deployed Crew
+
+1. Log in to [CrewAI Enterprise](https://app.crewai.com)
+2. Click on the crew name from your projects list
+3. You'll be taken to the crew's detail page
+
+<Frame>
+  ![Crew Dashboard](/images/enterprise/crew-dashboard.png)
+</Frame>
+
+### Step 2: Initiate Execution
+
+From your crew's detail page, you have two options to kickoff an execution:
+
+#### Option A: Quick Kickoff
+
+1. Click the `Kickoff` link in the Test Endpoints section
+2. Enter the required input parameters for your crew in the JSON editor
+3. Click the `Send Request` button
+
+<Frame>
+  ![Kickoff Endpoint](/images/enterprise/kickoff-endpoint.png)
+</Frame>
+
+#### Option B: Using the Visual Interface
+
+1. Click the `Run` tab in the crew detail page
+2. Enter the required inputs in the form fields
+3. Click the `Run Crew` button
+
+<Frame>
+  ![Run Crew](/images/enterprise/run-crew.png)
+</Frame>
+
+### Step 3: Monitor Execution Progress
+
+After initiating the execution:
+
+1. You'll receive a response containing a `kickoff_id` - **copy this ID**
+2. This ID is essential for tracking your execution
+
+<Frame>
+  ![Copy Task ID](/images/enterprise/copy-task-id.png)
+</Frame>
+
+### Step 4: Check Execution Status
+
+To monitor the progress of your execution:
+
+1. Click the "Status" endpoint in the Test Endpoints section
+2. Paste the `kickoff_id` into the designated field
+3. Click the "Get Status" button
+
+<Frame>
+  ![Get Status](/images/enterprise/get-status.png)
+</Frame>
+
+The status response will show:
+- Current execution state (`running`, `completed`, etc.)
+- Details about which tasks are in progress
+- Any outputs produced so far
+
+### Step 5: View Final Results
+
+Once execution is complete:
+
+1. The status will change to `completed`
+2. You can view the full execution results and outputs
+3. For a more detailed view, check the `Executions` tab in the crew detail page
+
+## Method 2: Using the API
+
+You can also kickoff crews programmatically using the CrewAI Enterprise REST API.
+
+### Authentication
+
+All API requests require a bearer token for authentication:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
+```
+
+Your bearer token is available on the Status tab of your crew's detail page.
+
+### Checking Crew Health
+
+Before executing operations, you can verify that your crew is running properly:
+
+```bash
+curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
+```
+
+A successful response will return a message indicating the crew is operational:
+
+```
+Healthy%
+```
+
+### Step 1: Retrieve Required Inputs
+
+First, determine what inputs your crew requires:
+
+```bash
+curl -X GET \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/inputs
+```
+
+The response will be a JSON object containing an array of required input parameters, for example:
+
+```json
+{"inputs":["topic","current_year"]}
+```
+
+This example shows that this particular crew requires two inputs: `topic` and `current_year`.
+
+### Step 2: Kickoff Execution
+
+Initiate execution by providing the required inputs:
+
+```bash
+curl -X POST \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  -d '{"inputs": {"topic": "AI Agent Frameworks", "current_year": "2025"}}' \
+  https://your-crew-url.crewai.com/kickoff
+```
+
+The response will include a `kickoff_id` that you'll need for tracking:
+
+```json
+{"kickoff_id":"abcd1234-5678-90ef-ghij-klmnopqrstuv"}
+```
+
+### Step 3: Check Execution Status
+
+Monitor the execution progress using the kickoff_id:
+
+```bash
+curl -X GET \
+  -H "Authorization: Bearer YOUR_CREW_TOKEN" \
+  https://your-crew-url.crewai.com/status/abcd1234-5678-90ef-ghij-klmnopqrstuv
+```
+
+## Handling Executions
+
+### Long-Running Executions
+
+For executions that may take a long time:
+
+1. Consider implementing a polling mechanism to check status periodically
+2. Use webhooks (if available) for notification when execution completes
+3. Implement error handling for potential timeouts
+
+### Execution Context
+
+The execution context includes:
+
+- Inputs provided at kickoff
+- Environment variables configured during deployment
+- Any state maintained between tasks
+
+### Debugging Failed Executions
+
+If an execution fails:
+
+1. Check the "Executions" tab for detailed logs
+2. Review the "Traces" tab for step-by-step execution details
+3. Look for LLM responses and tool usage in the trace details
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with execution issues or questions about the Enterprise platform.
+</Card>
+

docs/enterprise/guides/react-component-export.mdx

@@ -0,0 +1,103 @@
+---
+title: "React Component Export"
+description: "Learn how to export and integrate CrewAI Enterprise React components into your applications"
+icon: "react"
+---
+
+This guide explains how to export CrewAI Enterprise crews as React components and integrate them into your own applications.
+
+## Exporting a React Component
+
+<Steps>
+    <Step title="Export the Component">
+        Click on the ellipsis (three dots on the right of your deployed crew) and select the export option and save the file locally. We will be using `CrewLead.jsx` for our example.
+
+        <Frame>
+            <img src="/images/enterprise/export-react-component.png" alt="Export React Component" />
+        </Frame>
+    </Step>
+</Steps>
+
+## Setting Up Your React Environment
+
+To run this React component locally, you'll need to set up a React development environment and integrate this component into a React project.
+
+<Steps>
+    <Step title="Install Node.js">
+        - Download and install Node.js from the official website: https://nodejs.org/
+        - Choose the LTS (Long Term Support) version for stability.
+    </Step>
+
+    <Step title="Create a new React project">
+        - Open Command Prompt or PowerShell
+        - Navigate to the directory where you want to create your project
+        - Run the following command to create a new React project:
+
+            ```bash
+            npx create-react-app my-crew-app
+            ```
+        - Change into the project directory:
+        
+            ```bash
+            cd my-crew-app
+            ```
+    </Step>
+
+    <Step title="Install necessary dependencies">
+        ```bash
+        npm install react-dom
+        ```
+    </Step>
+
+    <Step title="Create the CrewLead component">
+        - Move the downloaded file `CrewLead.jsx` into the `src` folder of your project,
+    </Step>
+
+    <Step title="Modify your App.js to use the CrewLead component">
+        - Open `src/App.js`
+        - Replace its contents with something like this:
+
+        ```jsx
+        import React from 'react';
+        import CrewLead from './CrewLead';
+
+        function App() {
+            return (
+                <div className="App">
+                    <CrewLead baseUrl="YOUR_API_BASE_URL" bearerToken="YOUR_BEARER_TOKEN" />
+                </div>
+            );
+        }
+
+        export default App;
+        ```
+        - Replace `YOUR_API_BASE_URL` and `YOUR_BEARER_TOKEN` with the actual values for your API.
+    </Step>
+
+    <Step title="Start the development server">
+        - In your project directory, run:
+        
+            ```bash
+            npm start
+            ```
+        - This will start the development server, and your default web browser should open automatically to http://localhost:3000, where you'll see your React app running.
+    </Step>
+</Steps>
+
+## Customization
+
+You can then customise the `CrewLead.jsx` to add color, title etc
+
+<Frame>
+    <img src="/images/enterprise/customise-react-component.png" alt="Customise React Component" />
+</Frame>
+<Frame>
+    <img src="/images/enterprise/customise-react-component-2.png" alt="Customise React Component" />
+</Frame>
+
+## Next Steps
+
+- Customize the component styling to match your application's design
+- Add additional props for configuration
+- Integrate with your application's state management
+- Add error handling and loading states 

docs/enterprise/guides/salesforce-trigger.mdx

@@ -0,0 +1,44 @@
+---
+title: "Salesforce Trigger"
+description: "Trigger CrewAI crews from Salesforce workflows for CRM automation"
+icon: "salesforce"
+---
+
+CrewAI Enterprise can be triggered from Salesforce to automate customer relationship management workflows and enhance your sales operations.
+
+## Overview
+
+Salesforce is a leading customer relationship management (CRM) platform that helps businesses streamline their sales, service, and marketing operations. By setting up CrewAI triggers from Salesforce, you can:
+
+- Automate lead scoring and qualification
+- Generate personalized sales materials
+- Enhance customer service with AI-powered responses
+- Streamline data analysis and reporting
+
+## Demo
+
+<Frame>
+    <iframe width="100%" height="400" src="https://www.youtube.com/embed/oJunVqjjfu4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+</Frame>
+
+## Getting Started
+
+To set up Salesforce triggers:
+
+1. **Contact Support**: Reach out to CrewAI Enterprise support for assistance with Salesforce trigger setup
+2. **Review Requirements**: Ensure you have the necessary Salesforce permissions and API access
+3. **Configure Connection**: Work with the support team to establish the connection between CrewAI and your Salesforce instance
+4. **Test Triggers**: Verify the triggers work correctly with your specific use cases
+
+## Use Cases
+
+Common Salesforce + CrewAI trigger scenarios include:
+
+- **Lead Processing**: Automatically analyze and score incoming leads
+- **Proposal Generation**: Create customized proposals based on opportunity data
+- **Customer Insights**: Generate analysis reports from customer interaction history
+- **Follow-up Automation**: Create personalized follow-up messages and recommendations
+
+## Next Steps
+
+For detailed setup instructions and advanced configuration options, please contact CrewAI Enterprise support who can provide tailored guidance for your specific Salesforce environment and business needs. 

docs/enterprise/guides/slack-trigger.mdx

@@ -0,0 +1,61 @@
+---
+title: "Slack Trigger"
+description: "Trigger CrewAI crews directly from Slack using slash commands"
+icon: "slack"
+---
+
+This guide explains how to start a crew directly from Slack using CrewAI triggers.
+
+## Prerequisites
+
+- CrewAI Slack trigger installed and connected to your Slack workspace
+- At least one crew configured in CrewAI
+
+## Setup Steps
+
+<Steps>
+    <Step title="Ensure the CrewAI Slack trigger is set up">
+        In the CrewAI dashboard, navigate to the **Triggers** section.
+
+        <Frame>
+            <img src="/images/enterprise/slack-integration.png" alt="CrewAI Slack Integration" />
+        </Frame>
+
+        Verify that Slack is listed and is connected.
+    </Step>
+    <Step title="Open your Slack channel">
+        - Navigate to the channel where you want to kickoff the crew.
+        - Type the slash command "**/kickoff**" to initiate the crew kickoff process.
+        - You should see a  "**Kickoff crew**" appear as you type:
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew.png" alt="Kickoff crew" />
+            </Frame>
+        - Press Enter or select the "**Kickoff crew**" option. A dialog box titled "**Kickoff an AI Crew**" will appear.
+    </Step>
+    <Step title="Select the crew you want to start">
+        - In the dropdown menu labeled "**Select of the crews online:**", choose the crew you want to start.
+        - In the example below, "**prep-for-meeting**" is selected:
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-dropdown.png" alt="Kickoff crew dropdown" />
+            </Frame>
+        - If your crew requires any inputs, click the "**Add Inputs**" button to provide them.
+            <Note>
+                The "**Add Inputs**" button is shown in the example above but is not yet clicked.
+            </Note>
+    </Step>
+    <Step title="Click Kickoff and wait for the crew to complete">
+        - Once you've selected the crew and added any necessary inputs, click "**Kickoff**" to start the crew.
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-kickoff.png" alt="Kickoff crew" />
+            </Frame>
+        - The crew will start executing and you will see the results in the Slack channel.
+            <Frame>
+                <img src="/images/enterprise/kickoff-slack-crew-results.png" alt="Kickoff crew results" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Tips
+
+- Make sure you have the necessary permissions to use the `/kickoff` command in your Slack workspace.
+- If you don't see your desired crew in the dropdown, ensure it's properly configured and online in CrewAI. 

docs/enterprise/guides/team-management.mdx

@@ -0,0 +1,87 @@
+---
+title: "Team Management"
+description: "Learn how to invite and manage team members in your CrewAI Enterprise organization"
+icon: "users"
+---
+
+As an administrator of a CrewAI Enterprise account, you can easily invite new team members to join your organization. This guide will walk you through the process step-by-step.
+
+## Inviting Team Members
+
+<Steps>
+    <Step title="Access the Settings Page">
+        - Log in to your CrewAI Enterprise account
+        - Look for the gear icon (⚙️) in the top right corner of the dashboard
+        - Click on the gear icon to access the **Settings** page:
+            <Frame>
+                <img src="/images/enterprise/settings-page.png" alt="Settings Page" />
+            </Frame>
+    </Step>
+    <Step title="Navigate to the Members Section">
+        - On the Settings page, you'll see a `Members` tab
+        - Click on the `Members` tab to access the **Members** page:
+            <Frame>
+                <img src="/images/enterprise/members-tab.png" alt="Members Tab" />
+            </Frame>
+    </Step>
+    <Step title="Invite New Members">
+        - In the Members section, you'll see a list of current members (including yourself)
+        - Locate the `Email` input field
+        - Enter the email address of the person you want to invite
+        - Click the `Invite` button to send the invitation
+    </Step>
+    <Step title="Repeat as Needed">
+        - You can repeat this process to invite multiple team members
+        - Each invited member will receive an email invitation to join your organization
+    </Step>
+</Steps>
+
+## Adding Roles
+
+You can add roles to your team members to control their access to different parts of the platform.
+
+<Steps>
+    <Step title="Access the Settings Page">
+        - Log in to your CrewAI Enterprise account
+        - Look for the gear icon (⚙️) in the top right corner of the dashboard
+        - Click on the gear icon to access the **Settings** page:
+            <Frame>
+                <img src="/images/enterprise/settings-page.png" alt="Settings Page" />
+            </Frame>
+    </Step>
+    <Step title="Navigate to the Members Section">
+        - On the Settings page, you'll see a `Roles` tab
+        - Click on the `Roles` tab to access the **Roles** page.
+            <Frame>
+                <img src="/images/enterprise/roles-tab.png" alt="Roles Tab" />
+            </Frame>
+        - Click on the `Add Role` button to add a new role.
+        - Enter the details and permissions of the role and click the `Create Role` button to create the role.
+            <Frame>
+                <img src="/images/enterprise/add-role-modal.png" alt="Add Role Modal" />
+            </Frame>
+    </Step>
+    <Step title="Add Roles to Members">
+        - In the Members section, you'll see a list of current members (including yourself)
+           <Frame>
+                <img src="/images/enterprise/member-accepted-invitation.png" alt="Member Accepted Invitation" />
+           </Frame>
+        - Once the member has accepted the invitation, you can add a role to them.
+        - Navigate back to `Roles` tab
+        - Go to the member you want to add a role to and under the `Role` column, click on the dropdown
+        - Select the role you want to add to the member
+        - Click the `Update` button to save the role
+            <Frame>
+                <img src="/images/enterprise/assign-role.png" alt="Add Role to Member" />
+            </Frame>
+    </Step>
+</Steps>
+
+## Important Notes
+
+- **Admin Privileges**: Only users with administrative privileges can invite new members
+- **Email Accuracy**: Ensure you have the correct email addresses for your team members
+- **Invitation Acceptance**: Invited members will need to accept the invitation to join your organization
+- **Email Notifications**: You may want to inform your team members to check their email (including spam folders) for the invitation
+
+By following these steps, you can easily expand your team and collaborate more effectively within your CrewAI Enterprise organization. 

docs/enterprise/guides/update-crew.mdx

@@ -0,0 +1,89 @@
+---
+title: "Update Crew"
+description: "Updating a Crew on CrewAI Enterprise"
+icon: "pencil"
+---
+
+<Note>
+After deploying your crew to CrewAI Enterprise, you may need to make updates to the code, security settings, or configuration. 
+This guide explains how to perform these common update operations.
+</Note>
+
+## Why Update Your Crew?
+
+CrewAI won't automatically pick up GitHub updates by default, so you'll need to manually trigger updates, unless you checked the `Auto-update` option when deploying your crew.
+
+There are several reasons you might want to update your crew deployment:
+- You want to update the code with a latest commit you pushed to GitHub
+- You want to reset the bearer token for security reasons
+- You want to update environment variables
+
+## 1. Updating Your Crew Code for a Latest Commit
+
+When you've pushed new commits to your GitHub repository and want to update your deployment:
+
+1. Navigate to your crew in the CrewAI Enterprise platform
+2. Click on the `Re-deploy` button on your crew details page
+
+<Frame>
+  ![Re-deploy Button](/images/enterprise/redeploy-button.png)
+</Frame>
+
+This will trigger an update that you can track using the progress bar. The system will pull the latest code from your repository and rebuild your deployment.
+
+## 2. Resetting Bearer Token
+
+If you need to generate a new bearer token (for example, if you suspect the current token might have been compromised):
+
+1. Navigate to your crew in the CrewAI Enterprise platform
+2. Find the `Bearer Token` section
+3. Click the `Reset` button next to your current token
+
+<Frame>
+  ![Reset Token](/images/enterprise/reset-token.png)
+</Frame>
+
+<Warning>
+Resetting your bearer token will invalidate the previous token immediately. Make sure to update any applications or scripts that are using the old token.
+</Warning>
+
+## 3. Updating Environment Variables
+
+To update the environment variables for your crew:
+
+1. First access the deployment page by clicking on your crew's name
+
+<Frame>
+  ![Environment Variables Button](/images/enterprise/env-vars-button.png)
+</Frame>
+
+2. Locate the `Environment Variables` section (you will need to click the `Settings` icon to access it)
+3. Edit the existing variables or add new ones in the fields provided
+4. Click the `Update` button next to each variable you modify
+
+<Frame>
+  ![Update Environment Variables](/images/enterprise/update-env-vars.png)
+</Frame>
+
+5. Finally, click the `Update Deployment` button at the bottom of the page to apply the changes
+
+<Note>
+Updating environment variables will trigger a new deployment, but this will only update the environment configuration and not the code itself.
+</Note>
+
+## After Updating
+
+After performing any update:
+
+1. The system will rebuild and redeploy your crew
+2. You can monitor the deployment progress in real-time
+3. Once complete, test your crew to ensure the changes are working as expected
+
+<Tip>
+If you encounter any issues after updating, you can view deployment logs in the platform or contact support for assistance.
+</Tip>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with updating your crew or troubleshooting deployment issues.
+</Card>
+

docs/enterprise/guides/webhook-automation.mdx

@@ -0,0 +1,121 @@
+---
+title: "Webhook Automation"
+description: "Automate CrewAI Enterprise workflows using webhooks with platforms like ActivePieces, Zapier, and Make.com"
+icon: "webhook"
+---
+
+CrewAI Enterprise allows you to automate your workflow using webhooks. This article will guide you through the process of setting up and using webhooks to kickoff your crew execution, with a focus on integration with ActivePieces, a workflow automation platform similar to Zapier and Make.com.
+
+## Setting Up Webhooks
+
+<Steps>
+    <Step title="Accessing the Kickoff Interface">
+        - Navigate to the CrewAI Enterprise dashboard
+        - Look for the `/kickoff` section, which is used to start the crew execution
+            <Frame>
+                <img src="/images/enterprise/kickoff-interface.png" alt="Kickoff Interface" />
+            </Frame>
+    </Step>
+
+    <Step title="Configuring the JSON Content">
+        In the JSON Content section, you'll need to provide the following information:
+
+        - **inputs**: A JSON object containing:
+            - `company`: The name of the company (e.g., "tesla")
+            - `product_name`: The name of the product (e.g., "crewai")
+            - `form_response`: The type of response (e.g., "financial")
+            - `icp_description`: A brief description of the Ideal Customer Profile
+            - `product_description`: A short description of the product
+            - `taskWebhookUrl`, `stepWebhookUrl`, `crewWebhookUrl`: URLs for various webhook endpoints (ActivePieces, Zapier, Make.com or another compatible platform)
+    </Step>
+
+    <Step title="Integrating with ActivePieces">
+        In this example we will be using ActivePieces. You can use other platforms such as Zapier and Make.com
+
+        To integrate with ActivePieces:
+
+        1. Set up a new flow in ActivePieces
+        2. Add a trigger (e.g., `Every Day` schedule)
+            <Frame>
+                <img src="/images/enterprise/activepieces-trigger.png" alt="ActivePieces Trigger" />
+            </Frame>
+
+        3. Add an HTTP action step
+            - Set the action to `Send HTTP request`
+            - Use `POST` as the method
+            - Set the URL to your CrewAI Enterprise kickoff endpoint
+            - Add necessary headers (e.g., `Bearer Token`)
+                <Frame>
+                    <img src="/images/enterprise/activepieces-headers.png" alt="ActivePieces Headers" />
+                </Frame>
+
+            - In the body, include the JSON content as configured in step 2
+                <Frame>
+                    <img src="/images/enterprise/activepieces-body.png" alt="ActivePieces Body" />
+                </Frame>
+
+            - The crew will then kickoff at the pre-defined time.
+    </Step>
+
+    <Step title="Setting Up the Webhook">
+        1. Create a new flow in ActivePieces and name it 
+            <Frame>
+                <img src="/images/enterprise/activepieces-flow.png" alt="ActivePieces Flow" />
+            </Frame>
+
+        2. Add a webhook step as the trigger:
+            - Select `Catch Webhook` as the trigger type
+            - This will generate a unique URL that will receive HTTP requests and trigger your flow
+                <Frame>
+                    <img src="/images/enterprise/activepieces-webhook.png" alt="ActivePieces Webhook" />
+                </Frame>
+
+            - Configure the email to use crew webhook body text
+                <Frame>
+                    <img src="/images/enterprise/activepieces-email.png" alt="ActivePieces Email" />
+                </Frame>
+    </Step>
+</Steps>
+
+## Webhook Output Examples
+
+<Tabs>
+    <Tab title="Step Webhook">
+        `stepWebhookUrl` - Callback that will be executed upon each agent inner thought
+
+        ```json
+        {
+            "action": "**Preliminary Research Report on the Financial Industry for crewai Enterprise Solution**\n1. Industry Overview and Trends\nThe financial industry in ....\nConclusion:\nThe financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+        }
+        ```
+    </Tab>
+    <Tab title="Task Webhook">
+        `taskWebhookUrl` - Callback that will be executed upon the end of each task
+
+        ```json
+        {
+            "description": "Using the information gathered from the lead's data, conduct preliminary research on the lead's industry, company background, and potential use cases for crewai. Focus on finding relevant data that can aid in scoring the lead and planning a strategy to pitch them crewai.The financial industry presents a fertile ground for implementing AI solutions like crewai, particularly in areas such as digital customer engagement, risk management, and regulatory compliance. Further engagement with the lead is recommended to better tailor the crewai solution to their specific needs and scale.",
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0"
+        }
+        ```
+    </Tab>
+    <Tab title="Crew Webhook">
+        `crewWebhookUrl` - Callback that will be executed upon the end of the crew execution
+
+        ```json
+        {
+            "task_id": "97eba64f-958c-40a0-b61c-625fe635a3c0",
+            "result": {
+                "lead_score": "Customer service enhancement, and compliance are particularly relevant.",
+                "talking_points": [
+                    "Highlight how crewai's AI solutions can transform customer service with automated, personalized experiences and 24/7 support, improving both customer satisfaction and operational efficiency.",
+                    "Discuss crewai's potential to help the institution achieve its sustainability goals through better data analysis and decision-making, contributing to responsible investing and green initiatives.",
+                    "Emphasize crewai's ability to enhance compliance with evolving regulations through efficient data processing and reporting, reducing the risk of non-compliance penalties.",
+                    "Stress the adaptability of crewai to support both extensive multinational operations and smaller, targeted projects, ensuring the solution grows with the institution's needs."
+                ]
+            }
+        }
+        ```
+    </Tab>
+</Tabs> 

docs/enterprise/guides/zapier-trigger.mdx

@@ -0,0 +1,103 @@
+---
+title: "Zapier Trigger"
+description: "Trigger CrewAI crews from Zapier workflows to automate cross-app workflows"
+icon: "bolt"
+---
+
+This guide will walk you through the process of setting up Zapier triggers for CrewAI Enterprise, allowing you to automate workflows between CrewAI Enterprise and other applications.
+
+## Prerequisites
+
+- A CrewAI Enterprise account
+- A Zapier account
+- A Slack account (for this specific example)
+
+## Step-by-Step Setup
+
+<Steps>
+    <Step title="Set Up the Slack Trigger">
+        - In Zapier, create a new Zap.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-1.png" alt="Zapier 1" />
+        </Frame>
+    </Step>
+
+    <Step title="Choose Slack as your trigger app">
+        <Frame>
+            <img src="/images/enterprise/zapier-2.png" alt="Zapier 2" />
+        </Frame>
+        - Select `New Pushed Message` as the Trigger Event.
+        - Connect your Slack account if you haven't already.
+    </Step>
+
+    <Step title="Configure the CrewAI Enterprise Action">
+        - Add a new action step to your Zap.
+        - Choose CrewAI+ as your action app and Kickoff as the Action Event
+
+        <Frame>
+            <img src="/images/enterprise/zapier-3.png" alt="Zapier 5" />
+        </Frame>
+    </Step>
+
+    <Step title="Connect your CrewAI Enterprise account">
+        - Connect your CrewAI Enterprise account.
+        - Select the appropriate Crew for your workflow.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-4.png" alt="Zapier 6" />
+        </Frame>
+        - Configure the inputs for the Crew using the data from the Slack message.
+    </Step>
+
+    <Step title="Format the CrewAI Enterprise Output">
+        - Add another action step to format the text output from CrewAI Enterprise.
+        - Use Zapier's formatting tools to convert the Markdown output to HTML.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-5.png" alt="Zapier 8" />
+        </Frame>
+        <Frame>
+            <img src="/images/enterprise/zapier-6.png" alt="Zapier 9" />
+        </Frame>
+    </Step>
+
+    <Step title="Send the Output via Email">
+        - Add a final action step to send the formatted output via email.
+        - Choose your preferred email service (e.g., Gmail, Outlook).
+        - Configure the email details, including recipient, subject, and body.
+        - Insert the formatted CrewAI Enterprise output into the email body.
+
+        <Frame>
+            <img src="/images/enterprise/zapier-7.png" alt="Zapier 7" />
+        </Frame>
+    </Step>
+
+    <Step title="Kick Off the crew from Slack">
+        - Enter the text in your Slack channel
+
+        <Frame>
+            <img src="/images/enterprise/zapier-7b.png" alt="Zapier 10" />
+        </Frame>
+
+        - Select the 3 ellipsis button and then chose Push to Zapier
+
+        <Frame>
+            <img src="/images/enterprise/zapier-8.png" alt="Zapier 11" />
+        </Frame>
+    </Step>
+
+    <Step title="Select the crew and then Push to Kick Off">
+        <Frame>
+            <img src="/images/enterprise/zapier-9.png" alt="Zapier 12" />
+        </Frame>
+    </Step>
+</Steps>
+
+## Tips for Success
+
+- Ensure that your CrewAI Enterprise inputs are correctly mapped from the Slack message.
+- Test your Zap thoroughly before turning it on to catch any potential issues.
+- Consider adding error handling steps to manage potential failures in the workflow.
+
+By following these steps, you'll have successfully set up Zapier triggers for CrewAI Enterprise, allowing for automated workflows triggered by Slack messages and resulting in email notifications with CrewAI Enterprise output. 

