Merge branch 'main' into lg-pytest-improvements

Previously, if a registered event handler raised an exception during execution,
it could crash the entire application or interrupt the event dispatch process.
This change wraps handler execution in a try/except block within the `emit` method,
ensuring that exceptions are caught and logged without affecting other handlers or flow.

This improves the resilience of the event bus, especially when handling third-party
or temporary listeners.

.github/security.md

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

.github/workflows/linter.yml

@@ -5,32 +5,12 @@ on: [pull_request]
 jobs:
   lint:
     runs-on: ubuntu-latest
-    env:
-      TARGET_BRANCH: ${{ github.event.pull_request.base.ref }}
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
 
-      - name: Fetch Target Branch
-        run: git fetch origin $TARGET_BRANCH --depth=1
-
-      - name: Install Ruff
-        run: pip install ruff
-
-      - name: Get Changed Python Files
-        id: changed-files
+      - name: Install Requirements
         run: |
-          merge_base=$(git merge-base origin/"$TARGET_BRANCH" HEAD)
-          changed_files=$(git diff --name-only --diff-filter=ACMRTUB "$merge_base" | grep '\.py$' || true)
-          echo "files<<EOF" >> $GITHUB_OUTPUT
-          echo "$changed_files" >> $GITHUB_OUTPUT
-          echo "EOF" >> $GITHUB_OUTPUT
+          pip install ruff
 
-      - name: Run Ruff on Changed Files
-        if: ${{ steps.changed-files.outputs.files != '' }}
-        run: |
-          echo "${{ steps.changed-files.outputs.files }}" \
-            | tr ' ' '\n' \
-            | grep -v 'src/crewai/cli/templates/' \
-            | xargs -I{} ruff check "{}"
+      - name: Run Ruff Linter
+        run: ruff check

.github/workflows/mkdocs.yml

@@ -0,0 +1,45 @@
+name: Deploy MkDocs
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Calculate requirements hash
+        id: req-hash
+        run: echo "::set-output name=hash::$(sha256sum requirements-doc.txt | awk '{print $1}')"
+
+      - name: Setup cache
+        uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ steps.req-hash.outputs.hash }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+
+      - name: Install Requirements
+        run: |
+          sudo apt-get update &&
+          sudo apt-get install pngquant &&
+          pip install mkdocs-material mkdocs-material-extensions pillow cairosvg
+
+        env:
+          GH_TOKEN: ${{ secrets.GH_TOKEN }}
+
+      - name: Build and deploy MkDocs
+        run: mkdocs gh-deploy --force

.github/workflows/tests.yml

@@ -7,18 +7,14 @@ permissions:
 
 env:
   OPENAI_API_KEY: fake-api-key
-  PYTHONUNBUFFERED: 1
 
 jobs:
   tests:
-    name: tests (${{ matrix.python-version }})
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
-      fail-fast: true
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
-        group: [1, 2, 3, 4, 5, 6, 7, 8]
+        python-version: ['3.10', '3.11', '3.12']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -27,9 +23,6 @@ jobs:
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
-          cache-dependency-glob: |
-            **/pyproject.toml
-            **/uv.lock
 
       - name: Set up Python ${{ matrix.python-version }}
         run: uv python install ${{ matrix.python-version }}
@@ -37,14 +30,5 @@ jobs:
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run tests (group ${{ matrix.group }} of 8)
-        run: |
-          uv run pytest \
-            --block-network \
-            --timeout=30 \
-            -vv \
-            --splits 8 \
-            --group ${{ matrix.group }} \
-            --durations=10 \
-            -n auto \
-            --maxfail=3
+      - name: Run tests
+        run: uv run pytest --block-network --timeout=60 -vv

.gitignore

@@ -21,9 +21,9 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
+agentops.log
 test_flow.html
 crewairules.mdc
 plan.md
 conceptual_plan.md
-build_image
-chromadb-*.lock
+build_image

.ruff.toml

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

README.md

@@ -1,75 +1,32 @@
-<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>
+<div align="center">
 
-<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>
+![Logo of CrewAI](./docs/crewai_logo.png)
 
-<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>
 
-<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>
+</div>
 
 ### 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.
@@ -78,9 +35,21 @@ 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)
@@ -104,7 +73,7 @@ intelligent automations.
 ## Why CrewAI?
 
 <div align="center" style="margin-bottom: 30px;">
-  <img src="docs/images/asset.png" alt="CrewAI Logo" width="100%">
+  <img src="docs/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:
@@ -119,15 +88,9 @@ CrewAI empowers developers and enterprises to confidently build intelligent auto
 
 ## Getting Started
 
-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
+### 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
 
@@ -136,20 +99,18 @@ 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:
 
+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
@@ -161,20 +122,18 @@ To get started with CrewAI, follow these simple steps:
 
 ### 1. Installation
 
-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.
+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.
 
 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
@@ -184,11 +143,10 @@ 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**
 
+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`
@@ -403,7 +361,7 @@ In addition to the sequential process, you can use the hierarchical process, whi
 
 ## Key Features
 
-CrewAI stands apart as a lean, standalone, high-performance multi-AI Agent 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 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.
@@ -449,8 +407,7 @@ You can test different real life examples of AI crews in the [CrewAI-examples re
 
 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.
+- `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:
@@ -538,7 +495,6 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
 ```
 
 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
@@ -548,7 +504,7 @@ This example demonstrates how to:
 
 CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
 
-Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models.
+Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
 
 ## How CrewAI Compares
 
@@ -559,6 +515,7 @@ 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
@@ -649,10 +606,10 @@ 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)
@@ -660,7 +617,6 @@ 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)
@@ -668,110 +624,84 @@ 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?
 
+
+### 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/changelog.mdx

@@ -0,0 +1,283 @@
+---
+title: Changelog
+description: View the latest updates and changes to CrewAI
+icon: timeline
+---
+
+<Update label="2025-04-30" 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="2025-04-28" 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="2025-04-07" 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="2025-03-17" 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`
+  - Enhanced Event Listener with rich visualization and improved logging
+  - Added fingerprints
+
+  **Bug Fixes**
+  - Fixed Mistral issues
+  - Fixed a bug in documentation
+  - Fixed type check error in fingerprint property
+
+  **Documentation Updates**
+  - Improved tool documentation
+  - Updated installation guide for the `uv` tool package
+  - Added instructions for upgrading crewAI with the `uv` tool
+  - Added documentation for `ApifyActorsTool`
+</Update>
+
+<Update label="2025-03-10" description="v0.105.0">
+  **Core Improvements & Fixes**
+  - Fixed issues with missing template variables and user memory configuration
+  - Improved async flow support and addressed agent response formatting
+  - Enhanced memory reset functionality and fixed CLI memory commands
+  - Fixed type issues, tool calling properties, and telemetry decoupling
+
+  **New Features & Enhancements**
+  - Added Flow state export and improved state utilities
+  - Enhanced agent knowledge setup with optional crew embedder
+  - Introduced event emitter for better observability and LLM call tracking
+  - Added support for Python 3.10 and ChatOllama from langchain_ollama
+  - Integrated context window size support for the o3-mini model
+  - Added support for multiple router calls
+
+  **Documentation & Guides**
+  - Improved documentation layout and hierarchical structure
+  - Added QdrantVectorSearchTool guide and clarified event listener usage
+  - Fixed typos in prompts and updated Amazon Bedrock model listings
+</Update>
+
+<Update label="2025-02-12" description="v0.102.0">
+  **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
+  - Memory & Storage Fixes: Fixed short-term memory handling with Bedrock, ensured correct embedder initialization, and added a reset memories function in the crew class
+  - Training & Execution Reliability: Fixed broken training and interpolation issues with dict and list input types
+
+  **New Features & Enhancements**
+  - Advanced Knowledge Management: Improved naming conventions and enhanced embedding configuration with custom embedder support
+  - Expanded Logging & Observability: Added JSON format support for logging and integrated MLflow tracing documentation
+  - Data Handling Improvements: Updated excel_knowledge_source.py to process multi-tab files
+  - General Performance & Codebase Clean-Up: Streamlined enterprise code alignment and resolved linting issues
+  - Adding new tool: `QdrantVectorSearchTool`
+
+  **Documentation & Guides**
+  - Updated AI & Memory Docs: Improved Bedrock, Google AI, and long-term memory documentation
+  - Task & Workflow Clarity: Added "Human Input" row to Task Attributes, Langfuse guide, and FileWriterTool documentation
+  - Fixed Various Typos & Formatting Issues
+</Update>
+
+<Update label="2025-01-28" description="v0.100.0">
+  **Features**
+  - Add Composio docs
+  - Add SageMaker as a LLM provider
+  
+  **Fixes**
+  - Overall LLM connection issues
+  - Using safe accessors on training
+  - Add version check to crew_chat.py
+  
+  **Documentation**
+  - New docs for crewai chat
+  - Improve formatting and clarity in CLI and Composio Tool docs
+</Update>
+
+<Update label="2025-01-20" description="v0.98.0">
+  **Features**
+  - Conversation crew v1
+  - Add unique ID to flow states
+  - Add @persist decorator with FlowPersistence interface
+  
+  **Integrations**
+  - Add SambaNova integration
+  - Add NVIDIA NIM provider in cli
+  - Introducing VoyageAI
+  
+  **Fixes**
+  - Fix API Key Behavior and Entity Handling in Mem0 Integration
+  - Fixed core invoke loop logic and relevant tests
+  - Make tool inputs actual objects and not strings
+  - Add important missing parts to creating tools
+  - Drop litellm version to prevent windows issue
+  - Before kickoff if inputs are none
+  - Fixed typos, nested pydantic model issue, and docling issues
+</Update>
+
+<Update label="2025-01-04" description="v0.95.0">
+  **New Features**
+  - Adding Multimodal Abilities to Crew
+  - Programatic Guardrails
+  - HITL multiple rounds
+  - Gemini 2.0 Support
+  - CrewAI Flows Improvements
+  - Add Workflow Permissions
+  - Add support for langfuse with litellm
+  - Portkey Integration with CrewAI
+  - Add interpolate_only method and improve error handling
+  - Docling Support
+  - Weviate Support
+  
+  **Fixes**
+  - output_file not respecting system path
+  - disk I/O error when resetting short-term memory
+  - CrewJSONEncoder now accepts enums
+  - Python max version
+  - Interpolation for output_file in Task
+  - Handle coworker role name case/whitespace properly
+  - Add tiktoken as explicit dependency and document Rust requirement
+  - Include agent knowledge in planning process
+  - Change storage initialization to None for KnowledgeStorage
+  - Fix optional storage checks
+  - include event emitter in flows
+  - Docstring, Error Handling, and Type Hints Improvements
+  - Suppressed userWarnings from litellm pydantic issues
+</Update>
+
+<Update label="2024-12-05" description="v0.86.0">
+  **Changes**
+  - Remove all references to pipeline and pipeline router
+  - Add Nvidia NIM as provider in Custom LLM
+  - Add knowledge demo + improve knowledge docs
+  - Add HITL multiple rounds of followup
+  - New docs about yaml crew with decorators
+  - Simplify template crew
+</Update>
+
+<Update label="2024-12-04" description="v0.85.0">
+  **Features**
+  - Added knowledge to agent level
+  - Feat/remove langchain
+  - Improve typed task outputs
+  - Log in to Tool Repository on crewai login
+  
+  **Fixes**
+  - Fixes issues with result as answer not properly exiting LLM loop
+  - Fix missing key name when running with ollama provider
+  - Fix spelling issue found
+  
+  **Documentation**
+  - Update readme for running mypy
+  - Add knowledge to mint.json
+  - Update Github actions
+  - Update Agents docs to include two approaches for creating an agent
+  - Improvements to LLM Configuration and Usage
+</Update>
+
+<Update label="2024-11-25" description="v0.83.0">
+  **New Features**
+  - New before_kickoff and after_kickoff crew callbacks
+  - Support to pre-seed agents with Knowledge
+  - Add support for retrieving user preferences and memories using Mem0
+  
+  **Fixes**
+  - Fix Async Execution
+  - Upgrade chroma and adjust embedder function generator
+  - Update CLI Watson supported models + docs
+  - Reduce level for Bandit
+  - Fixing all tests
+  
+  **Documentation**
+  - Update Docs
+</Update>
+
+<Update label="2024-11-13" description="v0.80.0">
+  **Fixes**
+  - Fixing Tokens callback replacement bug
+  - Fixing Step callback issue
+  - Add cached prompt tokens info on usage metrics
+  - Fix crew_train_success test
+</Update>

docs/common-room-tracking.js

@@ -1,18 +0,0 @@
-(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

@@ -21,7 +21,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 <Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
 CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
 
-![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+![Visual Agent Builder Screenshot](../images/enterprise/crew-studio-quickstart)
 
 The Visual Agent Builder enables:
 - Intuitive agent configuration with form-based interfaces
@@ -43,6 +43,7 @@ The Visual Agent Builder enables:
 | **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.                                                       |
@@ -54,11 +55,6 @@ The Visual Agent Builder enables:
 | **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.                                                 |
@@ -71,7 +67,7 @@ There are two ways to create agents in CrewAI: using **YAML configuration (recom
 
 Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/agents.yaml` file and modify the template to match your requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
@@ -155,6 +151,7 @@ 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
@@ -165,11 +162,6 @@ 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
@@ -234,44 +226,6 @@ 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
@@ -295,16 +249,6 @@ multimodal_agent = Agent(
   - `"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
@@ -312,7 +256,7 @@ multimodal_agent = Agent(
 
 <Note>
 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>  
 
 <Note>
 When using custom templates, you can use variables like `{role}`, `{goal}`, and `{backstory}` in your templates. These will be automatically populated during execution.
@@ -362,267 +306,6 @@ 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>
-
-## Direct Agent Interaction with `kickoff()`
-
-Agents can be used directly without going through a task or crew workflow using the `kickoff()` method. This provides a simpler way to interact with an agent when you don't need the full crew orchestration capabilities.
-
-### How `kickoff()` Works
-
-The `kickoff()` method allows you to send messages directly to an agent and get a response, similar to how you would interact with an LLM but with all the agent's capabilities (tools, reasoning, etc.).
-
-```python Code
-from crewai import Agent
-from crewai_tools import SerperDevTool
-
-# Create an agent
-researcher = Agent(
-    role="AI Technology Researcher",
-    goal="Research the latest AI developments",
-    tools=[SerperDevTool()],
-    verbose=True
-)
-
-# Use kickoff() to interact directly with the agent
-result = researcher.kickoff("What are the latest developments in language models?")
-
-# Access the raw response
-print(result.raw)
-```
-
-### Parameters and Return Values
-
-| Parameter         | Type                                | Description                                                               |
-| :---------------- | :---------------------------------- | :------------------------------------------------------------------------ |
-| `messages`        | `Union[str, List[Dict[str, str]]]`  | Either a string query or a list of message dictionaries with role/content |
-| `response_format` | `Optional[Type[Any]]`               | Optional Pydantic model for structured output                             |
-
-The method returns a `LiteAgentOutput` object with the following properties:
-
-- `raw`: String containing the raw output text
-- `pydantic`: Parsed Pydantic model (if a `response_format` was provided)
-- `agent_role`: Role of the agent that produced the output
-- `usage_metrics`: Token usage metrics for the execution
-
-### Structured Output
-
-You can get structured output by providing a Pydantic model as the `response_format`:
-
-```python Code
-from pydantic import BaseModel
-from typing import List
-
-class ResearchFindings(BaseModel):
-    main_points: List[str]
-    key_technologies: List[str]
-    future_predictions: str
-
-# Get structured output
-result = researcher.kickoff(
-    "Summarize the latest developments in AI for 2025",
-    response_format=ResearchFindings
-)
-
-# Access structured data
-print(result.pydantic.main_points)
-print(result.pydantic.future_predictions)
-```
-
-### Multiple Messages
-
-You can also provide a conversation history as a list of message dictionaries:
-
-```python Code
-messages = [
-    {"role": "user", "content": "I need information about large language models"},
-    {"role": "assistant", "content": "I'd be happy to help with that! What specifically would you like to know?"},
-    {"role": "user", "content": "What are the latest developments in 2025?"}
-]
-
-result = researcher.kickoff(messages)
-```
-
-### Async Support
-
-An asynchronous version is available via `kickoff_async()` with the same parameters:
-
-```python Code
-import asyncio
-
-async def main():
-    result = await researcher.kickoff_async("What are the latest developments in AI?")
-    print(result.raw)
-
-asyncio.run(main())
-```
-
-<Note>
-The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler execution flow while preserving all of the agent's configuration (role, goal, backstory, tools, etc.).
-</Note>
-
 ## Important Considerations and Best Practices
 
 ### Security and Code Execution
@@ -637,17 +320,11 @@ The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler e
 - 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` when using custom embedding models
+- Configure `embedder_config` 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
@@ -655,13 +332,6 @@ The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler e
   - 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)
@@ -684,6 +354,7 @@ The `kickoff()` method uses a `LiteAgent` internally, which provides a simpler e
    - 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

@@ -4,9 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
 icon: terminal
 ---
 
-<Warning>Since release 0.140.0, CrewAI Enterprise started a process of migrating their login provider. As such, the authentication flow via CLI was updated. Users that use Google to login, or that created their account after July 3rd, 2025 will be unable to log in with older versions of the `crewai` library.</Warning>
-
-## Overview
+# CrewAI CLI Documentation
 
 The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 
@@ -88,7 +86,7 @@ crewai replay [OPTIONS]
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell Terminal
+```shell Terminal    
 crewai replay -t task_123456
 ```
 
@@ -112,8 +110,6 @@ crewai reset-memories [OPTIONS]
 - `-s, --short`: Reset SHORT TERM memory
 - `-e, --entities`: Reset ENTITIES memory
 - `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
-- `-kn, --knowledge`: Reset KNOWLEDGE storage
-- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
 - `-a, --all`: Reset ALL memories
 
 Example:
@@ -134,7 +130,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell Terminal
+```shell Terminal    
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
@@ -151,7 +147,7 @@ Starting from version 0.103.0, the `crewai run` command can be used to run both
 </Note>
 
 <Note>
-Make sure to run these commands from the directory where your CrewAI project is set up.
+Make sure to run these commands from the directory where your CrewAI project is set up. 
 Some commands may require additional configuration or setup within your project structure.
 </Note>
 
@@ -188,7 +184,10 @@ def crew(self) -> Crew:
 Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
 
 - **Authentication**: You need to be authenticated to deploy to CrewAI Enterprise.
-    You can login or create an account with:
+    ```shell Terminal
+    crewai signup
+    ```
+    If you already have an account, you can login with:
     ```shell Terminal
     crewai login
     ```
@@ -199,43 +198,12 @@ Deploy the crew or flow to [CrewAI Enterprise](https://app.crewai.com).
     ```
     - 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).
 
@@ -284,13 +252,13 @@ Watch this video tutorial for a step-by-step demonstration of deploying your cre
 
 ### 11. API Keys
 
-When running ```crewai create crew``` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
+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.
 
-Once you've selected an LLM provider and model, you will be prompted for API keys.
+Once you've selected an LLM provider, you will be prompted for API keys.
 
-#### Available LLM Providers
+#### Initial API key providers
 
-Here's a list of the most popular LLM providers suggested by the CLI:
+The CLI will initially prompt for API keys for the following services:
 
 * OpenAI
 * Groq
@@ -298,11 +266,11 @@ Here's a list of the most popular LLM providers suggested by the CLI:
 * Google Gemini
 * SambaNova
 
-When you select a provider, the CLI will then show you available models for that provider and prompt you to enter your API key.
+When you select a provider, the CLI will prompt you to enter your API key.
 
 #### Other Options
 
-If you select "other", you will be able to select from a list of LiteLLM supported providers.
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
 
 When you select a provider, the CLI will prompt you to enter the Key name and the API key.
 
@@ -310,81 +278,5 @@ See the following link for each provider's key name:
 
 * [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
 
-### 12. Configuration Management
 
-Manage CLI configuration settings for CrewAI.
 
-```shell Terminal
-crewai config [COMMAND] [OPTIONS]
-```
-
-#### Commands:
-
-- `list`: Display all CLI configuration parameters
-```shell Terminal
-crewai config list
-```
-
-- `set`: Set a CLI configuration parameter
-```shell Terminal
-crewai config set <key> <value>
-```
-
-- `reset`: Reset all CLI configuration parameters to default values
-```shell Terminal
-crewai config reset
-```
-
-#### Available Configuration Parameters
-
-- `enterprise_base_url`: Base URL of the CrewAI Enterprise instance
-- `oauth2_provider`: OAuth2 provider used for authentication (e.g., workos, okta, auth0)
-- `oauth2_audience`: OAuth2 audience value, typically used to identify the target API or resource
-- `oauth2_client_id`: OAuth2 client ID issued by the provider, used during authentication requests
-- `oauth2_domain`: OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens
-
-#### Examples
-
-Display current configuration:
-```shell Terminal
-crewai config list
-```
-
-Example output:
-```
-CrewAI CLI Configuration
-┏━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Setting            ┃ Value                              ┃ Description                                                                                      ┃
-┡━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ enterprise_base_url│ https://app.crewai.com             │ Base URL of the CrewAI Enterprise instance                                                       │
-│ org_name           │ Not set                            │ Name of the currently active organization                                                        │
-│ org_uuid           │ Not set                            │ UUID of the currently active organization                                                        │
-│ oauth2_provider    │ workos                             │ OAuth2 provider used for authentication (e.g., workos, okta, auth0).                             │
-│ oauth2_audience    │ client_01YYY                       │ OAuth2 audience value, typically used to identify the target API or resource.                    │
-│ oauth2_client_id   │ client_01XXX                       │ OAuth2 client ID issued by the provider, used during authentication requests.                    │
-│ oauth2_domain      │ login.crewai.com                   │ OAuth2 provider's domain (e.g., your-org.auth0.com) used for issuing tokens.                     │
-```
-
-Set the enterprise base URL:
-```shell Terminal
-crewai config set enterprise_base_url https://my-enterprise.crewai.com
-```
-
-Set OAuth2 provider:
-```shell Terminal
-crewai config set oauth2_provider auth0
-```
-
-Set OAuth2 domain:
-```shell Terminal
-crewai config set oauth2_domain my-company.auth0.com
-```
-
-Reset all configuration to defaults:
-```shell Terminal
-crewai config reset
-```
-
-<Note>
-Configuration settings are stored in `~/.config/crewai/settings.json`. Some settings like organization name and UUID are read-only and managed through authentication and organization commands. Tool repository related settings are hidden and cannot be set directly by users.
-</Note>

docs/concepts/collaboration.mdx

@@ -0,0 +1,51 @@
+---
+title: Collaboration
+description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
+icon: screen-users
+---
+
+## Collaboration Fundamentals      
+
+Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
+
+- **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.
+
+## Enhanced Attributes for Improved Collaboration
+
+The `Crew` class has been enriched with several attributes to support advanced functionalities:
+
+| 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** (`prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json)                                                                                      |
+| **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.                                                                                           |
+
+## Delegation (Dividing to Conquer)
+
+Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
+
+## Implementing Collaboration and Delegation
+
+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.
+
+## Example Scenario
+
+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.
+
+## Conclusion
+
+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.

docs/concepts/crews.mdx

@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 icon: people-group
 ---
 
-## Overview
+## What is a Crew?
 
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
@@ -20,18 +20,18 @@ 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`.                                                                                                                                                                     |
-| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |                                                                                                                                                                                       |
+| **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"}`.                                                                                                                                |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
-| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
 
 <Tip>
 **Crew Max RPM**: 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.
@@ -45,7 +45,7 @@ There are two ways to create crews in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define crews and is consistent with how agents and tasks are defined in CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, you can define your crew in a class that inherits from `CrewBase` and uses decorators to define agents, tasks, and the crew itself.
 
 #### Example Crew Class with Decorators
 
@@ -66,8 +66,8 @@ class YourCrewName:
     # To see an example agent and task defined in YAML, checkout the following:
     # - Task: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
     # - Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    agents_config = 'config/agents.yaml'
-    tasks_config = 'config/tasks.yaml'
+    agents_config = 'config/agents.yaml' 
+    tasks_config = 'config/tasks.yaml' 
 
     @before_kickoff
     def prepare_inputs(self, inputs):
@@ -111,18 +111,12 @@ class YourCrewName:
     def crew(self) -> Crew:
         return Crew(
             agents=self.agents,  # Automatically collected by the @agent decorator
-            tasks=self.tasks,    # Automatically collected by the @task decorator.
+            tasks=self.tasks,    # Automatically collected by the @task decorator. 
             process=Process.sequential,
             verbose=True,
         )
 ```
 
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={"any": "input here"})
-```
-
 <Note>
 Tasks will be executed in the order they are defined.
 </Note>
@@ -190,11 +184,6 @@ class YourCrewName:
             verbose=True
         )
 ```
-How to run the above code:
-
-```python code
-YourCrewName().crew().kickoff(inputs={})
-```
 
 In this example:
 
@@ -257,7 +246,7 @@ print(f"Token Usage: {crew_output.token_usage}")
 You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
 In case of `True(Boolean)` will save as `logs.txt`.
 
-In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
+In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
 
 ```python Code
 # Save crew logs
@@ -325,12 +314,12 @@ for result in results:
 
 # Example of using kickoff_async
 inputs = {'topic': 'AI in healthcare'}
-async_result = await my_crew.kickoff_async(inputs=inputs)
+async_result = 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 = await my_crew.kickoff_for_each_async(inputs=inputs_array)
+async_results = 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
 ---
 
-## Overview
+# Event Listeners
 
 CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
 
@@ -21,7 +21,7 @@ When specific actions occur in CrewAI (like a Crew starting execution, an Agent
 <Note type="info" title="Enterprise Enhancement: Prompt Tracing">
 CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
 
-![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
+![Prompt Tracing Dashboard](../images/enterprise/prompt-tracing.png)
 
 With Prompt Tracing you can:
 - View the complete history of all prompts sent to your LLM
@@ -177,7 +177,14 @@ class MyCustomCrew:
     # Your crew implementation...
 ```
 
-This is how third-party event listeners are registered in the CrewAI codebase.
+This is exactly how CrewAI's built-in `agentops_listener` is registered. In the CrewAI codebase, you'll find:
+
+```python
+# src/crewai/utilities/events/third_party/__init__.py
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Available Event Types
 
@@ -217,20 +224,6 @@ CrewAI provides a wide range of events that you can listen for:
 - **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
 - **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
 
-### Knowledge Events
-
-- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
-- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
-- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
-- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
-- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
-- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
-
-### 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
@@ -248,17 +241,6 @@ CrewAI provides a wide range of events that you can listen for:
 - **LLMCallFailedEvent**: Emitted when an LLM call fails
 - **LLMStreamChunkEvent**: Emitted for each chunk received during streaming LLM responses
 
-### Memory Events
-
-- **MemoryQueryStartedEvent**: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
-- **MemoryQueryCompletedEvent**: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
-- **MemoryQueryFailedEvent**: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
-- **MemorySaveStartedEvent**: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
-- **MemorySaveCompletedEvent**: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
-- **MemorySaveFailedEvent**: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
-- **MemoryRetrievalStartedEvent**: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
-- **MemoryRetrievalCompletedEvent**: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.
-
 ## Event Handler Structure
 
 Each event handler receives two parameters:
@@ -273,6 +255,77 @@ The structure of the event object depends on the event type, but all events inhe
 
 Additional fields vary by event type. For example, `CrewKickoffCompletedEvent` includes `crew_name` and `output` fields.
 
+## Real-World Example: Integration with AgentOps
+
+CrewAI includes an example of a third-party integration with [AgentOps](https://github.com/AgentOps-AI/agentops), a monitoring and observability platform for AI agents. Here's how it's implemented:
+
+```python
+from typing import Optional
+
+from crewai.utilities.events import (
+    CrewKickoffCompletedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+
+try:
+    import agentops
+    AGENTOPS_INSTALLED = True
+except ImportError:
+    AGENTOPS_INSTALLED = False
+
+class AgentOpsListener(BaseEventListener):
+    tool_event: Optional["agentops.ToolEvent"] = None
+    session: Optional["agentops.Session"] = None
+
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        if not AGENTOPS_INSTALLED:
+            return
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
+            self.session = agentops.init()
+            for agent in source.agents:
+                if self.session:
+                    self.session.create_agent(
+                        name=agent.role,
+                        agent_id=str(agent.id),
+                    )
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
+            if self.session:
+                self.session.end_session(
+                    end_state="Success",
+                    end_state_reason="Finished Execution",
+                )
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+            self.tool_event = agentops.ToolEvent(name=event.tool_name)
+            if self.session:
+                self.session.record(self.tool_event)
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
+```
+
+This listener initializes an AgentOps session when a Crew starts, registers agents with AgentOps, tracks tool usage, and ends the session when the Crew completes.
+
+The AgentOps listener is registered in CrewAI's event system through the import in `src/crewai/utilities/events/third_party/__init__.py`:
+
+```python
+from .agentops_listener import agentops_listener
+```
+
+This ensures the `agentops_listener` is loaded when the `crewai.utilities.events` package is imported.
 