docs/enterprise/integrations/asana.mdx

@@ -0,0 +1,253 @@
+---
+title: Asana Integration
+description: "Team task and project coordination with Asana integration for CrewAI."
+icon: "circle"
+---
+
+## Overview
+
+Enable your agents to manage tasks, projects, and team coordination through Asana. Create tasks, update project status, manage assignments, and streamline your team's workflow with AI-powered automation.
+
+## Prerequisites
+
+Before using the Asana integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- An Asana account with appropriate permissions
+- Connected your Asana account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Asana Integration
+
+### 1. Connect Your Asana Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Asana** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for task and project management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="ASANA_CREATE_COMMENT">
+    **Description:** Create a comment in Asana.
+
+    **Parameters:**
+    - `task` (string, required): Task ID - The ID of the Task the comment will be added to. The comment will be authored by the currently authenticated user.
+    - `text` (string, required): Text (example: "This is a comment.").
+  </Accordion>
+
+  <Accordion title="ASANA_CREATE_PROJECT">
+    **Description:** Create a project in Asana.
+
+    **Parameters:**
+    - `name` (string, required): Name (example: "Stuff to buy").
+    - `workspace` (string, required): Workspace - Use Connect Portal Workflow Settings to allow users to select which Workspace to create Projects in. Defaults to the user's first Workspace if left blank.
+    - `team` (string, optional): Team - Use Connect Portal Workflow Settings to allow users to select which Team to share this Project with. Defaults to the user's first Team if left blank.
+    - `notes` (string, optional): Notes (example: "These are things we need to purchase.").
+  </Accordion>
+
+  <Accordion title="ASANA_GET_PROJECTS">
+    **Description:** Get a list of projects in Asana.
+
+    **Parameters:**
+    - `archived` (string, optional): Archived - Choose "true" to show archived projects, "false" to display only active projects, or "default" to show both archived and active projects.
+      - Options: `default`, `true`, `false`
+  </Accordion>
+
+  <Accordion title="ASANA_GET_PROJECT_BY_ID">
+    **Description:** Get a project by ID in Asana.
+
+    **Parameters:**
+    - `projectFilterId` (string, required): Project ID.
+  </Accordion>
+
+  <Accordion title="ASANA_CREATE_TASK">
+    **Description:** Create a task in Asana.
+
+    **Parameters:**
+    - `name` (string, required): Name (example: "Task Name").
+    - `workspace` (string, optional): Workspace - Use Connect Portal Workflow Settings to allow users to select which Workspace to create Tasks in. Defaults to the user's first Workspace if left blank..
+    - `project` (string, optional): Project - Use Connect Portal Workflow Settings to allow users to select which Project to create this Task in.
+    - `notes` (string, optional): Notes.
+    - `dueOnDate` (string, optional): Due On - The date on which this task is due. Cannot be used together with Due At. (example: "YYYY-MM-DD").
+    - `dueAtDate` (string, optional): Due At - The date and time (ISO timestamp) at which this task is due. Cannot be used together with Due On. (example: "2019-09-15T02:06:58.147Z").
+    - `assignee` (string, optional): Assignee - The ID of the Asana user this task will be assigned to. Use Connect Portal Workflow Settings to allow users to select an Assignee.
+    - `gid` (string, optional): External ID - An ID from your application to associate this task with. You can use this ID to sync updates to this task later.
+  </Accordion>
+
+  <Accordion title="ASANA_UPDATE_TASK">
+    **Description:** Update a task in Asana.
+
+    **Parameters:**
+    - `taskId` (string, required): Task ID - The ID of the Task that will be updated.
+    - `completeStatus` (string, optional): Completed Status.
+      - Options: `true`, `false`
+    - `name` (string, optional): Name (example: "Task Name").
+    - `notes` (string, optional): Notes.
+    - `dueOnDate` (string, optional): Due On - The date on which this task is due. Cannot be used together with Due At. (example: "YYYY-MM-DD").
+    - `dueAtDate` (string, optional): Due At - The date and time (ISO timestamp) at which this task is due. Cannot be used together with Due On. (example: "2019-09-15T02:06:58.147Z").
+    - `assignee` (string, optional): Assignee - The ID of the Asana user this task will be assigned to. Use Connect Portal Workflow Settings to allow users to select an Assignee.
+    - `gid` (string, optional): External ID - An ID from your application to associate this task with. You can use this ID to sync updates to this task later.
+  </Accordion>
+
+  <Accordion title="ASANA_GET_TASKS">
+    **Description:** Get a list of tasks in Asana.
+
+    **Parameters:**
+    - `workspace` (string, optional): Workspace - The ID of the Workspace to filter tasks on. Use Connect Portal Workflow Settings to allow users to select a Workspace.
+    - `project` (string, optional): Project - The ID of the Project to filter tasks on. Use Connect Portal Workflow Settings to allow users to select a Project.
+    - `assignee` (string, optional): Assignee - The ID of the assignee to filter tasks on. Use Connect Portal Workflow Settings to allow users to select an Assignee.
+    - `completedSince` (string, optional): Completed since - Only return tasks that are either incomplete or that have been completed since this time (ISO or Unix timestamp). (example: "2014-04-25T16:15:47-04:00").
+  </Accordion>
+
+  <Accordion title="ASANA_GET_TASKS_BY_ID">
+    **Description:** Get a list of tasks by ID in Asana.
+
+    **Parameters:**
+    - `taskId` (string, required): Task ID.
+  </Accordion>
+
+  <Accordion title="ASANA_GET_TASK_BY_EXTERNAL_ID">
+    **Description:** Get a task by external ID in Asana.
+
+    **Parameters:**
+    - `gid` (string, required): External ID - The ID that this task is associated or synced with, from your application.
+  </Accordion>
+
+  <Accordion title="ASANA_ADD_TASK_TO_SECTION">
+    **Description:** Add a task to a section in Asana.
+
+    **Parameters:**
+    - `sectionId` (string, required): Section ID - The ID of the section to add this task to.
+    - `taskId` (string, required): Task ID - The ID of the task. (example: "1204619611402340").
+    - `beforeTaskId` (string, optional): Before Task ID - The ID of a task in this section that this task will be inserted before. Cannot be used with After Task ID. (example: "1204619611402340").
+    - `afterTaskId` (string, optional): After Task ID - The ID of a task in this section that this task will be inserted after. Cannot be used with Before Task ID. (example: "1204619611402340").
+  </Accordion>
+
+  <Accordion title="ASANA_GET_TEAMS">
+    **Description:** Get a list of teams in Asana.
+
+    **Parameters:**
+    - `workspace` (string, required): Workspace - Returns the teams in this workspace visible to the authorized user.
+  </Accordion>
+
+  <Accordion title="ASANA_GET_WORKSPACES">
+    **Description:** Get a list of workspaces in Asana.
+
+    **Parameters:** None required.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Asana Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Asana tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Asana capabilities
+asana_agent = Agent(
+    role="Project Manager",
+    goal="Manage tasks and projects in Asana efficiently",
+    backstory="An AI assistant specialized in project management and task coordination.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new project
+create_project_task = Task(
+    description="Create a new project called 'Q1 Marketing Campaign' in the Marketing workspace",
+    agent=asana_agent,
+    expected_output="Confirmation that the project was created successfully with project ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[asana_agent],
+    tasks=[create_project_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Asana Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Asana tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["asana_create_task", "asana_update_task", "asana_get_tasks"]
+)
+
+task_manager_agent = Agent(
+    role="Task Manager",
+    goal="Create and manage tasks efficiently",
+    backstory="An AI assistant that focuses on task creation and management.",
+    tools=enterprise_tools
+)
+
+# Task to create and assign a task
+task_management = Task(
+    description="Create a task called 'Review quarterly reports' and assign it to the appropriate team member",
+    agent=task_manager_agent,
+    expected_output="Task created and assigned successfully"
+)
+
+crew = Crew(
+    agents=[task_manager_agent],
+    tasks=[task_management]
+)
+
+crew.kickoff()
+```
+
+### Advanced Project Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+project_coordinator = Agent(
+    role="Project Coordinator",
+    goal="Coordinate project activities and track progress",
+    backstory="An experienced project coordinator who ensures projects run smoothly.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple Asana operations
+coordination_task = Task(
+    description="""
+    1. Get all active projects in the workspace
+    2. For each project, get the list of incomplete tasks
+    3. Create a summary report task in the 'Management Reports' project
+    4. Add comments to overdue tasks to request status updates
+    """,
+    agent=project_coordinator,
+    expected_output="Summary report created and status update requests sent for overdue tasks"
+)
+
+crew = Crew(
+    agents=[project_coordinator],
+    tasks=[coordination_task]
+)
+
+crew.kickoff()
+```

docs/enterprise/integrations/box.mdx

@@ -0,0 +1,268 @@
+---
+title: Box Integration
+description: "File storage and document management with Box integration for CrewAI."
+icon: "box"
+---
+
+## Overview
+
+Enable your agents to manage files, folders, and documents through Box. Upload files, organize folder structures, search content, and streamline your team's document management with AI-powered automation.
+
+## Prerequisites
+
+Before using the Box integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Box account with appropriate permissions
+- Connected your Box account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Box Integration
+
+### 1. Connect Your Box Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Box** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for file and folder management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="BOX_SAVE_FILE">
+    **Description:** Save a file from URL in Box.
+
+    **Parameters:**
+    - `fileAttributes` (object, required): Attributes - File metadata including name, parent folder, and timestamps.
+      ```json
+      {
+        "content_created_at": "2012-12-12T10:53:43-08:00",
+        "content_modified_at": "2012-12-12T10:53:43-08:00",
+        "name": "qwerty.png",
+        "parent": { "id": "1234567" }
+      }
+      ```
+    - `file` (string, required): File URL - Files must be smaller than 50MB in size. (example: "https://picsum.photos/200/300").
+  </Accordion>
+
+  <Accordion title="BOX_SAVE_FILE_FROM_OBJECT">
+    **Description:** Save a file in Box.
+
+    **Parameters:**
+    - `file` (string, required): File - Accepts a File Object containing file data. Files must be smaller than 50MB in size.
+    - `fileName` (string, required): File Name (example: "qwerty.png").
+    - `folder` (string, optional): Folder - Use Connect Portal Workflow Settings to allow users to select the File's Folder destination. Defaults to the user's root folder if left blank.
+  </Accordion>
+
+  <Accordion title="BOX_GET_FILE_BY_ID">
+    **Description:** Get a file by ID in Box.
+
+    **Parameters:**
+    - `fileId` (string, required): File ID - The unique identifier that represents a file. (example: "12345").
+  </Accordion>
+
+  <Accordion title="BOX_LIST_FILES">
+    **Description:** List files in Box.
+
+    **Parameters:**
+    - `folderId` (string, required): Folder ID - The unique identifier that represents a folder. (example: "0").
+    - `filterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "direction",
+                "operator": "$stringExactlyMatches",
+                "value": "ASC"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="BOX_CREATE_FOLDER">
+    **Description:** Create a folder in Box.
+
+    **Parameters:**
+    - `folderName` (string, required): Name - The name for the new folder. (example: "New Folder").
+    - `folderParent` (object, required): Parent Folder - The parent folder where the new folder will be created.
+      ```json
+      {
+        "id": "123456"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="BOX_MOVE_FOLDER">
+    **Description:** Move a folder in Box.
+
+    **Parameters:**
+    - `folderId` (string, required): Folder ID - The unique identifier that represents a folder. (example: "0").
+    - `folderName` (string, required): Name - The name for the folder. (example: "New Folder").
+    - `folderParent` (object, required): Parent Folder - The new parent folder destination.
+      ```json
+      {
+        "id": "123456"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="BOX_GET_FOLDER_BY_ID">
+    **Description:** Get a folder by ID in Box.
+
+    **Parameters:**
+    - `folderId` (string, required): Folder ID - The unique identifier that represents a folder. (example: "0").
+  </Accordion>
+
+  <Accordion title="BOX_SEARCH_FOLDERS">
+    **Description:** Search folders in Box.
+
+    **Parameters:**
+    - `folderId` (string, required): Folder ID - The folder to search within.
+    - `filterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "sort",
+                "operator": "$stringExactlyMatches",
+                "value": "name"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="BOX_DELETE_FOLDER">
+    **Description:** Delete a folder in Box.
+
+    **Parameters:**
+    - `folderId` (string, required): Folder ID - The unique identifier that represents a folder. (example: "0").
+    - `recursive` (boolean, optional): Recursive - Delete a folder that is not empty by recursively deleting the folder and all of its content.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Box Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Box tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Box capabilities
+box_agent = Agent(
+    role="Document Manager",
+    goal="Manage files and folders in Box efficiently",
+    backstory="An AI assistant specialized in document management and file organization.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a folder structure
+create_structure_task = Task(
+    description="Create a folder called 'Project Files' in the root directory and upload a document from URL",
+    agent=box_agent,
+    expected_output="Folder created and file uploaded successfully"
+)
+
+# Run the task
+crew = Crew(
+    agents=[box_agent],
+    tasks=[create_structure_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Box Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Box tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["box_create_folder", "box_save_file", "box_list_files"]
+)
+
+file_organizer_agent = Agent(
+    role="File Organizer",
+    goal="Organize and manage file storage efficiently",
+    backstory="An AI assistant that focuses on file organization and storage management.",
+    tools=enterprise_tools
+)
+
+# Task to organize files
+organization_task = Task(
+    description="Create a folder structure for the marketing team and organize existing files",
+    agent=file_organizer_agent,
+    expected_output="Folder structure created and files organized"
+)
+
+crew = Crew(
+    agents=[file_organizer_agent],
+    tasks=[organization_task]
+)
+
+crew.kickoff()
+```
+
+### Advanced File Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+file_manager = Agent(
+    role="File Manager",
+    goal="Maintain organized file structure and manage document lifecycle",
+    backstory="An experienced file manager who ensures documents are properly organized and accessible.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple Box operations
+management_task = Task(
+    description="""
+    1. List all files in the root folder
+    2. Create monthly archive folders for the current year
+    3. Move old files to appropriate archive folders
+    4. Generate a summary report of the file organization
+    """,
+    agent=file_manager,
+    expected_output="Files organized into archive structure with summary report"
+)
+
+crew = Crew(
+    agents=[file_manager],
+    tasks=[management_task]
+)
+
+crew.kickoff()
+```

docs/enterprise/integrations/clickup.mdx

@@ -0,0 +1,293 @@
+---
+title: ClickUp Integration
+description: "Task and productivity management with ClickUp integration for CrewAI."
+icon: "list-check"
+---
+
+## Overview
+
+Enable your agents to manage tasks, projects, and productivity workflows through ClickUp. Create and update tasks, organize projects, manage team assignments, and streamline your productivity management with AI-powered automation.
+
+## Prerequisites
+
+Before using the ClickUp integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A ClickUp account with appropriate permissions
+- Connected your ClickUp account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up ClickUp Integration
+
+### 1. Connect Your ClickUp Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **ClickUp** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for task and project management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="CLICKUP_SEARCH_TASKS">
+    **Description:** Search for tasks in ClickUp using advanced filters.
+
+    **Parameters:**
+    - `taskFilterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "statuses%5B%5D",
+                "operator": "$stringExactlyMatches",
+                "value": "open"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `space_ids%5B%5D`, `project_ids%5B%5D`, `list_ids%5B%5D`, `statuses%5B%5D`, `include_closed`, `assignees%5B%5D`, `tags%5B%5D`, `due_date_gt`, `due_date_lt`, `date_created_gt`, `date_created_lt`, `date_updated_gt`, `date_updated_lt`
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_TASK_IN_LIST">
+    **Description:** Get tasks in a specific list in ClickUp.
+
+    **Parameters:**
+    - `listId` (string, required): List - Select a List to get tasks from. Use Connect Portal User Settings to allow users to select a ClickUp List.
+    - `taskFilterFormula` (string, optional): Search for tasks that match specified filters. For example: name=task1.
+  </Accordion>
+
+  <Accordion title="CLICKUP_CREATE_TASK">
+    **Description:** Create a task in ClickUp.
+
+    **Parameters:**
+    - `listId` (string, required): List - Select a List to create this task in. Use Connect Portal User Settings to allow users to select a ClickUp List.
+    - `name` (string, required): Name - The task name.
+    - `description` (string, optional): Description - Task description.
+    - `status` (string, optional): Status - Select a Status for this task. Use Connect Portal User Settings to allow users to select a ClickUp Status.
+    - `assignees` (string, optional): Assignees - Select a Member (or an array of member IDs) to be assigned to this task. Use Connect Portal User Settings to allow users to select a ClickUp Member.
+    - `dueDate` (string, optional): Due Date - Specify a date for this task to be due on.
+    - `additionalFields` (string, optional): Additional Fields - Specify additional fields to include on this task as JSON.
+  </Accordion>
+
+  <Accordion title="CLICKUP_UPDATE_TASK">
+    **Description:** Update a task in ClickUp.
+
+    **Parameters:**
+    - `taskId` (string, required): Task ID - The ID of the task to update.
+    - `listId` (string, required): List - Select a List to create this task in. Use Connect Portal User Settings to allow users to select a ClickUp List.
+    - `name` (string, optional): Name - The task name.
+    - `description` (string, optional): Description - Task description.
+    - `status` (string, optional): Status - Select a Status for this task. Use Connect Portal User Settings to allow users to select a ClickUp Status.
+    - `assignees` (string, optional): Assignees - Select a Member (or an array of member IDs) to be assigned to this task. Use Connect Portal User Settings to allow users to select a ClickUp Member.
+    - `dueDate` (string, optional): Due Date - Specify a date for this task to be due on.
+    - `additionalFields` (string, optional): Additional Fields - Specify additional fields to include on this task as JSON.
+  </Accordion>
+
+  <Accordion title="CLICKUP_DELETE_TASK">
+    **Description:** Delete a task in ClickUp.
+
+    **Parameters:**
+    - `taskId` (string, required): Task ID - The ID of the task to delete.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_LIST">
+    **Description:** Get List information in ClickUp.
+
+    **Parameters:**
+    - `spaceId` (string, required): Space ID - The ID of the space containing the lists.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_CUSTOM_FIELDS_IN_LIST">
+    **Description:** Get Custom Fields in a List in ClickUp.
+
+    **Parameters:**
+    - `listId` (string, required): List ID - The ID of the list to get custom fields from.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_ALL_FIELDS_IN_LIST">
+    **Description:** Get All Fields in a List in ClickUp.
+
+    **Parameters:**
+    - `listId` (string, required): List ID - The ID of the list to get all fields from.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_SPACE">
+    **Description:** Get Space information in ClickUp.
+
+    **Parameters:**
+    - `spaceId` (string, optional): Space ID - The ID of the space to retrieve.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_FOLDERS">
+    **Description:** Get Folders in ClickUp.
+
+    **Parameters:**
+    - `spaceId` (string, required): Space ID - The ID of the space containing the folders.
+  </Accordion>
+
+  <Accordion title="CLICKUP_GET_MEMBER">
+    **Description:** Get Member information in ClickUp.
+
+    **Parameters:** None required.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic ClickUp Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (ClickUp tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with ClickUp capabilities
+clickup_agent = Agent(
+    role="Task Manager",
+    goal="Manage tasks and projects in ClickUp efficiently",
+    backstory="An AI assistant specialized in task management and productivity coordination.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new task
+create_task = Task(
+    description="Create a task called 'Review Q1 Reports' in the Marketing list with high priority",
+    agent=clickup_agent,
+    expected_output="Task created successfully with task ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[clickup_agent],
+    tasks=[create_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific ClickUp Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific ClickUp tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["clickup_create_task", "clickup_update_task", "clickup_search_tasks"]
+)
+
+task_coordinator = Agent(
+    role="Task Coordinator",
+    goal="Create and manage tasks efficiently",
+    backstory="An AI assistant that focuses on task creation and status management.",
+    tools=enterprise_tools
+)
+
+# Task to manage task workflow
+task_workflow = Task(
+    description="Create a task for project planning and assign it to the development team",
+    agent=task_coordinator,
+    expected_output="Task created and assigned successfully"
+)
+
+crew = Crew(
+    agents=[task_coordinator],
+    tasks=[task_workflow]
+)
+
+crew.kickoff()
+```
+
+### Advanced Project Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+project_manager = Agent(
+    role="Project Manager",
+    goal="Coordinate project activities and track team productivity",
+    backstory="An experienced project manager who ensures projects are delivered on time.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple ClickUp operations
+project_coordination = Task(
+    description="""
+    1. Get all open tasks in the current space
+    2. Identify overdue tasks and update their status
+    3. Create a weekly report task summarizing project progress
+    4. Assign the report task to the team lead
+    """,
+    agent=project_manager,
+    expected_output="Project status updated and weekly report task created and assigned"
+)
+
+crew = Crew(
+    agents=[project_manager],
+    tasks=[project_coordination]
+)
+
+crew.kickoff()
+```
+
+### Task Search and Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+task_analyst = Agent(
+    role="Task Analyst",
+    goal="Analyze task patterns and optimize team productivity",
+    backstory="An AI assistant that analyzes task data to improve team efficiency.",
+    tools=[enterprise_tools]
+)
+
+# Task to analyze and optimize task distribution
+task_analysis = Task(
+    description="""
+    Search for all tasks assigned to team members in the last 30 days,
+    analyze completion patterns, and create optimization recommendations
+    """,
+    agent=task_analyst,
+    expected_output="Task analysis report with optimization recommendations"
+)
+
+crew = Crew(
+    agents=[task_analyst],
+    tasks=[task_analysis]
+)
+
+crew.kickoff()
+```
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with ClickUp integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/github.mdx

@@ -0,0 +1,323 @@
+---
+title: GitHub Integration
+description: "Repository and issue management with GitHub integration for CrewAI."
+icon: "github"
+---
+
+## Overview
+
+Enable your agents to manage repositories, issues, and releases through GitHub. Create and update issues, manage releases, track project development, and streamline your software development workflow with AI-powered automation.
+
+## Prerequisites
+
+Before using the GitHub integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A GitHub account with appropriate repository permissions
+- Connected your GitHub account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up GitHub Integration
+
+### 1. Connect Your GitHub Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **GitHub** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for repository and issue management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="GITHUB_CREATE_ISSUE">
+    **Description:** Create an issue in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Issue. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Issue.
+    - `title` (string, required): Issue Title - Specify the title of the issue to create.
+    - `body` (string, optional): Issue Body - Specify the body contents of the issue to create.
+    - `assignees` (string, optional): Assignees - Specify the assignee(s)' GitHub login as an array of strings for this issue. (example: `["octocat"]`).
+  </Accordion>
+
+  <Accordion title="GITHUB_UPDATE_ISSUE">
+    **Description:** Update an issue in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Issue. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Issue.
+    - `issue_number` (string, required): Issue Number - Specify the number of the issue to update.
+    - `title` (string, required): Issue Title - Specify the title of the issue to update.
+    - `body` (string, optional): Issue Body - Specify the body contents of the issue to update.
+    - `assignees` (string, optional): Assignees - Specify the assignee(s)' GitHub login as an array of strings for this issue. (example: `["octocat"]`).
+    - `state` (string, optional): State - Specify the updated state of the issue.
+      - Options: `open`, `closed`
+  </Accordion>
+
+  <Accordion title="GITHUB_GET_ISSUE_BY_NUMBER">
+    **Description:** Get an issue by number in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Issue. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Issue.
+    - `issue_number` (string, required): Issue Number - Specify the number of the issue to fetch.
+  </Accordion>
+
+  <Accordion title="GITHUB_LOCK_ISSUE">
+    **Description:** Lock an issue in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Issue. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Issue.
+    - `issue_number` (string, required): Issue Number - Specify the number of the issue to lock.
+    - `lock_reason` (string, required): Lock Reason - Specify a reason for locking the issue or pull request conversation.
+      - Options: `off-topic`, `too heated`, `resolved`, `spam`
+  </Accordion>
+
+  <Accordion title="GITHUB_SEARCH_ISSUE">
+    **Description:** Search for issues in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Issue. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Issue.
+    - `filter` (object, required): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "assignee",
+                "operator": "$stringExactlyMatches",
+                "value": "octocat"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `assignee`, `creator`, `mentioned`, `labels`
+  </Accordion>
+
+  <Accordion title="GITHUB_CREATE_RELEASE">
+    **Description:** Create a release in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Release. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Release.
+    - `tag_name` (string, required): Name - Specify the name of the release tag to be created. (example: "v1.0.0").
+    - `target_commitish` (string, optional): Target - Specify the target of the release. This can either be a branch name or a commit SHA. Defaults to the main branch. (example: "master").
+    - `body` (string, optional): Body - Specify a description for this release.
+    - `draft` (string, optional): Draft - Specify whether the created release should be a draft (unpublished) release.
+      - Options: `true`, `false`
+    - `prerelease` (string, optional): Prerelease - Specify whether the created release should be a prerelease.
+      - Options: `true`, `false`
+    - `discussion_category_name` (string, optional): Discussion Category Name - If specified, a discussion of the specified category is created and linked to the release. The value must be a category that already exists in the repository.
+    - `generate_release_notes` (string, optional): Release Notes - Specify whether the created release should automatically create release notes using the provided name and body specified.
+      - Options: `true`, `false`
+  </Accordion>
+
+  <Accordion title="GITHUB_UPDATE_RELEASE">
+    **Description:** Update a release in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Release. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Release.
+    - `id` (string, required): Release ID - Specify the ID of the release to update.
+    - `tag_name` (string, optional): Name - Specify the name of the release tag to be updated. (example: "v1.0.0").
+    - `target_commitish` (string, optional): Target - Specify the target of the release. This can either be a branch name or a commit SHA. Defaults to the main branch. (example: "master").
+    - `body` (string, optional): Body - Specify a description for this release.
+    - `draft` (string, optional): Draft - Specify whether the created release should be a draft (unpublished) release.
+      - Options: `true`, `false`
+    - `prerelease` (string, optional): Prerelease - Specify whether the created release should be a prerelease.
+      - Options: `true`, `false`
+    - `discussion_category_name` (string, optional): Discussion Category Name - If specified, a discussion of the specified category is created and linked to the release. The value must be a category that already exists in the repository.
+    - `generate_release_notes` (string, optional): Release Notes - Specify whether the created release should automatically create release notes using the provided name and body specified.
+      - Options: `true`, `false`
+  </Accordion>
+
+  <Accordion title="GITHUB_GET_RELEASE_BY_ID">
+    **Description:** Get a release by ID in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Release. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Release.
+    - `id` (string, required): Release ID - Specify the release ID of the release to fetch.
+  </Accordion>
+
+  <Accordion title="GITHUB_GET_RELEASE_BY_TAG_NAME">
+    **Description:** Get a release by tag name in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Release. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Release.
+    - `tag_name` (string, required): Name - Specify the tag of the release to fetch. (example: "v1.0.0").
+  </Accordion>
+
+  <Accordion title="GITHUB_DELETE_RELEASE">
+    **Description:** Delete a release in GitHub.
+
+    **Parameters:**
+    - `owner` (string, required): Owner - Specify the name of the account owner of the associated repository for this Release. (example: "abc").
+    - `repo` (string, required): Repository - Specify the name of the associated repository for this Release.
+    - `id` (string, required): Release ID - Specify the ID of the release to delete.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic GitHub Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (GitHub tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with GitHub capabilities
+github_agent = Agent(
+    role="Repository Manager",
+    goal="Manage GitHub repositories, issues, and releases efficiently",
+    backstory="An AI assistant specialized in repository management and issue tracking.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new issue
+create_issue_task = Task(
+    description="Create a bug report issue for the login functionality in the main repository",
+    agent=github_agent,
+    expected_output="Issue created successfully with issue number"
+)
+
+# Run the task
+crew = Crew(
+    agents=[github_agent],
+    tasks=[create_issue_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific GitHub Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific GitHub tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["github_create_issue", "github_update_issue", "github_search_issue"]
+)
+
+issue_manager = Agent(
+    role="Issue Manager",
+    goal="Create and manage GitHub issues efficiently",
+    backstory="An AI assistant that focuses on issue tracking and management.",
+    tools=enterprise_tools
+)
+
+# Task to manage issue workflow
+issue_workflow = Task(
+    description="Create a feature request issue and assign it to the development team",
+    agent=issue_manager,
+    expected_output="Feature request issue created and assigned successfully"
+)
+
+crew = Crew(
+    agents=[issue_manager],
+    tasks=[issue_workflow]
+)
+
+crew.kickoff()
+```
+
+### Release Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+release_manager = Agent(
+    role="Release Manager",
+    goal="Manage software releases and versioning",
+    backstory="An experienced release manager who handles version control and release processes.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new release
+release_task = Task(
+    description="""
+    Create a new release v2.1.0 for the project with:
+    - Auto-generated release notes
+    - Target the main branch
+    - Include a description of new features and bug fixes
+    """,
+    agent=release_manager,
+    expected_output="Release v2.1.0 created successfully with release notes"
+)
+
+crew = Crew(
+    agents=[release_manager],
+    tasks=[release_task]
+)
+
+crew.kickoff()
+```
+
+### Issue Tracking and Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+project_coordinator = Agent(
+    role="Project Coordinator",
+    goal="Track and coordinate project issues and development progress",
+    backstory="An AI assistant that helps coordinate development work and track project progress.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple GitHub operations
+coordination_task = Task(
+    description="""
+    1. Search for all open issues assigned to the current milestone
+    2. Identify overdue issues and update their priority labels
+    3. Create a weekly progress report issue
+    4. Lock resolved issues that have been inactive for 30 days
+    """,
+    agent=project_coordinator,
+    expected_output="Project coordination completed with progress report and issue management"
+)
+
+crew = Crew(
+    agents=[project_coordinator],
+    tasks=[coordination_task]
+)
+
+crew.kickoff()
+```
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with GitHub integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/gmail.mdx