 ## Advanced Usage: Scoped Handlers
 

docs/concepts/flows.mdx

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

docs/concepts/knowledge.mdx

@@ -0,0 +1,656 @@
+---
+title: Knowledge
+description: What is knowledge in CrewAI and how to use it.
+icon: book
+---
+
+## What is Knowledge?
+
+Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
+Think of it as giving your agents a reference library they can consult while working.
+
+<Info>
+  Key benefits of using Knowledge:
+  - Enhance agents with domain-specific information
+  - Support decisions with real-world data
+  - Maintain context across conversations
+  - Ground responses in factual information
+</Info>
+
+## Supported Knowledge Sources
+
+CrewAI supports various types of knowledge sources out of the box:
+
+<CardGroup cols={2}>
+  <Card title="Text Sources" icon="text">
+    - Raw strings
+    - Text files (.txt)
+    - PDF documents
+  </Card>
+  <Card title="Structured Data" icon="table">
+    - CSV files
+    - Excel spreadsheets
+    - JSON documents
+  </Card>
+</CardGroup>
+
+## Supported Knowledge Parameters
+
+| Parameter                    | Type                                | Required | Description                                                                                                                                           |
+| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `sources`                  | **List[BaseKnowledgeSource]**        | Yes      | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.           |
+| `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
+| `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
+
+
+<Tip>
+Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
+Simply add the relevant knowledge sources your agent or crew needs to function.
+
+Knowledge sources can be added at the agent or crew level.
+Crew level knowledge sources will be used by **all agents** in the crew.
+Agent level knowledge sources will be used by the **specific agent** that is preloaded with the knowledge.
+</Tip>
+
+## Quickstart Example
+
+<Tip>
+For file-Based Knowledge Sources, make sure to place your files in a `knowledge` directory at the root of your project. 
+Also, use relative paths from the `knowledge` directory when creating the source.
+</Tip>
+
+Here's an example using string-based knowledge:
+
+```python Code
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+# Create a knowledge source
+content = "Users name is John. He is 30 years old and lives in San Francisco."
+string_source = StringKnowledgeSource(
+    content=content,
+)
+
+# Create an LLM with a temperature of 0 to ensure deterministic outputs
+llm = LLM(model="gpt-4o-mini", temperature=0)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="About User",
+    goal="You know everything about the user.",
+    backstory="""You are a master at understanding people and their preferences.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=llm,
+)
+task = Task(
+    description="Answer the following questions about the user: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge_sources=[string_source], # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
+```
+
+
+Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 
+
+<Note>
+  You need to install `docling` for the following example to work: `uv add docling`
+</Note>
+
+
+
+```python Code
+from crewai import LLM, Agent, Crew, Process, Task
+from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
+
+# Create a knowledge source
+content_source = CrewDoclingSource(
+    file_paths=[
+        "https://lilianweng.github.io/posts/2024-11-28-reward-hacking",
+        "https://lilianweng.github.io/posts/2024-07-07-hallucination",
+    ],
+)
+
+# Create an LLM with a temperature of 0 to ensure deterministic outputs
+llm = LLM(model="gpt-4o-mini", temperature=0)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="About papers",
+    goal="You know everything about the papers.",
+    backstory="""You are a master at understanding papers and their content.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=llm,
+)
+task = Task(
+    description="Answer the following questions about the papers: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge_sources=[
+        content_source
+    ],  # Enable knowledge by adding the sources here. You can also add more sources to the sources list.
+)
+
+result = crew.kickoff(
+    inputs={
+        "question": "What is the reward hacking paper about? Be sure to provide sources."
+    }
+)
+```
+
+## Knowledge Configuration
+
+You can configure the knowledge configuration for the crew or agent.
+
+```python Code
+from crewai.knowledge.knowledge_config import KnowledgeConfig
+
+knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
+
+agent = Agent(
+    ...
+    knowledge_config=knowledge_config
+)
+```
+
+<Tip>
+  `results_limit`: is the number of relevant documents to return. Default is 3.
+  `score_threshold`: is the minimum score for a document to be considered relevant. Default is 0.35.
+</Tip>
+
+## More Examples
+
+Here are examples of how to use different types of knowledge sources:
+
+Note: Please ensure that you create the ./knowldge folder. All source files (e.g., .txt, .pdf, .xlsx, .json) should be placed in this folder for centralized management.
+
+### Text File Knowledge Source
+```python
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
+
+# Create a text file knowledge source
+text_source = TextFileKnowledgeSource(
+    file_paths=["document.txt", "another.txt"]
+)
+
+# Create crew with text file source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[text_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[text_source]
+)
+```
+
+### PDF Knowledge Source
+```python
+from crewai.knowledge.source.pdf_knowledge_source import PDFKnowledgeSource
+
+# Create a PDF knowledge source
+pdf_source = PDFKnowledgeSource(
+    file_paths=["document.pdf", "another.pdf"]
+)
+
+# Create crew with PDF knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[pdf_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[pdf_source]
+)
+```
+
+### CSV Knowledge Source
+```python
+from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource
+
+# Create a CSV knowledge source
+csv_source = CSVKnowledgeSource(
+    file_paths=["data.csv"]
+)
+
+# Create crew with CSV knowledge source or on agent level
+agent = Agent(
+    ...
+    knowledge_sources=[csv_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[csv_source]
+)
+```
+
+### Excel Knowledge Source
+```python
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
+
+# Create an Excel knowledge source
+excel_source = ExcelKnowledgeSource(
+    file_paths=["spreadsheet.xlsx"]
+)
+
+# Create crew with Excel knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[excel_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[excel_source]
+)
+```
+
+### JSON Knowledge Source
+```python
+from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource
+
+# Create a JSON knowledge source
+json_source = JSONKnowledgeSource(
+    file_paths=["data.json"]
+)
+
+# Create crew with JSON knowledge source on agents or crew level
+agent = Agent(
+    ...
+    knowledge_sources=[json_source]
+)
+
+crew = Crew(
+    ...
+    knowledge_sources=[json_source]
+)
+```
+
+## Knowledge Configuration
+
+### Chunking Configuration
+
+Knowledge sources automatically chunk content for better processing. 
+You can configure chunking behavior in your knowledge sources:
+
+```python
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+source = StringKnowledgeSource(
+    content="Your content here",
+    chunk_size=4000,      # Maximum size of each chunk (default: 4000)
+    chunk_overlap=200     # Overlap between chunks (default: 200)
+)
+```
+
+The chunking configuration helps in:
+- Breaking down large documents into manageable pieces
+- Maintaining context through chunk overlap
+- Optimizing retrieval accuracy
+
+### Embeddings Configuration
+
+You can also configure the embedder for the knowledge store. 
+This is useful if you want to use a different embedder for the knowledge store than the one used for the agents.
+The `embedder` parameter supports various embedding model providers that include:
+- `openai`: OpenAI's embedding models
+- `google`: Google's text embedding models
+- `azure`: Azure OpenAI embeddings
+- `ollama`: Local embeddings with Ollama
+- `vertexai`: Google Cloud VertexAI embeddings
+- `cohere`: Cohere's embedding models
+- `voyageai`: VoyageAI's embedding models
+- `bedrock`: AWS Bedrock embeddings
+- `huggingface`: Hugging Face models
+- `watson`: IBM Watson embeddings
+
+Here's an example of how to configure the embedder for the knowledge store using Google's `text-embedding-004` model:
+<CodeGroup>
+```python Example
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+import os
+
+# Get the GEMINI API key
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+
+# Create a knowledge source
+content = "Users name is John. He is 30 years old and lives in San Francisco."
+string_source = StringKnowledgeSource(
+    content=content,
+)
+
+# Create an LLM with a temperature of 0 to ensure deterministic outputs
+gemini_llm = LLM(
+    model="gemini/gemini-1.5-pro-002",
+    api_key=GEMINI_API_KEY,
+    temperature=0,
+)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="About User",
+    goal="You know everything about the user.",
+    backstory="""You are a master at understanding people and their preferences.""",
+    verbose=True,
+    allow_delegation=False,
+    llm=gemini_llm,
+    embedder={
+        "provider": "google",
+        "config": {
+            "model": "models/text-embedding-004",
+            "api_key": GEMINI_API_KEY,
+        }
+    }
+)
+
+task = Task(
+    description="Answer the following questions about the user: {question}",
+    expected_output="An answer to the question.",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    knowledge_sources=[string_source],
+    embedder={
+        "provider": "google",
+        "config": {
+            "model": "models/text-embedding-004",
+            "api_key": GEMINI_API_KEY,
+        }
+    }
+)
+
+result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"})
+```
+```text Output
+# Agent: About User
+## Task: Answer the following questions about the user: What city does John live in and how old is he?
+
+# Agent: About User
+## Final Answer: 
+John is 30 years old and lives in San Francisco.
+```
+</CodeGroup>
+## Clearing Knowledge
+
+If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
+
+```bash Command
+crewai reset-memories --knowledge
+```
+
+This is useful when you've updated your knowledge sources and want to ensure that the agents are using the most recent information.
+
+## Agent-Specific Knowledge
+
+While knowledge can be provided at the crew level using `crew.knowledge_sources`, individual agents can also have their own knowledge sources using the `knowledge_sources` parameter:
+
+```python Code
+from crewai import Agent, Task, Crew
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+
+# Create agent-specific knowledge about a product
+product_specs = StringKnowledgeSource(
+    content="""The XPS 13 laptop features:
+    - 13.4-inch 4K display
+    - Intel Core i7 processor
+    - 16GB RAM
+    - 512GB SSD storage
+    - 12-hour battery life""",
+    metadata={"category": "product_specs"}
+)
+
+# Create a support agent with product knowledge
+support_agent = Agent(
+    role="Technical Support Specialist",
+    goal="Provide accurate product information and support.",
+    backstory="You are an expert on our laptop products and specifications.",
+    knowledge_sources=[product_specs]  # Agent-specific knowledge
+)
+
+# Create a task that requires product knowledge
+support_task = Task(
+    description="Answer this customer question: {question}",
+    agent=support_agent
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[support_agent],
+    tasks=[support_task]
+)
+
+# Get answer about the laptop's specifications
+result = crew.kickoff(
+    inputs={"question": "What is the storage capacity of the XPS 13?"}
+)
+```
+
+<Info>
+  Benefits of agent-specific knowledge:
+  - Give agents specialized information for their roles
+  - Maintain separation of concerns between agents
+  - Combine with crew-level knowledge for layered information access
+</Info>
+
+## Custom Knowledge Sources
+
+CrewAI allows you to create custom knowledge sources for any type of data by extending the `BaseKnowledgeSource` class. Let's create a practical example that fetches and processes space news articles.
+
+#### Space News Knowledge Source Example
+
+<CodeGroup>
+
+```python Code
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+import requests
+from datetime import datetime
+from typing import Dict, Any
+from pydantic import BaseModel, Field
+
+class SpaceNewsKnowledgeSource(BaseKnowledgeSource):
+    """Knowledge source that fetches data from Space News API."""
+
+    api_endpoint: str = Field(description="API endpoint URL")
+    limit: int = Field(default=10, description="Number of articles to fetch")
+
+    def load_content(self) -> Dict[Any, str]:
+        """Fetch and format space news articles."""
+        try:
+            response = requests.get(
+                f"{self.api_endpoint}?limit={self.limit}"
+            )
+            response.raise_for_status()
+
+            data = response.json()
+            articles = data.get('results', [])
+
+            formatted_data = self.validate_content(articles)
+            return {self.api_endpoint: formatted_data}
+        except Exception as e:
+            raise ValueError(f"Failed to fetch space news: {str(e)}")
+
+    def validate_content(self, articles: list) -> str:
+        """Format articles into readable text."""
+        formatted = "Space News Articles:\n\n"
+        for article in articles:
+            formatted += f"""
+                Title: {article['title']}
+                Published: {article['published_at']}
+                Summary: {article['summary']}
+                News Site: {article['news_site']}
+                URL: {article['url']}
+                -------------------"""
+        return formatted
+
+    def add(self) -> None:
+        """Process and store the articles."""
+        content = self.load_content()
+        for _, text in content.items():
+            chunks = self._chunk_text(text)
+            self.chunks.extend(chunks)
+
+        self._save_documents()
+
+# Create knowledge source
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
+    limit=10,
+)
+
+# Create specialized agent
+space_analyst = Agent(
+    role="Space News Analyst",
+    goal="Answer questions about space news accurately and comprehensively",
+    backstory="""You are a space industry analyst with expertise in space exploration,
+    satellite technology, and space industry trends. You excel at answering questions
+    about space news and providing detailed, accurate information.""",
+    knowledge_sources=[recent_news],
+    llm=LLM(model="gpt-4", temperature=0.0)
+)
+
+# Create task that handles user questions
+analysis_task = Task(
+    description="Answer this question about space news: {user_question}",
+    expected_output="A detailed answer based on the recent space news articles",
+    agent=space_analyst
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[space_analyst],
+    tasks=[analysis_task],
+    verbose=True,
+    process=Process.sequential
+)
+
+# Example usage
+result = crew.kickoff(
+    inputs={"user_question": "What are the latest developments in space exploration?"}
+)
+```
+
+```output Output
+# Agent: Space News Analyst
+## Task: Answer this question about space news: What are the latest developments in space exploration?
+
+
+# Agent: Space News Analyst
+## Final Answer:
+The latest developments in space exploration, based on recent space news articles, include the following:
+
+1. SpaceX has received the final regulatory approvals to proceed with the second integrated Starship/Super Heavy launch, scheduled for as soon as the morning of Nov. 17, 2023. This is a significant step in SpaceX's ambitious plans for space exploration and colonization. [Source: SpaceNews](https://spacenews.com/starship-cleared-for-nov-17-launch/)
+
+2. SpaceX has also informed the US Federal Communications Commission (FCC) that it plans to begin launching its first next-generation Starlink Gen2 satellites. This represents a major upgrade to the Starlink satellite internet service, which aims to provide high-speed internet access worldwide. [Source: Teslarati](https://www.teslarati.com/spacex-first-starlink-gen2-satellite-launch-2022/)
+
+3. AI startup Synthetaic has raised $15 million in Series B funding. The company uses artificial intelligence to analyze data from space and air sensors, which could have significant applications in space exploration and satellite technology. [Source: SpaceNews](https://spacenews.com/ai-startup-synthetaic-raises-15-million-in-series-b-funding/)
+
+4. The Space Force has formally established a unit within the U.S. Indo-Pacific Command, marking a permanent presence in the Indo-Pacific region. This could have significant implications for space security and geopolitics. [Source: SpaceNews](https://spacenews.com/space-force-establishes-permanent-presence-in-indo-pacific-region/)
+
+5. Slingshot Aerospace, a space tracking and data analytics company, is expanding its network of ground-based optical telescopes to increase coverage of low Earth orbit. This could improve our ability to track and analyze objects in low Earth orbit, including satellites and space debris. [Source: SpaceNews](https://spacenews.com/slingshots-space-tracking-network-to-extend-coverage-of-low-earth-orbit/)
+
+6. The National Natural Science Foundation of China has outlined a five-year project for researchers to study the assembly of ultra-large spacecraft. This could lead to significant advancements in spacecraft technology and space exploration capabilities. [Source: SpaceNews](https://spacenews.com/china-researching-challenges-of-kilometer-scale-ultra-large-spacecraft/)
+
+7. The Center for AEroSpace Autonomy Research (CAESAR) at Stanford University is focusing on spacecraft autonomy. The center held a kickoff event on May 22, 2024, to highlight the industry, academia, and government collaboration it seeks to foster. This could lead to significant advancements in autonomous spacecraft technology. [Source: SpaceNews](https://spacenews.com/stanford-center-focuses-on-spacecraft-autonomy/)
+```
+
+</CodeGroup>
+#### Key Components Explained
+
+1. **Custom Knowledge Source (`SpaceNewsKnowledgeSource`)**:
+
+   - Extends `BaseKnowledgeSource` for integration with CrewAI
+   - Configurable API endpoint and article limit
+   - Implements three key methods:
+     - `load_content()`: Fetches articles from the API
+     - `_format_articles()`: Structures the articles into readable text
+     - `add()`: Processes and stores the content
+
+2. **Agent Configuration**:
+
+   - Specialized role as a Space News Analyst
+   - Uses the knowledge source to access space news
+
+3. **Task Setup**:
+
+   - Takes a user question as input through `{user_question}`
+   - Designed to provide detailed answers based on the knowledge source
+
+4. **Crew Orchestration**:
+   - Manages the workflow between agent and task
+   - Handles input/output through the kickoff method
+
+This example demonstrates how to:
+
+- Create a custom knowledge source that fetches real-time data
+- Process and format external data for AI consumption
+- Use the knowledge source to answer specific user questions
+- Integrate everything seamlessly with CrewAI's agent system
+
+#### About the Spaceflight News API
+
+The example uses the [Spaceflight News API](https://api.spaceflightnewsapi.net/v4/docs/), which:
+
+- Provides free access to space-related news articles
+- Requires no authentication
+- Returns structured data about space news
+- Supports pagination and filtering
+
+You can customize the API query by modifying the endpoint URL:
+
+```python
+# Fetch more articles
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles",
+    limit=20,  # Increase the number of articles
+)
+
+# Add search parameters
+recent_news = SpaceNewsKnowledgeSource(
+    api_endpoint="https://api.spaceflightnewsapi.net/v4/articles?search=NASA", # Search for NASA news
+    limit=10,
+)
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Content Organization">
+    - Keep chunk sizes appropriate for your content type
+    - Consider content overlap for context preservation
+    - Organize related information into separate knowledge sources
+  </Accordion>
+
+  <Accordion title="Performance Tips">
+    - Adjust chunk sizes based on content complexity 
+    - Configure appropriate embedding models
+    - Consider using local embedding providers for faster processing
+  </Accordion>
+</AccordionGroup>

docs/concepts/llms.mdx

@@ -4,10 +4,9 @@ description: 'A comprehensive guide to configuring and using Large Language Mode
 icon: 'microchip-ai'
 ---
 
-## Overview
-
-CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
-
+<Note>
+  CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
+</Note>
 
 ## What are LLMs?
 
@@ -28,19 +27,23 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
   </Card>
 </CardGroup>
 
-## Setting up your LLM
+## Setting Up Your LLM
 
-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.
+There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
 
 <Tabs>
   <Tab title="1. Environment Variables">
-    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.
+    The simplest way to get started. Set these variables in your environment:
 
-    ```bash .env
-    MODEL=model-id  # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
+    ```bash
+    # Required: Your API key for authentication
+    OPENAI_API_KEY=<your-api-key>
 
-    # Be sure to set your API keys here too. See the Provider
-    # section below.
+    # 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>
     ```
 
     <Warning>
@@ -50,13 +53,13 @@ There are different places in CrewAI code where you can specify the model to use
   <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 agents.yaml {6}
+    ```yaml
     researcher:
         role: Research Specialist
         goal: Conduct comprehensive research and analysis
         backstory: A dedicated research professional with years of experience
         verbose: true
-        llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
+        llm: openai/gpt-4o-mini # your model here
         # (see provider configuration examples below for more)
     ```
 
@@ -71,23 +74,23 @@ There are different places in CrewAI code where you can specify the model to use
   <Tab title="3. Direct Code">
     For maximum flexibility, configure LLMs directly in your Python code:
 
-    ```python {4,8}
+    ```python
     from crewai import LLM
 
     # Basic configuration
-    llm = LLM(model="model-id-here")  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+    llm = LLM(model="gpt-4")
 
     # Advanced configuration with detailed parameters
     llm = LLM(
-        model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
+        model="gpt-4o-mini",
         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
     )
     ```
 
@@ -107,6 +110,7 @@ There are different places in CrewAI code where you can specify the model to use
 
 ## 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.
 
@@ -152,39 +156,6 @@ 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
@@ -203,55 +174,19 @@ In this section, you'll find detailed examples that help you select, configure,
     ```
   </Accordion>
 
-  <Accordion title="Google (Gemini API)">
-    Set your API key in your `.env` file. If you need a key, or need to find an
-    existing key, check [AI Studio](https://aistudio.google.com/apikey).
+  <Accordion title="Google">
+    Set the following environment variables in your `.env` file:
 
-    ```toml .env
+    ```toml Code
+    # Option 1: Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
+
+    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
+    # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Example usage in your CrewAI project:
-    ```python Code
-    from crewai import LLM
-
-    llm = LLM(
-        model="gemini/gemini-2.0-flash",
-        temperature=0.7,
-    )
-    ```
-
-    ### Gemini models
-
-    Google offers a range of powerful models optimized for different use cases.
-
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
-
-    The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
-
-    ### Gemma
-
-    The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
-
-    | Model          | Context Window |
-    |----------------|----------------|
-    | gemma-3-1b-it  | 32k tokens     |
-    | gemma-3-4b-it  | 32k tokens     |
-    | gemma-3-12b-it | 32k tokens     |
-    | gemma-3-27b-it | 128k tokens    |
-
-  </Accordion>
-  <Accordion title="Google (Vertex AI)">
-    Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
+    Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
     ```python Code
     import json
 
@@ -270,23 +205,19 @@ In this section, you'll find detailed examples that help you select, configure,
     from crewai import LLM
 
     llm = LLM(
-        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
+        model="gemini/gemini-1.5-pro-latest",
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )
     ```
-
     Google offers a range of powerful models optimized for different use cases:
 
-    | Model                          | Context Window | Best For                                                          |
-    |--------------------------------|----------------|-------------------------------------------------------------------|
-    | gemini-2.5-flash-preview-04-17 | 1M tokens      | Adaptive thinking, cost efficiency                                |
-    | gemini-2.5-pro-preview-05-06   | 1M tokens      | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
-    | gemini-2.0-flash               | 1M tokens      | Next generation features, speed, thinking, and realtime streaming |
-    | gemini-2.0-flash-lite          | 1M tokens      | Cost efficiency and low latency                                   |
-    | gemini-1.5-flash               | 1M tokens      | Balanced multimodal model, good for most tasks                    |
-    | gemini-1.5-flash-8B            | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
-    | gemini-1.5-pro                 | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
+    | Model                  | Context Window | Best For                                                          |
+    |-----------------------|----------------|------------------------------------------------------------------|
+    | gemini-2.0-flash-exp  | 1M tokens      | Higher quality at faster speed, multimodal model, good for most tasks |
+    | gemini-1.5-flash      | 1M tokens      | Balanced multimodal model, good for most tasks                    |
+    | gemini-1.5-flash-8B   | 1M tokens      | Fastest, most cost-efficient, good for high-frequency tasks       |
+    | gemini-1.5-pro        | 2M tokens      | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
   </Accordion>
 
   <Accordion title="Azure">
@@ -452,7 +383,7 @@ 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 architecture to deliver compute efficient content generation |
+    | microsoft/phi-3.5-moe-instruct                                      | 128K tokens    | Advanced LLM based on Mixture of Experts architecure 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. |
@@ -476,19 +407,19 @@ 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
 
@@ -510,7 +441,7 @@ In this section, you'll find detailed examples that help you select, configure,
                 config=self.agents_config['researcher'], # type: ignore[index]
                 llm=local_nvidia_nim_llm
             )
-
+        
         # ...
     ```
   </Accordion>
@@ -684,28 +615,6 @@ In this section, you'll find detailed examples that help you select, configure,
       - openrouter/deepseek/deepseek-chat
     </Info>
   </Accordion>
-
-  <Accordion title="Nebius AI Studio">
-    Set the following environment variables in your `.env` file:
-    ```toml Code
-    NEBIUS_API_KEY=<your-api-key>
-    ```
-
-    Example usage in your CrewAI project:
-    ```python Code
-    llm = LLM(
-        model="nebius/Qwen/Qwen3-30B-A3B"
-    )
-    ```
-
-    <Info>
-      Nebius AI Studio features:
-      - Large collection of open source models
-      - Higher rate limits
-      - Competitive pricing
-      - Good balance of speed and quality
-    </Info>
-  </Accordion>
 </AccordionGroup>
 
 ## Streaming Responses
@@ -728,78 +637,23 @@ 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.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()
+    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())
     ```
-
-    <Tip>
-      [Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
-    </Tip>
-  </Tab>
-
-  <Tab title="Agent & Task Tracking">
-    All LLM events in CrewAI include agent and task information, allowing you to track and filter LLM interactions by specific agents or tasks:
-
-    ```python
-    from crewai import LLM, Agent, Task, Crew
-    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(source, event):
-                if researcher.id == event.agent_id:
-                    print("\n==============\n Got event:", event, "\n==============\n")
-
-
-    my_listener = MyCustomListener()
-
-    llm = LLM(model="gpt-4o-mini", temperature=0, stream=True)
-
-    researcher = Agent(
-        role="About User",
-        goal="You know everything about the user.",
-        backstory="""You are a master at understanding people and their preferences.""",
-        llm=llm,
-    )
-
-    search = Task(
-        description="Answer the following questions about the user: {question}",
-        expected_output="An answer to the question.",
-        agent=researcher,
-    )
-
-    crew = Crew(agents=[researcher], tasks=[search])
-
-    result = crew.kickoff(
-        inputs={"question": "..."}
-    )
-    ```
-
-    <Info>
-      This feature is particularly useful for:
-      - Debugging specific agent behaviors
-      - Logging LLM usage by task type
-      - Auditing which agents are making what types of LLM calls
-      - Performance monitoring of specific tasks
-    </Info>
   </Tab>
 </Tabs>
 
@@ -896,24 +750,6 @@ Learn how to get the most out of your LLM configuration:
       Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
     </Info>
   </Accordion>
-
-  <Accordion title="Drop Additional Parameters">
-    CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
-    For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
-
-    ```python
-    from crewai import LLM
-    import os
-
-    os.environ["OPENAI_API_KEY"] = "<api-key>"
-
-    o3_llm = LLM(
-        model="o3",
-        drop_params=True,
-        additional_drop_params=["stop"]
-    )
-    ```
-  </Accordion>
 </AccordionGroup>
 
 ## Common Issues and Solutions
@@ -949,7 +785,7 @@ 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/memory.mdx

@@ -0,0 +1,731 @@
+---
+title: Memory
+description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
+icon: database
+---
+
+## Introduction to Memory Systems in CrewAI
+
+The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. 
+This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember, 
+reason, and learn from past interactions.
+
+## Memory System Components
+
+| Component            | Description                                                                                                             |
+| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
+| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
+| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
+| **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
+| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **External Memory**  | Enables integration with external memory systems and providers (like Mem0), allowing for specialized memory storage and retrieval across different applications. Supports custom storage implementations for flexible memory management. |
+| **User Memory**      | ⚠️ **DEPRECATED**: This component is deprecated and will be removed in a future version. Please use [External Memory](#using-external-memory) instead. |
+
+## How Memory Systems Empower Agents
+
+1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
+
+2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
+
+3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
+
+## Implementing Memory in Your Crew
+
+When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
+By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. 
+The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model. 
+It's also possible to initialize the memory instance with your own instance.
+
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
+The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
+The data storage files are saved into a platform-specific location found using the appdirs package,
+and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
+
+### Example: Configuring Memory for a Crew
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with memory capabilities
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True
+)
+```
+
+### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
+
+```python Code
+from crewai import Crew, Process
+from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
+from crewai.memory.storage.rag_storage import RAGStorage
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from typing import List, Optional
+
+# Assemble your crew with memory capabilities
+my_crew: Crew = Crew(
+    agents = [...],
+    tasks = [...],
+    process = Process.sequential,
+    memory = True,
+    # Long-term memory for persistent storage across sessions
+    long_term_memory = LongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path="/my_crew1/long_term_memory_storage.db"
+        )
+    ),
+    # Short-term memory for current context using RAG
+    short_term_memory = ShortTermMemory(
+        storage = RAGStorage(
+                embedder_config={
+                    "provider": "openai",
+                    "config": {
+                        "model": 'text-embedding-3-small'
+                    }
+                },
+                type="short_term",
+                path="/my_crew1/"
+            )
+        ),
+    ),
+    # Entity memory for tracking key information about entities
+    entity_memory = EntityMemory(
+        storage=RAGStorage(
+            embedder_config={
+                "provider": "openai",
+                "config": {
+                    "model": 'text-embedding-3-small'
+                }
+            },
+            type="short_term",
+            path="/my_crew1/"
+        )
+    ),
+    verbose=True,
+)
+```
+
+## Security Considerations
+
+When configuring memory storage:
+- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
+- Never hardcode sensitive information like database credentials
+- Consider access permissions for storage directories
+- Use relative paths when possible to maintain portability
+
+Example using environment variables:
+```python
+import os
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+
+# Configure storage path using environment variable
+storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path="{storage_path}/memory.db".format(storage_path=storage_path)
+        )
+    )
+)
+```
+
+## Configuration Examples
+
+### Basic Memory Configuration
+```python
+from crewai import Crew
+from crewai.memory import LongTermMemory
+
+# Simple memory configuration
+crew = Crew(memory=True)  # Uses default storage locations
+```
+Note that External Memory won’t be defined when `memory=True` is set, as we can’t infer which external memory would be suitable for your case
+
+### Custom Storage Configuration
+```python
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+
+# Configure custom storage paths
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(db_path="./memory.db")
+    )
+)
+```
+
+## Integrating Mem0 for Enhanced User Memory
+
+[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
+
+
+### Using Mem0 API platform
+
+To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences. In this case `user_memory` is set to `MemoryClient` from mem0.
+
+
+```python Code
+import os
+from crewai import Crew, Process
+from mem0 import MemoryClient
+
+# Set environment variables for Mem0
+os.environ["MEM0_API_KEY"] = "m0-xx"
+
+# Step 1: Create a Crew with User Memory
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
+    },
+)
+```
+
+#### Additional Memory Configuration Options
+If you want to access a specific organization and project, you can set the `org_id` and `project_id` parameters in the memory configuration.
+
+```python Code
+from crewai import Crew
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john", "org_id": "my_org_id", "project_id": "my_project_id"},
+        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
+    },
+)
+```
+
+### Using Local Mem0 memory
+If you want to use local mem0 memory, with a custom configuration, you can set a parameter `local_mem0_config` in the config itself.
+If both os environment key is set and local_mem0_config is given, the API platform takes higher priority over the local configuration.
+Check [this](https://docs.mem0.ai/open-source/python-quickstart#run-mem0-locally) mem0 local configuration docs for more understanding.
+In this case `user_memory` is set to `Memory` from mem0.
+
+
+```python Code
+from crewai import Crew
+
+
+#local mem0 config
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "gpt-4"
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "text-embedding-3-small"
+        }
+    },
+    "graph_store": {
+        "provider": "neo4j",
+        "config": {
+            "url": "neo4j+s://your-instance",
+            "username": "neo4j",
+            "password": "password"
+        }
+    },
+    "history_db_path": "/path/to/history.db",
+    "version": "v1.1",
+    "custom_fact_extraction_prompt": "Optional custom prompt for fact extraction for memory",
+    "custom_update_memory_prompt": "Optional custom prompt for update memory"
+}
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john", 'local_mem0_config': config},
+        "user_memory" : {} #Set user_memory explicitly to a dictionary, we are working on this issue.
+    },
+)
+```
+
+### Using External Memory
+
+External Memory is a powerful feature that allows you to integrate external memory systems with your CrewAI applications. This is particularly useful when you want to use specialized memory providers or maintain memory across different applications.
+Since it’s an external memory, we’re not able to add a default value to it - unlike with Long Term and Short Term memory. 
+
+#### Basic Usage with Mem0
+
+The most common way to use External Memory is with Mem0 as the provider:
+
+```python
+import os
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+
+os.environ["MEM0_API_KEY"] = "YOUR-API-KEY"
+
+agent = Agent(
+    role="You are a helpful assistant",
+    goal="Plan a vacation for the user",
+    backstory="You are a helpful assistant that can plan a vacation for the user",
+    verbose=True,
+)
+task = Task(
+    description="Give things related to the user's vacation",
+    expected_output="A plan for the vacation",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    external_memory=ExternalMemory(
+        embedder_config={"provider": "mem0", "config": {"user_id": "U-123"}} # you can provide an entire Mem0 configuration
+    ),
+)
+
+crew.kickoff(
+    inputs={"question": "which destination is better for a beach vacation?"}
+)
+```
+
+#### Using External Memory with Custom Storage
+
+You can also create custom storage implementations for External Memory. Here's an example of how to create a custom storage:
+
+```python
+from crewai import Agent, Crew, Process, Task
+from crewai.memory.external.external_memory import ExternalMemory
+from crewai.memory.storage.interface import Storage
+
+
+class CustomStorage(Storage):
+    def __init__(self):
+        self.memories = []
+
+    def save(self, value, metadata=None, agent=None):
+        self.memories.append({"value": value, "metadata": metadata, "agent": agent})
+
+    def search(self, query, limit=10, score_threshold=0.5):
+        # Implement your search logic here
+        return []
+
+    def reset(self):
+        self.memories = []
+
+
+# Create external memory with custom storage
+external_memory = ExternalMemory(
+    storage=CustomStorage(),
+    embedder_config={"provider": "mem0", "config": {"user_id": "U-123"}},
+)
+
+agent = Agent(
+    role="You are a helpful assistant",
+    goal="Plan a vacation for the user",
+    backstory="You are a helpful assistant that can plan a vacation for the user",
+    verbose=True,
+)
+task = Task(
+    description="Give things related to the user's vacation",
+    expected_output="A plan for the vacation",
+    agent=agent,
+)
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+    external_memory=external_memory,
+)
+
+crew.kickoff(
+    inputs={"question": "which destination is better for a beach vacation?"}
+)
+```
+
+
+## Additional Embedding Providers
+
+### Using OpenAI embeddings (already default)
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": 'text-embedding-3-small'
+        }
+    }
+)
+```
+Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
+
+Example:
+```python Code
+from crewai import Crew, Agent, Task, Process
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": 'text-embedding-3-small'
+        }
+    }
+)
+```
+
+### Using Ollama embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
+)
+```
+
+### Using Google AI embeddings
+
+#### Prerequisites
+Before using Google AI embeddings, ensure you have:
+- Access to the Gemini API
+- The necessary API keys and permissions
+
+You will need to update your *pyproject.toml* dependencies:
+```YAML
+dependencies = [
+    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
+    "crewai[tools]>=0.100.0,<1.0.0"
+]
+```
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "google",
+        "config": {
+            "api_key": "<YOUR_API_KEY>",
+            "model": "<model_name>"
+        }
+    }
+)
+```
+
+### Using Azure OpenAI embeddings
+
+```python Code
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "api_base": "YOUR_API_BASE_PATH",
+            "api_version": "YOUR_API_VERSION",
+            "model_name": 'text-embedding-3-small'
+        }
+    }
+)
+```
+
+### Using Vertex AI embeddings
+
+```python Code
+from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "project_id"="YOUR_PROJECT_ID",
+            "region"="YOUR_REGION",
+            "api_key"="YOUR_API_KEY",
+            "model_name"="textembedding-gecko"
+        }
+    }
+)
+```
+
+### Using Cohere embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "cohere",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "model": "<model_name>"
+        }
+    }
+)
+```
+### Using VoyageAI embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "voyageai",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "model": "<model_name>"
+        }
+    }
+)
+```
+### Using HuggingFace embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "huggingface",
+        "config": {
+            "api_url": "<api_url>",
+        }
+    }
+)
+```
+
+### Using Watson embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "model": "<model_name>",
+            "api_url": "<api_url>",
+            "api_key": "<YOUR_API_KEY>",
+            "project_id": "<YOUR_PROJECT_ID>",
+        }
+    }
+)
+```
+
+### Using Amazon Bedrock embeddings
+
+```python Code
+# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
+
+import os
+import boto3
+from crewai import Crew, Agent, Task, Process
+
+boto3_session = boto3.Session(
+    region_name=os.environ.get("AWS_REGION_NAME"),
+    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
+    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
+)
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    embedder={
+    "provider": "bedrock",
+        "config":{
+            "session": boto3_session,
+            "model": "amazon.titan-embed-text-v2:0",
+            "vector_dimension": 1024
+        }
+    }
+    verbose=True
+)
+```
+
+### Adding Custom Embedding Function
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+from chromadb import Documents, EmbeddingFunction, Embeddings
+
+# Create a custom embedding function
+class CustomEmbedder(EmbeddingFunction):
+    def __call__(self, input: Documents) -> Embeddings:
+        # generate embeddings
+        return [1, 2, 3] # this is a dummy embedding
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "custom",
+        "config": {
+            "embedder": CustomEmbedder()
+        }
+    }
+)
+```
+
+### Resetting Memory via cli
+
+```shell
+crewai reset-memories [OPTIONS]
+```
+
+#### Resetting Memory Options
+
+| Option             | Description                      | Type           | Default |
+| :----------------- | :------------------------------- | :------------- | :------ |
+| `-l`, `--long`     | Reset LONG TERM memory.          | Flag (boolean) | False   |
+| `-s`, `--short`    | Reset SHORT TERM memory.         | Flag (boolean) | False   |
+| `-e`, `--entities` | Reset ENTITIES memory.           | Flag (boolean) | False   |
+| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False   |
+| `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False   |
+| `-a`, `--all`      | Reset ALL memories.              | Flag (boolean) | False   |
+
+Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
+
+
+
+
+### Resetting Memory via crew object
+
+```python
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "custom",
+        "config": {
+            "embedder": CustomEmbedder()
+        }
+    }
+)
+
+my_crew.reset_memories(command_type = 'all') # Resets all the memory
+```
+
+#### Resetting Memory Options
+
+| Command Type       | Description                      |
+| :----------------- | :------------------------------- |
+| `long`             | Reset LONG TERM memory.          | 
+| `short`            | Reset SHORT TERM memory.         | 
+| `entities`         | Reset ENTITIES memory.           | 
+| `kickoff_outputs`  | Reset LATEST KICKOFF TASK OUTPUTS. |
+| `knowledge`        | Reset KNOWLEDGE memory.          |
+| `all`              | Reset ALL memories.              |
+
+
+## Benefits of Using CrewAI's Memory System
+
+- 🦾 **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
+- 🫡 **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.
+
+## Conclusion
+
+Integrating CrewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, 
+you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/concepts/planning.mdx

@@ -1,10 +1,10 @@
 ---
 title: Planning
 description: Learn how to add planning to your CrewAI Crew and improve their performance.
-icon: ruler-combined
+icon: brain
 ---
 
-## Overview
+## Introduction
 
 The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, 
 all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
@@ -29,10 +29,6 @@ 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

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

docs/concepts/tasks.mdx

@@ -4,7 +4,7 @@ description: Detailed guide on managing and creating tasks within the CrewAI fra
 icon: list-check
 ---
 
-## Overview
+## Overview of a Task
 
 In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
 
@@ -15,7 +15,7 @@ Tasks within CrewAI can be collaborative, requiring multiple agents to work toge
 <Note type="info" title="Enterprise Enhancement: Visual Task Builder">
 CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
 
-![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
+![Task Builder Screenshot](../images/enterprise/crew-studio-quickstart.png)
 
 The Visual Task Builder enables:
 - Drag-and-drop task creation
@@ -51,14 +51,11 @@ 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.                                                                               |
-| **Create Directory** _(optional)_ | `create_directory` | `Optional[bool]`             | Whether to create the directory for output_file if it doesn't exist. Defaults to True.                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
-| **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
 
 ## Creating Tasks
 
@@ -68,7 +65,7 @@ There are two ways to create tasks in CrewAI: using **YAML configuration (recomm
 
 Using YAML configuration provides a cleaner, more maintainable way to define tasks. We strongly recommend using this approach to define tasks in your CrewAI projects.
 
-After creating your CrewAI project as outlined in the [Installation](/en/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
+After creating your CrewAI project as outlined in the [Installation](/installation) section, navigate to the `src/latest_ai_development/config/tasks.yaml` file and modify the template to match your specific task requirements.
 
 <Note>
 Variables in your YAML files (like `{topic}`) will be replaced with values from your inputs when running the crew:
@@ -97,7 +94,6 @@ 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
 ```
 