@@ -0,0 +1,356 @@
+---
+title: Gmail Integration
+description: "Email and contact management with Gmail integration for CrewAI."
+icon: "envelope"
+---
+
+## Overview
+
+Enable your agents to manage emails, contacts, and drafts through Gmail. Send emails, search messages, manage contacts, create drafts, and streamline your email communications with AI-powered automation.
+
+## Prerequisites
+
+Before using the Gmail integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Gmail account with appropriate permissions
+- Connected your Gmail account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Gmail Integration
+
+### 1. Connect Your Gmail Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Gmail** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for email and contact management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="GMAIL_SEND_EMAIL">
+    **Description:** Send an email in Gmail.
+
+    **Parameters:**
+    - `toRecipients` (array, required): To - Specify the recipients as either a single string or a JSON array.
+      ```json
+      [
+        "recipient1@domain.com",
+        "recipient2@domain.com"
+      ]
+      ```
+    - `from` (string, required): From - Specify the email of the sender.
+    - `subject` (string, required): Subject - Specify the subject of the message.
+    - `messageContent` (string, required): Message Content - Specify the content of the email message as plain text or HTML.
+    - `attachments` (string, optional): Attachments - Accepts either a single file object or a JSON array of file objects.
+    - `additionalHeaders` (object, optional): Additional Headers - Specify any additional header fields here.
+      ```json
+      {
+        "reply-to": "Sender Name <sender@domain.com>"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GMAIL_GET_EMAIL_BY_ID">
+    **Description:** Get an email by ID in Gmail.
+
+    **Parameters:**
+    - `userId` (string, required): User ID - Specify the user's email address. (example: "user@domain.com").
+    - `messageId` (string, required): Message ID - Specify the ID of the message to retrieve.
+  </Accordion>
+
+  <Accordion title="GMAIL_SEARCH_FOR_EMAIL">
+    **Description:** Search for emails in Gmail using advanced filters.
+
+    **Parameters:**
+    - `emailFilterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "from",
+                "operator": "$stringContains",
+                "value": "example@domain.com"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `from`, `to`, `date`, `label`, `subject`, `cc`, `bcc`, `category`, `deliveredto:`, `size`, `filename`, `older_than`, `newer_than`, `list`, `is:important`, `is:unread`, `is:snoozed`, `is:starred`, `is:read`, `has:drive`, `has:document`, `has:spreadsheet`, `has:presentation`, `has:attachment`, `has:youtube`, `has:userlabels`
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "page_cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GMAIL_DELETE_EMAIL">
+    **Description:** Delete an email in Gmail.
+
+    **Parameters:**
+    - `userId` (string, required): User ID - Specify the user's email address. (example: "user@domain.com").
+    - `messageId` (string, required): Message ID - Specify the ID of the message to trash.
+  </Accordion>
+
+  <Accordion title="GMAIL_CREATE_A_CONTACT">
+    **Description:** Create a contact in Gmail.
+
+    **Parameters:**
+    - `givenName` (string, required): Given Name - Specify the Given Name of the Contact to create. (example: "John").
+    - `familyName` (string, required): Family Name - Specify the Family Name of the Contact to create. (example: "Doe").
+    - `email` (string, required): Email - Specify the Email Address of the Contact to create.
+    - `additionalFields` (object, optional): Additional Fields - Additional contact information.
+      ```json
+      {
+        "addresses": [
+          {
+            "streetAddress": "1000 North St.",
+            "city": "Los Angeles"
+          }
+        ]
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GMAIL_GET_CONTACT_BY_RESOURCE_NAME">
+    **Description:** Get a contact by resource name in Gmail.
+
+    **Parameters:**
+    - `resourceName` (string, required): Resource Name - Specify the resource name of the contact to fetch.
+  </Accordion>
+
+  <Accordion title="GMAIL_SEARCH_FOR_CONTACT">
+    **Description:** Search for a contact in Gmail.
+
+    **Parameters:**
+    - `searchTerm` (string, required): Term - Specify a search term to search for near or exact matches on the names, nickNames, emailAddresses, phoneNumbers, or organizations Contact properties.
+  </Accordion>
+
+  <Accordion title="GMAIL_DELETE_CONTACT">
+    **Description:** Delete a contact in Gmail.
+
+    **Parameters:**
+    - `resourceName` (string, required): Resource Name - Specify the resource name of the contact to delete.
+  </Accordion>
+
+  <Accordion title="GMAIL_CREATE_DRAFT">
+    **Description:** Create a draft in Gmail.
+
+    **Parameters:**
+    - `toRecipients` (array, optional): To - Specify the recipients as either a single string or a JSON array.
+      ```json
+      [
+        "recipient1@domain.com",
+        "recipient2@domain.com"
+      ]
+      ```
+    - `from` (string, optional): From - Specify the email of the sender.
+    - `subject` (string, optional): Subject - Specify the subject of the message.
+    - `messageContent` (string, optional): Message Content - Specify the content of the email message as plain text or HTML.
+    - `attachments` (string, optional): Attachments - Accepts either a single file object or a JSON array of file objects.
+    - `additionalHeaders` (object, optional): Additional Headers - Specify any additional header fields here.
+      ```json
+      {
+        "reply-to": "Sender Name <sender@domain.com>"
+      }
+      ```
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Gmail Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Gmail tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Gmail capabilities
+gmail_agent = Agent(
+    role="Email Manager",
+    goal="Manage email communications and contacts efficiently",
+    backstory="An AI assistant specialized in email management and communication.",
+    tools=[enterprise_tools]
+)
+
+# Task to send a follow-up email
+send_email_task = Task(
+    description="Send a follow-up email to john@example.com about the project update meeting",
+    agent=gmail_agent,
+    expected_output="Email sent successfully with confirmation"
+)
+
+# Run the task
+crew = Crew(
+    agents=[gmail_agent],
+    tasks=[send_email_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Gmail Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Gmail tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["gmail_send_email", "gmail_search_for_email", "gmail_create_draft"]
+)
+
+email_coordinator = Agent(
+    role="Email Coordinator",
+    goal="Coordinate email communications and manage drafts",
+    backstory="An AI assistant that focuses on email coordination and draft management.",
+    tools=enterprise_tools
+)
+
+# Task to prepare and send emails
+email_coordination = Task(
+    description="Search for emails from the marketing team, create a summary draft, and send it to stakeholders",
+    agent=email_coordinator,
+    expected_output="Summary email sent to stakeholders"
+)
+
+crew = Crew(
+    agents=[email_coordinator],
+    tasks=[email_coordination]
+)
+
+crew.kickoff()
+```
+
+### Contact Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+contact_manager = Agent(
+    role="Contact Manager",
+    goal="Manage and organize email contacts efficiently",
+    backstory="An experienced contact manager who maintains organized contact databases.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage contacts
+contact_task = Task(
+    description="""
+    1. Search for contacts from the 'example.com' domain
+    2. Create new contacts for recent email senders not in the contact list
+    3. Update contact information with recent interaction data
+    """,
+    agent=contact_manager,
+    expected_output="Contact database updated with new contacts and recent interactions"
+)
+
+crew = Crew(
+    agents=[contact_manager],
+    tasks=[contact_task]
+)
+
+crew.kickoff()
+```
+
+### Email Search and Analysis
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+email_analyst = Agent(
+    role="Email Analyst",
+    goal="Analyze email patterns and provide insights",
+    backstory="An AI assistant that analyzes email data to provide actionable insights.",
+    tools=[enterprise_tools]
+)
+
+# Task to analyze email patterns
+analysis_task = Task(
+    description="""
+    Search for all unread emails from the last 7 days,
+    categorize them by sender domain,
+    and create a summary report of communication patterns
+    """,
+    agent=email_analyst,
+    expected_output="Email analysis report with communication patterns and recommendations"
+)
+
+crew = Crew(
+    agents=[email_analyst],
+    tasks=[analysis_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Email Workflows
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+workflow_manager = Agent(
+    role="Email Workflow Manager",
+    goal="Automate email workflows and responses",
+    backstory="An AI assistant that manages automated email workflows and responses.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple Gmail operations
+workflow_task = Task(
+    description="""
+    1. Search for emails with 'urgent' in the subject from the last 24 hours
+    2. Create draft responses for each urgent email
+    3. Send automated acknowledgment emails to senders
+    4. Create a summary report of urgent items requiring attention
+    """,
+    agent=workflow_manager,
+    expected_output="Urgent emails processed with automated responses and summary report"
+)
+
+crew = Crew(
+    agents=[workflow_manager],
+    tasks=[workflow_task]
+)
+
+crew.kickoff()
+```
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Gmail integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/google_calendar.mdx

@@ -0,0 +1,391 @@
+---
+title: Google Calendar Integration
+description: "Event and schedule management with Google Calendar integration for CrewAI."
+icon: "calendar"
+---
+
+## Overview
+
+Enable your agents to manage calendar events, schedules, and availability through Google Calendar. Create and update events, manage attendees, check availability, and streamline your scheduling workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Google Calendar integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Google account with Google Calendar access
+- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Google Calendar Integration
+
+### 1. Connect Your Google Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Google Calendar** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for calendar and contact access
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="GOOGLE_CALENDAR_CREATE_EVENT">
+    **Description:** Create an event in Google Calendar.
+
+    **Parameters:**
+    - `eventName` (string, required): Event name.
+    - `startTime` (string, required): Start time - Accepts Unix timestamp or ISO8601 date formats.
+    - `endTime` (string, optional): End time - Defaults to one hour after the start time if left blank.
+    - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank.
+    - `attendees` (string, optional): Attendees - Accepts an array of email addresses or email addresses separated by commas.
+    - `eventLocation` (string, optional): Event location.
+    - `eventDescription` (string, optional): Event description.
+    - `eventId` (string, optional): Event ID - An ID from your application to associate this event with. You can use this ID to sync updates to this event later.
+    - `includeMeetLink` (boolean, optional): Include Google Meet link? - Automatically creates Google Meet conference link for this event.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_UPDATE_EVENT">
+    **Description:** Update an existing event in Google Calendar.
+
+    **Parameters:**
+    - `eventId` (string, required): Event ID - The ID of the event to update.
+    - `eventName` (string, optional): Event name.
+    - `startTime` (string, optional): Start time - Accepts Unix timestamp or ISO8601 date formats.
+    - `endTime` (string, optional): End time - Defaults to one hour after the start time if left blank.
+    - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank.
+    - `attendees` (string, optional): Attendees - Accepts an array of email addresses or email addresses separated by commas.
+    - `eventLocation` (string, optional): Event location.
+    - `eventDescription` (string, optional): Event description.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_LIST_EVENTS">
+    **Description:** List events from Google Calendar.
+
+    **Parameters:**
+    - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank.
+    - `after` (string, optional): After - Filters events that start after the provided date (Unix in milliseconds or ISO timestamp). (example: "2025-04-12T10:00:00Z or 1712908800000").
+    - `before` (string, optional): Before - Filters events that end before the provided date (Unix in milliseconds or ISO timestamp). (example: "2025-04-12T10:00:00Z or 1712908800000").
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_GET_EVENT_BY_ID">
+    **Description:** Get a specific event by ID from Google Calendar.
+
+    **Parameters:**
+    - `eventId` (string, required): Event ID.
+    - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_DELETE_EVENT">
+    **Description:** Delete an event from Google Calendar.
+
+    **Parameters:**
+    - `eventId` (string, required): Event ID - The ID of the calendar event to be deleted.
+    - `calendar` (string, optional): Calendar - Use Connect Portal Workflow Settings to allow users to select which calendar the event will be added to. Defaults to the user's primary calendar if left blank.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_GET_CONTACTS">
+    **Description:** Get contacts from Google Calendar.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "page_cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_SEARCH_CONTACTS">
+    **Description:** Search for contacts in Google Calendar.
+
+    **Parameters:**
+    - `query` (string, optional): Search query to search contacts.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_LIST_DIRECTORY_PEOPLE">
+    **Description:** List directory people.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "page_cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_SEARCH_DIRECTORY_PEOPLE">
+    **Description:** Search directory people.
+
+    **Parameters:**
+    - `query` (string, required): Search query to search contacts.
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "page_cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_LIST_OTHER_CONTACTS">
+    **Description:** List other contacts.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "page_cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_SEARCH_OTHER_CONTACTS">
+    **Description:** Search other contacts.
+
+    **Parameters:**
+    - `query` (string, optional): Search query to search contacts.
+  </Accordion>
+
+  <Accordion title="GOOGLE_CALENDAR_GET_AVAILABILITY">
+    **Description:** Get availability information for calendars.
+
+    **Parameters:**
+    - `timeMin` (string, required): The start of the interval. In ISO format.
+    - `timeMax` (string, required): The end of the interval. In ISO format.
+    - `timeZone` (string, optional): Time zone used in the response. Optional. The default is UTC.
+    - `items` (array, optional): List of calendars and/or groups to query. Defaults to the user default calendar.
+      ```json
+      [
+        {
+          "id": "calendar_id_1"
+        },
+        {
+          "id": "calendar_id_2"
+        }
+      ]
+      ```
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Calendar Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Google Calendar tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Google Calendar capabilities
+calendar_agent = Agent(
+    role="Schedule Manager",
+    goal="Manage calendar events and scheduling efficiently",
+    backstory="An AI assistant specialized in calendar management and scheduling coordination.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a meeting
+create_meeting_task = Task(
+    description="Create a team standup meeting for tomorrow at 9 AM with the development team",
+    agent=calendar_agent,
+    expected_output="Meeting created successfully with Google Meet link"
+)
+
+# Run the task
+crew = Crew(
+    agents=[calendar_agent],
+    tasks=[create_meeting_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Calendar Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Google Calendar tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["google_calendar_create_event", "google_calendar_list_events", "google_calendar_get_availability"]
+)
+
+meeting_coordinator = Agent(
+    role="Meeting Coordinator",
+    goal="Coordinate meetings and check availability",
+    backstory="An AI assistant that focuses on meeting scheduling and availability management.",
+    tools=enterprise_tools
+)
+
+# Task to schedule a meeting with availability check
+schedule_meeting = Task(
+    description="Check availability for next week and schedule a project review meeting with stakeholders",
+    agent=meeting_coordinator,
+    expected_output="Meeting scheduled after checking availability of all participants"
+)
+
+crew = Crew(
+    agents=[meeting_coordinator],
+    tasks=[schedule_meeting]
+)
+
+crew.kickoff()
+```
+
+### Event Management and Updates
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+event_manager = Agent(
+    role="Event Manager",
+    goal="Manage and update calendar events efficiently",
+    backstory="An experienced event manager who handles event logistics and updates.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage event updates
+event_management = Task(
+    description="""
+    1. List all events for this week
+    2. Update any events that need location changes to include video conference links
+    3. Send calendar invitations to new team members for recurring meetings
+    """,
+    agent=event_manager,
+    expected_output="Weekly events updated with proper locations and new attendees added"
+)
+
+crew = Crew(
+    agents=[event_manager],
+    tasks=[event_management]
+)
+
+crew.kickoff()
+```
+
+### Contact and Availability Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+availability_coordinator = Agent(
+    role="Availability Coordinator",
+    goal="Coordinate availability and manage contacts for scheduling",
+    backstory="An AI assistant that specializes in availability management and contact coordination.",
+    tools=[enterprise_tools]
+)
+
+# Task to coordinate availability
+availability_task = Task(
+    description="""
+    1. Search for contacts in the engineering department
+    2. Check availability for all engineers next Friday afternoon
+    3. Create a team meeting for the first available 2-hour slot
+    4. Include Google Meet link and send invitations
+    """,
+    agent=availability_coordinator,
+    expected_output="Team meeting scheduled based on availability with all engineers invited"
+)
+
+crew = Crew(
+    agents=[availability_coordinator],
+    tasks=[availability_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Scheduling Workflows
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+scheduling_automator = Agent(
+    role="Scheduling Automator",
+    goal="Automate scheduling workflows and calendar management",
+    backstory="An AI assistant that automates complex scheduling scenarios and calendar workflows.",
+    tools=[enterprise_tools]
+)
+
+# Complex scheduling automation task
+automation_task = Task(
+    description="""
+    1. List all upcoming events for the next two weeks
+    2. Identify any scheduling conflicts or back-to-back meetings
+    3. Suggest optimal meeting times by checking availability
+    4. Create buffer time between meetings where needed
+    5. Update event descriptions with agenda items and meeting links
+    """,
+    agent=scheduling_automator,
+    expected_output="Calendar optimized with resolved conflicts, buffer times, and updated meeting details"
+)
+
+crew = Crew(
+    agents=[scheduling_automator],
+    tasks=[automation_task]
+)
+
+crew.kickoff()
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Authentication Errors**
+- Ensure your Google account has the necessary permissions for calendar access
+- Verify that the OAuth connection includes all required scopes for Google Calendar API
+- Check if calendar sharing settings allow the required access level
+
+**Event Creation Issues**
+- Verify that time formats are correct (ISO8601 or Unix timestamps)
+- Ensure attendee email addresses are properly formatted
+- Check that the target calendar exists and is accessible
+- Verify time zones are correctly specified
+
+**Availability and Time Conflicts**
+- Use proper ISO format for time ranges when checking availability
+- Ensure time zones are consistent across all operations
+- Verify that calendar IDs are correct when checking multiple calendars
+
+**Contact and People Search**
+- Ensure search queries are properly formatted
+- Check that directory access permissions are granted
+- Verify that contact information is up to date and accessible
+
+**Event Updates and Deletions**
+- Verify that event IDs are correct and events exist
+- Ensure you have edit permissions for the events
+- Check that calendar ownership allows modifications
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Google Calendar integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/google_sheets.mdx