@@ -186,9 +182,9 @@ 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"
 )
 ```
@@ -261,54 +257,6 @@ 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:
@@ -334,11 +282,9 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+### Using Task Guardrails
 
-### Function-Based Guardrails
-
-To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
+To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -373,16 +319,18 @@ blog_task = Task(
    - Type hints are recommended but optional
 
 2. **Return Values**:
-   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
+   - 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
-from crewai import TaskOutput, LLMGuardrail
+from crewai import TaskOutput
 
 def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
     try:
@@ -800,91 +748,184 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
+## Task Guardrails
 
+Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
+
+### Basic Usage
+
+#### Define your own logic to validate
+
+```python Code
+from typing import Tuple, Union
+from crewai import Task
+
+def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
+    """Validate that the output is valid JSON."""
+    try:
+        json_data = json.loads(result)
+        return (True, json_data)
+    except json.JSONDecodeError:
+        return (False, "Output must be valid JSON")
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=validate_json_output
+)
+```
+
+#### 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.
+2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
+3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
+   - If `success` is `True`, `data` is the validated/transformed result
+   - If `success` is `False`, `data` is the error message
+4. **Result Routing**:
+   - On success (`True`), the result is automatically passed to the next task
+   - On failure (`False`), the error is sent back to the agent to generate a new answer
+
+### Common Use Cases
+
+#### Data Format Validation
+```python Code
+def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure the output contains a valid email address."""
+    import re
+    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
+    if re.match(email_pattern, result.strip()):
+        return (True, result.strip())
+    return (False, "Output must be a valid email address")
+```
+
+#### Content Filtering
+```python Code
+def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
+    """Remove or validate sensitive information."""
+    sensitive_patterns = ['SSN:', 'password:', 'secret:']
+    for pattern in sensitive_patterns:
+        if pattern.lower() in result.lower():
+            return (False, f"Output contains sensitive information ({pattern})")
+    return (True, result)
+```
+
+#### Data Transformation
+```python Code
+def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
+    """Ensure phone numbers are in a consistent format."""
+    import re
+    digits = re.sub(r'\D', '', result)
+    if len(digits) == 10:
+        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
+        return (True, formatted)
+    return (False, "Output must be a 10-digit phone number")
+```
+
+### Advanced Features
+
+#### Chaining Multiple Validations
+```python Code
+def chain_validations(*validators):
+    """Chain multiple validators together."""
+    def combined_validator(result):
+        for validator in validators:
+            success, data = validator(result)
+            if not success:
+                return (False, data)
+            result = data
+        return (True, result)
+    return combined_validator
+
+# Usage
+task = Task(
+    description="Get user contact info",
+    expected_output="Email and phone",
+    guardrail=chain_validations(
+        validate_email_format,
+        filter_sensitive_info
+    )
+)
+```
+
+#### Custom Retry Logic
+```python Code
+task = Task(
+    description="Generate data",
+    expected_output="Valid data",
+    guardrail=validate_data,
+    max_retries=5  # Override default retry limit
+)
+```
 
 ## Creating Directories when Saving Files
 
-The `create_directory` parameter controls whether CrewAI should automatically create directories when saving task outputs to files. This feature is particularly useful for organizing outputs and ensuring that file paths are correctly structured, especially when working with complex project hierarchies.
-
-### Default Behavior
-
-By default, `create_directory=True`, which means CrewAI will automatically create any missing directories in the output file path:
+You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
 
 ```python Code
-# Default behavior - directories are created automatically
-report_task = Task(
-    description='Generate a comprehensive market analysis report',
-    expected_output='A detailed market analysis with charts and insights',
-    agent=analyst_agent,
-    output_file='reports/2025/market_analysis.md',  # Creates 'reports/2025/' if it doesn't exist
-    markdown=True
+# ...
+
+save_output_task = Task(
+    description='Save the summarized AI news to a file',
+    expected_output='File saved successfully',
+    agent=research_agent,
+    tools=[file_save_tool],
+    output_file='outputs/ai_news_summary.txt',
+    create_directory=True
 )
-```
 
-### Disabling Directory Creation
-
-If you want to prevent automatic directory creation and ensure that the directory already exists, set `create_directory=False`:
-
-```python Code
-# Strict mode - directory must already exist
-strict_output_task = Task(
-    description='Save critical data that requires existing infrastructure',
-    expected_output='Data saved to pre-configured location',
-    agent=data_agent,
-    output_file='secure/vault/critical_data.json',
-    create_directory=False  # Will raise RuntimeError if 'secure/vault/' doesn't exist
-)
-```
-
-### YAML Configuration
-
-You can also configure this behavior in your YAML task definitions:
-
-```yaml tasks.yaml
-analysis_task:
-  description: >
-    Generate quarterly financial analysis
-  expected_output: >
-    A comprehensive financial report with quarterly insights
-  agent: financial_analyst
-  output_file: reports/quarterly/q4_2024_analysis.pdf
-  create_directory: true  # Automatically create 'reports/quarterly/' directory
-
-audit_task:
-  description: >
-    Perform compliance audit and save to existing audit directory
-  expected_output: >
-    A compliance audit report
-  agent: auditor
-  output_file: audit/compliance_report.md
-  create_directory: false  # Directory must already exist
-```
-
-### Use Cases
-
-**Automatic Directory Creation (`create_directory=True`):**
-- Development and prototyping environments
-- Dynamic report generation with date-based folders
-- Automated workflows where directory structure may vary
-- Multi-tenant applications with user-specific folders
-
-**Manual Directory Management (`create_directory=False`):**
-- Production environments with strict file system controls
-- Security-sensitive applications where directories must be pre-configured
-- Systems with specific permission requirements
-- Compliance environments where directory creation is audited
-
-### Error Handling
-
-When `create_directory=False` and the directory doesn't exist, CrewAI will raise a `RuntimeError`:
-
-```python Code
-try:
-    result = crew.kickoff()
-except RuntimeError as e:
-    # Handle missing directory error
-    print(f"Directory creation failed: {e}")
-    # Create directory manually or use fallback location
+#...
 ```
 
 Check out the video below to see how to use structured outputs in CrewAI:

docs/concepts/testing.mdx

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

docs/concepts/tools.mdx

@@ -4,7 +4,7 @@ description: Understanding and leveraging tools within the CrewAI framework for
 icon: screwdriver-wrench
 ---
 
-## Overview
+## Introduction
 
 CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
@@ -18,6 +18,8 @@ enabling everything from simple searches to complex interactions and effective t
 <Note type="info" title="Enterprise Enhancement: Tools Repository">
 CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
 
+![Tools Repository Screenshot](../images/enterprise/tools-repository.png)
+
 The Enterprise Tools Repository includes:
 - Pre-built connectors for popular enterprise systems
 - Custom tool creation interface
@@ -32,7 +34,6 @@ The Enterprise Tools Repository includes:
 - **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
 
@@ -178,62 +179,6 @@ 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

docs/concepts/training.mdx

@@ -0,0 +1,67 @@
+---
+title: Training
+description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
+icon: dumbbell
+---
+
+## Introduction
+
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
+
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+This helps the agents improve their understanding, decision-making, and problem-solving abilities.
+
+### Training Your Crew Using the CLI
+
+To use the training feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+```shell
+crewai train -n <n_iterations> <filename> (optional)
+```
+<Tip>
+  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
+</Tip>
+
+### Training Your Crew Programmatically
+
+To train your crew programmatically, use the following steps:
+
+1. Define the number of iterations for training.
+2. Specify the input parameters for the training process.
+3. Execute the training command within a try-except block to handle potential errors.
+
+```python Code
+n_iterations = 2
+inputs = {"topic": "CrewAI Training"}
+filename = "your_model.pkl"
+
+try:
+    YourCrewName_Crew().crew().train(
+      n_iterations=n_iterations, 
+      inputs=inputs, 
+      filename=filename
+    )
+
+except Exception as e:
+    raise Exception(f"An error occurred while training the crew: {e}")
+```
+
+### Key Points to Note
+
+- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
+- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
+- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
+
+It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
+
+Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
+
+Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
+
+Happy training with CrewAI! 🚀
+  

docs/en/api-reference/inputs.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /inputs"
-description: "Get required inputs for your crew"
-openapi: "/enterprise-api.en.yaml GET /inputs"
----
-
-

docs/en/api-reference/introduction.mdx

@@ -1,119 +0,0 @@
----
-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/en/api-reference/kickoff.mdx

@@ -1,7 +0,0 @@
----
-title: "POST /kickoff"
-description: "Start a crew execution"
-openapi: "/enterprise-api.en.yaml POST /kickoff"
----
-
-

docs/en/api-reference/status.mdx

@@ -1,7 +0,0 @@
----
-title: "GET /status/{kickoff_id}"
-description: "Get execution status"
-openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
----
-
-

docs/en/concepts/collaboration.mdx

@@ -1,362 +0,0 @@
----
-title: Collaboration
-description: How to enable agents to work together, delegate tasks, and communicate effectively within CrewAI teams.
-icon: screen-users
----
-
-## Overview
-
-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.
-
-## Quick Start: Enable Collaboration
-
-```python
-from crewai import Agent, Crew, Task
-
-# 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
-)
-
-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
-)
-
-# Agents can now collaborate automatically
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[...],
-    verbose=True
-)
-```
-
-## How Agent Collaboration Works
-
-When `allow_delegation=True`, CrewAI automatically provides agents with two powerful tools:
-
-### 1. **Delegate Work Tool**
-Allows agents to assign tasks to teammates with specific expertise.
-
-```python
-# Agent automatically gets this tool:
-# Delegate work to coworker(task: str, context: str, coworker: str)
-```
-
-### 2. **Ask Question Tool** 
-Enables agents to ask specific questions to gather information from colleagues.
-
-```python
-# Agent automatically gets this tool:
-# Ask question to coworker(question: str, context: str, coworker: str)
-```
-
-## 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/en/concepts/reasoning.mdx

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

docs/en/concepts/training.mdx

@@ -1,196 +0,0 @@
----
-title: Training
-description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
-icon: dumbbell
----
-
-## 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.
-
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
-This helps the agents improve their understanding, decision-making, and problem-solving abilities.
-
-### Training Your Crew Using the CLI
-
-To use the training feature, follow these steps:
-
-1. Open your terminal or command prompt.
-2. Navigate to the directory where your CrewAI project is located.
-3. Run the following command:
-
-```shell
-crewai train -n <n_iterations> -f <filename.pkl>
-```
-<Tip>
-  Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
-</Tip>
-
-<Note>
-  If you omit `-f`, the output defaults to `trained_agents_data.pkl` in the current working directory. You can pass an absolute path to control where the file is written.
-</Note>
-
-### Training your Crew programmatically
-
-To train your crew programmatically, use the following steps:
-
-1. Define the number of iterations for training.
-2. Specify the input parameters for the training process.
-3. Execute the training command within a try-except block to handle potential errors.
-
-```python Code
-n_iterations = 2
-inputs = {"topic": "CrewAI Training"}
-filename = "your_model.pkl"
-
-try:
-    YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations,
-      inputs=inputs,
-      filename=filename
-    )
-
-except Exception as e:
-    raise Exception(f"An error occurred while training the crew: {e}")
-```
-
-## How trained data is used by agents
-
-CrewAI uses the training artifacts in two ways: during training to incorporate your human feedback, and after training to guide agents with consolidated suggestions.
-
-### Training data flow
-
-```mermaid
-flowchart TD
-  A["Start training<br/>CLI: crewai train -n -f<br/>or Python: crew.train(...)"] --> B["Setup training mode<br/>- task.human_input = true<br/>- disable delegation<br/>- init training_data.pkl + trained file"]
-
-  subgraph "Iterations"
-    direction LR
-    C["Iteration i<br/>initial_output"] --> D["User human_feedback"]
-    D --> E["improved_output"]
-    E --> F["Append to training_data.pkl<br/>by agent_id and iteration"]
-  end
-
-  B --> C
-  F --> G{"More iterations?"}
-  G -- "Yes" --> C
-  G -- "No" --> H["Evaluate per agent<br/>aggregate iterations"]
-
-  H --> I["Consolidate<br/>suggestions[] + quality + final_summary"]
-  I --> J["Save by agent role to trained file<br/>(default: trained_agents_data.pkl)"]
-
-  J --> K["Normal (non-training) runs"]
-  K --> L["Auto-load suggestions<br/>from trained_agents_data.pkl"]
-  L --> M["Append to prompt<br/>for consistent improvements"]
-```
-
-### During training runs
-
-- On each iteration, the system records for every agent:
-  - `initial_output`: the agent’s first answer
-  - `human_feedback`: your inline feedback when prompted
-  - `improved_output`: the agent’s follow-up answer after feedback
-- This data is stored in a working file named `training_data.pkl` keyed by the agent’s internal ID and iteration.
-- While training is active, the agent automatically appends your prior human feedback to its prompt to enforce those instructions on subsequent attempts within the training session.
-  Training is interactive: tasks set `human_input = true`, so running in a non-interactive environment will block on user input.
-
-### After training completes
-
-- When `train(...)` finishes, CrewAI evaluates the collected training data per agent and produces a consolidated result containing:
-  - `suggestions`: clear, actionable instructions distilled from your feedback and the difference between initial/improved outputs
-  - `quality`: a 0–10 score capturing improvement
-  - `final_summary`: a step-by-step set of action items for future tasks
-- These consolidated results are saved to the filename you pass to `train(...)` (default via CLI is `trained_agents_data.pkl`). Entries are keyed by the agent’s `role` so they can be applied across sessions.
-- During normal (non-training) execution, each agent automatically loads its consolidated `suggestions` and appends them to the task prompt as mandatory instructions. This gives you consistent improvements without changing your agent definitions.
-
-### File summary
-
-- `training_data.pkl` (ephemeral, per-session):
-  - Structure: `agent_id -> { iteration_number: { initial_output, human_feedback, improved_output } }`
-  - Purpose: capture raw data and human feedback during training
-  - Location: saved in the current working directory (CWD)
-- `trained_agents_data.pkl` (or your custom filename):
-  - Structure: `agent_role -> { suggestions: string[], quality: number, final_summary: string }`
-  - Purpose: persist consolidated guidance for future runs
-  - Location: written to the CWD by default; use `-f` to set a custom (including absolute) path
-
-## Small Language Model Considerations
-
-<Warning>
-  When using smaller language models (≤7B parameters) for training data evaluation, be aware that they may face challenges with generating structured outputs and following complex instructions.
-</Warning>
-
-### Limitations of Small Models in Training Evaluation
-
-<CardGroup cols={2}>
-  <Card title="JSON Output Accuracy" icon="triangle-exclamation">
-    Smaller models often struggle with producing valid JSON responses needed for structured training evaluations, leading to parsing errors and incomplete data.
-  </Card>
-  <Card title="Evaluation Quality" icon="chart-line">
-    Models under 7B parameters may provide less nuanced evaluations with limited reasoning depth compared to larger models.
-  </Card>
-  <Card title="Instruction Following" icon="list-check">
-    Complex training evaluation criteria may not be fully followed or considered by smaller models.
-  </Card>
-  <Card title="Consistency" icon="rotate">
-    Evaluations across multiple training iterations may lack consistency with smaller models.
-  </Card>
-</CardGroup>
-
-### Recommendations for Training
-
-<Tabs>
-  <Tab title="Best Practice">
-    For optimal training quality and reliable evaluations, we strongly recommend using models with at least 7B parameters or larger:
-
-    ```python
-    from crewai import Agent, Crew, Task, LLM
-
-    # Recommended minimum for training evaluation
-    llm = LLM(model="mistral/open-mistral-7b")
-
-    # Better options for reliable training evaluation
-    llm = LLM(model="anthropic/claude-3-sonnet-20240229-v1:0")
-    llm = LLM(model="gpt-4o")
-
-    # Use this LLM with your agents
-    agent = Agent(
-        role="Training Evaluator",
-        goal="Provide accurate training feedback",
-        llm=llm
-    )
-    ```
-
-    <Tip>
-      More powerful models provide higher quality feedback with better reasoning, leading to more effective training iterations.
-    </Tip>
-  </Tab>
-  <Tab title="Small Model Usage">
-    If you must use smaller models for training evaluation, be aware of these constraints:
-
-    ```python
-    # Using a smaller model (expect some limitations)
-    llm = LLM(model="huggingface/microsoft/Phi-3-mini-4k-instruct")
-    ```
-
-    <Warning>
-      While CrewAI includes optimizations for small models, expect less reliable and less nuanced evaluation results that may require more human intervention during training.
-    </Warning>
-  </Tab>
-</Tabs>
-
-### Key Points to Note
-
-- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
-- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
-- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
-- Trained guidance is applied at prompt time; it does not modify your Python/YAML agent configuration.
-- Agents automatically load trained suggestions from a file named `trained_agents_data.pkl` located in the current working directory. If you trained to a different filename, either rename it to `trained_agents_data.pkl` before running, or adjust the loader in code.
-- You can change the output filename when calling `crewai train` with `-f/--filename`. Absolute paths are supported if you want to save outside the CWD.
-
-It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
-
-Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
-
-Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.

docs/en/enterprise/features/agent-repositories.mdx

@@ -1,155 +0,0 @@
----
-title: 'Agent Repositories'
-description: 'Learn how to use Agent Repositories to share and reuse your agents across teams and projects'
-icon: 'database'
----
-
-Agent Repositories allow enterprise users to store, share, and reuse agent definitions across teams and projects. This feature enables organizations to maintain a centralized library of standardized agents, promoting consistency and reducing duplication of effort.
-
-## Benefits of Agent Repositories
-
-- **Standardization**: Maintain consistent agent definitions across your organization
-- **Reusability**: Create an agent once and use it in multiple crews and projects
-- **Governance**: Implement organization-wide policies for agent configurations
-- **Collaboration**: Enable teams to share and build upon each other's work
-
-## Using Agent Repositories
-
-### Prerequisites
-
-1. You must have an account at CrewAI, try the [free plan](https://app.crewai.com).
-2. You need to be authenticated using the CrewAI CLI.
-3. If you have more than one organization, make sure you are switched to the correct organization using the CLI command:
-
-```bash
-crewai org switch <org_id>
-```
-
-### Creating and Managing Agents in Repositories
-
-To create and manage agents in repositories,Enterprise Dashboard.
-
-### Loading Agents from Repositories
-
-You can load agents from repositories in your code using the `from_repository` parameter:
-
-```python
-from crewai import Agent
-
-# Create an agent by loading it from a repository
-# The agent is loaded with all its predefined configurations
-researcher = Agent(
-    from_repository="market-research-agent"
-)
-
-```
-
-### Overriding Repository Settings
-
-You can override specific settings from the repository by providing them in the configuration:
-
-```python
-researcher = Agent(
-    from_repository="market-research-agent",
-    goal="Research the latest trends in AI development",  # Override the repository goal
-    verbose=True  # Add a setting not in the repository
-)
-```
-
-### Example: Creating a Crew with Repository Agents
-
-```python
-from crewai import Crew, Agent, Task
-
-# Load agents from repositories
-researcher = Agent(
-    from_repository="market-research-agent"
-)
-
-writer = Agent(
-    from_repository="content-writer-agent"
-)
-
-# Create tasks
-research_task = Task(
-    description="Research the latest trends in AI",
-    agent=researcher
-)
-
-writing_task = Task(
-    description="Write a comprehensive report based on the research",
-    agent=writer
-)
-
-# Create the crew
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, writing_task],
-    verbose=True
-)
-
-# Run the crew
-result = crew.kickoff()
-```
-
-### Example: Using `kickoff()` with Repository Agents
-
-You can also use repository agents directly with the `kickoff()` method for simpler interactions:
-
-```python
-from crewai import Agent
-from pydantic import BaseModel
-from typing import List
-
-# Define a structured output format
-class MarketAnalysis(BaseModel):
-    key_trends: List[str]
-    opportunities: List[str]
-    recommendation: str
-
-# Load an agent from repository
-analyst = Agent(
-    from_repository="market-analyst-agent",
-    verbose=True
-)
-
-# Get a free-form response
-result = analyst.kickoff("Analyze the AI market in 2025")
-print(result.raw)  # Access the raw response
-
-# Get structured output
-structured_result = analyst.kickoff(
-    "Provide a structured analysis of the AI market in 2025",
-    response_format=MarketAnalysis
-)
-
-# Access structured data
-print(f"Key Trends: {structured_result.pydantic.key_trends}")
-print(f"Recommendation: {structured_result.pydantic.recommendation}")
-```
-
-## Best Practices
-
-1. **Naming Convention**: Use clear, descriptive names for your repository agents
-2. **Documentation**: Include comprehensive descriptions for each agent
-3. **Tool Management**: Ensure that tools referenced by repository agents are available in your environment
-4. **Access Control**: Manage permissions to ensure only authorized team members can modify repository agents
-
-## Organization Management
-
-To switch between organizations or see your current organization, use the CrewAI CLI:
-
-```bash
-# View current organization
-crewai org current
-
-# Switch to a different organization
-crewai org switch <org_id>
-
-# List all available organizations
-crewai org list
-```
-
-<Note>
-When loading agents from repositories, you must be authenticated and switched to the correct organization. If you receive errors, check your authentication status and organization settings using the CLI commands above.
-</Note>

docs/en/enterprise/features/hallucination-guardrail.mdx

@@ -1,250 +0,0 @@
----
-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/en/enterprise/features/integrations.mdx

@@ -1,185 +0,0 @@
----
-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["gmail_find_email"]
-
-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/en/enterprise/features/rbac.mdx

@@ -1,103 +0,0 @@
----
-title: "Role-Based Access Control (RBAC)"
-description: "Control access to crews, tools, and data with roles, scopes, and granular permissions."
-icon: "shield"
----
-
-## Overview
-
-RBAC in CrewAI Enterprise enables secure, scalable access management through a combination of organization‑level roles and automation‑level visibility controls.
-
-<Frame>
-  <img src="/images/enterprise/users_and_roles.png" alt="RBAC overview in CrewAI Enterprise" />
-  
-</Frame>
-
-## Users and Roles
-
-Each member in your CrewAI workspace is assigned a role, which determines their access across various features. 
-
-You can:
-
-- Use predefined roles (Owner, Member)
-- Create custom roles tailored to specific permissions
-- Assign roles at any time through the settings panel
-
-You can configure users and roles in Settings → Roles.
-
-<Steps>
-  <Step title="Open Roles settings">
-    Go to <b>Settings → Roles</b> in CrewAI Enterprise.
-  </Step>
-  <Step title="Choose a role type">
-    Use a predefined role (<b>Owner</b>, <b>Member</b>) or click <b>Create role</b> to define a custom one.
-  </Step>
-  <Step title="Assign to members">
-    Select users and assign the role. You can change this anytime.
-  </Step>
-</Steps>
-
-### Configuration summary
-
-| Area | Where to configure | Options |
-|:---|:---|:---|
-| Users & Roles | Settings → Roles | Predefined: Owner, Member; Custom roles |
-| Automation visibility | Automation → Settings → Visibility | Private; Whitelist users/roles |
-
-## Automation‑level Access Control
-
-In addition to organization‑wide roles, CrewAI Automations support fine‑grained visibility settings that let you restrict access to specific automations by user or role.
-
-This is useful for:
-
-- Keeping sensitive or experimental automations private
-- Managing visibility across large teams or external collaborators
-- Testing automations in isolated contexts
-
-Deployments can be configured as private, meaning only whitelisted users and roles will be able to:
-
-- View the deployment
-- Run it or interact with its API
-- Access its logs, metrics, and settings
-
-The organization owner always has access, regardless of visibility settings.
-
-You can configure automation‑level access control in Automation → Settings → Visibility tab.
-
-<Steps>
-  <Step title="Open Visibility tab">
-    Navigate to <b>Automation → Settings → Visibility</b>.
-  </Step>
-  <Step title="Set visibility">
-    Choose <b>Private</b> to restrict access. The organization owner always retains access.