@@ -0,0 +1,321 @@
+---
+title: Google Sheets Integration
+description: "Spreadsheet data synchronization with Google Sheets integration for CrewAI."
+icon: "google"
+---
+
+## Overview
+
+Enable your agents to manage spreadsheet data through Google Sheets. Read rows, create new entries, update existing data, and streamline your data management workflows with AI-powered automation. Perfect for data tracking, reporting, and collaborative data management.
+
+## Prerequisites
+
+Before using the Google Sheets integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Google account with Google Sheets access
+- Connected your Google account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+- Spreadsheets with proper column headers for data operations
+
+## Setting Up Google Sheets Integration
+
+### 1. Connect Your Google Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Google Sheets** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for spreadsheet access
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="GOOGLE_SHEETS_GET_ROW">
+    **Description:** Get rows from a Google Sheets spreadsheet.
+
+    **Parameters:**
+    - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet.
+    - `limit` (string, optional): Limit rows - Limit the maximum number of rows to return.
+  </Accordion>
+
+  <Accordion title="GOOGLE_SHEETS_CREATE_ROW">
+    **Description:** Create a new row in a Google Sheets spreadsheet.
+
+    **Parameters:**
+    - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet..
+    - `worksheet` (string, required): Worksheet - Your worksheet must have column headers.
+    - `additionalFields` (object, required): Fields - Include fields to create this row with, as an object with keys of Column Names. Use Connect Portal Workflow Settings to allow users to select a Column Mapping.
+      ```json
+      {
+        "columnName1": "columnValue1",
+        "columnName2": "columnValue2",
+        "columnName3": "columnValue3",
+        "columnName4": "columnValue4"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="GOOGLE_SHEETS_UPDATE_ROW">
+    **Description:** Update existing rows in a Google Sheets spreadsheet.
+
+    **Parameters:**
+    - `spreadsheetId` (string, required): Spreadsheet - Use Connect Portal Workflow Settings to allow users to select a spreadsheet. Defaults to using the first worksheet in the selected spreadsheet.
+    - `worksheet` (string, required): Worksheet - Your worksheet must have column headers.
+    - `filterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions to identify which rows to update.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "status",
+                "operator": "$stringExactlyMatches",
+                "value": "pending"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available operators: `$stringContains`, `$stringDoesNotContain`, `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$numberGreaterThan`, `$numberLessThan`, `$numberEquals`, `$numberDoesNotEqual`, `$dateTimeAfter`, `$dateTimeBefore`, `$dateTimeEquals`, `$booleanTrue`, `$booleanFalse`, `$exists`, `$doesNotExist`
+    - `additionalFields` (object, required): Fields - Include fields to update, as an object with keys of Column Names. Use Connect Portal Workflow Settings to allow users to select a Column Mapping.
+      ```json
+      {
+        "columnName1": "newValue1",
+        "columnName2": "newValue2",
+        "columnName3": "newValue3",
+        "columnName4": "newValue4"
+      }
+      ```
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Google Sheets Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Google Sheets tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Google Sheets capabilities
+sheets_agent = Agent(
+    role="Data Manager",
+    goal="Manage spreadsheet data and track information efficiently",
+    backstory="An AI assistant specialized in data management and spreadsheet operations.",
+    tools=[enterprise_tools]
+)
+
+# Task to add new data to a spreadsheet
+data_entry_task = Task(
+    description="Add a new customer record to the customer database spreadsheet with name, email, and signup date",
+    agent=sheets_agent,
+    expected_output="New customer record added successfully to the spreadsheet"
+)
+
+# Run the task
+crew = Crew(
+    agents=[sheets_agent],
+    tasks=[data_entry_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Google Sheets Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Google Sheets tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["google_sheets_get_row", "google_sheets_create_row"]
+)
+
+data_collector = Agent(
+    role="Data Collector",
+    goal="Collect and organize data in spreadsheets",
+    backstory="An AI assistant that focuses on data collection and organization.",
+    tools=enterprise_tools
+)
+
+# Task to collect and organize data
+data_collection = Task(
+    description="Retrieve current inventory data and add new product entries to the inventory spreadsheet",
+    agent=data_collector,
+    expected_output="Inventory data retrieved and new products added successfully"
+)
+
+crew = Crew(
+    agents=[data_collector],
+    tasks=[data_collection]
+)
+
+crew.kickoff()
+```
+
+### Data Analysis and Reporting
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+data_analyst = Agent(
+    role="Data Analyst",
+    goal="Analyze spreadsheet data and generate insights",
+    backstory="An experienced data analyst who extracts insights from spreadsheet data.",
+    tools=[enterprise_tools]
+)
+
+# Task to analyze data and create reports
+analysis_task = Task(
+    description="""
+    1. Retrieve all sales data from the current month's spreadsheet
+    2. Analyze the data for trends and patterns
+    3. Create a summary report in a new row with key metrics
+    """,
+    agent=data_analyst,
+    expected_output="Sales data analyzed and summary report created with key insights"
+)
+
+crew = Crew(
+    agents=[data_analyst],
+    tasks=[analysis_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Data Updates
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+data_updater = Agent(
+    role="Data Updater",
+    goal="Automatically update and maintain spreadsheet data",
+    backstory="An AI assistant that maintains data accuracy and updates records automatically.",
+    tools=[enterprise_tools]
+)
+
+# Task to update data based on conditions
+update_task = Task(
+    description="""
+    1. Find all pending orders in the orders spreadsheet
+    2. Update their status to 'processing'
+    3. Add a timestamp for when the status was updated
+    4. Log the changes in a separate tracking sheet
+    """,
+    agent=data_updater,
+    expected_output="All pending orders updated to processing status with timestamps logged"
+)
+
+crew = Crew(
+    agents=[data_updater],
+    tasks=[update_task]
+)
+
+crew.kickoff()
+```
+
+### Complex Data Management Workflow
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+workflow_manager = Agent(
+    role="Data Workflow Manager",
+    goal="Manage complex data workflows across multiple spreadsheets",
+    backstory="An AI assistant that orchestrates complex data operations across multiple spreadsheets.",
+    tools=[enterprise_tools]
+)
+
+# Complex workflow task
+workflow_task = Task(
+    description="""
+    1. Get all customer data from the main customer spreadsheet
+    2. Create monthly summary entries for active customers
+    3. Update customer status based on activity in the last 30 days
+    4. Generate a monthly report with customer metrics
+    5. Archive inactive customer records to a separate sheet
+    """,
+    agent=workflow_manager,
+    expected_output="Monthly customer workflow completed with updated statuses and generated reports"
+)
+
+crew = Crew(
+    agents=[workflow_manager],
+    tasks=[workflow_task]
+)
+
+crew.kickoff()
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Permission Errors**
+- Ensure your Google account has edit access to the target spreadsheets
+- Verify that the OAuth connection includes required scopes for Google Sheets API
+- Check that spreadsheets are shared with the authenticated account
+
+**Spreadsheet Structure Issues**
+- Ensure worksheets have proper column headers before creating or updating rows
+- Verify that column names in `additionalFields` match the actual column headers
+- Check that the specified worksheet exists in the spreadsheet
+
+**Data Type and Format Issues**
+- Ensure data values match the expected format for each column
+- Use proper date formats for date columns (ISO format recommended)
+- Verify that numeric values are properly formatted for number columns
+
+**Filter Formula Issues**
+- Ensure filter formulas follow the correct JSON structure for disjunctive normal form
+- Use valid field names that match actual column headers
+- Test simple filters before building complex multi-condition queries
+- Verify that operator types match the data types in the columns
+
+**Row Limits and Performance**
+- Be mindful of row limits when using `GOOGLE_SHEETS_GET_ROW`
+- Consider pagination for large datasets
+- Use specific filters to reduce the amount of data processed
+
+**Update Operations**
+- Ensure filter conditions properly identify the intended rows for updates
+- Test filter conditions with small datasets before large updates
+- Verify that all required fields are included in update operations
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Google Sheets integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/hubspot.mdx

@@ -0,0 +1,579 @@
+---
+title: "HubSpot Integration"
+description: "Manage companies and contacts in HubSpot with CrewAI."
+icon: "briefcase"
+---
+
+## Overview
+
+Enable your agents to manage companies and contacts within HubSpot. Create new records and streamline your CRM processes with AI-powered automation.
+
+## Prerequisites
+
+Before using the HubSpot integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription.
+- A HubSpot account with appropriate permissions.
+- Connected your HubSpot account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors).
+
+## Setting Up HubSpot Integration
+
+### 1. Connect Your HubSpot Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors).
+2. Find **HubSpot** in the Authentication Integrations section.
+3. Click **Connect** and complete the OAuth flow.
+4. Grant the necessary permissions for company and contact management.
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account).
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="HUBSPOT_CREATE_RECORD_COMPANIES">
+    **Description:** Create a new company record in HubSpot.
+
+    **Parameters:**
+    - `name` (string, required): Name of the company.
+    - `domain` (string, optional): Company Domain Name.
+    - `industry` (string, optional): Industry. Must be one of the predefined values from HubSpot.
+    - `phone` (string, optional): Phone Number.
+    - `hubspot_owner_id` (string, optional): Company owner ID.
+    - `type` (string, optional): Type of the company. Available values: `PROSPECT`, `PARTNER`, `RESELLER`, `VENDOR`, `OTHER`.
+    - `city` (string, optional): City.
+    - `state` (string, optional): State/Region.
+    - `zip` (string, optional): Postal Code.
+    - `numberofemployees` (number, optional): Number of Employees.
+    - `annualrevenue` (number, optional): Annual Revenue.
+    - `timezone` (string, optional): Time Zone.
+    - `description` (string, optional): Description.
+    - `linkedin_company_page` (string, optional): LinkedIn Company Page URL.
+    - `company_email` (string, optional): Company Email.
+    - `first_name` (string, optional): First Name of a contact at the company.
+    - `last_name` (string, optional): Last Name of a contact at the company.
+    - `about_us` (string, optional): About Us.
+    - `hs_csm_sentiment` (string, optional): CSM Sentiment. Available values: `at_risk`, `neutral`, `healthy`.
+    - `closedate` (string, optional): Close Date.
+    - `hs_keywords` (string, optional): Company Keywords. Must be one of the predefined values.
+    - `country` (string, optional): Country/Region.
+    - `hs_country_code` (string, optional): Country/Region Code.
+    - `hs_employee_range` (string, optional): Employee range.
+    - `facebook_company_page` (string, optional): Facebook Company Page URL.
+    - `facebookfans` (number, optional): Number of Facebook Fans.
+    - `hs_gps_coordinates` (string, optional): GPS Coordinates.
+    - `hs_gps_error` (string, optional): GPS Error.
+    - `googleplus_page` (string, optional): Google Plus Page URL.
+    - `owneremail` (string, optional): HubSpot Owner Email.
+    - `ownername` (string, optional): HubSpot Owner Name.
+    - `hs_ideal_customer_profile` (string, optional): Ideal Customer Profile Tier. Available values: `tier_1`, `tier_2`, `tier_3`.
+    - `hs_industry_group` (string, optional): Industry group.
+    - `is_public` (boolean, optional): Is Public.
+    - `hs_last_metered_enrichment_timestamp` (string, optional): Last Metered Enrichment Timestamp.
+    - `hs_lead_status` (string, optional): Lead Status. Available values: `NEW`, `OPEN`, `IN_PROGRESS`, `OPEN_DEAL`, `UNQUALIFIED`, `ATTEMPTED_TO_CONTACT`, `CONNECTED`, `BAD_TIMING`.
+    - `lifecyclestage` (string, optional): Lifecycle Stage. Available values: `subscriber`, `lead`, `marketingqualifiedlead`, `salesqualifiedlead`, `opportunity`, `customer`, `evangelist`, `other`.
+    - `linkedinbio` (string, optional): LinkedIn Bio.
+    - `hs_linkedin_handle` (string, optional): LinkedIn handle.
+    - `hs_live_enrichment_deadline` (string, optional): Live enrichment deadline.
+    - `hs_logo_url` (string, optional): Logo URL.
+    - `hs_analytics_source` (string, optional): Original Traffic Source.
+    - `hs_pinned_engagement_id` (number, optional): Pinned Engagement ID.
+    - `hs_quick_context` (string, optional): Quick context.
+    - `hs_revenue_range` (string, optional): Revenue range.
+    - `hs_state_code` (string, optional): State/Region Code.
+    - `address` (string, optional): Street Address.
+    - `address2` (string, optional): Street Address 2.
+    - `hs_is_target_account` (boolean, optional): Target Account.
+    - `hs_target_account` (string, optional): Target Account Tier. Available values: `tier_1`, `tier_2`, `tier_3`.
+    - `hs_target_account_recommendation_snooze_time` (string, optional): Target Account Recommendation Snooze Time.
+    - `hs_target_account_recommendation_state` (string, optional): Target Account Recommendation State. Available values: `DISMISSED`, `NONE`, `SNOOZED`.
+    - `total_money_raised` (string, optional): Total Money Raised.
+    - `twitterbio` (string, optional): Twitter Bio.
+    - `twitterfollowers` (number, optional): Twitter Followers.
+    - `twitterhandle` (string, optional): Twitter Handle.
+    - `web_technologies` (string, optional): Web Technologies used. Must be one of the predefined values.
+    - `website` (string, optional): Website URL.
+    - `founded_year` (string, optional): Year Founded.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_CREATE_RECORD_CONTACTS">
+    **Description:** Create a new contact record in HubSpot.
+
+    **Parameters:**
+    - `email` (string, required): Email address of the contact.
+    - `firstname` (string, optional): First Name.
+    - `lastname` (string, optional): Last Name.
+    - `phone` (string, optional): Phone Number.
+    - `hubspot_owner_id` (string, optional): Contact owner.
+    - `lifecyclestage` (string, optional): Lifecycle Stage. Available values: `subscriber`, `lead`, `marketingqualifiedlead`, `salesqualifiedlead`, `opportunity`, `customer`, `evangelist`, `other`.
+    - `hs_lead_status` (string, optional): Lead Status. Available values: `NEW`, `OPEN`, `IN_PROGRESS`, `OPEN_DEAL`, `UNQUALIFIED`, `ATTEMPTED_TO_CONTACT`, `CONNECTED`, `BAD_TIMING`.
+    - `annualrevenue` (string, optional): Annual Revenue.
+    - `hs_buying_role` (string, optional): Buying Role.
+    - `cc_emails` (string, optional): CC Emails.
+    - `ch_customer_id` (string, optional): Chargify Customer ID.
+    - `ch_customer_reference` (string, optional): Chargify Customer Reference.
+    - `chargify_sites` (string, optional): Chargify Site(s).
+    - `city` (string, optional): City.
+    - `hs_facebook_ad_clicked` (boolean, optional): Clicked Facebook ad.
+    - `hs_linkedin_ad_clicked` (string, optional): Clicked LinkedIn Ad.
+    - `hs_clicked_linkedin_ad` (string, optional): Clicked on a LinkedIn Ad.
+    - `closedate` (string, optional): Close Date.
+    - `company` (string, optional): Company Name.
+    - `company_size` (string, optional): Company size.
+    - `country` (string, optional): Country/Region.
+    - `hs_country_region_code` (string, optional): Country/Region Code.
+    - `date_of_birth` (string, optional): Date of birth.
+    - `degree` (string, optional): Degree.
+    - `hs_email_customer_quarantined_reason` (string, optional): Email address quarantine reason.
+    - `hs_role` (string, optional): Employment Role. Must be one of the predefined values.
+    - `hs_seniority` (string, optional): Employment Seniority. Must be one of the predefined values.
+    - `hs_sub_role` (string, optional): Employment Sub Role. Must be one of the predefined values.
+    - `hs_employment_change_detected_date` (string, optional): Employment change detected date.
+    - `hs_enriched_email_bounce_detected` (boolean, optional): Enriched Email Bounce Detected.
+    - `hs_facebookid` (string, optional): Facebook ID.
+    - `hs_facebook_click_id` (string, optional): Facebook click id.
+    - `fax` (string, optional): Fax Number.
+    - `field_of_study` (string, optional): Field of study.
+    - `followercount` (number, optional): Follower Count.
+    - `gender` (string, optional): Gender.
+    - `hs_google_click_id` (string, optional): Google ad click id.
+    - `graduation_date` (string, optional): Graduation date.
+    - `owneremail` (string, optional): HubSpot Owner Email (legacy).
+    - `ownername` (string, optional): HubSpot Owner Name (legacy).
+    - `industry` (string, optional): Industry.
+    - `hs_inferred_language_codes` (string, optional): Inferred Language Codes. Must be one of the predefined values.
+    - `jobtitle` (string, optional): Job Title.
+    - `hs_job_change_detected_date` (string, optional): Job change detected date.
+    - `job_function` (string, optional): Job function.
+    - `hs_journey_stage` (string, optional): Journey Stage. Must be one of the predefined values.
+    - `kloutscoregeneral` (number, optional): Klout Score.
+    - `hs_last_metered_enrichment_timestamp` (string, optional): Last Metered Enrichment Timestamp.
+    - `hs_latest_source` (string, optional): Latest Traffic Source.
+    - `hs_latest_source_timestamp` (string, optional): Latest Traffic Source Date.
+    - `hs_legal_basis` (string, optional): Legal basis for processing contact's data.
+    - `linkedinbio` (string, optional): LinkedIn Bio.
+    - `linkedinconnections` (number, optional): LinkedIn Connections.
+    - `hs_linkedin_url` (string, optional): LinkedIn URL.
+    - `hs_linkedinid` (string, optional): Linkedin ID.
+    - `hs_live_enrichment_deadline` (string, optional): Live enrichment deadline.
+    - `marital_status` (string, optional): Marital Status.
+    - `hs_content_membership_email` (string, optional): Member email.
+    - `hs_content_membership_notes` (string, optional): Membership Notes.
+    - `message` (string, optional): Message.
+    - `military_status` (string, optional): Military status.
+    - `mobilephone` (string, optional): Mobile Phone Number.
+    - `numemployees` (string, optional): Number of Employees.
+    - `hs_analytics_source` (string, optional): Original Traffic Source.
+    - `photo` (string, optional): Photo.
+    - `hs_pinned_engagement_id` (number, optional): Pinned engagement ID.
+    - `zip` (string, optional): Postal Code.
+    - `hs_language` (string, optional): Preferred language. Must be one of the predefined values.
+    - `associatedcompanyid` (number, optional): Primary Associated Company ID.
+    - `hs_email_optout_survey_reason` (string, optional): Reason for opting out of email.
+    - `relationship_status` (string, optional): Relationship Status.
+    - `hs_returning_to_office_detected_date` (string, optional): Returning to office detected date.
+    - `salutation` (string, optional): Salutation.
+    - `school` (string, optional): School.
+    - `seniority` (string, optional): Seniority.
+    - `hs_feedback_show_nps_web_survey` (boolean, optional): Should be shown an NPS web survey.
+    - `start_date` (string, optional): Start date.
+    - `state` (string, optional): State/Region.
+    - `hs_state_code` (string, optional): State/Region Code.
+    - `hs_content_membership_status` (string, optional): Status.
+    - `address` (string, optional): Street Address.
+    - `tax_exempt` (string, optional): Tax Exempt.
+    - `hs_timezone` (string, optional): Time Zone. Must be one of the predefined values.
+    - `twitterbio` (string, optional): Twitter Bio.
+    - `hs_twitterid` (string, optional): Twitter ID.
+    - `twitterprofilephoto` (string, optional): Twitter Profile Photo.
+    - `twitterhandle` (string, optional): Twitter Username.
+    - `vat_number` (string, optional): VAT Number.
+    - `ch_verified` (string, optional): Verified for ACH/eCheck Payments.
+    - `website` (string, optional): Website URL.
+    - `hs_whatsapp_phone_number` (string, optional): WhatsApp Phone Number.
+    - `work_email` (string, optional): Work email.
+    - `hs_googleplusid` (string, optional): googleplus ID.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_CREATE_RECORD_DEALS">
+    **Description:** Create a new deal record in HubSpot.
+
+    **Parameters:**
+    - `dealname` (string, required): Name of the deal.
+    - `amount` (number, optional): The value of the deal.
+    - `dealstage` (string, optional): The pipeline stage of the deal.
+    - `pipeline` (string, optional): The pipeline the deal belongs to.
+    - `closedate` (string, optional): The date the deal is expected to close.
+    - `hubspot_owner_id` (string, optional): The owner of the deal.
+    - `dealtype` (string, optional): The type of deal. Available values: `newbusiness`, `existingbusiness`.
+    - `description` (string, optional): A description of the deal.
+    - `hs_priority` (string, optional): The priority of the deal. Available values: `low`, `medium`, `high`.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_CREATE_RECORD_ENGAGEMENTS">
+    **Description:** Create a new engagement (e.g., note, email, call, meeting, task) in HubSpot.
+
+    **Parameters:**
+    - `engagementType` (string, required): The type of engagement. Available values: `NOTE`, `EMAIL`, `CALL`, `MEETING`, `TASK`.
+    - `hubspot_owner_id` (string, optional): The user the activity is assigned to.
+    - `hs_timestamp` (string, optional): The date and time of the activity.
+    - `hs_note_body` (string, optional): The body of the note. (Used for `NOTE`)
+    - `hs_task_subject` (string, optional): The title of the task. (Used for `TASK`)
+    - `hs_task_body` (string, optional): The notes for the task. (Used for `TASK`)
+    - `hs_task_status` (string, optional): The status of the task. (Used for `TASK`)
+    - `hs_meeting_title` (string, optional): The title of the meeting. (Used for `MEETING`)
+    - `hs_meeting_body` (string, optional): The description for the meeting. (Used for `MEETING`)
+    - `hs_meeting_start_time` (string, optional): The start time of the meeting. (Used for `MEETING`)
+    - `hs_meeting_end_time` (string, optional): The end time of the meeting. (Used for `MEETING`)
+  </Accordion>
+
+  <Accordion title="HUBSPOT_UPDATE_RECORD_COMPANIES">
+    **Description:** Update an existing company record in HubSpot.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the company to update.
+    - `name` (string, optional): Name of the company.
+    - `domain` (string, optional): Company Domain Name.
+    - `industry` (string, optional): Industry.
+    - `phone` (string, optional): Phone Number.
+    - `city` (string, optional): City.
+    - `state` (string, optional): State/Region.
+    - `zip` (string, optional): Postal Code.
+    - `numberofemployees` (number, optional): Number of Employees.
+    - `annualrevenue` (number, optional): Annual Revenue.
+    - `description` (string, optional): Description.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_CREATE_RECORD_ANY">
+    **Description:** Create a record for a specified object type in HubSpot.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID of the custom object.
+    - Additional parameters depend on the custom object's schema.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_UPDATE_RECORD_CONTACTS">
+    **Description:** Update an existing contact record in HubSpot.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the contact to update.
+    - `firstname` (string, optional): First Name.
+    - `lastname` (string, optional): Last Name.
+    - `email` (string, optional): Email address.
+    - `phone` (string, optional): Phone Number.
+    - `company` (string, optional): Company Name.
+    - `jobtitle` (string, optional): Job Title.
+    - `lifecyclestage` (string, optional): Lifecycle Stage.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_UPDATE_RECORD_DEALS">
+    **Description:** Update an existing deal record in HubSpot.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the deal to update.
+    - `dealname` (string, optional): Name of the deal.
+    - `amount` (number, optional): The value of the deal.
+    - `dealstage` (string, optional): The pipeline stage of the deal.
+    - `pipeline` (string, optional): The pipeline the deal belongs to.
+    - `closedate` (string, optional): The date the deal is expected to close.
+    - `dealtype` (string, optional): The type of deal.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_UPDATE_RECORD_ENGAGEMENTS">
+    **Description:** Update an existing engagement in HubSpot.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the engagement to update.
+    - `hs_note_body` (string, optional): The body of the note.
+    - `hs_task_subject` (string, optional): The title of the task.
+    - `hs_task_body` (string, optional): The notes for the task.
+    - `hs_task_status` (string, optional): The status of the task.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_UPDATE_RECORD_ANY">
+    **Description:** Update a record for a specified object type in HubSpot.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update.
+    - `recordType` (string, required): The object type ID of the custom object.
+    - Additional parameters depend on the custom object's schema.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORDS_COMPANIES">
+    **Description:** Get a list of company records from HubSpot.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORDS_CONTACTS">
+    **Description:** Get a list of contact records from HubSpot.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORDS_DEALS">
+    **Description:** Get a list of deal records from HubSpot.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORDS_ENGAGEMENTS">
+    **Description:** Get a list of engagement records from HubSpot.
+
+    **Parameters:**
+    - `objectName` (string, required): The type of engagement to fetch (e.g., "notes").
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORDS_ANY">
+    **Description:** Get a list of records for any specified object type in HubSpot.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID of the custom object.
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORD_BY_ID_COMPANIES">
+    **Description:** Get a single company record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the company to retrieve.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORD_BY_ID_CONTACTS">
+    **Description:** Get a single contact record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the contact to retrieve.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORD_BY_ID_DEALS">
+    **Description:** Get a single deal record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the deal to retrieve.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORD_BY_ID_ENGAGEMENTS">
+    **Description:** Get a single engagement record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the engagement to retrieve.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_RECORD_BY_ID_ANY">
+    **Description:** Get a single record of any specified object type by its ID.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID of the custom object.
+    - `recordId` (string, required): The ID of the record to retrieve.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_SEARCH_RECORDS_COMPANIES">
+    **Description:** Search for company records in HubSpot using a filter formula.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): A filter in disjunctive normal form (OR of ANDs).
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_SEARCH_RECORDS_CONTACTS">
+    **Description:** Search for contact records in HubSpot using a filter formula.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): A filter in disjunctive normal form (OR of ANDs).
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_SEARCH_RECORDS_DEALS">
+    **Description:** Search for deal records in HubSpot using a filter formula.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): A filter in disjunctive normal form (OR of ANDs).
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_SEARCH_RECORDS_ENGAGEMENTS">
+    **Description:** Search for engagement records in HubSpot using a filter formula.
+
+    **Parameters:**
+    - `engagementFilterFormula` (object, optional): A filter for engagements.
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_SEARCH_RECORDS_ANY">
+    **Description:** Search for records of any specified object type in HubSpot.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID to search.
+    - `filterFormula` (string, optional): The filter formula to apply.
+    - `paginationParameters` (object, optional): Use `pageCursor` to fetch subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DELETE_RECORD_COMPANIES">
+    **Description:** Delete a company record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the company to delete.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DELETE_RECORD_CONTACTS">
+    **Description:** Delete a contact record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the contact to delete.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DELETE_RECORD_DEALS">
+    **Description:** Delete a deal record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the deal to delete.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DELETE_RECORD_ENGAGEMENTS">
+    **Description:** Delete an engagement record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the engagement to delete.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DELETE_RECORD_ANY">
+    **Description:** Delete a record of any specified object type by its ID.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID of the custom object.
+    - `recordId` (string, required): The ID of the record to delete.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_GET_CONTACTS_BY_LIST_ID">
+    **Description:** Get contacts from a specific list by its ID.
+
+    **Parameters:**
+    - `listId` (string, required): The ID of the list to get contacts from.
+    - `paginationParameters` (object, optional): Use `pageCursor` for subsequent pages.
+  </Accordion>
+
+  <Accordion title="HUBSPOT_DESCRIBE_ACTION_SCHEMA">
+    **Description:** Get the expected schema for a given object type and operation.
+
+    **Parameters:**
+    - `recordType` (string, required): The object type ID (e.g., 'companies').
+    - `operation` (string, required): The operation type (e.g., 'CREATE_RECORD').
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic HubSpot Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (HubSpot tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with HubSpot capabilities
+hubspot_agent = Agent(
+    role="CRM Manager",
+    goal="Manage company and contact records in HubSpot",
+    backstory="An AI assistant specialized in CRM management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new company
+create_company_task = Task(
+    description="Create a new company in HubSpot with name 'Innovate Corp' and domain 'innovatecorp.com'.",
+    agent=hubspot_agent,
+    expected_output="Company created successfully with confirmation"
+)
+
+# Run the task
+crew = Crew(
+    agents=[hubspot_agent],
+    tasks=[create_company_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific HubSpot Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only the tool to create contacts
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["hubspot_create_record_contacts"]
+)
+
+contact_creator = Agent(
+    role="Contact Creator",
+    goal="Create new contacts in HubSpot",
+    backstory="An AI assistant that focuses on creating new contact entries in the CRM.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a contact
+create_contact = Task(
+    description="Create a new contact for 'John Doe' with email 'john.doe@example.com'.",
+    agent=contact_creator,
+    expected_output="Contact created successfully in HubSpot."
+)
+
+crew = Crew(
+    agents=[contact_creator],
+    tasks=[create_contact]
+)
+
+crew.kickoff()
+```
+
+### Contact Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+crm_manager = Agent(
+    role="CRM Manager",
+    goal="Manage and organize HubSpot contacts efficiently.",
+    backstory="An experienced CRM manager who maintains an organized contact database.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage contacts
+contact_task = Task(
+    description="Create a new contact for 'Jane Smith' at 'Global Tech Inc.' with email 'jane.smith@globaltech.com'.",
+    agent=crm_manager,
+    expected_output="Contact database updated with the new contact."
+)
+
+crew = Crew(
+    agents=[crm_manager],
+    tasks=[contact_task]
+)
+
+crew.kickoff()
+```
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with HubSpot integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/jira.mdx

@@ -0,0 +1,394 @@
+---
+title: Jira Integration
+description: "Issue tracking and project management with Jira integration for CrewAI."
+icon: "bug"
+---
+
+## Overview
+
+Enable your agents to manage issues, projects, and workflows through Jira. Create and update issues, track project progress, manage assignments, and streamline your project management with AI-powered automation.
+
+## Prerequisites
+
+Before using the Jira integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Jira account with appropriate project permissions
+- Connected your Jira account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Jira Integration
+
+### 1. Connect Your Jira Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Jira** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for issue and project management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="JIRA_CREATE_ISSUE">
+    **Description:** Create an issue in Jira.
+
+    **Parameters:**
+    - `summary` (string, required): Summary - A brief one-line summary of the issue. (example: "The printer stopped working").
+    - `project` (string, optional): Project - The project which the issue belongs to. Defaults to the user's first project if not provided. Use Connect Portal Workflow Settings to allow users to select a Project.
+    - `issueType` (string, optional): Issue type - Defaults to Task if not provided.
+    - `jiraIssueStatus` (string, optional): Status - Defaults to the project's first status if not provided.
+    - `assignee` (string, optional): Assignee - Defaults to the authenticated user if not provided.
+    - `descriptionType` (string, optional): Description Type - Select the Description Type.
+      - Options: `description`, `descriptionJSON`
+    - `description` (string, optional): Description - A detailed description of the issue. This field appears only when 'descriptionType' = 'description'.
+    - `additionalFields` (string, optional): Additional Fields - Specify any other fields that should be included in JSON format. Use Connect Portal Workflow Settings to allow users to select which Issue Fields to update.
+      ```json
+      {
+        "customfield_10001": "value"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="JIRA_UPDATE_ISSUE">
+    **Description:** Update an issue in Jira.
+
+    **Parameters:**
+    - `issueKey` (string, required): Issue Key (example: "TEST-1234").
+    - `summary` (string, optional): Summary - A brief one-line summary of the issue. (example: "The printer stopped working").
+    - `issueType` (string, optional): Issue type - Use Connect Portal Workflow Settings to allow users to select an Issue Type.
+    - `jiraIssueStatus` (string, optional): Status - Use Connect Portal Workflow Settings to allow users to select a Status.
+    - `assignee` (string, optional): Assignee - Use Connect Portal Workflow Settings to allow users to select an Assignee.
+    - `descriptionType` (string, optional): Description Type - Select the Description Type.
+      - Options: `description`, `descriptionJSON`
+    - `description` (string, optional): Description - A detailed description of the issue. This field appears only when 'descriptionType' = 'description'.
+    - `additionalFields` (string, optional): Additional Fields - Specify any other fields that should be included in JSON format.
+  </Accordion>
+
+  <Accordion title="JIRA_GET_ISSUE_BY_KEY">
+    **Description:** Get an issue by key in Jira.
+
+    **Parameters:**
+    - `issueKey` (string, required): Issue Key (example: "TEST-1234").
+  </Accordion>
+
+  <Accordion title="JIRA_FILTER_ISSUES">
+    **Description:** Search issues in Jira using filters.
+
+    **Parameters:**
+    - `jqlQuery` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "status",
+                "operator": "$stringExactlyMatches",
+                "value": "Open"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available operators: `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringIsIn`, `$stringIsNotIn`, `$stringContains`, `$stringDoesNotContain`, `$stringGreaterThan`, `$stringLessThan`
+    - `limit` (string, optional): Limit results - Limit the maximum number of issues to return. Defaults to 10 if left blank.
+  </Accordion>
+
+  <Accordion title="JIRA_SEARCH_BY_JQL">
+    **Description:** Search issues by JQL in Jira.
+
+    **Parameters:**
+    - `jqlQuery` (string, required): JQL Query (example: "project = PROJECT").
+    - `paginationParameters` (object, optional): Pagination parameters for paginated results.
+      ```json
+      {
+        "pageCursor": "cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="JIRA_UPDATE_ISSUE_ANY">
+    **Description:** Update any issue in Jira. Use DESCRIBE_ACTION_SCHEMA to get properties schema for this function.
+
+    **Parameters:** No specific parameters - use JIRA_DESCRIBE_ACTION_SCHEMA first to get the expected schema.
+  </Accordion>
+
+  <Accordion title="JIRA_DESCRIBE_ACTION_SCHEMA">
+    **Description:** Get the expected schema for an issue type. Use this function first if no other function matches the issue type you want to operate on.
+
+    **Parameters:**
+    - `issueTypeId` (string, required): Issue Type ID.
+    - `projectKey` (string, required): Project key.
+    - `operation` (string, required): Operation Type value, for example CREATE_ISSUE or UPDATE_ISSUE.
+  </Accordion>
+
+  <Accordion title="JIRA_GET_PROJECTS">
+    **Description:** Get Projects in Jira.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Pagination Parameters.
+      ```json
+      {
+        "pageCursor": "cursor_string"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="JIRA_GET_ISSUE_TYPES_BY_PROJECT">
+    **Description:** Get Issue Types by project in Jira.
+
+    **Parameters:**
+    - `project` (string, required): Project key.
+  </Accordion>
+
+  <Accordion title="JIRA_GET_ISSUE_TYPES">
+    **Description:** Get all Issue Types in Jira.
+
+    **Parameters:** None required.
+  </Accordion>
+
+  <Accordion title="JIRA_GET_ISSUE_STATUS_BY_PROJECT">
+    **Description:** Get issue statuses for a given project.
+
+    **Parameters:**
+    - `project` (string, required): Project key.
+  </Accordion>
+
+  <Accordion title="JIRA_GET_ALL_ASSIGNEES_BY_PROJECT">
+    **Description:** Get assignees for a given project.
+
+    **Parameters:**
+    - `project` (string, required): Project key.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Jira Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Jira tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Jira capabilities
+jira_agent = Agent(
+    role="Issue Manager",
+    goal="Manage Jira issues and track project progress efficiently",
+    backstory="An AI assistant specialized in issue tracking and project management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a bug report
+create_bug_task = Task(
+    description="Create a bug report for the login functionality with high priority and assign it to the development team",
+    agent=jira_agent,
+    expected_output="Bug report created successfully with issue key"
+)
+
+# Run the task
+crew = Crew(
+    agents=[jira_agent],
+    tasks=[create_bug_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Jira Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Jira tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["jira_create_issue", "jira_update_issue", "jira_search_by_jql"]
+)
+
+issue_coordinator = Agent(
+    role="Issue Coordinator",
+    goal="Create and manage Jira issues efficiently",
+    backstory="An AI assistant that focuses on issue creation and management.",
+    tools=enterprise_tools
+)
+
+# Task to manage issue workflow
+issue_workflow = Task(
+    description="Create a feature request issue and update the status of related issues",
+    agent=issue_coordinator,
+    expected_output="Feature request created and related issues updated"
+)
+
+crew = Crew(
+    agents=[issue_coordinator],
+    tasks=[issue_workflow]
+)
+
+crew.kickoff()
+```
+
+### Project Analysis and Reporting
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+project_analyst = Agent(
+    role="Project Analyst",
+    goal="Analyze project data and generate insights from Jira",
+    backstory="An experienced project analyst who extracts insights from project management data.",
+    tools=[enterprise_tools]
+)
+
+# Task to analyze project status
+analysis_task = Task(
+    description="""
+    1. Get all projects and their issue types
+    2. Search for all open issues across projects
+    3. Analyze issue distribution by status and assignee
+    4. Create a summary report issue with findings
+    """,
+    agent=project_analyst,
+    expected_output="Project analysis completed with summary report created"
+)
+
+crew = Crew(
+    agents=[project_analyst],
+    tasks=[analysis_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Issue Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+automation_manager = Agent(
+    role="Automation Manager",
+    goal="Automate issue management and workflow processes",
+    backstory="An AI assistant that automates repetitive issue management tasks.",
+    tools=[enterprise_tools]
+)
+
+# Task to automate issue management
+automation_task = Task(
+    description="""
+    1. Search for all unassigned issues using JQL
+    2. Get available assignees for each project
+    3. Automatically assign issues based on workload and expertise
+    4. Update issue priorities based on age and type
+    5. Create weekly sprint planning issues
+    """,
+    agent=automation_manager,
+    expected_output="Issues automatically assigned and sprint planning issues created"
+)
+
+crew = Crew(
+    agents=[automation_manager],
+    tasks=[automation_task]
+)
+
+crew.kickoff()
+```
+
+### Advanced Schema-Based Operations
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+schema_specialist = Agent(
+    role="Schema Specialist",
+    goal="Handle complex Jira operations using dynamic schemas",
+    backstory="An AI assistant that can work with dynamic Jira schemas and custom issue types.",
+    tools=[enterprise_tools]
+)
+
+# Task using schema-based operations
+schema_task = Task(
+    description="""
+    1. Get all projects and their custom issue types
+    2. For each custom issue type, describe the action schema
+    3. Create issues using the dynamic schema for complex custom fields
+    4. Update issues with custom field values based on business rules
+    """,
+    agent=schema_specialist,
+    expected_output="Custom issues created and updated using dynamic schemas"
+)
+
+crew = Crew(
+    agents=[schema_specialist],
+    tasks=[schema_task]
+)
+
+crew.kickoff()
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Permission Errors**
+- Ensure your Jira account has necessary permissions for the target projects
+- Verify that the OAuth connection includes required scopes for Jira API
+- Check if you have create/edit permissions for issues in the specified projects
+
+**Invalid Project or Issue Keys**
+- Double-check project keys and issue keys for correct format (e.g., "PROJ-123")
+- Ensure projects exist and are accessible to your account
+- Verify that issue keys reference existing issues
+
+**Issue Type and Status Issues**
+- Use JIRA_GET_ISSUE_TYPES_BY_PROJECT to get valid issue types for a project
+- Use JIRA_GET_ISSUE_STATUS_BY_PROJECT to get valid statuses
+- Ensure issue types and statuses are available in the target project
+
+**JQL Query Problems**
+- Test JQL queries in Jira's issue search before using in API calls
+- Ensure field names in JQL are spelled correctly and exist in your Jira instance
+- Use proper JQL syntax for complex queries
+
+**Custom Fields and Schema Issues**
+- Use JIRA_DESCRIBE_ACTION_SCHEMA to get the correct schema for complex issue types
+- Ensure custom field IDs are correct (e.g., "customfield_10001")
+- Verify that custom fields are available in the target project and issue type
+
+**Filter Formula Issues**
+- Ensure filter formulas follow the correct JSON structure for disjunctive normal form
+- Use valid field names that exist in your Jira configuration
+- Test simple filters before building complex multi-condition queries
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Jira integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/linear.mdx

@@ -0,0 +1,453 @@
+---
+title: Linear Integration
+description: "Software project and bug tracking with Linear integration for CrewAI."
+icon: "list-check"
+---
+
+## Overview
+
+Enable your agents to manage issues, projects, and development workflows through Linear. Create and update issues, manage project timelines, organize teams, and streamline your software development process with AI-powered automation.
+
+## Prerequisites
+
+Before using the Linear integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Linear account with appropriate workspace permissions
+- Connected your Linear account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Linear Integration
+
+### 1. Connect Your Linear Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Linear** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for issue and project management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="LINEAR_CREATE_ISSUE">
+    **Description:** Create a new issue in Linear.
+
+    **Parameters:**
+    - `teamId` (string, required): Team ID - Specify the Team ID of the parent for this new issue. Use Connect Portal Workflow Settings to allow users to select a Team ID. (example: "a70bdf0f-530a-4887-857d-46151b52b47c").
+    - `title` (string, required): Title - Specify a title for this issue.
+    - `description` (string, optional): Description - Specify a description for this issue.
+    - `statusId` (string, optional): Status - Specify the state or status of this issue.
+    - `priority` (string, optional): Priority - Specify the priority of this issue as an integer.
+    - `dueDate` (string, optional): Due Date - Specify the due date of this issue in ISO 8601 format.
+    - `cycleId` (string, optional): Cycle ID - Specify the cycle associated with this issue.
+    - `additionalFields` (object, optional): Additional Fields.
+      ```json
+      {
+        "assigneeId": "a70bdf0f-530a-4887-857d-46151b52b47c",
+        "labelIds": ["a70bdf0f-530a-4887-857d-46151b52b47c"]
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="LINEAR_UPDATE_ISSUE">
+    **Description:** Update an issue in Linear.
+
+    **Parameters:**
+    - `issueId` (string, required): Issue ID - Specify the Issue ID of the issue to update. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977").
+    - `title` (string, optional): Title - Specify a title for this issue.
+    - `description` (string, optional): Description - Specify a description for this issue.
+    - `statusId` (string, optional): Status - Specify the state or status of this issue.
+    - `priority` (string, optional): Priority - Specify the priority of this issue as an integer.
+    - `dueDate` (string, optional): Due Date - Specify the due date of this issue in ISO 8601 format.
+    - `cycleId` (string, optional): Cycle ID - Specify the cycle associated with this issue.
+    - `additionalFields` (object, optional): Additional Fields.
+      ```json
+      {
+        "assigneeId": "a70bdf0f-530a-4887-857d-46151b52b47c",
+        "labelIds": ["a70bdf0f-530a-4887-857d-46151b52b47c"]
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="LINEAR_GET_ISSUE_BY_ID">
+    **Description:** Get an issue by ID in Linear.
+
+    **Parameters:**
+    - `issueId` (string, required): Issue ID - Specify the record ID of the issue to fetch. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977").
+  </Accordion>
+
+  <Accordion title="LINEAR_GET_ISSUE_BY_ISSUE_IDENTIFIER">
+    **Description:** Get an issue by issue identifier in Linear.
+
+    **Parameters:**
+    - `externalId` (string, required): External ID - Specify the human-readable Issue identifier of the issue to fetch. (example: "ABC-1").
+  </Accordion>
+
+  <Accordion title="LINEAR_SEARCH_ISSUE">
+    **Description:** Search issues in Linear.
+
+    **Parameters:**
+    - `queryTerm` (string, required): Query Term - The search term to look for.
+    - `issueFilterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "title",
+                "operator": "$stringContains",
+                "value": "bug"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `title`, `number`, `project`, `createdAt`
+      Available operators: `$stringExactlyMatches`, `$stringDoesNotExactlyMatch`, `$stringIsIn`, `$stringIsNotIn`, `$stringStartsWith`, `$stringDoesNotStartWith`, `$stringEndsWith`, `$stringDoesNotEndWith`, `$stringContains`, `$stringDoesNotContain`, `$stringGreaterThan`, `$stringLessThan`, `$numberGreaterThanOrEqualTo`, `$numberLessThanOrEqualTo`, `$numberGreaterThan`, `$numberLessThan`, `$dateTimeAfter`, `$dateTimeBefore`
+  </Accordion>
+
+  <Accordion title="LINEAR_DELETE_ISSUE">
+    **Description:** Delete an issue in Linear.
+
+    **Parameters:**
+    - `issueId` (string, required): Issue ID - Specify the record ID of the issue to delete. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977").
+  </Accordion>
+
+  <Accordion title="LINEAR_ARCHIVE_ISSUE">
+    **Description:** Archive an issue in Linear.
+
+    **Parameters:**
+    - `issueId` (string, required): Issue ID - Specify the record ID of the issue to archive. (example: "90fbc706-18cd-42c9-ae66-6bd344cc8977").
+  </Accordion>
+
+  <Accordion title="LINEAR_CREATE_SUB_ISSUE">
+    **Description:** Create a sub-issue in Linear.
+
+    **Parameters:**
+    - `parentId` (string, required): Parent ID - Specify the Issue ID for the parent of this new issue.
+    - `teamId` (string, required): Team ID - Specify the Team ID of the parent for this new sub-issue. Use Connect Portal Workflow Settings to allow users to select a Team ID. (example: "a70bdf0f-530a-4887-857d-46151b52b47c").
+    - `title` (string, required): Title - Specify a title for this issue.
+    - `description` (string, optional): Description - Specify a description for this issue.
+    - `additionalFields` (object, optional): Additional Fields.
+      ```json
+      {
+        "lead": "linear_user_id"
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="LINEAR_CREATE_PROJECT">
+    **Description:** Create a new project in Linear.
+
+    **Parameters:**
+    - `teamIds` (object, required): Team ID - Specify the team ID(s) this project is associated with as a string or a JSON array. Use Connect Portal User Settings to allow your user to select a Team ID.
+      ```json
+      [
+        "a70bdf0f-530a-4887-857d-46151b52b47c",
+        "4ac7..."
+      ]
+      ```
+    - `projectName` (string, required): Project Name - Specify the name of the project. (example: "My Linear Project").
+    - `description` (string, optional): Project Description - Specify a description for this project.
+    - `additionalFields` (object, optional): Additional Fields.
+      ```json
+      {
+        "state": "planned",
+        "description": ""
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="LINEAR_UPDATE_PROJECT">
+    **Description:** Update a project in Linear.
+
+    **Parameters:**
+    - `projectId` (string, required): Project ID - Specify the ID of the project to update. (example: "a6634484-6061-4ac7-9739-7dc5e52c796b").
+    - `projectName` (string, optional): Project Name - Specify the name of the project to update. (example: "My Linear Project").
+    - `description` (string, optional): Project Description - Specify a description for this project.
+    - `additionalFields` (object, optional): Additional Fields.
+      ```json
+      {
+        "state": "planned",
+        "description": ""
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="LINEAR_GET_PROJECT_BY_ID">
+    **Description:** Get a project by ID in Linear.
+
+    **Parameters:**
+    - `projectId` (string, required): Project ID - Specify the Project ID of the project to fetch. (example: "a6634484-6061-4ac7-9739-7dc5e52c796b").
+  </Accordion>
+
+  <Accordion title="LINEAR_DELETE_PROJECT">
+    **Description:** Delete a project in Linear.
+
+    **Parameters:**
+    - `projectId` (string, required): Project ID - Specify the Project ID of the project to delete. (example: "a6634484-6061-4ac7-9739-7dc5e52c796b").
+  </Accordion>
+
+  <Accordion title="LINEAR_SEARCH_TEAMS">
+    **Description:** Search teams in Linear.
+
+    **Parameters:**
+    - `teamFilterFormula` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "name",
+                "operator": "$stringContains",
+                "value": "Engineering"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `id`, `name`
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Linear Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Linear tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Linear capabilities
+linear_agent = Agent(
+    role="Development Manager",
+    goal="Manage Linear issues and track development progress efficiently",
+    backstory="An AI assistant specialized in software development project management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a bug report
+create_bug_task = Task(
+    description="Create a high-priority bug report for the authentication system and assign it to the backend team",
+    agent=linear_agent,
+    expected_output="Bug report created successfully with issue ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[linear_agent],
+    tasks=[create_bug_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Linear Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Linear tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["linear_create_issue", "linear_update_issue", "linear_search_issue"]
+)
+
+issue_manager = Agent(
+    role="Issue Manager",
+    goal="Create and manage Linear issues efficiently",
+    backstory="An AI assistant that focuses on issue creation and lifecycle management.",
+    tools=enterprise_tools
+)
+
+# Task to manage issue workflow
+issue_workflow = Task(
+    description="Create a feature request issue and update the status of related issues to reflect current progress",
+    agent=issue_manager,
+    expected_output="Feature request created and related issues updated"
+)
+
+crew = Crew(
+    agents=[issue_manager],
+    tasks=[issue_workflow]
+)
+
+crew.kickoff()
+```
+
+### Project and Team Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+project_coordinator = Agent(
+    role="Project Coordinator",
+    goal="Coordinate projects and teams in Linear efficiently",
+    backstory="An experienced project coordinator who manages development cycles and team workflows.",
+    tools=[enterprise_tools]
+)
+
+# Task to coordinate project setup
+project_coordination = Task(
+    description="""
+    1. Search for engineering teams in Linear
+    2. Create a new project for Q2 feature development
+    3. Associate the project with relevant teams
+    4. Create initial project milestones as issues
+    """,
+    agent=project_coordinator,
+    expected_output="Q2 project created with teams assigned and initial milestones established"
+)
+
+crew = Crew(
+    agents=[project_coordinator],
+    tasks=[project_coordination]
+)
+
+crew.kickoff()
+```
+
+### Issue Hierarchy and Sub-task Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+task_organizer = Agent(
+    role="Task Organizer",
+    goal="Organize complex issues into manageable sub-tasks",
+    backstory="An AI assistant that breaks down complex development work into organized sub-tasks.",
+    tools=[enterprise_tools]
+)
+
+# Task to create issue hierarchy
+hierarchy_task = Task(
+    description="""
+    1. Search for large feature issues that need to be broken down
+    2. For each complex issue, create sub-issues for different components
+    3. Update the parent issues with proper descriptions and links to sub-issues
+    4. Assign sub-issues to appropriate team members based on expertise
+    """,
+    agent=task_organizer,
+    expected_output="Complex issues broken down into manageable sub-tasks with proper assignments"
+)
+
+crew = Crew(
+    agents=[task_organizer],
+    tasks=[hierarchy_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Development Workflow
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+workflow_automator = Agent(
+    role="Workflow Automator",
+    goal="Automate development workflow processes in Linear",
+    backstory="An AI assistant that automates repetitive development workflow tasks.",
+    tools=[enterprise_tools]
+)
+
+# Complex workflow automation task
+automation_task = Task(
+    description="""
+    1. Search for issues that have been in progress for more than 7 days
+    2. Update their priorities based on due dates and project importance
+    3. Create weekly sprint planning issues for each team
+    4. Archive completed issues from the previous cycle
+    5. Generate project status reports as new issues
+    """,
+    agent=workflow_automator,
+    expected_output="Development workflow automated with updated priorities, sprint planning, and status reports"
+)
+
+crew = Crew(
+    agents=[workflow_automator],
+    tasks=[automation_task]
+)
+
+crew.kickoff()
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Permission Errors**
+- Ensure your Linear account has necessary permissions for the target workspace
+- Verify that the OAuth connection includes required scopes for Linear API
+- Check if you have create/edit permissions for issues and projects in the workspace
+
+**Invalid IDs and References**
+- Double-check team IDs, issue IDs, and project IDs for correct UUID format
+- Ensure referenced entities (teams, projects, cycles) exist and are accessible
+- Verify that issue identifiers follow the correct format (e.g., "ABC-1")
+
+**Team and Project Association Issues**
+- Use LINEAR_SEARCH_TEAMS to get valid team IDs before creating issues or projects
+- Ensure teams exist and are active in your workspace
+- Verify that team IDs are properly formatted as UUIDs
+
+**Issue Status and Priority Problems**
+- Check that status IDs reference valid workflow states for the team
+- Ensure priority values are within the valid range for your Linear configuration
+- Verify that custom fields and labels exist before referencing them
+
+**Date and Time Format Issues**
+- Use ISO 8601 format for due dates and timestamps
+- Ensure time zones are handled correctly for due date calculations
+- Verify that date values are valid and in the future for due dates
+
+**Search and Filter Issues**
+- Ensure search queries are properly formatted and not empty
+- Use valid field names in filter formulas: `title`, `number`, `project`, `createdAt`
+- Test simple filters before building complex multi-condition queries
+- Verify that operator types match the data types of the fields being filtered
+
+**Sub-issue Creation Problems**
+- Ensure parent issue IDs are valid and accessible
+- Verify that the team ID for sub-issues matches or is compatible with the parent issue's team
+- Check that parent issues are not already archived or deleted
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Linear integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/notion.mdx

@@ -0,0 +1,509 @@
+---
+title: Notion Integration
+description: "Page and database management with Notion integration for CrewAI."
+icon: "book"
+---
+
+## Overview
+
+Enable your agents to manage pages, databases, and content through Notion. Create and update pages, manage content blocks, organize knowledge bases, and streamline your documentation workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Notion integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Notion account with appropriate workspace permissions
+- Connected your Notion account through the [Integrations page](https://app.crewai.com/crewai_plus/connectors)
+
+## Setting Up Notion Integration
+
+### 1. Connect Your Notion Account
+
+1. Navigate to [CrewAI Enterprise Integrations](https://app.crewai.com/crewai_plus/connectors)
+2. Find **Notion** in the Authentication Integrations section
+3. Click **Connect** and complete the OAuth flow
+4. Grant the necessary permissions for page and database management
+5. Copy your Enterprise Token from [Account Settings](https://app.crewai.com/crewai_plus/settings/account)
+
+### 2. Install Required Package
+
+```bash
+uv add crewai-tools
+```
+
+## Available Actions
+
+<AccordionGroup>
+  <Accordion title="NOTION_CREATE_PAGE">
+    **Description:** Create a page in Notion.
+
+    **Parameters:**
+    - `parent` (object, required): Parent - The parent page or database where the new page is inserted, represented as a JSON object with a page_id or database_id key.
+      ```json
+      {
+        "database_id": "DATABASE_ID"
+      }
+      ```
+    - `properties` (object, required): Properties - The values of the page's properties. If the parent is a database, then the schema must match the parent database's properties.
+      ```json
+      {
+        "title": [
+          {
+            "text": {
+              "content": "My Page"
+            }
+          }
+        ]
+      }
+      ```
+    - `icon` (object, required): Icon - The page icon.
+      ```json
+      {
+        "emoji": "🥬"
+      }
+      ```
+    - `children` (object, optional): Children - Content blocks to add to the page.
+      ```json
+      [
+        {
+          "object": "block",
+          "type": "heading_2",
+          "heading_2": {
+            "rich_text": [
+              {
+                "type": "text",
+                "text": {
+                  "content": "Lacinato kale"
+                }
+              }
+            ]
+          }
+        }
+      ]
+      ```
+    - `cover` (object, optional): Cover - The page cover image.
+      ```json
+      {
+        "external": {
+          "url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg"
+        }
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="NOTION_UPDATE_PAGE">
+    **Description:** Update a page in Notion.
+
+    **Parameters:**
+    - `pageId` (string, required): Page ID - Specify the ID of the Page to Update. (example: "59833787-2cf9-4fdf-8782-e53db20768a5").
+    - `icon` (object, required): Icon - The page icon.
+      ```json
+      {
+        "emoji": "🥬"
+      }
+      ```
+    - `archived` (boolean, optional): Archived - Whether the page is archived (deleted). Set to true to archive a page. Set to false to un-archive (restore) a page.
+    - `properties` (object, optional): Properties - The property values to update for the page.
+      ```json
+      {
+        "title": [
+          {
+            "text": {
+              "content": "My Updated Page"
+            }
+          }
+        ]
+      }
+      ```
+    - `cover` (object, optional): Cover - The page cover image.
+      ```json
+      {
+        "external": {
+          "url": "https://upload.wikimedia.org/wikipedia/commons/6/62/Tuscankale.jpg"
+        }
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="NOTION_GET_PAGE_BY_ID">
+    **Description:** Get a page by ID in Notion.
+
+    **Parameters:**
+    - `pageId` (string, required): Page ID - Specify the ID of the Page to Get. (example: "59833787-2cf9-4fdf-8782-e53db20768a5").
+  </Accordion>
+
+  <Accordion title="NOTION_ARCHIVE_PAGE">
+    **Description:** Archive a page in Notion.
+
+    **Parameters:**
+    - `pageId` (string, required): Page ID - Specify the ID of the Page to Archive. (example: "59833787-2cf9-4fdf-8782-e53db20768a5").
+  </Accordion>
+
+  <Accordion title="NOTION_SEARCH_PAGES">
+    **Description:** Search pages in Notion using filters.
+
+    **Parameters:**
+    - `searchByTitleFilterSearch` (object, optional): A filter in disjunctive normal form - OR of AND groups of single conditions.
+      ```json
+      {
+        "operator": "OR",
+        "conditions": [
+          {
+            "operator": "AND",
+            "conditions": [
+              {
+                "field": "query",
+                "operator": "$stringExactlyMatches",
+                "value": "meeting notes"
+              }
+            ]
+          }
+        ]
+      }
+      ```
+      Available fields: `query`, `filter.value`, `direction`, `page_size`
+  </Accordion>
+
+  <Accordion title="NOTION_GET_PAGE_CONTENT">
+    **Description:** Get page content (blocks) in Notion.
+
+    **Parameters:**
+    - `blockId` (string, required): Page ID - Specify a Block or Page ID to receive all of its block's children in order. (example: "59833787-2cf9-4fdf-8782-e53db20768a5").
+  </Accordion>
+
+  <Accordion title="NOTION_UPDATE_BLOCK">
+    **Description:** Update a block in Notion.
+
+    **Parameters:**
+    - `blockId` (string, required): Block ID - Specify the ID of the Block to Update. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
+    - `archived` (boolean, optional): Archived - Set to true to archive (delete) a block. Set to false to un-archive (restore) a block.
+    - `paragraph` (object, optional): Paragraph content.
+      ```json
+      {
+        "rich_text": [
+          {
+            "type": "text",
+            "text": {
+              "content": "Lacinato kale",
+              "link": null
+            }
+          }
+        ],
+        "color": "default"
+      }
+      ```
+    - `image` (object, optional): Image block.
+      ```json
+      {
+        "type": "external",
+        "external": {
+          "url": "https://website.domain/images/image.png"
+        }
+      }
+      ```
+    - `bookmark` (object, optional): Bookmark block.
+      ```json
+      {
+        "caption": [],
+        "url": "https://companywebsite.com"
+      }
+      ```
+    - `code` (object, optional): Code block.
+      ```json
+      {
+        "rich_text": [
+          {
+            "type": "text",
+            "text": {
+              "content": "const a = 3"
+            }
+          }
+        ],
+        "language": "javascript"
+      }
+      ```
+    - `pdf` (object, optional): PDF block.
+      ```json
+      {
+        "type": "external",
+        "external": {
+          "url": "https://website.domain/files/doc.pdf"
+        }
+      }
+      ```
+    - `table` (object, optional): Table block.
+      ```json
+      {
+        "table_width": 2,
+        "has_column_header": false,
+        "has_row_header": false
+      }
+      ```
+    - `tableOfContent` (object, optional): Table of Contents block.
+      ```json
+      {
+        "color": "default"
+      }
+      ```
+    - `additionalFields` (object, optional): Additional block types.
+      ```json
+      {
+        "child_page": {
+          "title": "Lacinato kale"
+        },
+        "child_database": {
+          "title": "My database"
+        }
+      }
+      ```
+  </Accordion>
+
+  <Accordion title="NOTION_GET_BLOCK_BY_ID">
+    **Description:** Get a block by ID in Notion.
+
+    **Parameters:**
+    - `blockId` (string, required): Block ID - Specify the ID of the Block to Get. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
+  </Accordion>
+
+  <Accordion title="NOTION_DELETE_BLOCK">
+    **Description:** Delete a block in Notion.
+
+    **Parameters:**
+    - `blockId` (string, required): Block ID - Specify the ID of the Block to Delete. (example: "9bc30ad4-9373-46a5-84ab-0a7845ee52e6").
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Notion Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Notion tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Notion capabilities
+notion_agent = Agent(
+    role="Documentation Manager",
+    goal="Manage documentation and knowledge base in Notion efficiently",
+    backstory="An AI assistant specialized in content management and documentation.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a meeting notes page
+create_notes_task = Task(
+    description="Create a new meeting notes page in the team database with today's date and agenda items",
+    agent=notion_agent,
+    expected_output="Meeting notes page created successfully with structured content"
+)
+
+# Run the task
+crew = Crew(
+    agents=[notion_agent],
+    tasks=[create_notes_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Notion Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Notion tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["notion_create_page", "notion_update_block", "notion_search_pages"]
+)
+
+content_manager = Agent(
+    role="Content Manager",
+    goal="Create and manage content pages efficiently",
+    backstory="An AI assistant that focuses on content creation and management.",
+    tools=enterprise_tools
+)
+
+# Task to manage content workflow
+content_workflow = Task(
+    description="Create a new project documentation page and add structured content blocks for requirements and specifications",
+    agent=content_manager,
+    expected_output="Project documentation created with organized content sections"
+)
+
+crew = Crew(
+    agents=[content_manager],
+    tasks=[content_workflow]
+)
+
+crew.kickoff()
+```
+
+### Knowledge Base Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+knowledge_curator = Agent(
+    role="Knowledge Curator",
+    goal="Curate and organize knowledge base content in Notion",
+    backstory="An experienced knowledge manager who organizes and maintains comprehensive documentation.",
+    tools=[enterprise_tools]
+)
+
+# Task to curate knowledge base
+curation_task = Task(
+    description="""
+    1. Search for existing documentation pages related to our new product feature
+    2. Create a comprehensive feature documentation page with proper structure
+    3. Add code examples, images, and links to related resources
+    4. Update existing pages with cross-references to the new documentation
+    """,
+    agent=knowledge_curator,
+    expected_output="Feature documentation created and integrated with existing knowledge base"
+)
+
+crew = Crew(
+    agents=[knowledge_curator],
+    tasks=[curation_task]
+)
+
+crew.kickoff()
+```
+
+### Content Structure and Organization
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+content_organizer = Agent(
+    role="Content Organizer",
+    goal="Organize and structure content blocks for optimal readability",
+    backstory="An AI assistant that specializes in content structure and user experience.",
+    tools=[enterprise_tools]
+)
+
+# Task to organize content structure
+organization_task = Task(
+    description="""
+    1. Get content from existing project pages
+    2. Analyze the structure and identify improvement opportunities
+    3. Update content blocks to use proper headings, tables, and formatting
+    4. Add table of contents and improve navigation between related pages
+    5. Create templates for future documentation consistency
+    """,
+    agent=content_organizer,
+    expected_output="Content reorganized with improved structure and navigation"
+)
+
+crew = Crew(
+    agents=[content_organizer],
+    tasks=[organization_task]
+)
+
+crew.kickoff()
+```
+
+### Automated Documentation Workflows
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+doc_automator = Agent(
+    role="Documentation Automator",
+    goal="Automate documentation workflows and maintenance",
+    backstory="An AI assistant that automates repetitive documentation tasks.",
+    tools=[enterprise_tools]
+)
+
+# Complex documentation automation task
+automation_task = Task(
+    description="""
+    1. Search for pages that haven't been updated in the last 30 days
+    2. Review and update outdated content blocks
+    3. Create weekly team update pages with consistent formatting
+    4. Add status indicators and progress tracking to project pages
+    5. Generate monthly documentation health reports
+    6. Archive completed project pages and organize them in archive sections
+    """,
+    agent=doc_automator,
+    expected_output="Documentation automated with updated content, weekly reports, and organized archives"
+)
+
+crew = Crew(
+    agents=[doc_automator],
+    tasks=[automation_task]
+)
+
+crew.kickoff()
+```
+
+## Troubleshooting
+
+### Common Issues
+
+**Permission Errors**
+- Ensure your Notion account has edit access to the target workspace
+- Verify that the OAuth connection includes required scopes for Notion API
+- Check that pages and databases are shared with the authenticated integration
+
+**Invalid Page and Block IDs**
+- Double-check page IDs and block IDs for correct UUID format
+- Ensure referenced pages and blocks exist and are accessible
+- Verify that parent page or database IDs are valid when creating new pages
+
+**Property Schema Issues**
+- Ensure page properties match the database schema when creating pages in databases
+- Verify that property names and types are correct for the target database
+- Check that required properties are included when creating or updating pages
+
+**Content Block Structure**
+- Ensure block content follows Notion's rich text format specifications
+- Verify that nested block structures are properly formatted
+- Check that media URLs are accessible and properly formatted
+
+**Search and Filter Issues**
+- Ensure search queries are properly formatted and not empty
+- Use valid field names in filter formulas: `query`, `filter.value`, `direction`, `page_size`
+- Test simple searches before building complex filter conditions
+
+**Parent-Child Relationships**
+- Verify that parent page or database exists before creating child pages
+- Ensure proper permissions exist for the parent container
+- Check that database schemas allow the properties you're trying to set
+
+**Rich Text and Media Content**
+- Ensure URLs for external images, PDFs, and bookmarks are accessible
+- Verify that rich text formatting follows Notion's API specifications
+- Check that code block language types are supported by Notion
+
+**Archive and Deletion Operations**
+- Understand the difference between archiving (reversible) and deleting (permanent)
+- Verify that you have permissions to archive or delete the target content
+- Be cautious with bulk operations that might affect multiple pages or blocks
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Notion integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/salesforce.mdx

@@ -0,0 +1,632 @@
+---
+title: Salesforce Integration
+description: "CRM and sales automation with Salesforce integration for CrewAI."
+icon: "salesforce"
+---
+
+## Overview
+
+Enable your agents to manage customer relationships, sales processes, and data through Salesforce. Create and update records, manage leads and opportunities, execute SOQL queries, and streamline your CRM workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Salesforce integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Salesforce account with appropriate permissions
+- Connected your Salesforce account through the [Integrations page](https://app.crewai.com/integrations)
+
+## Available Tools
+
+### **Record Management**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_CREATE_RECORD_CONTACT">
+    **Description:** Create a new Contact record in Salesforce.
+
+    **Parameters:**
+    - `FirstName` (string, optional): First Name
+    - `LastName` (string, required): Last Name - This field is required
+    - `accountId` (string, optional): Account ID - The Account that the Contact belongs to
+    - `Email` (string, optional): Email address
+    - `Title` (string, optional): Title of the contact, such as CEO or Vice President
+    - `Description` (string, optional): A description of the Contact
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Contact fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_RECORD_LEAD">
+    **Description:** Create a new Lead record in Salesforce.
+
+    **Parameters:**
+    - `FirstName` (string, optional): First Name
+    - `LastName` (string, required): Last Name - This field is required
+    - `Company` (string, required): Company - This field is required
+    - `Email` (string, optional): Email address
+    - `Phone` (string, optional): Phone number
+    - `Website` (string, optional): Website URL
+    - `Title` (string, optional): Title of the contact, such as CEO or Vice President
+    - `Status` (string, optional): Lead Status - Use Connect Portal Workflow Settings to select Lead Status
+    - `Description` (string, optional): A description of the Lead
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Lead fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_RECORD_OPPORTUNITY">
+    **Description:** Create a new Opportunity record in Salesforce.
+
+    **Parameters:**
+    - `Name` (string, required): The Opportunity name - This field is required
+    - `StageName` (string, optional): Opportunity Stage - Use Connect Portal Workflow Settings to select stage
+    - `CloseDate` (string, optional): Close Date in YYYY-MM-DD format - Defaults to 30 days from current date
+    - `AccountId` (string, optional): The Account that the Opportunity belongs to
+    - `Amount` (string, optional): Estimated total sale amount
+    - `Description` (string, optional): A description of the Opportunity
+    - `OwnerId` (string, optional): The Salesforce user assigned to work on this Opportunity
+    - `NextStep` (string, optional): Description of next task in closing Opportunity
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Opportunity fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_RECORD_TASK">
+    **Description:** Create a new Task record in Salesforce.
+
+    **Parameters:**
+    - `whatId` (string, optional): Related to ID - The ID of the Account or Opportunity this Task is related to
+    - `whoId` (string, optional): Name ID - The ID of the Contact or Lead this Task is related to
+    - `subject` (string, required): Subject of the task
+    - `activityDate` (string, optional): Activity Date in YYYY-MM-DD format
+    - `description` (string, optional): A description of the Task
+    - `taskSubtype` (string, required): Task Subtype - Options: task, email, listEmail, call
+    - `Status` (string, optional): Status - Options: Not Started, In Progress, Completed
+    - `ownerId` (string, optional): Assigned To ID - The Salesforce user assigned to this Task
+    - `callDurationInSeconds` (string, optional): Call Duration in seconds
+    - `isReminderSet` (boolean, optional): Whether reminder is set
+    - `reminderDateTime` (string, optional): Reminder Date/Time in ISO format
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Task fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_RECORD_ACCOUNT">
+    **Description:** Create a new Account record in Salesforce.
+
+    **Parameters:**
+    - `Name` (string, required): The Account name - This field is required
+    - `OwnerId` (string, optional): The Salesforce user assigned to this Account
+    - `Website` (string, optional): Website URL
+    - `Phone` (string, optional): Phone number
+    - `Description` (string, optional): Account description
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Account fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_RECORD_ANY">
+    **Description:** Create a record of any object type in Salesforce.
+
+    **Note:** This is a flexible tool for creating records of custom or unknown object types.
+  </Accordion>
+</AccordionGroup>
+
+### **Record Updates**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_UPDATE_RECORD_CONTACT">
+    **Description:** Update an existing Contact record in Salesforce.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update
+    - `FirstName` (string, optional): First Name
+    - `LastName` (string, optional): Last Name
+    - `accountId` (string, optional): Account ID - The Account that the Contact belongs to
+    - `Email` (string, optional): Email address
+    - `Title` (string, optional): Title of the contact
+    - `Description` (string, optional): A description of the Contact
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Contact fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_UPDATE_RECORD_LEAD">
+    **Description:** Update an existing Lead record in Salesforce.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update
+    - `FirstName` (string, optional): First Name
+    - `LastName` (string, optional): Last Name
+    - `Company` (string, optional): Company name
+    - `Email` (string, optional): Email address
+    - `Phone` (string, optional): Phone number
+    - `Website` (string, optional): Website URL
+    - `Title` (string, optional): Title of the contact
+    - `Status` (string, optional): Lead Status
+    - `Description` (string, optional): A description of the Lead
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Lead fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_UPDATE_RECORD_OPPORTUNITY">
+    **Description:** Update an existing Opportunity record in Salesforce.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update
+    - `Name` (string, optional): The Opportunity name
+    - `StageName` (string, optional): Opportunity Stage
+    - `CloseDate` (string, optional): Close Date in YYYY-MM-DD format
+    - `AccountId` (string, optional): The Account that the Opportunity belongs to
+    - `Amount` (string, optional): Estimated total sale amount
+    - `Description` (string, optional): A description of the Opportunity
+    - `OwnerId` (string, optional): The Salesforce user assigned to work on this Opportunity
+    - `NextStep` (string, optional): Description of next task in closing Opportunity
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Opportunity fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_UPDATE_RECORD_TASK">
+    **Description:** Update an existing Task record in Salesforce.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update
+    - `whatId` (string, optional): Related to ID - The ID of the Account or Opportunity this Task is related to
+    - `whoId` (string, optional): Name ID - The ID of the Contact or Lead this Task is related to
+    - `subject` (string, optional): Subject of the task
+    - `activityDate` (string, optional): Activity Date in YYYY-MM-DD format
+    - `description` (string, optional): A description of the Task
+    - `Status` (string, optional): Status - Options: Not Started, In Progress, Completed
+    - `ownerId` (string, optional): Assigned To ID - The Salesforce user assigned to this Task
+    - `callDurationInSeconds` (string, optional): Call Duration in seconds
+    - `isReminderSet` (boolean, optional): Whether reminder is set
+    - `reminderDateTime` (string, optional): Reminder Date/Time in ISO format
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Task fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_UPDATE_RECORD_ACCOUNT">
+    **Description:** Update an existing Account record in Salesforce.
+
+    **Parameters:**
+    - `recordId` (string, required): The ID of the record to update
+    - `Name` (string, optional): The Account name
+    - `OwnerId` (string, optional): The Salesforce user assigned to this Account
+    - `Website` (string, optional): Website URL
+    - `Phone` (string, optional): Phone number
+    - `Description` (string, optional): Account description
+    - `additionalFields` (object, optional): Additional fields in JSON format for custom Account fields
+  </Accordion>
+
+  <Accordion title="SALESFORCE_UPDATE_RECORD_ANY">
+    **Description:** Update a record of any object type in Salesforce.
+
+    **Note:** This is a flexible tool for updating records of custom or unknown object types.
+  </Accordion>
+</AccordionGroup>
+
+### **Record Retrieval**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_CONTACT">
+    **Description:** Get a Contact record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): Record ID of the Contact
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_LEAD">
+    **Description:** Get a Lead record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): Record ID of the Lead
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_OPPORTUNITY">
+    **Description:** Get an Opportunity record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): Record ID of the Opportunity
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_TASK">
+    **Description:** Get a Task record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): Record ID of the Task
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_ACCOUNT">
+    **Description:** Get an Account record by its ID.
+
+    **Parameters:**
+    - `recordId` (string, required): Record ID of the Account
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_ID_ANY">
+    **Description:** Get a record of any object type by its ID.
+
+    **Parameters:**
+    - `recordType` (string, required): Record Type (e.g., "CustomObject__c")
+    - `recordId` (string, required): Record ID
+  </Accordion>
+</AccordionGroup>
+
+### **Record Search**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_CONTACT">
+    **Description:** Search for Contact records with advanced filtering.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `sortBy` (string, optional): Sort field (e.g., "CreatedDate")
+    - `sortDirection` (string, optional): Sort direction - Options: ASC, DESC
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_LEAD">
+    **Description:** Search for Lead records with advanced filtering.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `sortBy` (string, optional): Sort field (e.g., "CreatedDate")
+    - `sortDirection` (string, optional): Sort direction - Options: ASC, DESC