-  </Step>
-  <Step title="Whitelist access">
-    Add specific users and roles allowed to view, run, and access logs/metrics/settings.
-  </Step>
-  <Step title="Save and verify">
-    Save changes, then confirm that non‑whitelisted users cannot view or run the automation.
-  </Step>
-</Steps>
-
-### Private visibility: access outcomes
-
-| Action | Owner | Whitelisted user/role | Not whitelisted |
-|:---|:---|:---|:---|
-| View automation | ✓ | ✓ | ✗ |
-| Run automation/API | ✓ | ✓ | ✗ |
-| Access logs/metrics/settings | ✓ | ✓ | ✗ |
-
-<Tip>
-The organization owner always has access. In private mode, only whitelisted users and roles can view, run, and access logs/metrics/settings.
-</Tip>
-
-<Frame>
-  <img src="/images/enterprise/visibility.png" alt="Automation Visibility settings in CrewAI Enterprise" />
-  
-</Frame>
-
-<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
-  Contact our support team for assistance with RBAC questions.
-</Card>
-
-

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

@@ -1,51 +0,0 @@
----
-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/en/enterprise/guides/hubspot-trigger.mdx

@@ -1,53 +0,0 @@
----
-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/en/enterprise/guides/human-in-the-loop.mdx

@@ -1,78 +0,0 @@
----
-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/en/enterprise/guides/react-component-export.mdx

@@ -1,103 +0,0 @@
----
-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/en/enterprise/guides/salesforce-trigger.mdx

@@ -1,44 +0,0 @@
----
-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/en/enterprise/guides/slack-trigger.mdx

@@ -1,61 +0,0 @@
----
-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/en/enterprise/guides/team-management.mdx

@@ -1,87 +0,0 @@
----
-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/en/enterprise/guides/webhook-automation.mdx

@@ -1,121 +0,0 @@
----
-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/en/enterprise/guides/zapier-trigger.mdx

@@ -1,103 +0,0 @@
----
-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/en/enterprise/integrations/asana.mdx

@@ -1,253 +0,0 @@
----
-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/en/enterprise/integrations/box.mdx

@@ -1,268 +0,0 @@
----
-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/en/enterprise/integrations/clickup.mdx

@@ -1,293 +0,0 @@
----
-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/en/enterprise/integrations/github.mdx

@@ -1,323 +0,0 @@
----
-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/en/enterprise/integrations/gmail.mdx

@@ -1,356 +0,0 @@
----
-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/en/enterprise/integrations/google_calendar.mdx

@@ -1,391 +0,0 @@
----
-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/en/enterprise/integrations/google_sheets.mdx

@@ -1,321 +0,0 @@
----
-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/en/enterprise/integrations/hubspot.mdx

@@ -1,579 +0,0 @@
----
-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/en/enterprise/integrations/jira.mdx

@@ -1,394 +0,0 @@
----
-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/en/enterprise/integrations/linear.mdx

@@ -1,453 +0,0 @@
----
-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/en/enterprise/integrations/notion.mdx

@@ -1,509 +0,0 @@
----
-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/en/enterprise/integrations/salesforce.mdx

@@ -1,632 +0,0 @@
----
-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/en/enterprise/integrations/shopify.mdx

@@ -1,382 +0,0 @@
----
-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/en/enterprise/integrations/slack.mdx

@@ -1,293 +0,0 @@
----
-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/en/enterprise/integrations/stripe.mdx

@@ -1,305 +0,0 @@
----
-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/en/enterprise/integrations/zendesk.mdx

@@ -1,343 +0,0 @@
----
-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/en/enterprise/resources/frequently-asked-questions.mdx

@@ -1,151 +0,0 @@
----
-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](/en/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/en/examples/cookbooks.mdx

@@ -1,22 +0,0 @@
----
-title: CrewAI Cookbooks
-description: Feature-focused quickstarts and notebooks for learning patterns fast.
-icon: book
----
-
-## Quickstarts & Demos
-
-<CardGroup cols={2}>
-  <Card title="Task Guardrails" icon="shield-check" href="https://github.com/crewAIInc/crewAI-quickstarts/tree/main/Task%20Guardrails"> 
-    Interactive notebooks for hands-on exploration.
-  </Card>
-  <Card title="Browse Quickstarts" icon="bolt" href="https://github.com/crewAIInc/crewAI-quickstarts">
-    Feature demos and small projects showcasing specific CrewAI capabilities.
-  </Card>
-</CardGroup>
-
-<Tip>
-Use Cookbooks to learn a pattern quickly, then jump to Full Examples for production‑grade implementations.
-</Tip>
-
-

docs/en/examples/example.mdx

@@ -1,85 +0,0 @@
----
-title: CrewAI Examples
-description: Explore curated examples organized by Crews, Flows, Integrations, and Notebooks.
-icon: rocket-launch
----
-
-## Crews
-
-<CardGroup cols={3}>
-  <Card title="Marketing Strategy" icon="bullhorn" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/marketing_strategy">
-    Multi‑agent marketing campaign planning.
-  </Card>
-  <Card title="Surprise Trip" icon="plane" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/surprise_trip">
-    Personalized surprise travel planning.
-  </Card>
-  <Card title="Match Profile to Positions" icon="id-card" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/match_profile_to_positions">
-    CV‑to‑job matching with vector search.
-  </Card>
-  <Card title="Job Posting" icon="newspaper" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/job-posting">
-    Automated job description creation.
-  </Card>
-  <Card title="Game Builder Crew" icon="gamepad" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/game-builder-crew">
-    Multi‑agent team that designs and builds Python games.
-  </Card>
-  <Card title="Recruitment" icon="user-group" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews/recruitment">
-    Candidate sourcing and evaluation.
-  </Card>
-  <Card title="Browse all Crews" icon="users" href="https://github.com/crewAIInc/crewAI-examples/tree/main/crews">
-    See the full list of crew examples.
-  </Card>
-</CardGroup>
-
-## Flows
-
-<CardGroup cols={3}>
-  <Card title="Content Creator Flow" icon="pen" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/content_creator_flow">
-    Multi‑crew content generation with routing.
-  </Card>
-  <Card title="Email Auto Responder" icon="envelope" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/email_auto_responder_flow">
-    Automated email monitoring and replies.
-  </Card>
-  <Card title="Lead Score Flow" icon="chart-line" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/lead_score_flow">
-    Lead qualification with human‑in‑the‑loop.
-  </Card>
-  <Card title="Meeting Assistant Flow" icon="calendar" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/meeting_assistant_flow">
-    Notes processing with integrations.
-  </Card>
-  <Card title="Self Evaluation Loop" icon="rotate" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/self_evaluation_loop_flow">
-    Iterative self‑improvement workflows.
-  </Card>
-  <Card title="Write a Book (Flows)" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows/write_a_book_with_flows">
-    Parallel chapter generation.
-  </Card>
-  <Card title="Browse all Flows" icon="diagram-project" href="https://github.com/crewAIInc/crewAI-examples/tree/main/flows">
-    See the full list of flow examples.
-  </Card>
-</CardGroup>
-
-## Integrations
-
-<CardGroup cols={3}>
-  <Card title="CrewAI ↔ LangGraph" icon="link" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/crewai-langgraph">
-    Integration with LangGraph framework.
-  </Card>
-  <Card title="Azure OpenAI" icon="cloud" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/azure_model">
-    Using CrewAI with Azure OpenAI.
-  </Card>
-  <Card title="NVIDIA Models" icon="microchip" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations/nvidia_models">
-    NVIDIA ecosystem integrations.
-  </Card>
-  <Card title="Browse Integrations" icon="puzzle-piece" href="https://github.com/crewAIInc/crewAI-examples/tree/main/integrations">
-    See all integration examples.
-  </Card>
-</CardGroup>
-
-## Notebooks
-
-<CardGroup cols={2}>
-  <Card title="Simple QA Crew + Flow" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks/Simple%20QA%20Crew%20%2B%20Flow">
-    Simple QA Crew + Flow.
-  </Card>
-  <Card title="All Notebooks" icon="book" href="https://github.com/crewAIInc/crewAI-examples/tree/main/Notebooks">
-    Interactive examples for learning and experimentation.
-  </Card>
-</CardGroup>

docs/en/learn/custom-llm.mdx

@@ -1,350 +0,0 @@
----
-title: Custom LLM Implementation
-description: Learn how to create custom LLM implementations in CrewAI.
-icon: code
----
-
-## Overview
-
-CrewAI supports custom LLM implementations through the `BaseLLM` abstract base class. This allows you to integrate any LLM provider that doesn't have built-in support in LiteLLM, or implement custom authentication mechanisms.
-
-## Quick Start
-
-Here's a minimal custom LLM implementation:
-
-```python
-from crewai import BaseLLM
-from typing import Any, Dict, List, Optional, Union
-import requests
-
-class CustomLLM(BaseLLM):
-    def __init__(self, model: str, api_key: str, endpoint: str, temperature: Optional[float] = None):
-        # IMPORTANT: Call super().__init__() with required parameters
-        super().__init__(model=model, temperature=temperature)
-        
-        self.api_key = api_key
-        self.endpoint = endpoint
-        
-    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."""
-        # Convert string to message format if needed
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-        
-        # Prepare request
-        payload = {
-            "model": self.model,
-            "messages": messages,
-            "temperature": self.temperature,
-        }
-        
-        # Add tools if provided and supported
-        if tools and self.supports_function_calling():
-            payload["tools"] = tools
-        
-        # Make API call
-        response = requests.post(
-            self.endpoint,
-            headers={
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            },
-            json=payload,
-            timeout=30
-        )
-        response.raise_for_status()
-        
-        result = response.json()
-        return result["choices"][0]["message"]["content"]
-        
-    def supports_function_calling(self) -> bool:
-        """Override if your LLM supports function calling."""
-        return True  # Change to False if your LLM doesn't support tools
-        
-    def get_context_window_size(self) -> int:
-        """Return the context window size of your LLM."""
-        return 8192  # Adjust based on your model's actual context window
-```
-
-## Using Your Custom LLM
-
-```python
-from crewai import Agent, Task, Crew
-
-# Assuming you have the CustomLLM class defined above
-# Create your custom LLM
-custom_llm = CustomLLM(
-    model="my-custom-model",
-    api_key="your-api-key",
-    endpoint="https://api.example.com/v1/chat/completions",
-    temperature=0.7
-)
-
-# Use with an agent
-agent = Agent(
-    role="Research Assistant",
-    goal="Find and analyze information",
-    backstory="You are a research assistant.",
-    llm=custom_llm
-)
-
-# Create and execute tasks
-task = Task(
-    description="Research the latest developments in AI",
-    expected_output="A comprehensive summary",
-    agent=agent
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Required Methods
-
-### Constructor: `__init__()`
-
-**Critical**: You must call `super().__init__(model, temperature)` with the required parameters:
-
-```python
-def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
-    # REQUIRED: Call parent constructor with model and temperature
-    super().__init__(model=model, temperature=temperature)
-    
-    # Your custom initialization
-    self.api_key = api_key
-```
-
-### Abstract Method: `call()`
-
-The `call()` method is the heart of your LLM implementation. It must:
-
-- Accept messages (string or list of dicts with 'role' and 'content')
-- Return a string response
-- Handle tools and function calling if supported
-- Raise appropriate exceptions for errors
-
-### Optional Methods
-
-```python
-def supports_function_calling(self) -> bool:
-    """Return True if your LLM supports function calling."""
-    return True  # Default is True
-
-def supports_stop_words(self) -> bool:
-    """Return True if your LLM supports stop sequences."""
-    return True  # Default is True
-
-def get_context_window_size(self) -> int:
-    """Return the context window size."""
-    return 4096  # Default is 4096
-```
-
-## Common Patterns
-
-### Error Handling
-
-```python
-import requests
-
-def call(self, messages, tools=None, callbacks=None, available_functions=None):
-    try:
-        response = requests.post(
-            self.endpoint,
-            headers={"Authorization": f"Bearer {self.api_key}"},
-            json=payload,
-            timeout=30
-        )
-        response.raise_for_status()
-        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) as e:
-        raise ValueError(f"Invalid response format: {str(e)}")
-```
-
-### Custom Authentication
-
-```python
-from crewai import BaseLLM
-from typing import Optional
-
-class CustomAuthLLM(BaseLLM):
-    def __init__(self, model: str, auth_token: str, endpoint: str, temperature: Optional[float] = None):
-        super().__init__(model=model, temperature=temperature)
-        self.auth_token = auth_token
-        self.endpoint = endpoint
-    
-    def call(self, messages, tools=None, callbacks=None, available_functions=None):
-        headers = {
-            "Authorization": f"Custom {self.auth_token}",  # Custom auth format
-            "Content-Type": "application/json"
-        }
-        # Rest of implementation...
-```
-
-### Stop Words Support
-
-CrewAI automatically adds `"\nObservation:"` as a stop word to control agent behavior. If your LLM supports stop words:
-
-```python
-def call(self, messages, tools=None, callbacks=None, available_functions=None):
-    payload = {
-        "model": self.model,
-        "messages": messages,
-        "stop": self.stop  # Include stop words in API call
-    }
-    # Make API call...
-
-def supports_stop_words(self) -> bool:
-    return True  # Your LLM supports stop sequences
-```
-
-If your LLM doesn't support stop words natively:
-
-```python
-def call(self, messages, tools=None, callbacks=None, available_functions=None):
-    response = self._make_api_call(messages, tools)
-    content = response["choices"][0]["message"]["content"]
-    
-    # Manually truncate at stop words
-    if self.stop:
-        for stop_word in self.stop:
-            if stop_word in content:
-                content = content.split(stop_word)[0]
-                break
-    
-    return content
-
-def supports_stop_words(self) -> bool:
-    return False  # Tell CrewAI we handle stop words manually
-```
-
-## Function Calling
-
-If your LLM supports function calling, implement the complete flow:
-
-```python
-import json
-
-def call(self, messages, tools=None, callbacks=None, available_functions=None):
-    # Convert string to message format
-    if isinstance(messages, str):
-        messages = [{"role": "user", "content": messages}]
-    
-    # Make API call
-    response = self._make_api_call(messages, tools)
-    message = response["choices"][0]["message"]
-    
-    # Check for function calls
-    if "tool_calls" in message and available_functions:
-        return self._handle_function_calls(
-            message["tool_calls"], messages, tools, available_functions
-        )
-    
-    return message["content"]
-
-def _handle_function_calls(self, tool_calls, messages, tools, available_functions):
-    """Handle function calling with proper message flow."""
-    for tool_call in tool_calls:
-        function_name = tool_call["function"]["name"]
-        
-        if function_name in available_functions:
-            # Parse and execute function
-            function_args = json.loads(tool_call["function"]["arguments"])
-            function_result = available_functions[function_name](**function_args)
-            
-            # Add function call and result to message history
-            messages.append({
-                "role": "assistant",
-                "content": None,
-                "tool_calls": [tool_call]
-            })
-            messages.append({
-                "role": "tool",
-                "tool_call_id": tool_call["id"],
-                "name": function_name,
-                "content": str(function_result)
-            })
-            
-            # Call LLM again with updated context
-            return self.call(messages, tools, None, available_functions)
-    
-    return "Function call failed"
-```
-
-## Troubleshooting
-
-### Common Issues
-
-**Constructor Errors**
-```python
-# ❌ Wrong - missing required parameters
-def __init__(self, api_key: str):
-    super().__init__()
-
-# ✅ Correct
-def __init__(self, model: str, api_key: str, temperature: Optional[float] = None):
-    super().__init__(model=model, temperature=temperature)
-```
-
-**Function Calling Not Working**
-- Ensure `supports_function_calling()` returns `True`
-- Check that you handle `tool_calls` in the response
-- Verify `available_functions` parameter is used correctly
-
-**Authentication Failures**
-- Verify API key format and permissions
-- Check authentication header format
-- Ensure endpoint URLs are correct
-
-**Response Parsing Errors**
-- Validate response structure before accessing nested fields
-- Handle cases where content might be None
-- Add proper error handling for malformed responses
-
-## Testing Your Custom LLM
-
-```python
-from crewai import Agent, Task, Crew
-
-def test_custom_llm():
-    llm = CustomLLM(
-        model="test-model",
-        api_key="test-key",
-        endpoint="https://api.test.com"
-    )
-    
-    # Test basic call
-    result = llm.call("Hello, world!")
-    assert isinstance(result, str)
-    assert len(result) > 0
-    
-    # Test with CrewAI agent
-    agent = Agent(
-        role="Test Agent",
-        goal="Test custom LLM",
-        backstory="A test agent.",
-        llm=llm
-    )
-    
-    task = Task(
-        description="Say hello",
-        expected_output="A greeting",
-        agent=agent
-    )
-    
-    crew = Crew(agents=[agent], tasks=[task])
-    result = crew.kickoff()
-    assert "hello" in result.raw.lower()
-```
-
-This guide covers the essentials of implementing custom LLMs in CrewAI.

docs/en/learn/dalle-image-generation.mdx

@@ -1,73 +0,0 @@
----
-title: "Image Generation with DALL-E"
-description: "Learn how to use DALL-E for AI-powered image generation in your CrewAI projects"
-icon: "image"
----
-
-CrewAI supports integration with OpenAI's DALL-E, allowing your AI agents to generate images as part of their tasks. This guide will walk you through how to set up and use the DALL-E tool in your CrewAI projects.
-
-## Prerequisites
-
-- crewAI installed (latest version)
-- OpenAI API key with access to DALL-E
-
-## Setting Up the DALL-E Tool
-
-<Steps>
-    <Step title="Import the DALL-E tool">
-        ```python
-        from crewai_tools import DallETool
-        ```
-    </Step>
-
-    <Step title="Add the DALL-E tool to your agent configuration">
-        ```python
-        @agent
-        def researcher(self) -> Agent:
-            return Agent(
-                config=self.agents_config['researcher'],
-                tools=[SerperDevTool(), DallETool()],  # Add DallETool to the list of tools
-                allow_delegation=False,
-                verbose=True
-            )
-        ```
-    </Step>
-</Steps>
-
-## Using the DALL-E Tool
-
-Once you've added the DALL-E tool to your agent, it can generate images based on text prompts. The tool will return a URL to the generated image, which can be used in the agent's output or passed to other agents for further processing.
-
-### Example Agent Configuration
-
-```yaml
-role: >
-    LinkedIn Profile Senior Data Researcher
-goal: >
-    Uncover detailed LinkedIn profiles based on provided name {name} and domain {domain}
-    Generate a Dall-e image based on domain {domain}
-backstory: >
-    You're a seasoned researcher with a knack for uncovering the most relevant LinkedIn profiles.
-    Known for your ability to navigate LinkedIn efficiently, you excel at gathering and presenting
-    professional information clearly and concisely.
-```
-
-### Expected Output
-
-The agent with the DALL-E tool will be able to generate the image and provide a URL in its response. You can then download the image.
-
-<Frame>
-    <img src="/images/enterprise/dall-e-image.png" alt="DALL-E Image" />
-</Frame>
-
-## Best Practices
-
-1. **Be specific in your image generation prompts** to get the best results.
-2. **Consider generation time** - Image generation can take some time, so factor this into your task planning.
-3. **Follow usage policies** - Always comply with OpenAI's usage policies when generating images.
-
-## Troubleshooting
-
-1. **Check API access** - Ensure your OpenAI API key has access to DALL-E.
-2. **Version compatibility** - Check that you're using the latest version of crewAI and crewai-tools.
-3. **Tool configuration** - Verify that the DALL-E tool is correctly added to the agent's tool list. 

docs/en/learn/human-in-the-loop.mdx

@@ -1,78 +0,0 @@
----
-title: "Human-in-the-Loop (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/en/learn/llm-selection-guide.mdx

@@ -1,729 +0,0 @@
----
-title: 'Strategic LLM Selection Guide'
-description: 'Strategic framework for choosing the right LLM for your CrewAI AI agents and writing effective task and agent definitions'
-icon: 'brain-circuit'
----
-
-## The CrewAI Approach to LLM Selection
-
-Rather than prescriptive model recommendations, we advocate for a **thinking framework** that helps you make informed decisions based on your specific use case, constraints, and requirements. The LLM landscape evolves rapidly, with new models emerging regularly and existing ones being updated frequently. What matters most is developing a systematic approach to evaluation that remains relevant regardless of which specific models are available.
-
-<Note>
-This guide focuses on strategic thinking rather than specific model recommendations, as the LLM landscape evolves rapidly.
-</Note>
-
-## Quick Decision Framework
-
-<Steps>
-  <Step title="Analyze Your Tasks">