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_OPPORTUNITY">
+    **Description:** Search for Opportunity records with advanced filtering.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `sortBy` (string, optional): Sort field (e.g., "CreatedDate")
+    - `sortDirection` (string, optional): Sort direction - Options: ASC, DESC
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_TASK">
+    **Description:** Search for Task records with advanced filtering.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `sortBy` (string, optional): Sort field (e.g., "CreatedDate")
+    - `sortDirection` (string, optional): Sort direction - Options: ASC, DESC
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_ACCOUNT">
+    **Description:** Search for Account records with advanced filtering.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `sortBy` (string, optional): Sort field (e.g., "CreatedDate")
+    - `sortDirection` (string, optional): Sort direction - Options: ASC, DESC
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_SEARCH_RECORDS_ANY">
+    **Description:** Search for records of any object type.
+
+    **Parameters:**
+    - `recordType` (string, required): Record Type to search
+    - `filterFormula` (string, optional): Filter search criteria
+    - `includeAllFields` (boolean, optional): Include all fields in results
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+</AccordionGroup>
+
+### **List View Retrieval**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_CONTACT">
+    **Description:** Get Contact records from a specific List View.
+
+    **Parameters:**
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_LEAD">
+    **Description:** Get Lead records from a specific List View.
+
+    **Parameters:**
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_OPPORTUNITY">
+    **Description:** Get Opportunity records from a specific List View.
+
+    **Parameters:**
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_TASK">
+    **Description:** Get Task records from a specific List View.
+
+    **Parameters:**
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_ACCOUNT">
+    **Description:** Get Account records from a specific List View.
+
+    **Parameters:**
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+
+  <Accordion title="SALESFORCE_GET_RECORD_BY_VIEW_ID_ANY">
+    **Description:** Get records of any object type from a specific List View.
+
+    **Parameters:**
+    - `recordType` (string, required): Record Type
+    - `listViewId` (string, required): List View ID
+    - `paginationParameters` (object, optional): Pagination settings with pageCursor
+  </Accordion>
+</AccordionGroup>
+
+### **Custom Fields**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_CONTACT">
+    **Description:** Deploy custom fields for Contact objects.
+
+    **Parameters:**
+    - `label` (string, required): Field Label for displays and internal reference
+    - `type` (string, required): Field Type - Options: Checkbox, Currency, Date, Email, Number, Percent, Phone, Picklist, MultiselectPicklist, Text, TextArea, LongTextArea, Html, Time, Url
+    - `defaultCheckboxValue` (boolean, optional): Default value for checkbox fields
+    - `length` (string, required): Length for numeric/text fields
+    - `decimalPlace` (string, required): Decimal places for numeric fields
+    - `pickListValues` (string, required): Values for picklist fields (separated by new lines)
+    - `visibleLines` (string, required): Visible lines for multiselect/text area fields
+    - `description` (string, optional): Field description
+    - `helperText` (string, optional): Helper text shown on hover
+    - `defaultFieldValue` (string, optional): Default field value
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_LEAD">
+    **Description:** Deploy custom fields for Lead objects.
+
+    **Parameters:**
+    - `label` (string, required): Field Label for displays and internal reference
+    - `type` (string, required): Field Type - Options: Checkbox, Currency, Date, Email, Number, Percent, Phone, Picklist, MultiselectPicklist, Text, TextArea, LongTextArea, Html, Time, Url
+    - `defaultCheckboxValue` (boolean, optional): Default value for checkbox fields
+    - `length` (string, required): Length for numeric/text fields
+    - `decimalPlace` (string, required): Decimal places for numeric fields
+    - `pickListValues` (string, required): Values for picklist fields (separated by new lines)
+    - `visibleLines` (string, required): Visible lines for multiselect/text area fields
+    - `description` (string, optional): Field description
+    - `helperText` (string, optional): Helper text shown on hover
+    - `defaultFieldValue` (string, optional): Default field value
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_OPPORTUNITY">
+    **Description:** Deploy custom fields for Opportunity objects.
+
+    **Parameters:**
+    - `label` (string, required): Field Label for displays and internal reference
+    - `type` (string, required): Field Type - Options: Checkbox, Currency, Date, Email, Number, Percent, Phone, Picklist, MultiselectPicklist, Text, TextArea, LongTextArea, Html, Time, Url
+    - `defaultCheckboxValue` (boolean, optional): Default value for checkbox fields
+    - `length` (string, required): Length for numeric/text fields
+    - `decimalPlace` (string, required): Decimal places for numeric fields
+    - `pickListValues` (string, required): Values for picklist fields (separated by new lines)
+    - `visibleLines` (string, required): Visible lines for multiselect/text area fields
+    - `description` (string, optional): Field description
+    - `helperText` (string, optional): Helper text shown on hover
+    - `defaultFieldValue` (string, optional): Default field value
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_TASK">
+    **Description:** Deploy custom fields for Task objects.
+
+    **Parameters:**
+    - `label` (string, required): Field Label for displays and internal reference
+    - `type` (string, required): Field Type - Options: Checkbox, Currency, Date, Email, Number, Percent, Phone, Picklist, MultiselectPicklist, Text, TextArea, Time, Url
+    - `defaultCheckboxValue` (boolean, optional): Default value for checkbox fields
+    - `length` (string, required): Length for numeric/text fields
+    - `decimalPlace` (string, required): Decimal places for numeric fields
+    - `pickListValues` (string, required): Values for picklist fields (separated by new lines)
+    - `visibleLines` (string, required): Visible lines for multiselect fields
+    - `description` (string, optional): Field description
+    - `helperText` (string, optional): Helper text shown on hover
+    - `defaultFieldValue` (string, optional): Default field value
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_ACCOUNT">
+    **Description:** Deploy custom fields for Account objects.
+
+    **Parameters:**
+    - `label` (string, required): Field Label for displays and internal reference
+    - `type` (string, required): Field Type - Options: Checkbox, Currency, Date, Email, Number, Percent, Phone, Picklist, MultiselectPicklist, Text, TextArea, LongTextArea, Html, Time, Url
+    - `defaultCheckboxValue` (boolean, optional): Default value for checkbox fields
+    - `length` (string, required): Length for numeric/text fields
+    - `decimalPlace` (string, required): Decimal places for numeric fields
+    - `pickListValues` (string, required): Values for picklist fields (separated by new lines)
+    - `visibleLines` (string, required): Visible lines for multiselect/text area fields
+    - `description` (string, optional): Field description
+    - `helperText` (string, optional): Helper text shown on hover
+    - `defaultFieldValue` (string, optional): Default field value
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_FIELD_ANY">
+    **Description:** Deploy custom fields for any object type.
+
+    **Note:** This is a flexible tool for creating custom fields on custom or unknown object types.
+  </Accordion>
+</AccordionGroup>
+
+### **Advanced Operations**
+
+<AccordionGroup>
+  <Accordion title="SALESFORCE_WRITE_SOQL_QUERY">
+    **Description:** Execute custom SOQL queries against your Salesforce data.
+
+    **Parameters:**
+    - `query` (string, required): SOQL Query (e.g., "SELECT Id, Name FROM Account WHERE Name = 'Example'")
+  </Accordion>
+
+  <Accordion title="SALESFORCE_CREATE_CUSTOM_OBJECT">
+    **Description:** Deploy a new custom object in Salesforce.
+
+    **Parameters:**
+    - `label` (string, required): Object Label for tabs, page layouts, and reports
+    - `pluralLabel` (string, required): Plural Label (e.g., "Accounts")
+    - `description` (string, optional): A description of the Custom Object
+    - `recordName` (string, required): Record Name that appears in layouts and searches (e.g., "Account Name")
+  </Accordion>
+
+  <Accordion title="SALESFORCE_DESCRIBE_ACTION_SCHEMA">
+    **Description:** Get the expected schema for operations on specific object types.
+
+    **Parameters:**
+    - `recordType` (string, required): Record Type to describe
+    - `operation` (string, required): Operation Type (e.g., "CREATE_RECORD" or "UPDATE_RECORD")
+
+    **Note:** Use this function first when working with custom objects to understand their schema before performing operations.
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Salesforce Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Salesforce tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Salesforce capabilities
+salesforce_agent = Agent(
+    role="CRM Manager",
+    goal="Manage customer relationships and sales processes efficiently",
+    backstory="An AI assistant specialized in CRM operations and sales automation.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new lead
+create_lead_task = Task(
+    description="Create a new lead for John Doe from Example Corp with email john.doe@example.com",
+    agent=salesforce_agent,
+    expected_output="Lead created successfully with lead ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[salesforce_agent],
+    tasks=[create_lead_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Salesforce Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Salesforce tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["salesforce_create_record_lead", "salesforce_update_record_opportunity", "salesforce_search_records_contact"]
+)
+
+sales_manager = Agent(
+    role="Sales Manager",
+    goal="Manage leads and opportunities in the sales pipeline",
+    backstory="An experienced sales manager who handles lead qualification and opportunity management.",
+    tools=enterprise_tools
+)
+
+# Task to manage sales pipeline
+pipeline_task = Task(
+    description="Create a qualified lead and convert it to an opportunity with $50,000 value",
+    agent=sales_manager,
+    expected_output="Lead created and opportunity established successfully"
+)
+
+crew = Crew(
+    agents=[sales_manager],
+    tasks=[pipeline_task]
+)
+
+crew.kickoff()
+```
+
+### Contact and Account Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+account_manager = Agent(
+    role="Account Manager",
+    goal="Manage customer accounts and maintain strong relationships",
+    backstory="An AI assistant that specializes in account management and customer relationship building.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage customer accounts
+account_task = Task(
+    description="""
+    1. Create a new account for TechCorp Inc.
+    2. Add John Doe as the primary contact for this account
+    3. Create a follow-up task for next week to check on their project status
+    """,
+    agent=account_manager,
+    expected_output="Account, contact, and follow-up task created successfully"
+)
+
+crew = Crew(
+    agents=[account_manager],
+    tasks=[account_task]
+)
+
+crew.kickoff()
+```
+
+### Advanced SOQL Queries and Reporting
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+data_analyst = Agent(
+    role="Sales Data Analyst",
+    goal="Generate insights from Salesforce data using SOQL queries",
+    backstory="An analytical AI that excels at extracting meaningful insights from CRM data.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving SOQL queries and data analysis
+analysis_task = Task(
+    description="""
+    1. Execute a SOQL query to find all opportunities closing this quarter
+    2. Search for contacts at companies with opportunities over $100K
+    3. Create a summary report of the sales pipeline status
+    4. Update high-value opportunities with next steps
+    """,
+    agent=data_analyst,
+    expected_output="Comprehensive sales pipeline analysis with actionable insights"
+)
+
+crew = Crew(
+    agents=[data_analyst],
+    tasks=[analysis_task]
+)
+
+crew.kickoff()
+```
+
+This comprehensive documentation covers all the Salesforce tools organized by functionality, making it easy for users to find the specific operations they need for their CRM automation tasks.
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Salesforce integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/shopify.mdx

@@ -0,0 +1,382 @@
+---
+title: Shopify Integration
+description: "E-commerce and online store management with Shopify integration for CrewAI."
+icon: "shopify"
+---
+
+## Overview
+
+Enable your agents to manage e-commerce operations through Shopify. Handle customers, orders, products, inventory, and store analytics to streamline your online business with AI-powered automation.
+
+## Prerequisites
+
+Before using the Shopify integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Shopify store with appropriate admin permissions
+- Connected your Shopify store through the [Integrations page](https://app.crewai.com/integrations)
+
+## Available Tools
+
+### **Customer Management**
+
+<AccordionGroup>
+  <Accordion title="SHOPIFY_GET_CUSTOMERS">
+    **Description:** Retrieve a list of customers from your Shopify store.
+
+    **Parameters:**
+    - `customerIds` (string, optional): Comma-separated list of customer IDs to filter by (example: "207119551, 207119552")
+    - `createdAtMin` (string, optional): Only return customers created after this date (ISO or Unix timestamp)
+    - `createdAtMax` (string, optional): Only return customers created before this date (ISO or Unix timestamp)
+    - `updatedAtMin` (string, optional): Only return customers updated after this date (ISO or Unix timestamp)
+    - `updatedAtMax` (string, optional): Only return customers updated before this date (ISO or Unix timestamp)
+    - `limit` (string, optional): Maximum number of customers to return (defaults to 250)
+  </Accordion>
+
+  <Accordion title="SHOPIFY_SEARCH_CUSTOMERS">
+    **Description:** Search for customers using advanced filtering criteria.
+
+    **Parameters:**
+    - `filterFormula` (object, optional): Advanced filter in disjunctive normal form with field-specific operators
+    - `limit` (string, optional): Maximum number of customers to return (defaults to 250)
+  </Accordion>
+
+  <Accordion title="SHOPIFY_CREATE_CUSTOMER">
+    **Description:** Create a new customer in your Shopify store.
+
+    **Parameters:**
+    - `firstName` (string, required): Customer's first name
+    - `lastName` (string, required): Customer's last name
+    - `email` (string, required): Customer's email address
+    - `company` (string, optional): Company name
+    - `streetAddressLine1` (string, optional): Street address
+    - `streetAddressLine2` (string, optional): Street address line 2
+    - `city` (string, optional): City
+    - `state` (string, optional): State or province code
+    - `country` (string, optional): Country
+    - `zipCode` (string, optional): Zip code
+    - `phone` (string, optional): Phone number
+    - `tags` (string, optional): Tags as array or comma-separated list
+    - `note` (string, optional): Customer note
+    - `sendEmailInvite` (boolean, optional): Whether to send email invitation
+    - `metafields` (object, optional): Additional metafields in JSON format
+  </Accordion>
+
+  <Accordion title="SHOPIFY_UPDATE_CUSTOMER">
+    **Description:** Update an existing customer in your Shopify store.
+
+    **Parameters:**
+    - `customerId` (string, required): The ID of the customer to update
+    - `firstName` (string, optional): Customer's first name
+    - `lastName` (string, optional): Customer's last name
+    - `email` (string, optional): Customer's email address
+    - `company` (string, optional): Company name
+    - `streetAddressLine1` (string, optional): Street address
+    - `streetAddressLine2` (string, optional): Street address line 2
+    - `city` (string, optional): City
+    - `state` (string, optional): State or province code
+    - `country` (string, optional): Country
+    - `zipCode` (string, optional): Zip code
+    - `phone` (string, optional): Phone number
+    - `tags` (string, optional): Tags as array or comma-separated list
+    - `note` (string, optional): Customer note
+    - `sendEmailInvite` (boolean, optional): Whether to send email invitation
+    - `metafields` (object, optional): Additional metafields in JSON format
+  </Accordion>
+</AccordionGroup>
+
+### **Order Management**
+
+<AccordionGroup>
+  <Accordion title="SHOPIFY_GET_ORDERS">
+    **Description:** Retrieve a list of orders from your Shopify store.
+
+    **Parameters:**
+    - `orderIds` (string, optional): Comma-separated list of order IDs to filter by (example: "450789469, 450789470")
+    - `createdAtMin` (string, optional): Only return orders created after this date (ISO or Unix timestamp)
+    - `createdAtMax` (string, optional): Only return orders created before this date (ISO or Unix timestamp)
+    - `updatedAtMin` (string, optional): Only return orders updated after this date (ISO or Unix timestamp)
+    - `updatedAtMax` (string, optional): Only return orders updated before this date (ISO or Unix timestamp)
+    - `limit` (string, optional): Maximum number of orders to return (defaults to 250)
+  </Accordion>
+
+  <Accordion title="SHOPIFY_CREATE_ORDER">
+    **Description:** Create a new order in your Shopify store.
+
+    **Parameters:**
+    - `email` (string, required): Customer email address
+    - `lineItems` (object, required): Order line items in JSON format with title, price, quantity, and variant_id
+    - `sendReceipt` (boolean, optional): Whether to send order receipt
+    - `fulfillmentStatus` (string, optional): Fulfillment status - Options: fulfilled, null, partial, restocked
+    - `financialStatus` (string, optional): Financial status - Options: pending, authorized, partially_paid, paid, partially_refunded, refunded, voided
+    - `inventoryBehaviour` (string, optional): Inventory behavior - Options: bypass, decrement_ignoring_policy, decrement_obeying_policy
+    - `note` (string, optional): Order note
+  </Accordion>
+
+  <Accordion title="SHOPIFY_UPDATE_ORDER">
+    **Description:** Update an existing order in your Shopify store.
+
+    **Parameters:**
+    - `orderId` (string, required): The ID of the order to update
+    - `email` (string, optional): Customer email address
+    - `lineItems` (object, optional): Updated order line items in JSON format
+    - `sendReceipt` (boolean, optional): Whether to send order receipt
+    - `fulfillmentStatus` (string, optional): Fulfillment status - Options: fulfilled, null, partial, restocked
+    - `financialStatus` (string, optional): Financial status - Options: pending, authorized, partially_paid, paid, partially_refunded, refunded, voided
+    - `inventoryBehaviour` (string, optional): Inventory behavior - Options: bypass, decrement_ignoring_policy, decrement_obeying_policy
+    - `note` (string, optional): Order note
+  </Accordion>
+
+  <Accordion title="SHOPIFY_GET_ABANDONED_CARTS">
+    **Description:** Retrieve abandoned carts from your Shopify store.
+
+    **Parameters:**
+    - `createdWithInLast` (string, optional): Restrict results to checkouts created within specified time
+    - `createdAfterId` (string, optional): Restrict results to after the specified ID
+    - `status` (string, optional): Show checkouts with given status - Options: open, closed (defaults to open)
+    - `createdAtMin` (string, optional): Only return carts created after this date (ISO or Unix timestamp)
+    - `createdAtMax` (string, optional): Only return carts created before this date (ISO or Unix timestamp)
+    - `limit` (string, optional): Maximum number of carts to return (defaults to 250)
+  </Accordion>
+</AccordionGroup>
+
+### **Product Management (REST API)**
+
+<AccordionGroup>
+  <Accordion title="SHOPIFY_GET_PRODUCTS">
+    **Description:** Retrieve a list of products from your Shopify store using REST API.
+
+    **Parameters:**
+    - `productIds` (string, optional): Comma-separated list of product IDs to filter by (example: "632910392, 632910393")
+    - `title` (string, optional): Filter by product title
+    - `productType` (string, optional): Filter by product type
+    - `vendor` (string, optional): Filter by vendor
+    - `status` (string, optional): Filter by status - Options: active, archived, draft
+    - `createdAtMin` (string, optional): Only return products created after this date (ISO or Unix timestamp)
+    - `createdAtMax` (string, optional): Only return products created before this date (ISO or Unix timestamp)
+    - `updatedAtMin` (string, optional): Only return products updated after this date (ISO or Unix timestamp)
+    - `updatedAtMax` (string, optional): Only return products updated before this date (ISO or Unix timestamp)
+    - `limit` (string, optional): Maximum number of products to return (defaults to 250)
+  </Accordion>
+
+  <Accordion title="SHOPIFY_CREATE_PRODUCT">
+    **Description:** Create a new product in your Shopify store using REST API.
+
+    **Parameters:**
+    - `title` (string, required): Product title
+    - `productType` (string, required): Product type/category
+    - `vendor` (string, required): Product vendor
+    - `productDescription` (string, optional): Product description (accepts plain text or HTML)
+    - `tags` (string, optional): Product tags as array or comma-separated list
+    - `price` (string, optional): Product price
+    - `inventoryPolicy` (string, optional): Inventory policy - Options: deny, continue
+    - `imageUrl` (string, optional): Product image URL
+    - `isPublished` (boolean, optional): Whether product is published
+    - `publishToPointToSale` (boolean, optional): Whether to publish to point of sale
+  </Accordion>
+
+  <Accordion title="SHOPIFY_UPDATE_PRODUCT">
+    **Description:** Update an existing product in your Shopify store using REST API.
+
+    **Parameters:**
+    - `productId` (string, required): The ID of the product to update
+    - `title` (string, optional): Product title
+    - `productType` (string, optional): Product type/category
+    - `vendor` (string, optional): Product vendor
+    - `productDescription` (string, optional): Product description (accepts plain text or HTML)
+    - `tags` (string, optional): Product tags as array or comma-separated list
+    - `price` (string, optional): Product price
+    - `inventoryPolicy` (string, optional): Inventory policy - Options: deny, continue
+    - `imageUrl` (string, optional): Product image URL
+    - `isPublished` (boolean, optional): Whether product is published
+    - `publishToPointToSale` (boolean, optional): Whether to publish to point of sale
+  </Accordion>
+</AccordionGroup>
+
+### **Product Management (GraphQL)**
+
+<AccordionGroup>
+  <Accordion title="SHOPIFY_GET_PRODUCTS_GRAPHQL">
+    **Description:** Retrieve products using advanced GraphQL filtering capabilities.
+
+    **Parameters:**
+    - `productFilterFormula` (object, optional): Advanced filter in disjunctive normal form with support for fields like id, title, vendor, status, handle, tag, created_at, updated_at, published_at
+  </Accordion>
+
+  <Accordion title="SHOPIFY_CREATE_PRODUCT_GRAPHQL">
+    **Description:** Create a new product using GraphQL API with enhanced media support.
+
+    **Parameters:**
+    - `title` (string, required): Product title
+    - `productType` (string, required): Product type/category
+    - `vendor` (string, required): Product vendor
+    - `productDescription` (string, optional): Product description (accepts plain text or HTML)
+    - `tags` (string, optional): Product tags as array or comma-separated list
+    - `media` (object, optional): Media objects with alt text, content type, and source URL
+    - `additionalFields` (object, optional): Additional product fields like status, requiresSellingPlan, giftCard
+  </Accordion>
+
+  <Accordion title="SHOPIFY_UPDATE_PRODUCT_GRAPHQL">
+    **Description:** Update an existing product using GraphQL API with enhanced media support.
+
+    **Parameters:**
+    - `productId` (string, required): The GraphQL ID of the product to update (e.g., "gid://shopify/Product/913144112")
+    - `title` (string, optional): Product title
+    - `productType` (string, optional): Product type/category
+    - `vendor` (string, optional): Product vendor
+    - `productDescription` (string, optional): Product description (accepts plain text or HTML)
+    - `tags` (string, optional): Product tags as array or comma-separated list
+    - `media` (object, optional): Updated media objects with alt text, content type, and source URL
+    - `additionalFields` (object, optional): Additional product fields like status, requiresSellingPlan, giftCard
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Shopify Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Shopify tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Shopify capabilities
+shopify_agent = Agent(
+    role="E-commerce Manager",
+    goal="Manage online store operations and customer relationships efficiently",
+    backstory="An AI assistant specialized in e-commerce operations and online store management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new customer
+create_customer_task = Task(
+    description="Create a new VIP customer Jane Smith with email jane.smith@example.com and phone +1-555-0123",
+    agent=shopify_agent,
+    expected_output="Customer created successfully with customer ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[shopify_agent],
+    tasks=[create_customer_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Shopify Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Shopify tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["shopify_create_customer", "shopify_create_order", "shopify_get_products"]
+)
+
+store_manager = Agent(
+    role="Store Manager",
+    goal="Manage customer orders and product catalog",
+    backstory="An experienced store manager who handles customer relationships and inventory management.",
+    tools=enterprise_tools
+)
+
+# Task to manage store operations
+store_task = Task(
+    description="Create a new customer and process their order for 2 Premium Coffee Mugs",
+    agent=store_manager,
+    expected_output="Customer created and order processed successfully"
+)
+
+crew = Crew(
+    agents=[store_manager],
+    tasks=[store_task]
+)
+
+crew.kickoff()
+```
+
+### Product Management with GraphQL
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+product_manager = Agent(
+    role="Product Manager",
+    goal="Manage product catalog and inventory with advanced GraphQL capabilities",
+    backstory="An AI assistant that specializes in product management and catalog optimization.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage product catalog
+catalog_task = Task(
+    description="""
+    1. Create a new product "Premium Coffee Mug" from Coffee Co vendor
+    2. Add high-quality product images and descriptions
+    3. Search for similar products from the same vendor
+    4. Update product tags and pricing strategy
+    """,
+    agent=product_manager,
+    expected_output="Product created and catalog optimized successfully"
+)
+
+crew = Crew(
+    agents=[product_manager],
+    tasks=[catalog_task]
+)
+
+crew.kickoff()
+```
+
+### Order and Customer Analytics
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+analytics_agent = Agent(
+    role="E-commerce Analyst",
+    goal="Analyze customer behavior and order patterns to optimize store performance",
+    backstory="An analytical AI that excels at extracting insights from e-commerce data.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving multiple operations
+analytics_task = Task(
+    description="""
+    1. Retrieve recent customer data and order history
+    2. Identify abandoned carts from the last 7 days
+    3. Analyze product performance and inventory levels
+    4. Generate recommendations for customer retention
+    """,
+    agent=analytics_agent,
+    expected_output="Comprehensive e-commerce analytics report with actionable insights"
+)
+
+crew = Crew(
+    agents=[analytics_agent],
+    tasks=[analytics_task]
+)
+
+crew.kickoff()
+```
+
+### Getting Help
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Shopify integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/slack.mdx

@@ -0,0 +1,293 @@
+---
+title: Slack Integration
+description: "Team communication and collaboration with Slack integration for CrewAI."
+icon: "slack"
+---
+
+## Overview
+
+Enable your agents to manage team communication through Slack. Send messages, search conversations, manage channels, and coordinate team activities to streamline your collaboration workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Slack integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Slack workspace with appropriate permissions
+- Connected your Slack workspace through the [Integrations page](https://app.crewai.com/integrations)
+
+## Available Tools
+
+### **User Management**
+
+<AccordionGroup>
+  <Accordion title="SLACK_LIST_MEMBERS">
+    **Description:** List all members in a Slack channel.
+
+    **Parameters:**
+    - No parameters required - retrieves all channel members
+  </Accordion>
+
+  <Accordion title="SLACK_GET_USER_BY_EMAIL">
+    **Description:** Find a user in your Slack workspace by their email address.
+
+    **Parameters:**
+    - `email` (string, required): The email address of a user in the workspace
+  </Accordion>
+
+  <Accordion title="SLACK_GET_USERS_BY_NAME">
+    **Description:** Search for users by their name or display name.
+
+    **Parameters:**
+    - `name` (string, required): User's real name to search for
+    - `displayName` (string, required): User's display name to search for
+    - `paginationParameters` (object, optional): Pagination settings
+      - `pageCursor` (string, optional): Page cursor for pagination
+  </Accordion>
+</AccordionGroup>
+
+### **Channel Management**
+
+<AccordionGroup>
+  <Accordion title="SLACK_LIST_CHANNELS">
+    **Description:** List all channels in your Slack workspace.
+
+    **Parameters:**
+    - No parameters required - retrieves all accessible channels
+  </Accordion>
+</AccordionGroup>
+
+### **Messaging**
+
+<AccordionGroup>
+  <Accordion title="SLACK_SEND_MESSAGE">
+    **Description:** Send a message to a Slack channel.
+
+    **Parameters:**
+    - `channel` (string, required): Channel name or ID - Use Connect Portal Workflow Settings to allow users to select a channel, or enter a channel name to create a new channel
+    - `message` (string, required): The message text to send
+    - `botName` (string, required): The name of the bot that sends this message
+    - `botIcon` (string, required): Bot icon - Can be either an image URL or an emoji (e.g., ":dog:")
+    - `blocks` (object, optional): Slack Block Kit JSON for rich message formatting with attachments and interactive elements
+    - `authenticatedUser` (boolean, optional): If true, message appears to come from your authenticated Slack user instead of the application (defaults to false)
+  </Accordion>
+
+  <Accordion title="SLACK_SEND_DIRECT_MESSAGE">
+    **Description:** Send a direct message to a specific user in Slack.
+
+    **Parameters:**
+    - `memberId` (string, required): Recipient user ID - Use Connect Portal Workflow Settings to allow users to select a workspace member
+    - `message` (string, required): The message text to send
+    - `botName` (string, required): The name of the bot that sends this message
+    - `botIcon` (string, required): Bot icon - Can be either an image URL or an emoji (e.g., ":dog:")
+    - `blocks` (object, optional): Slack Block Kit JSON for rich message formatting with attachments and interactive elements
+    - `authenticatedUser` (boolean, optional): If true, message appears to come from your authenticated Slack user instead of the application (defaults to false)
+  </Accordion>
+</AccordionGroup>
+
+### **Search & Discovery**
+
+<AccordionGroup>
+  <Accordion title="SLACK_SEARCH_MESSAGES">
+    **Description:** Search for messages across your Slack workspace.
+
+    **Parameters:**
+    - `query` (string, required): Search query using Slack search syntax to find messages that match specified criteria
+
+    **Search Query Examples:**
+    - `"project update"` - Search for messages containing "project update"
+    - `from:@john in:#general` - Search for messages from John in the #general channel
+    - `has:link after:2023-01-01` - Search for messages with links after January 1, 2023
+    - `in:@channel before:yesterday` - Search for messages in a specific channel before yesterday
+  </Accordion>
+</AccordionGroup>
+
+## Block Kit Integration
+
+Slack's Block Kit allows you to create rich, interactive messages. Here are some examples of how to use the `blocks` parameter:
+
+### Simple Text with Attachment
+```json
+[
+  {
+    "text": "I am a test message",
+    "attachments": [
+      {
+        "text": "And here's an attachment!"
+      }
+    ]
+  }
+]
+```
+
+### Rich Formatting with Sections
+```json
+[
+  {
+    "type": "section",
+    "text": {
+      "type": "mrkdwn",
+      "text": "*Project Update*\nStatus: ✅ Complete"
+    }
+  },
+  {
+    "type": "divider"
+  },
+  {
+    "type": "section",
+    "text": {
+      "type": "plain_text",
+      "text": "All tasks have been completed successfully."
+    }
+  }
+]
+```
+
+## Usage Examples
+
+### Basic Slack Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Slack tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Slack capabilities
+slack_agent = Agent(
+    role="Team Communication Manager",
+    goal="Facilitate team communication and coordinate collaboration efficiently",
+    backstory="An AI assistant specialized in team communication and workspace coordination.",
+    tools=[enterprise_tools]
+)
+
+# Task to send project updates
+update_task = Task(
+    description="Send a project status update to the #general channel with current progress",
+    agent=slack_agent,
+    expected_output="Project update message sent successfully to team channel"
+)
+
+# Run the task
+crew = Crew(
+    agents=[slack_agent],
+    tasks=[update_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Slack Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Slack tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["slack_send_message", "slack_send_direct_message", "slack_search_messages"]
+)
+
+communication_manager = Agent(
+    role="Communication Coordinator",
+    goal="Manage team communications and ensure important messages reach the right people",
+    backstory="An experienced communication coordinator who handles team messaging and notifications.",
+    tools=enterprise_tools
+)
+
+# Task to coordinate team communication
+coordination_task = Task(
+    description="Send task completion notifications to team members and update project channels",
+    agent=communication_manager,
+    expected_output="Team notifications sent and project channels updated successfully"
+)
+
+crew = Crew(
+    agents=[communication_manager],
+    tasks=[coordination_task]
+)
+
+crew.kickoff()
+```
+
+### Advanced Messaging with Block Kit
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+notification_agent = Agent(
+    role="Notification Manager",
+    goal="Create rich, interactive notifications and manage workspace communication",
+    backstory="An AI assistant that specializes in creating engaging team notifications and updates.",
+    tools=[enterprise_tools]
+)
+
+# Task to send rich notifications
+notification_task = Task(
+    description="""
+    1. Send a formatted project completion message to #general with progress charts
+    2. Send direct messages to team leads with task summaries
+    3. Create interactive notification with action buttons for team feedback
+    """,
+    agent=notification_agent,
+    expected_output="Rich notifications sent with interactive elements and formatted content"
+)
+
+crew = Crew(
+    agents=[notification_agent],
+    tasks=[notification_task]
+)
+
+crew.kickoff()
+```
+
+### Message Search and Analytics
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+analytics_agent = Agent(
+    role="Communication Analyst",
+    goal="Analyze team communication patterns and extract insights from conversations",
+    backstory="An analytical AI that excels at understanding team dynamics through communication data.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving search and analysis
+analysis_task = Task(
+    description="""
+    1. Search for recent project-related messages across all channels
+    2. Find users by email to identify team members
+    3. Analyze communication patterns and response times
+    4. Generate weekly team communication summary
+    """,
+    agent=analytics_agent,
+    expected_output="Comprehensive communication analysis with team insights and recommendations"
+)
+
+crew = Crew(
+    agents=[analytics_agent],
+    tasks=[analysis_task]
+)
+
+crew.kickoff()
+```
+
+## Contact Support
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for assistance with Slack integration setup or troubleshooting.
+</Card>

docs/enterprise/integrations/stripe.mdx

@@ -0,0 +1,305 @@
+---
+title: Stripe Integration
+description: "Payment processing and subscription management with Stripe integration for CrewAI."
+icon: "stripe"
+---
+
+## Overview
+
+Enable your agents to manage payments, subscriptions, and customer billing through Stripe. Handle customer data, process subscriptions, manage products, and track financial transactions to streamline your payment workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Stripe integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Stripe account with appropriate API permissions
+- Connected your Stripe account through the [Integrations page](https://app.crewai.com/integrations)
+
+## Available Tools
+
+### **Customer Management**
+
+<AccordionGroup>
+  <Accordion title="STRIPE_CREATE_CUSTOMER">
+    **Description:** Create a new customer in your Stripe account.
+
+    **Parameters:**
+    - `emailCreateCustomer` (string, required): Customer's email address
+    - `name` (string, optional): Customer's full name
+    - `description` (string, optional): Customer description for internal reference
+    - `metadataCreateCustomer` (object, optional): Additional metadata as key-value pairs (e.g., `{"field1": 1, "field2": 2}`)
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_CUSTOMER_BY_ID">
+    **Description:** Retrieve a specific customer by their Stripe customer ID.
+
+    **Parameters:**
+    - `idGetCustomer` (string, required): The Stripe customer ID to retrieve
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_CUSTOMERS">
+    **Description:** Retrieve a list of customers with optional filtering.
+
+    **Parameters:**
+    - `emailGetCustomers` (string, optional): Filter customers by email address
+    - `createdAfter` (string, optional): Filter customers created after this date (Unix timestamp)
+    - `createdBefore` (string, optional): Filter customers created before this date (Unix timestamp)
+    - `limitGetCustomers` (string, optional): Maximum number of customers to return (defaults to 10)
+  </Accordion>
+
+  <Accordion title="STRIPE_UPDATE_CUSTOMER">
+    **Description:** Update an existing customer's information.
+
+    **Parameters:**
+    - `customerId` (string, required): The ID of the customer to update
+    - `emailUpdateCustomer` (string, optional): Updated email address
+    - `name` (string, optional): Updated customer name
+    - `description` (string, optional): Updated customer description
+    - `metadataUpdateCustomer` (object, optional): Updated metadata as key-value pairs
+  </Accordion>
+</AccordionGroup>
+
+### **Subscription Management**
+
+<AccordionGroup>
+  <Accordion title="STRIPE_CREATE_SUBSCRIPTION">
+    **Description:** Create a new subscription for a customer.
+
+    **Parameters:**
+    - `customerIdCreateSubscription` (string, required): The customer ID for whom the subscription will be created
+    - `plan` (string, required): The plan ID for the subscription - Use Connect Portal Workflow Settings to allow users to select a plan
+    - `metadataCreateSubscription` (object, optional): Additional metadata for the subscription
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_SUBSCRIPTIONS">
+    **Description:** Retrieve subscriptions with optional filtering.
+
+    **Parameters:**
+    - `customerIdGetSubscriptions` (string, optional): Filter subscriptions by customer ID
+    - `subscriptionStatus` (string, optional): Filter by subscription status - Options: incomplete, incomplete_expired, trialing, active, past_due, canceled, unpaid
+    - `limitGetSubscriptions` (string, optional): Maximum number of subscriptions to return (defaults to 10)
+  </Accordion>
+</AccordionGroup>
+
+### **Product Management**
+
+<AccordionGroup>
+  <Accordion title="STRIPE_CREATE_PRODUCT">
+    **Description:** Create a new product in your Stripe catalog.
+
+    **Parameters:**
+    - `productName` (string, required): The product name
+    - `description` (string, optional): Product description
+    - `metadataProduct` (object, optional): Additional product metadata as key-value pairs
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_PRODUCT_BY_ID">
+    **Description:** Retrieve a specific product by its Stripe product ID.
+
+    **Parameters:**
+    - `productId` (string, required): The Stripe product ID to retrieve
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_PRODUCTS">
+    **Description:** Retrieve a list of products with optional filtering.
+
+    **Parameters:**
+    - `createdAfter` (string, optional): Filter products created after this date (Unix timestamp)
+    - `createdBefore` (string, optional): Filter products created before this date (Unix timestamp)
+    - `limitGetProducts` (string, optional): Maximum number of products to return (defaults to 10)
+  </Accordion>
+</AccordionGroup>
+
+### **Financial Operations**
+
+<AccordionGroup>
+  <Accordion title="STRIPE_GET_BALANCE_TRANSACTIONS">
+    **Description:** Retrieve balance transactions from your Stripe account.
+
+    **Parameters:**
+    - `balanceTransactionType` (string, optional): Filter by transaction type - Options: charge, refund, payment, payment_refund
+    - `paginationParameters` (object, optional): Pagination settings
+      - `pageCursor` (string, optional): Page cursor for pagination
+  </Accordion>
+
+  <Accordion title="STRIPE_GET_PLANS">
+    **Description:** Retrieve subscription plans from your Stripe account.
+
+    **Parameters:**
+    - `isPlanActive` (boolean, optional): Filter by plan status - true for active plans, false for inactive plans
+    - `paginationParameters` (object, optional): Pagination settings
+      - `pageCursor` (string, optional): Page cursor for pagination
+  </Accordion>
+</AccordionGroup>
+
+## Usage Examples
+
+### Basic Stripe Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Stripe tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Stripe capabilities
+stripe_agent = Agent(
+    role="Payment Manager",
+    goal="Manage customer payments, subscriptions, and billing operations efficiently",
+    backstory="An AI assistant specialized in payment processing and subscription management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new customer
+create_customer_task = Task(
+    description="Create a new premium customer John Doe with email john.doe@example.com",
+    agent=stripe_agent,
+    expected_output="Customer created successfully with customer ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[stripe_agent],
+    tasks=[create_customer_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Stripe Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Stripe tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["stripe_create_customer", "stripe_create_subscription", "stripe_get_balance_transactions"]
+)
+
+billing_manager = Agent(
+    role="Billing Manager",
+    goal="Handle customer billing, subscriptions, and payment processing",
+    backstory="An experienced billing manager who handles subscription lifecycle and payment operations.",
+    tools=enterprise_tools
+)
+
+# Task to manage billing operations
+billing_task = Task(
+    description="Create a new customer and set up their premium subscription plan",
+    agent=billing_manager,
+    expected_output="Customer created and subscription activated successfully"
+)
+
+crew = Crew(
+    agents=[billing_manager],
+    tasks=[billing_task]
+)
+
+crew.kickoff()
+```
+
+### Subscription Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+subscription_manager = Agent(
+    role="Subscription Manager",
+    goal="Manage customer subscriptions and optimize recurring revenue",
+    backstory="An AI assistant that specializes in subscription lifecycle management and customer retention.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage subscription operations
+subscription_task = Task(
+    description="""
+    1. Create a new product "Premium Service Plan" with advanced features
+    2. Set up subscription plans with different tiers
+    3. Create customers and assign them to appropriate plans
+    4. Monitor subscription status and handle billing issues
+    """,
+    agent=subscription_manager,
+    expected_output="Subscription management system configured with customers and active plans"
+)
+
+crew = Crew(
+    agents=[subscription_manager],
+    tasks=[subscription_task]
+)
+
+crew.kickoff()
+```
+
+### Financial Analytics and Reporting
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+financial_analyst = Agent(
+    role="Financial Analyst",
+    goal="Analyze payment data and generate financial insights",
+    backstory="An analytical AI that excels at extracting insights from payment and subscription data.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving financial analysis
+analytics_task = Task(
+    description="""
+    1. Retrieve balance transactions for the current month
+    2. Analyze customer payment patterns and subscription trends
+    3. Identify high-value customers and subscription performance
+    4. Generate monthly financial performance report
+    """,
+    agent=financial_analyst,
+    expected_output="Comprehensive financial analysis with payment insights and recommendations"
+)
+
+crew = Crew(
+    agents=[financial_analyst],
+    tasks=[analytics_task]
+)
+
+crew.kickoff()
+```
+
+## Subscription Status Reference
+
+Understanding subscription statuses:
+
+- **incomplete** - Subscription requires payment method or payment confirmation
+- **incomplete_expired** - Subscription expired before payment was confirmed
+- **trialing** - Subscription is in trial period
+- **active** - Subscription is active and current
+- **past_due** - Payment failed but subscription is still active
+- **canceled** - Subscription has been canceled
+- **unpaid** - Payment failed and subscription is no longer active
+
+## Metadata Usage
+
+Metadata allows you to store additional information about customers, subscriptions, and products:
+
+```json
+{
+  "customer_segment": "enterprise",
+  "acquisition_source": "google_ads",
+  "lifetime_value": "high",
+  "custom_field_1": "value1"
+}
+```
+
+This integration enables comprehensive payment and subscription management automation, allowing your AI agents to handle billing operations seamlessly within your Stripe ecosystem.

docs/enterprise/integrations/zendesk.mdx

@@ -0,0 +1,343 @@
+---
+title: Zendesk Integration
+description: "Customer support and helpdesk management with Zendesk integration for CrewAI."
+icon: "headset"
+---
+
+## Overview
+
+Enable your agents to manage customer support operations through Zendesk. Create and update tickets, manage users, track support metrics, and streamline your customer service workflows with AI-powered automation.
+
+## Prerequisites
+
+Before using the Zendesk integration, ensure you have:
+
+- A [CrewAI Enterprise](https://app.crewai.com) account with an active subscription
+- A Zendesk account with appropriate API permissions
+- Connected your Zendesk account through the [Integrations page](https://app.crewai.com/integrations)
+
+## Available Tools
+
+### **Ticket Management**
+
+<AccordionGroup>
+  <Accordion title="ZENDESK_CREATE_TICKET">
+    **Description:** Create a new support ticket in Zendesk.
+
+    **Parameters:**
+    - `ticketSubject` (string, required): Ticket subject line (e.g., "Help, my printer is on fire!")
+    - `ticketDescription` (string, required): First comment that appears on the ticket (e.g., "The smoke is very colorful.")
+    - `requesterName` (string, required): Name of the user requesting support (e.g., "Jane Customer")
+    - `requesterEmail` (string, required): Email of the user requesting support (e.g., "jane@example.com")
+    - `assigneeId` (string, optional): Zendesk Agent ID assigned to this ticket - Use Connect Portal Workflow Settings to allow users to select an assignee
+    - `ticketType` (string, optional): Ticket type - Options: problem, incident, question, task
+    - `ticketPriority` (string, optional): Priority level - Options: urgent, high, normal, low
+    - `ticketStatus` (string, optional): Ticket status - Options: new, open, pending, hold, solved, closed
+    - `ticketDueAt` (string, optional): Due date for task-type tickets (ISO 8601 timestamp)
+    - `ticketTags` (string, optional): Array of tags to apply (e.g., `["enterprise", "other_tag"]`)
+    - `ticketExternalId` (string, optional): External ID to link tickets to local records
+    - `ticketCustomFields` (object, optional): Custom field values in JSON format
+  </Accordion>
+
+  <Accordion title="ZENDESK_UPDATE_TICKET">
+    **Description:** Update an existing support ticket in Zendesk.
+
+    **Parameters:**
+    - `ticketId` (string, required): ID of the ticket to update (e.g., "35436")
+    - `ticketSubject` (string, optional): Updated ticket subject
+    - `requesterName` (string, required): Name of the user who requested this ticket
+    - `requesterEmail` (string, required): Email of the user who requested this ticket
+    - `assigneeId` (string, optional): Updated assignee ID - Use Connect Portal Workflow Settings
+    - `ticketType` (string, optional): Updated ticket type - Options: problem, incident, question, task
+    - `ticketPriority` (string, optional): Updated priority - Options: urgent, high, normal, low
+    - `ticketStatus` (string, optional): Updated status - Options: new, open, pending, hold, solved, closed
+    - `ticketDueAt` (string, optional): Updated due date (ISO 8601 timestamp)
+    - `ticketTags` (string, optional): Updated tags array
+    - `ticketExternalId` (string, optional): Updated external ID
+    - `ticketCustomFields` (object, optional): Updated custom field values
+  </Accordion>
+
+  <Accordion title="ZENDESK_GET_TICKET_BY_ID">
+    **Description:** Retrieve a specific ticket by its ID.
+
+    **Parameters:**
+    - `ticketId` (string, required): The ticket ID to retrieve (e.g., "35436")
+  </Accordion>
+
+  <Accordion title="ZENDESK_ADD_COMMENT_TO_TICKET">
+    **Description:** Add a comment or internal note to an existing ticket.
+
+    **Parameters:**
+    - `ticketId` (string, required): ID of the ticket to add comment to (e.g., "35436")
+    - `commentBody` (string, required): Comment message (accepts plain text or HTML, e.g., "Thanks for your help!")
+    - `isInternalNote` (boolean, optional): Set to true for internal notes instead of public replies (defaults to false)
+    - `isPublic` (boolean, optional): True for public comments, false for internal notes
+  </Accordion>
+
+  <Accordion title="ZENDESK_SEARCH_TICKETS">
+    **Description:** Search for tickets using various filters and criteria.
+
+    **Parameters:**
+    - `ticketSubject` (string, optional): Filter by text in ticket subject
+    - `ticketDescription` (string, optional): Filter by text in ticket description and comments
+    - `ticketStatus` (string, optional): Filter by status - Options: new, open, pending, hold, solved, closed
+    - `ticketType` (string, optional): Filter by type - Options: problem, incident, question, task, no_type
+    - `ticketPriority` (string, optional): Filter by priority - Options: urgent, high, normal, low, no_priority
+    - `requesterId` (string, optional): Filter by requester user ID
+    - `assigneeId` (string, optional): Filter by assigned agent ID
+    - `recipientEmail` (string, optional): Filter by original recipient email address
+    - `ticketTags` (string, optional): Filter by ticket tags
+    - `ticketExternalId` (string, optional): Filter by external ID
+    - `createdDate` (object, optional): Filter by creation date with operator (EQUALS, LESS_THAN_EQUALS, GREATER_THAN_EQUALS) and value
+    - `updatedDate` (object, optional): Filter by update date with operator and value
+    - `dueDate` (object, optional): Filter by due date with operator and value
+    - `sort_by` (string, optional): Sort field - Options: created_at, updated_at, priority, status, ticket_type
+    - `sort_order` (string, optional): Sort direction - Options: asc, desc
+  </Accordion>
+</AccordionGroup>
+
+### **User Management**
+
+<AccordionGroup>
+  <Accordion title="ZENDESK_CREATE_USER">
+    **Description:** Create a new user in Zendesk.
+
+    **Parameters:**
+    - `name` (string, required): User's full name
+    - `email` (string, optional): User's email address (e.g., "jane@example.com")
+    - `phone` (string, optional): User's phone number
+    - `role` (string, optional): User role - Options: admin, agent, end-user
+    - `externalId` (string, optional): Unique identifier from another system
+    - `details` (string, optional): Additional user details
+    - `notes` (string, optional): Internal notes about the user
+  </Accordion>
+
+  <Accordion title="ZENDESK_UPDATE_USER">
+    **Description:** Update an existing user's information.
+
+    **Parameters:**
+    - `userId` (string, required): ID of the user to update
+    - `name` (string, optional): Updated user name
+    - `email` (string, optional): Updated email (adds as secondary email on update)
+    - `phone` (string, optional): Updated phone number
+    - `role` (string, optional): Updated role - Options: admin, agent, end-user
+    - `externalId` (string, optional): Updated external ID
+    - `details` (string, optional): Updated user details
+    - `notes` (string, optional): Updated internal notes
+  </Accordion>
+
+  <Accordion title="ZENDESK_GET_USER_BY_ID">
+    **Description:** Retrieve a specific user by their ID.
+
+    **Parameters:**
+    - `userId` (string, required): The user ID to retrieve
+  </Accordion>
+
+  <Accordion title="ZENDESK_SEARCH_USERS">
+    **Description:** Search for users using various criteria.
+
+    **Parameters:**
+    - `name` (string, optional): Filter by user name
+    - `email` (string, optional): Filter by user email (e.g., "jane@example.com")
+    - `role` (string, optional): Filter by role - Options: admin, agent, end-user
+    - `externalId` (string, optional): Filter by external ID
+    - `sort_by` (string, optional): Sort field - Options: created_at, updated_at
+    - `sort_order` (string, optional): Sort direction - Options: asc, desc
+  </Accordion>
+</AccordionGroup>
+
+### **Administrative Tools**
+
+<AccordionGroup>
+  <Accordion title="ZENDESK_GET_TICKET_FIELDS">
+    **Description:** Retrieve all standard and custom fields available for tickets.
+
+    **Parameters:**
+    - `paginationParameters` (object, optional): Pagination settings
+      - `pageCursor` (string, optional): Page cursor for pagination
+  </Accordion>
+
+  <Accordion title="ZENDESK_GET_TICKET_AUDITS">
+    **Description:** Get audit records (read-only history) for tickets.
+
+    **Parameters:**
+    - `ticketId` (string, optional): Get audits for specific ticket (if empty, retrieves audits for all non-archived tickets, e.g., "1234")
+    - `paginationParameters` (object, optional): Pagination settings
+      - `pageCursor` (string, optional): Page cursor for pagination
+  </Accordion>
+</AccordionGroup>
+
+## Custom Fields
+
+Custom fields allow you to store additional information specific to your organization:
+
+```json
+[
+  { "id": 27642, "value": "745" },
+  { "id": 27648, "value": "yes" }
+]
+```
+
+## Ticket Priority Levels
+
+Understanding priority levels:
+
+- **urgent** - Critical issues requiring immediate attention
+- **high** - Important issues that should be addressed quickly
+- **normal** - Standard priority for most tickets
+- **low** - Minor issues that can be addressed when convenient
+
+## Ticket Status Workflow
+
+Standard ticket status progression:
+
+- **new** - Recently created, not yet assigned
+- **open** - Actively being worked on
+- **pending** - Waiting for customer response or external action
+- **hold** - Temporarily paused
+- **solved** - Issue resolved, awaiting customer confirmation
+- **closed** - Ticket completed and closed
+
+## Usage Examples
+
+### Basic Zendesk Agent Setup
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get enterprise tools (Zendesk tools will be included)
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+# Create an agent with Zendesk capabilities
+zendesk_agent = Agent(
+    role="Support Manager",
+    goal="Manage customer support tickets and provide excellent customer service",
+    backstory="An AI assistant specialized in customer support operations and ticket management.",
+    tools=[enterprise_tools]
+)
+
+# Task to create a new support ticket
+create_ticket_task = Task(
+    description="Create a high-priority support ticket for John Smith who is unable to access his account after password reset",
+    agent=zendesk_agent,
+    expected_output="Support ticket created successfully with ticket ID"
+)
+
+# Run the task
+crew = Crew(
+    agents=[zendesk_agent],
+    tasks=[create_ticket_task]
+)
+
+crew.kickoff()
+```
+
+### Filtering Specific Zendesk Tools
+
+```python
+from crewai_tools import CrewaiEnterpriseTools
+
+# Get only specific Zendesk tools
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token",
+    actions_list=["zendesk_create_ticket", "zendesk_update_ticket", "zendesk_add_comment_to_ticket"]
+)
+
+support_agent = Agent(
+    role="Customer Support Agent",
+    goal="Handle customer inquiries and resolve support issues efficiently",
+    backstory="An experienced support agent who specializes in ticket resolution and customer communication.",
+    tools=enterprise_tools
+)
+
+# Task to manage support workflow
+support_task = Task(
+    description="Create a ticket for login issues, add troubleshooting comments, and update status to resolved",
+    agent=support_agent,
+    expected_output="Support ticket managed through complete resolution workflow"
+)
+
+crew = Crew(
+    agents=[support_agent],
+    tasks=[support_task]
+)
+
+crew.kickoff()
+```
+
+### Advanced Ticket Management
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+ticket_manager = Agent(
+    role="Ticket Manager",
+    goal="Manage support ticket workflows and ensure timely resolution",
+    backstory="An AI assistant that specializes in support ticket triage and workflow optimization.",
+    tools=[enterprise_tools]
+)
+
+# Task to manage ticket lifecycle
+ticket_workflow = Task(
+    description="""
+    1. Create a new support ticket for account access issues
+    2. Add internal notes with troubleshooting steps
+    3. Update ticket priority based on customer tier
+    4. Add resolution comments and close the ticket
+    """,
+    agent=ticket_manager,
+    expected_output="Complete ticket lifecycle managed from creation to resolution"
+)
+
+crew = Crew(
+    agents=[ticket_manager],
+    tasks=[ticket_workflow]
+)
+
+crew.kickoff()
+```
+
+### Support Analytics and Reporting
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import CrewaiEnterpriseTools
+
+enterprise_tools = CrewaiEnterpriseTools(
+    enterprise_token="your_enterprise_token"
+)
+
+support_analyst = Agent(
+    role="Support Analyst",
+    goal="Analyze support metrics and generate insights for team performance",
+    backstory="An analytical AI that excels at extracting insights from support data and ticket patterns.",
+    tools=[enterprise_tools]
+)
+
+# Complex task involving analytics and reporting
+analytics_task = Task(
+    description="""
+    1. Search for all open tickets from the last 30 days
+    2. Analyze ticket resolution times and customer satisfaction
+    3. Identify common issues and support patterns
+    4. Generate weekly support performance report
+    """,
+    agent=support_analyst,
+    expected_output="Comprehensive support analytics report with performance insights and recommendations"
+)
+
+crew = Crew(
+    agents=[support_analyst],
+    tasks=[analytics_task]
+)
+
+crew.kickoff()
+```

docs/enterprise/introduction.mdx

@@ -0,0 +1,99 @@
+---
+title: "CrewAI Enterprise"
+description: "Deploy, monitor, and scale your AI agent workflows"
+icon: "globe"
+---
+
+## Introduction
+
+CrewAI Enterprise provides a platform for deploying, monitoring, and scaling your crews and agents in a production environment.
+
+<Frame>
+  <img src="/images/enterprise/crewai-enterprise-dashboard.png" alt="CrewAI Enterprise Dashboard" />
+</Frame>
+
+CrewAI Enterprise extends the power of the open-source framework with features designed for production deployments, collaboration, and scalability. Deploy your crews to a managed infrastructure and monitor their execution in real-time.
+
+## Key Features
+
+<CardGroup cols={2}>
+  <Card title="Crew Deployments" icon="rocket">
+    Deploy your crews to a managed infrastructure with a few clicks
+  </Card>
+  <Card title="API Access" icon="code">
+    Access your deployed crews via REST API for integration with existing systems
+  </Card>
+  <Card title="Observability" icon="chart-line">
+    Monitor your crews with detailed execution traces and logs
+  </Card>
+  <Card title="Tool Repository" icon="toolbox">
+    Publish and install tools to enhance your crews' capabilities
+  </Card>
+  <Card title="Webhook Streaming" icon="webhook">
+    Stream real-time events and updates to your systems
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    Create and customize crews using a no-code/low-code interface
+  </Card>
+</CardGroup>
+
+## Deployment Options
+
+<CardGroup cols={3}>
+  <Card title="GitHub Integration" icon="github">
+    Connect directly to your GitHub repositories to deploy code
+  </Card>
+  <Card title="Crew Studio" icon="palette">
+    Deploy crews created through the no-code Crew Studio interface
+  </Card>
+  <Card title="CLI Deployment" icon="terminal">
+    Use the CrewAI CLI for more advanced deployment workflows
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+<Steps>
+  <Step title="Sign up for an account">
+    Create your account at [app.crewai.com](https://app.crewai.com)
+    <Card 
+      title="Sign Up" 
+      icon="user"
+      href="https://app.crewai.com/signup"
+    >
+      Sign Up
+    </Card>
+  </Step>
+  <Step title="Build your first crew">
+    Use code or Crew Studio to build your crew
+    <Card 
+      title="Build Crew" 
+      icon="paintbrush"
+      href="/enterprise/guides/build-crew"
+    >
+      Build Crew
+    </Card>
+  </Step>
+  <Step title="Deploy your crew">
+    Deploy your crew to the Enterprise platform
+    <Card 
+      title="Deploy Crew" 
+      icon="rocket"
+      href="/enterprise/guides/deploy-crew"
+    >
+      Deploy Crew
+    </Card>
+  </Step>
+  <Step title="Access your crew">
+    Integrate with your crew via the generated API endpoints
+    <Card 
+      title="API Access" 
+      icon="code"
+      href="/enterprise/guides/use-crew-api"
+    >
+      Use the Crew API
+    </Card>
+  </Step>
+</Steps>
+
+For detailed instructions, check out our [deployment guide](/enterprise/guides/deploy-crew) or click the button below to get started.

docs/enterprise/resources/frequently-asked-questions.mdx

@@ -0,0 +1,151 @@
+---
+title: FAQs
+description: "Frequently asked questions about CrewAI Enterprise"
+icon: "circle-question"
+---
+
+<AccordionGroup>
+    <Accordion title="How is task execution handled in the hierarchical process?">
+        In the hierarchical process, a manager agent is automatically created and coordinates the workflow, delegating tasks and validating outcomes for streamlined and effective execution. The manager agent utilizes tools to facilitate task delegation and execution by agents under the manager's guidance. The manager LLM is crucial for the hierarchical process and must be set up correctly for proper function.
+    </Accordion>
+
+    <Accordion title="Where can I get the latest CrewAI documentation?">
+        The most up-to-date documentation for CrewAI is available on our official documentation website: https://docs.crewai.com/
+        <Card href="https://docs.crewai.com/" icon="books">CrewAI Docs</Card>
+    </Accordion>
+
+    <Accordion title="What are the key differences between Hierarchical and Sequential Processes in CrewAI?">
+        #### Hierarchical Process:
+        - Tasks are delegated and executed based on a structured chain of command
+        - A manager language model (`manager_llm`) must be specified for the manager agent
+        - Manager agent oversees task execution, planning, delegation, and validation
+        - Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities
+
+        #### Sequential Process:
+        - Tasks are executed one after another, ensuring tasks are completed in an orderly progression
+        - Output of one task serves as context for the next
+        - Task execution follows the predefined order in the task list
+
+        #### Which Process is Better for Complex Projects?
+        The hierarchical process is better suited for complex projects because it allows for:
+        - **Dynamic task allocation and delegation**: Manager agent can assign tasks based on agent capabilities
+        - **Structured validation and oversight**: Manager agent reviews task outputs and ensures completion
+        - **Complex task management**: Precise control over tool availability at the agent level
+    </Accordion>
+
+    <Accordion title="What are the benefits of using memory in the CrewAI framework?">
+        - **Adaptive Learning**: Crews become more efficient over time, adapting to new information and refining their approach to tasks
+        - **Enhanced Personalization**: Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences
+        - **Improved Problem Solving**: Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights
+    </Accordion>
+
+    <Accordion title="What is the purpose of setting a maximum RPM limit for an agent?">
+        Setting a maximum RPM limit for an agent prevents the agent from making too many requests to external services, which can help to avoid rate limits and improve performance.
+    </Accordion>
+
+    <Accordion title="What role does human input play in the execution of tasks within a CrewAI crew?">
+        Human input allows agents to request additional information or clarification when necessary. This feature is crucial in complex decision-making processes or when agents require more details to complete a task effectively.
+
+        To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
+
+        For detailed implementation guidance, see our [Human-in-the-Loop guide](/how-to/human-in-the-loop).
+    </Accordion>
+
+    <Accordion title="What advanced customization options are available for tailoring and enhancing agent behavior and capabilities in CrewAI?">
+        CrewAI provides a range of advanced customization options:
+        
+        - **Language Model Customization**: Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`)
+        - **Performance and Debugging Settings**: Adjust an agent's performance and monitor its operations
+        - **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization
+        - **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`)
+        - **Maximum Iterations**: The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task
+        - **Delegation and Autonomy**: Control an agent's ability to delegate or ask questions with the `allow_delegation` attribute (default: True)
+        - **Human Input Integration**: Agents can request additional information or clarification when necessary
+    </Accordion>
+
+    <Accordion title="In what scenarios is human input particularly useful in agent execution?">
+        Human input is particularly useful when:
+        - **Agents require additional information or clarification**: When agents encounter ambiguity or incomplete data
+        - **Agents need to make complex or sensitive decisions**: Human input can assist in ethical or nuanced decision-making
+        - **Oversight and validation of agent output**: Human input can help validate results and prevent errors
+        - **Customizing agent behavior**: Human input can provide feedback to refine agent responses over time
+        - **Identifying and resolving errors or limitations**: Human input helps address agent capability gaps
+    </Accordion>
+
+    <Accordion title="What are the different types of memory that are available in crewAI?">
+        The different types of memory available in CrewAI are:
+        - **Short-term memory**: Temporary storage for immediate context
+        - **Long-term memory**: Persistent storage for learned patterns and information
+        - **Entity memory**: Focused storage for specific entities and their attributes
+        - **Contextual memory**: Memory that maintains context across interactions
+
+        Learn more about the different types of memory:
+        <Card href="https://docs.crewai.com/concepts/memory" icon="brain">CrewAI Memory</Card>
+    </Accordion>
+
+    <Accordion title="How do I use Output Pydantic in a Task?">
+        To use Output Pydantic in a task, you need to define the expected output of the task as a Pydantic model. Here's a quick example:
+
+        <Steps>
+            <Step title="Define a Pydantic model">
+                ```python
+                from pydantic import BaseModel
+
+                class User(BaseModel):
+                    name: str
+                    age: int
+                ```
+            </Step>
+
+            <Step title="Create a task with Output Pydantic">
+                ```python
+                from crewai import Task, Crew, Agent
+                from my_models import User
+
+                task = Task(
+                    description="Create a user with the provided name and age",
+                    expected_output=User,  # This is the Pydantic model
+                    agent=agent,
+                    tools=[tool1, tool2]
+                )
+                ```
+            </Step>
+
+            <Step title="Set the output_pydantic attribute in your agent">
+                ```python
+                from crewai import Agent
+                from my_models import User
+
+                agent = Agent(
+                    role='User Creator',
+                    goal='Create users',
+                    backstory='I am skilled in creating user accounts',
+                    tools=[tool1, tool2],
+                    output_pydantic=User
+                )
+                ```
+            </Step>
+        </Steps>
+
+        Here's a tutorial on how to consistently get structured outputs from your agents:
+        <Frame>
+            <iframe 
+            height="400"
+            width="100%"
+            src="https://www.youtube.com/embed/dNpKQk5uxHw" 
+            title="YouTube video player" frameborder="0" 
+            allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" 
+            allowfullscreen></iframe>
+        </Frame>
+    </Accordion>
+
+    <Accordion title="How can I create custom tools for my CrewAI agents?">
+        You can create custom tools by subclassing the `BaseTool` class provided by CrewAI or by using the tool decorator. Subclassing involves defining a new class that inherits from `BaseTool`, specifying the name, description, and the `_run` method for operational logic. The tool decorator allows you to create a `Tool` object directly with the required attributes and a functional logic.
+
+        <Card href="https://docs.crewai.com/how-to/create-custom-tools" icon="code">CrewAI Tools Guide</Card>
+    </Accordion>
+
+    <Accordion title="How can you control the maximum number of requests per minute that the entire crew can perform?">
+        The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
+    </Accordion>
+</AccordionGroup>

docs/guides/advanced/customizing-prompts.mdx

@@ -4,11 +4,9 @@ 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:
+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:
 
 1. **Optimize for specific LLMs** – Different models (such as GPT-4, Claude, or Llama) thrive with prompt formats tailored to their unique architectures.
 2. **Change the language** – Build agents that operate exclusively in languages beyond English, handling nuances with precision.
@@ -22,13 +20,174 @@ This guide explores how to tap into CrewAI's prompts at a lower level, giving yo
 
 Under the hood, CrewAI employs a modular prompt system that you can customize extensively:
 
-- **Agent templates** – Govern each agent’s approach to their assigned role.
+- **Agent templates** – Govern each agent's approach to their assigned role.
 - **Prompt slices** – Control specialized behaviors such as tasks, tool usage, and output structure.
 - **Error handling** – Direct how agents respond to failures, exceptions, or timeouts.
 - **Tool-specific prompts** – Define detailed instructions for how tools are invoked or utilized.
 
 Check out the [original prompt templates in CrewAI's repository](https://github.com/crewAIInc/crewAI/blob/main/src/crewai/translations/en.json) to see how these elements are organized. From there, you can override or adapt them as needed to unlock advanced behaviors.
 
+## Understanding Default System Instructions
+
+<Warning>
+**Production Transparency Issue**: CrewAI automatically injects default instructions into your prompts that you might not be aware of. This section explains what's happening under the hood and how to gain full control.
+</Warning>
+
+When you define an agent with `role`, `goal`, and `backstory`, CrewAI automatically adds additional system instructions that control formatting and behavior. Understanding these default injections is crucial for production systems where you need full prompt transparency.
+
+### What CrewAI Automatically Injects
+
+Based on your agent configuration, CrewAI adds different default instructions:
+
+#### For Agents Without Tools
+```text
+"I MUST use these formats, my job depends on it!"
+```
+
+#### For Agents With Tools  
+```text
+"IMPORTANT: Use the following format in your response:
+
+Thought: you should always think about what to do
+Action: the action to take, only one name of [tool_names]
+Action Input: the input to the action, just a simple JSON object...
+```
+
+#### For Structured Outputs (JSON/Pydantic)
+```text
+"Ensure your final answer contains only the content in the following format: {output_format}
+Ensure the final output does not include any code block markers like ```json or ```python."
+```
+
+### Viewing the Complete System Prompt
+
+To see exactly what prompt is being sent to your LLM, you can inspect the generated prompt:
+
+```python
+from crewai import Agent, Crew, Task
+from crewai.utilities.prompts import Prompts
+
+# Create your agent
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze data and provide insights",
+    backstory="You are an expert data analyst with 10 years of experience.",
+    verbose=True
+)
+
+# Create a sample task
+task = Task(
+    description="Analyze the sales data and identify trends",
+    expected_output="A detailed analysis with key insights and trends",
+    agent=agent
+)
+
+# Create the prompt generator
+prompt_generator = Prompts(
+    agent=agent,
+    has_tools=len(agent.tools) > 0,
+    use_system_prompt=agent.use_system_prompt
+)
+
+# Generate and inspect the actual prompt
+generated_prompt = prompt_generator.task_execution()
+
+# Print the complete system prompt that will be sent to the LLM
+if "system" in generated_prompt:
+    print("=== SYSTEM PROMPT ===")
+    print(generated_prompt["system"])
+    print("\n=== USER PROMPT ===")
+    print(generated_prompt["user"])
+else:
+    print("=== COMPLETE PROMPT ===")
+    print(generated_prompt["prompt"])
+
+# You can also see how the task description gets formatted
+print("\n=== TASK CONTEXT ===")
+print(f"Task Description: {task.description}")
+print(f"Expected Output: {task.expected_output}")
+```
+
+### Overriding Default Instructions
+
+You have several options to gain full control over the prompts:
+
+#### Option 1: Custom Templates (Recommended)
+```python
+from crewai import Agent
+
+# Define your own system template without default instructions
+custom_system_template = """You are {role}. {backstory}
+Your goal is: {goal}
+
+Respond naturally and conversationally. Focus on providing helpful, accurate information."""
+
+custom_prompt_template = """Task: {input}
+
+Please complete this task thoughtfully."""
+
+agent = Agent(
+    role="Research Assistant", 
+    goal="Help users find accurate information",
+    backstory="You are a helpful research assistant.",
+    system_template=custom_system_template,
+    prompt_template=custom_prompt_template,
+    use_system_prompt=True  # Use separate system/user messages
+)
+```
+
+#### Option 2: Custom Prompt File
+Create a `custom_prompts.json` file to override specific prompt slices:
+
+```json
+{
+  "slices": {
+    "no_tools": "\nProvide your best answer in a natural, conversational way.",
+    "tools": "\nYou have access to these tools: {tools}\n\nUse them when helpful, but respond naturally.",
+    "formatted_task_instructions": "Format your response as: {output_format}"
+  }
+}
+```
+
+Then use it in your crew:
+
+```python
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    prompt_file="custom_prompts.json",
+    verbose=True
+)
+```
+
+#### Option 3: Disable System Prompts for o1 Models
+```python
+agent = Agent(
+    role="Analyst",
+    goal="Analyze data", 
+    backstory="Expert analyst",
+    use_system_prompt=False  # Disables system prompt separation
+)
+```
+
+### Debugging with Observability Tools
+
+For production transparency, integrate with observability platforms to monitor all prompts and LLM interactions. This allows you to see exactly what prompts (including default instructions) are being sent to your LLMs.
+
+See our [Observability documentation](/how-to/observability) for detailed integration guides with various platforms including Langfuse, MLflow, Weights & Biases, and custom logging solutions.
+
+### Best Practices for Production
+
+1. **Always inspect generated prompts** before deploying to production
+2. **Use custom templates** when you need full control over prompt content
+3. **Integrate observability tools** for ongoing prompt monitoring (see [Observability docs](/how-to/observability))
+4. **Test with different LLMs** as default instructions may work differently across models
+5. **Document your prompt customizations** for team transparency
+
+<Tip>
+The default instructions exist to ensure consistent agent behavior, but they can interfere with domain-specific requirements. Use the customization options above to maintain full control over your agent's behavior in production systems.
+</Tip>
+
 ## Best Practices for Managing Prompt Files
 
 When engaging in low-level prompt customization, follow these guidelines to keep things organized and maintainable:
@@ -46,7 +205,7 @@ One straightforward approach is to create a JSON file for the prompts you want t
 1. Craft a JSON file with your updated prompt slices.
 2. Reference that file via the `prompt_file` parameter in your Crew.
 
-CrewAI then merges your customizations with the defaults, so you don’t have to redefine every prompt. Here’s how:
+CrewAI then merges your customizations with the defaults, so you don't have to redefine every prompt. Here's how:
 
 ### Example: Basic Prompt Customization
 
@@ -95,14 +254,14 @@ With these few edits, you gain low-level control over how your agents communicat
 
 ## Optimizing for Specific Models
 
-Different models thrive on differently structured prompts. Making deeper adjustments can significantly boost performance by aligning your prompts with a model’s nuances.
+Different models thrive on differently structured prompts. Making deeper adjustments can significantly boost performance by aligning your prompts with a model's nuances.
 
 ### Example: Llama 3.3 Prompting Template
 
-For instance, when dealing with Meta’s Llama 3.3, deeper-level customization may reflect the recommended structure described at:
+For instance, when dealing with Meta's Llama 3.3, deeper-level customization may reflect the recommended structure described at:
 https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#prompt-template
 
-Here’s an example to highlight how you might fine-tune an Agent to leverage Llama 3.3 in code:
+Here's an example to highlight how you might fine-tune an Agent to leverage Llama 3.3 in code:
 
 ```python
 from crewai import Agent, Crew, Task, Process
@@ -150,8 +309,8 @@ Through this deeper configuration, you can exercise comprehensive, low-level con
 
 ## Conclusion
 
-Low-level prompt customization in CrewAI opens the door to super custom, complex use cases. By establishing well-organized prompt files (or direct inline templates), you can accommodate various models, languages, and specialized domains. This level of flexibility ensures you can craft precisely the AI behavior you need, all while knowing CrewAI still provides reliable defaults when you don’t override them.
+Low-level prompt customization in CrewAI opens the door to super custom, complex use cases. By establishing well-organized prompt files (or direct inline templates), you can accommodate various models, languages, and specialized domains. This level of flexibility ensures you can craft precisely the AI behavior you need, all while knowing CrewAI still provides reliable defaults when you don't override them.
 
 <Check>
-You now have the foundation for advanced prompt customizations in CrewAI. Whether you’re adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
+You now have the foundation for advanced prompt customizations in CrewAI. Whether you're adapting for model-specific structures or domain-specific constraints, this low-level approach lets you shape agent interactions in highly specialized ways.
 </Check>

docs/guides/advanced/fingerprinting.mdx

@@ -4,8 +4,6 @@ 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

@@ -4,8 +4,6 @@ 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

@@ -4,8 +4,6 @@ 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.
@@ -13,7 +11,7 @@ When building AI applications with CrewAI, one of the most important decisions y
 At the heart of this decision is understanding the relationship between **complexity** and **precision** in your application:
 
 <Frame caption="Complexity vs. Precision Matrix for CrewAI Applications">
-  <img src="../..//complexity_precision.png" alt="Complexity vs. Precision Matrix" />
+  <img src="../../images/complexity_precision.png" alt="Complexity vs. Precision Matrix" />
 </Frame>
 
 This matrix helps visualize how different approaches align with varying requirements for complexity and precision. Let's explore what each quadrant means and how it guides your architectural choices.

docs/guides/crews/first-crew.mdx

@@ -4,8 +4,6 @@ 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.
@@ -35,7 +33,8 @@ Let's get started building your first crew!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your OpenAI API key in your environment variables
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Project
@@ -55,7 +54,7 @@ This will generate a project with the basic structure needed for your crew. The
 - A main script to run the crew
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="../../crews.png" alt="CrewAI Framework Overview" />
+  <img src="../../images/crews.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 
@@ -92,7 +91,8 @@ For our research crew, we'll create two agents:
 1. A **researcher** who excels at finding and organizing information
 2. An **analyst** who can interpret research findings and create insightful reports
 
-Let's modify the `agents.yaml` file to define these specialized agents:
+Let's modify the `agents.yaml` file to define these specialized agents. Be sure
+to set `llm` to the provider you are using.
 
 ```yaml
 # src/research_crew/config/agents.yaml
@@ -107,7 +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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 
 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: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 ```
 
 Notice how each agent has a distinct role, goal, and backstory. These elements aren't just descriptive - they actively shape how the agent approaches its tasks. By crafting these carefully, you can create agents with specialized skills and perspectives that complement each other.
@@ -185,15 +185,20 @@ Let's modify the `crew.py` file:
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai_tools import SerperDevTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class ResearchCrew():
     """Research crew for comprehensive topic analysis and reporting"""
 
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
     @agent
     def researcher(self) -> Agent:
         return Agent(
-            config=self.agents_config['researcher'],
+            config=self.agents_config['researcher'], # type: ignore[index]
             verbose=True,
             tools=[SerperDevTool()]
         )
@@ -201,20 +206,20 @@ class ResearchCrew():
     @agent
     def analyst(self) -> Agent:
         return Agent(
-            config=self.agents_config['analyst'],
+            config=self.agents_config['analyst'], # type: ignore[index]
             verbose=True
         )
 
     @task
     def research_task(self) -> Task:
         return Task(
-            config=self.tasks_config['research_task']
+            config=self.tasks_config['research_task'] # type: ignore[index]
         )
 
     @task
     def analysis_task(self) -> Task:
         return Task(
-            config=self.tasks_config['analysis_task'],
+            config=self.tasks_config['analysis_task'], # type: ignore[index]
             output_file='output/report.md'
         )
 
@@ -277,12 +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:
 
-```
-OPENAI_API_KEY=your_openai_api_key
+```sh
 SERPER_API_KEY=your_serper_api_key
+# Add your provider's API key here too.
 ```
 
-You can get a Serper API key from [Serper.dev](https://serper.dev/).
+See the [LLM Setup guide](/concepts/llms#setting-up-your-llm) for details on configuring your provider of choice. You can get a Serper API key from [Serper.dev](https://serper.dev/).
 
 ## Step 8: Install Dependencies
 
@@ -387,4 +392,4 @@ Now that you've built your first crew, you can:
 
 <Check>
 Congratulations! You've successfully built your first CrewAI crew that can research and analyze any topic you provide. This foundational experience has equipped you with the skills to create increasingly sophisticated AI systems that can tackle complex, multi-stage problems through collaborative intelligence.
-</Check>
+</Check>

docs/guides/flows/first-flow.mdx

@@ -4,8 +4,6 @@ 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.
@@ -45,7 +43,8 @@ Let's dive in and build your first flow!
 Before starting, make sure you have:
 
 1. Installed CrewAI following the [installation guide](/installation)
-2. Set up your OpenAI API key in your environment variables
+2. Set up your LLM API key in your environment, following the [LLM setup
+   guide](/concepts/llms#setting-up-your-llm)
 3. Basic understanding of Python
 
 ## Step 1: Create a New CrewAI Flow Project
@@ -60,7 +59,7 @@ cd guide_creator_flow
 This will generate a project with the basic structure needed for your flow.
 
 <Frame caption="CrewAI Framework Overview">
-  <img src="../../flows.png" alt="CrewAI Framework Overview" />
+  <img src="../../images/flows.png" alt="CrewAI Framework Overview" />
 </Frame>
 
 ## Step 2: Understanding the Project Structure
@@ -107,6 +106,8 @@ Now, let's modify the generated files for the content writer crew. We'll set up
 
 1. First, update the agents configuration file to define our content creation team:
 
+   Remember to set `llm` to the provider you are using.
+
 ```yaml
 # src/guide_creator_flow/crews/content_crew/config/agents.yaml
 content_writer:
@@ -119,7 +120,7 @@ content_writer:
     You are a talented educational writer with expertise in creating clear, engaging
     content. You have a gift for explaining complex concepts in accessible language
     and organizing information in a way that helps readers build their understanding.
-  llm: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 
 content_reviewer:
   role: >
@@ -132,7 +133,7 @@ content_reviewer:
     content. You have an eye for detail, clarity, and coherence. You excel at
     improving content while maintaining the original author's voice and ensuring
     consistent quality across multiple sections.
-  llm: openai/gpt-4o-mini
+  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
 ```
 
 These agent definitions establish the specialized roles and perspectives that will shape how our AI agents approach content creation. Notice how each agent has a distinct purpose and expertise.
@@ -203,35 +204,40 @@ These task definitions provide detailed instructions to our agents, ensuring the
 # src/guide_creator_flow/crews/content_crew/content_crew.py
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import List
 
 @CrewBase
 class ContentCrew():
     """Content writing crew"""
 
+    agents: List[BaseAgent]
+    tasks: List[Task]
+
     @agent
     def content_writer(self) -> Agent:
         return Agent(
-            config=self.agents_config['content_writer'],
+            config=self.agents_config['content_writer'], # type: ignore[index]
             verbose=True
         )
 
     @agent
     def content_reviewer(self) -> Agent:
         return Agent(
-            config=self.agents_config['content_reviewer'],
+            config=self.agents_config['content_reviewer'], # type: ignore[index]
             verbose=True
         )
 
     @task
     def write_section_task(self) -> Task:
         return Task(
-            config=self.tasks_config['write_section_task']
+            config=self.tasks_config['write_section_task'] # type: ignore[index]
         )
 
     @task
     def review_section_task(self) -> Task:
         return Task(
-            config=self.tasks_config['review_section_task'],
+            config=self.tasks_config['review_section_task'], # type: ignore[index]
             context=[self.write_section_task()]
         )
 
@@ -263,6 +269,7 @@ Let's create our flow in the `main.py` file:
 ```python
 #!/usr/bin/env python
 import json
+import os
 from typing import List, Dict
 from pydantic import BaseModel, Field
 from crewai import LLM
@@ -341,6 +348,9 @@ class GuideCreatorFlow(Flow[GuideCreatorState]):
         outline_dict = json.loads(response)
         self.state.guide_outline = GuideOutline(**outline_dict)
 
+        # Ensure output directory exists before saving
+        os.makedirs("output", exist_ok=True)
+
         # Save the outline to a file
         with open("output/guide_outline.json", "w") as f:
             json.dump(outline_dict, f, indent=2)
@@ -432,10 +442,15 @@ This is the power of flows - combining different types of processing (user inter
 
 ## Step 6: Set Up Your Environment Variables
 
-Create a `.env` file in your project root with your API keys:
+Create a `.env` file in your project root with your API keys. See the [LLM setup
+guide](/concepts/llms#setting-up-your-llm) for details on configuring a provider.
 
-```
+```sh .env
 OPENAI_API_KEY=your_openai_api_key
+# or
+GEMINI_API_KEY=your_gemini_api_key
+# or
+ANTHROPIC_API_KEY=your_anthropic_api_key
 ```
 
 ## Step 7: Install Dependencies
@@ -538,7 +553,10 @@ Let's break down the key components of flows to help you understand how to build
 Flows allow you to make direct calls to language models when you need simple, structured responses:
 
 ```python
-llm = LLM(model="openai/gpt-4o-mini", response_format=GuideOutline)
+llm = LLM(
+    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    response_format=GuideOutline
+)
 response = llm.call(messages=messages)
 ```
 

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

@@ -4,8 +4,6 @@ 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.
@@ -279,22 +277,23 @@ This pattern allows you to combine direct data passing with state updates for ma
 
 One of CrewAI's most powerful features is the ability to persist flow state across executions. This enables workflows that can be paused, resumed, and even recovered after failures.
 
-### The @persist Decorator
+### The @persist() Decorator
 
-The `@persist` decorator automates state persistence, saving your flow's state at key points in execution.
+The `@persist()` decorator automates state persistence, saving your flow's state at key points in execution.
 
 #### Class-Level Persistence
 
-When applied at the class level, `@persist` saves state after every method execution:
+When applied at the class level, `@persist()` saves state after every method execution:
 
 ```python
-from crewai.flow.flow import Flow, listen, persist, start
+from crewai.flow.flow import Flow, listen, start
+from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
 class CounterState(BaseModel):
     value: int = 0
 
-@persist  # Apply to the entire flow class
+@persist()  # Apply to the entire flow class
 class PersistentCounterFlow(Flow[CounterState]):
     @start()
     def increment(self):
@@ -321,10 +320,11 @@ print(f"Second run result: {result2}")  # Will be higher due to persisted state
 
 #### Method-Level Persistence
 
-For more granular control, you can apply `@persist` to specific methods:
+For more granular control, you can apply `@persist()` to specific methods:
 
 ```python
-from crewai.flow.flow import Flow, listen, persist, start
+from crewai.flow.flow import Flow, listen, start
+from crewai.flow.persistence import persist
 
 class SelectivePersistFlow(Flow):
     @start()
@@ -332,7 +332,7 @@ class SelectivePersistFlow(Flow):
         self.state["count"] = 1
         return "First step"
 
-    @persist  # Only persist after this method
+    @persist()  # Only persist after this method
     @listen(first_step)
     def important_step(self, prev_result):
         self.state["count"] += 1

docs/how-to/portkey-observability.mdx

@@ -1,202 +0,0 @@
----
-title: Portkey Integration
-description: How to use Portkey with CrewAI
-icon: key
----
-
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-## Getting Started
-
-<Steps>
-    <Step title="Install CrewAI and Portkey">
-    ```bash
-    pip install -qU crewai portkey-ai
-    ```
-    </Step>
-    <Step title="Configure the LLM Client">
-    To build CrewAI Agents with Portkey, you'll need two keys:
-    - **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-    - **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-    ```python
-    from crewai import LLM
-    from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-    gpt_llm = LLM(
-        model="gpt-4",
-        base_url=PORTKEY_GATEWAY_URL,
-        api_key="dummy", # We are using Virtual key
-        extra_headers=createHeaders(
-            api_key="YOUR_PORTKEY_API_KEY",
-            virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-        )
-    )
-    ```
-    </Step>
-    <Step title="Create and Run Your First Agent">
-    ```python
-    from crewai import Agent, Task, Crew
-
-    # Define your agents with roles and goals
-    coder = Agent(
-        role='Software developer',
-        goal='Write clear, concise code on demand',
-        backstory='An expert coder with a keen eye for software trends.',
-        llm=gpt_llm
-    )
-
-    # Create tasks for your agents
-    task1 = Task(
-        description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-        expected_output="A clear and concise HTML code",
-        agent=coder
-    )
-
-    # Instantiate your crew
-    crew = Crew(
-        agents=[coder],
-        tasks=[task1],
-    )
-
-    result = crew.kickoff()
-    print(result)
-    ```
-    </Step>
-</Steps>
-
-## Key Features
-
-| Feature | Description |
-|:--------|:------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
-- **Simple Cache**: Perfect for exact matches
-- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
-- **Automatic Retries**: Handle temporary failures gracefully
-- **Request Timeouts**: Prevent hanging operations
-- **Conditional Routing**: Route requests based on specific conditions
-- **Fallbacks**: Set up automatic provider failovers
-- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
-- Cost per agent interaction
-- Response times and latency
-- Token usage and efficiency
-- Success/failure rates
-- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
-- Set budget limit and rate limts per Virtual Key (disposable API keys)
-- Implement role-based access control
-- Track system changes with audit logs
-- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
-- [📘 Portkey Documentation](https://docs.portkey.ai)
-- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
-- [🐦 Twitter](https://twitter.com/portkeyai)
-- [💬 Discord Community](https://discord.gg/DD7vgKK299)