-    Begin by deeply understanding what your tasks actually require. Consider the cognitive complexity involved, the depth of reasoning needed, the format of expected outputs, and the amount of context the model will need to process. This foundational analysis will guide every subsequent decision.
-  </Step>
-  <Step title="Map Model Capabilities">
-    Once you understand your requirements, map them to model strengths. Different model families excel at different types of work; some are optimized for reasoning and analysis, others for creativity and content generation, and others for speed and efficiency.
-  </Step>
-  <Step title="Consider Constraints">
-    Factor in your real-world operational constraints including budget limitations, latency requirements, data privacy needs, and infrastructure capabilities. The theoretically best model may not be the practically best choice for your situation.
-  </Step>
-  <Step title="Test and Iterate">
-    Start with reliable, well-understood models and optimize based on actual performance in your specific use case. Real-world results often differ from theoretical benchmarks, so empirical testing is crucial.
-  </Step>
-</Steps>
-
-## Core Selection Framework
-
-### a. Task-First Thinking
-
-The most critical step in LLM selection is understanding what your task actually demands. Too often, teams select models based on general reputation or benchmark scores without carefully analyzing their specific requirements. This approach leads to either over-engineering simple tasks with expensive, complex models, or under-powering sophisticated work with models that lack the necessary capabilities.
-
-<Tabs>
-  <Tab title="Reasoning Complexity">
-    - **Simple Tasks** represent the majority of everyday AI work and include basic instruction following, straightforward data processing, and simple formatting operations. These tasks typically have clear inputs and outputs with minimal ambiguity. The cognitive load is low, and the model primarily needs to follow explicit instructions rather than engage in complex reasoning.
-
-    - **Complex Tasks** require multi-step reasoning, strategic thinking, and the ability to handle ambiguous or incomplete information. These might involve analyzing multiple data sources, developing comprehensive strategies, or solving problems that require breaking down into smaller components. The model needs to maintain context across multiple reasoning steps and often must make inferences that aren't explicitly stated.
-
-    - **Creative Tasks** demand a different type of cognitive capability focused on generating novel, engaging, and contextually appropriate content. This includes storytelling, marketing copy creation, and creative problem-solving. The model needs to understand nuance, tone, and audience while producing content that feels authentic and engaging rather than formulaic.
-  </Tab>
-  
-  <Tab title="Output Requirements">
-    - **Structured Data** tasks require precision and consistency in format adherence. When working with JSON, XML, or database formats, the model must reliably produce syntactically correct output that can be programmatically processed. These tasks often have strict validation requirements and little tolerance for format errors, making reliability more important than creativity.
-
-    - **Creative Content** outputs demand a balance of technical competence and creative flair. The model needs to understand audience, tone, and brand voice while producing content that engages readers and achieves specific communication goals. Quality here is often subjective and requires models that can adapt their writing style to different contexts and purposes.
-
-    - **Technical Content** sits between structured data and creative content, requiring both precision and clarity. Documentation, code generation, and technical analysis need to be accurate and comprehensive while remaining accessible to the intended audience. The model must understand complex technical concepts and communicate them effectively.
-  </Tab>
-  
-  <Tab title="Context Needs">
-    - **Short Context** scenarios involve focused, immediate tasks where the model needs to process limited information quickly. These are often transactional interactions where speed and efficiency matter more than deep understanding. The model doesn't need to maintain extensive conversation history or process large documents.
-
-    - **Long Context** requirements emerge when working with substantial documents, extended conversations, or complex multi-part tasks. The model needs to maintain coherence across thousands of tokens while referencing earlier information accurately. This capability becomes crucial for document analysis, comprehensive research, and sophisticated dialogue systems.
-
-    - **Very Long Context** scenarios push the boundaries of what's currently possible, involving massive document processing, extensive research synthesis, or complex multi-session interactions. These use cases require models specifically designed for extended context handling and often involve trade-offs between context length and processing speed.
-  </Tab>
-</Tabs>
-
-### b. Model Capability Mapping
-
-Understanding model capabilities requires looking beyond marketing claims and benchmark scores to understand the fundamental strengths and limitations of different model architectures and training approaches.
-
-<AccordionGroup>
-  <Accordion title="Reasoning Models" icon="brain">
-    Reasoning models represent a specialized category designed specifically for complex, multi-step thinking tasks. These models excel when problems require careful analysis, strategic planning, or systematic problem decomposition. They typically employ techniques like chain-of-thought reasoning or tree-of-thought processing to work through complex problems step by step.
-
-    The strength of reasoning models lies in their ability to maintain logical consistency across extended reasoning chains and to break down complex problems into manageable components. They're particularly valuable for strategic planning, complex analysis, and situations where the quality of reasoning matters more than speed of response.
-
-    However, reasoning models often come with trade-offs in terms of speed and cost. They may also be less suitable for creative tasks or simple operations where their sophisticated reasoning capabilities aren't needed. Consider these models when your tasks involve genuine complexity that benefits from systematic, step-by-step analysis.
-  </Accordion>
-  
-  <Accordion title="General Purpose Models" icon="microchip">
-    General purpose models offer the most balanced approach to LLM selection, providing solid performance across a wide range of tasks without extreme specialization in any particular area. These models are trained on diverse datasets and optimized for versatility rather than peak performance in specific domains.
-
-    The primary advantage of general purpose models is their reliability and predictability across different types of work. They handle most standard business tasks competently, from research and analysis to content creation and data processing. This makes them excellent choices for teams that need consistent performance across varied workflows.
-
-    While general purpose models may not achieve the peak performance of specialized alternatives in specific domains, they offer operational simplicity and reduced complexity in model management. They're often the best starting point for new projects, allowing teams to understand their specific needs before potentially optimizing with more specialized models.
-  </Accordion>
-  
-  <Accordion title="Fast & Efficient Models" icon="bolt">
-    Fast and efficient models prioritize speed, cost-effectiveness, and resource efficiency over sophisticated reasoning capabilities. These models are optimized for high-throughput scenarios where quick responses and low operational costs are more important than nuanced understanding or complex reasoning.
-
-    These models excel in scenarios involving routine operations, simple data processing, function calling, and high-volume tasks where the cognitive requirements are relatively straightforward. They're particularly valuable for applications that need to process many requests quickly or operate within tight budget constraints.
-
-    The key consideration with efficient models is ensuring that their capabilities align with your task requirements. While they can handle many routine operations effectively, they may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation. They're best used for well-defined, routine operations where speed and cost matter more than sophistication.
-  </Accordion>
-  
-  <Accordion title="Creative Models" icon="pen">
-    Creative models are specifically optimized for content generation, writing quality, and creative thinking tasks. These models typically excel at understanding nuance, tone, and style while producing engaging, contextually appropriate content that feels natural and authentic.
-
-    The strength of creative models lies in their ability to adapt writing style to different audiences, maintain consistent voice and tone, and generate content that engages readers effectively. They often perform better on tasks involving storytelling, marketing copy, brand communications, and other content where creativity and engagement are primary goals.
-
-    When selecting creative models, consider not just their ability to generate text, but their understanding of audience, context, and purpose. The best creative models can adapt their output to match specific brand voices, target different audience segments, and maintain consistency across extended content pieces.
-  </Accordion>
-  
-  <Accordion title="Open Source Models" icon="code">
-    Open source models offer unique advantages in terms of cost control, customization potential, data privacy, and deployment flexibility. These models can be run locally or on private infrastructure, providing complete control over data handling and model behavior.
-
-    The primary benefits of open source models include elimination of per-token costs, ability to fine-tune for specific use cases, complete data privacy, and independence from external API providers. They're particularly valuable for organizations with strict data privacy requirements, budget constraints, or specific customization needs.
-
-    However, open source models require more technical expertise to deploy and maintain effectively. Teams need to consider infrastructure costs, model management complexity, and the ongoing effort required to keep models updated and optimized. The total cost of ownership may be higher than cloud-based alternatives when factoring in technical overhead.
-  </Accordion>
-</AccordionGroup>
-
-## Strategic Configuration Patterns
-
-### a. Multi-Model Approach
-
-<Tip>
-Use different models for different purposes within the same crew to optimize both performance and cost.
-</Tip>
-
-The most sophisticated CrewAI implementations often employ multiple models strategically, assigning different models to different agents based on their specific roles and requirements. This approach allows teams to optimize for both performance and cost by using the most appropriate model for each type of work.
-
-Planning agents benefit from reasoning models that can handle complex strategic thinking and multi-step analysis. These agents often serve as the "brain" of the operation, developing strategies and coordinating other agents' work. Content agents, on the other hand, perform best with creative models that excel at writing quality and audience engagement. Processing agents handling routine operations can use efficient models that prioritize speed and cost-effectiveness.
-
-**Example: Research and Analysis Crew**
-
-```python
-from crewai import Agent, Task, Crew, LLM
-
-# High-capability reasoning model for strategic planning
-manager_llm = LLM(model="gemini-2.5-flash-preview-05-20", temperature=0.1)
-
-# Creative model for content generation
-content_llm = LLM(model="claude-3-5-sonnet-20241022", temperature=0.7)
-
-# Efficient model for data processing
-processing_llm = LLM(model="gpt-4o-mini", temperature=0)
-
-research_manager = Agent(
-    role="Research Strategy Manager",
-    goal="Develop comprehensive research strategies and coordinate team efforts",
-    backstory="Expert research strategist with deep analytical capabilities",
-    llm=manager_llm,  # High-capability model for complex reasoning
-    verbose=True
-)
-
-content_writer = Agent(
-    role="Research Content Writer",
-    goal="Transform research findings into compelling, well-structured reports",
-    backstory="Skilled writer who excels at making complex topics accessible",
-    llm=content_llm,  # Creative model for engaging content
-    verbose=True
-)
-
-data_processor = Agent(
-    role="Data Analysis Specialist", 
-    goal="Extract and organize key data points from research sources",
-    backstory="Detail-oriented analyst focused on accuracy and efficiency",
-    llm=processing_llm,  # Fast, cost-effective model for routine tasks
-    verbose=True
-)
-
-crew = Crew(
-    agents=[research_manager, content_writer, data_processor],
-    tasks=[...],  # Your specific tasks
-    manager_llm=manager_llm,  # Manager uses the reasoning model
-    verbose=True
-)
-```
-
-The key to successful multi-model implementation is understanding how different agents interact and ensuring that model capabilities align with agent responsibilities. This requires careful planning but can result in significant improvements in both output quality and operational efficiency.
-
-### b. Component-Specific Selection
-
-<Tabs>
-  <Tab title="Manager LLM">
-    The manager LLM plays a crucial role in hierarchical CrewAI processes, serving as the coordination point for multiple agents and tasks. This model needs to excel at delegation, task prioritization, and maintaining context across multiple concurrent operations.
-
-    Effective manager LLMs require strong reasoning capabilities to make good delegation decisions, consistent performance to ensure predictable coordination, and excellent context management to track the state of multiple agents simultaneously. The model needs to understand the capabilities and limitations of different agents while optimizing task allocation for efficiency and quality.
-
-    Cost considerations are particularly important for manager LLMs since they're involved in every operation. The model needs to provide sufficient capability for effective coordination while remaining cost-effective for frequent use. This often means finding models that offer good reasoning capabilities without the premium pricing of the most sophisticated options.
-  </Tab>
-  
-  <Tab title="Function Calling LLM">
-    Function calling LLMs handle tool usage across all agents, making them critical for crews that rely heavily on external tools and APIs. These models need to excel at understanding tool capabilities, extracting parameters accurately, and handling tool responses effectively.
-
-    The most important characteristics for function calling LLMs are precision and reliability rather than creativity or sophisticated reasoning. The model needs to consistently extract the correct parameters from natural language requests and handle tool responses appropriately. Speed is also important since tool usage often involves multiple round trips that can impact overall performance.
-
-    Many teams find that specialized function calling models or general purpose models with strong tool support work better than creative or reasoning-focused models for this role. The key is ensuring that the model can reliably bridge the gap between natural language instructions and structured tool calls.
-  </Tab>
-  
-  <Tab title="Agent-Specific Overrides">
-    Individual agents can override crew-level LLM settings when their specific needs differ significantly from the general crew requirements. This capability allows for fine-tuned optimization while maintaining operational simplicity for most agents.
-
-    Consider agent-specific overrides when an agent's role requires capabilities that differ substantially from other crew members. For example, a creative writing agent might benefit from a model optimized for content generation, while a data analysis agent might perform better with a reasoning-focused model.
-
-    The challenge with agent-specific overrides is balancing optimization with operational complexity. Each additional model adds complexity to deployment, monitoring, and cost management. Teams should focus overrides on agents where the performance improvement justifies the additional complexity.
-  </Tab>
-</Tabs>
-
-## Task Definition Framework
-
-### a. Focus on Clarity Over Complexity
-
-Effective task definition is often more important than model selection in determining the quality of CrewAI outputs. Well-defined tasks provide clear direction and context that enable even modest models to perform well, while poorly defined tasks can cause even sophisticated models to produce unsatisfactory results.
-
-<AccordionGroup>
-  <Accordion title="Effective Task Descriptions" icon="list-check">
-    The best task descriptions strike a balance between providing sufficient detail and maintaining clarity. They should define the specific objective clearly enough that there's no ambiguity about what success looks like, while explaining the approach or methodology in enough detail that the agent understands how to proceed.
-
-    Effective task descriptions include relevant context and constraints that help the agent understand the broader purpose and any limitations they need to work within. They break complex work into focused steps that can be executed systematically, rather than presenting overwhelming, multi-faceted objectives that are difficult to approach systematically.
-
-    Common mistakes include being too vague about objectives, failing to provide necessary context, setting unclear success criteria, or combining multiple unrelated tasks into a single description. The goal is to provide enough information for the agent to succeed while maintaining focus on a single, clear objective.
-  </Accordion>
-  
-  <Accordion title="Expected Output Guidelines" icon="bullseye">
-    Expected output guidelines serve as a contract between the task definition and the agent, clearly specifying what the deliverable should look like and how it will be evaluated. These guidelines should describe both the format and structure needed, as well as the key elements that must be included for the output to be considered complete.
-
-    The best output guidelines provide concrete examples of quality indicators and define completion criteria clearly enough that both the agent and human reviewers can assess whether the task has been completed successfully. This reduces ambiguity and helps ensure consistent results across multiple task executions.
-
-    Avoid generic output descriptions that could apply to any task, missing format specifications that leave agents guessing about structure, unclear quality standards that make evaluation difficult, or failing to provide examples or templates that help agents understand expectations.
-  </Accordion>
-</AccordionGroup>
-
-### b. Task Sequencing Strategy
-
-<Tabs>
-  <Tab title="Sequential Dependencies">
-    Sequential task dependencies are essential when tasks build upon previous outputs, information flows from one task to another, or quality depends on the completion of prerequisite work. This approach ensures that each task has access to the information and context it needs to succeed.
-
-    Implementing sequential dependencies effectively requires using the context parameter to chain related tasks, building complexity gradually through task progression, and ensuring that each task produces outputs that serve as meaningful inputs for subsequent tasks. The goal is to maintain logical flow between dependent tasks while avoiding unnecessary bottlenecks.
-
-    Sequential dependencies work best when there's a clear logical progression from one task to another and when the output of one task genuinely improves the quality or feasibility of subsequent tasks. However, they can create bottlenecks if not managed carefully, so it's important to identify which dependencies are truly necessary versus those that are merely convenient.
-  </Tab>
-  
-  <Tab title="Parallel Execution">
-    Parallel execution becomes valuable when tasks are independent of each other, time efficiency is important, or different expertise areas are involved that don't require coordination. This approach can significantly reduce overall execution time while allowing specialized agents to work on their areas of strength simultaneously.
-
-    Successful parallel execution requires identifying tasks that can truly run independently, grouping related but separate work streams effectively, and planning for result integration when parallel tasks need to be combined into a final deliverable. The key is ensuring that parallel tasks don't create conflicts or redundancies that reduce overall quality.
-
-    Consider parallel execution when you have multiple independent research streams, different types of analysis that don't depend on each other, or content creation tasks that can be developed simultaneously. However, be mindful of resource allocation and ensure that parallel execution doesn't overwhelm your available model capacity or budget.
-  </Tab>
-</Tabs>
-
-## Optimizing Agent Configuration for LLM Performance
-
-### a. Role-Driven LLM Selection
-
-<Warning>
-Generic agent roles make it impossible to select the right LLM. Specific roles enable targeted model optimization.
-</Warning>
-
-The specificity of your agent roles directly determines which LLM capabilities matter most for optimal performance. This creates a strategic opportunity to match precise model strengths with agent responsibilities.
-
-**Generic vs. Specific Role Impact on LLM Choice:**
-
-When defining roles, think about the specific domain knowledge, working style, and decision-making frameworks that would be most valuable for the tasks the agent will handle. The more specific and contextual the role definition, the better the model can embody that role effectively.
-```python
-# ✅ Specific role - clear LLM requirements
-specific_agent = Agent(
-    role="SaaS Revenue Operations Analyst",  # Clear domain expertise needed
-    goal="Analyze recurring revenue metrics and identify growth opportunities",
-    backstory="Specialist in SaaS business models with deep understanding of ARR, churn, and expansion revenue",
-    llm=LLM(model="gpt-4o")  # Reasoning model justified for complex analysis
-)
-```
-
-**Role-to-Model Mapping Strategy:**
-
-- **"Research Analyst"** → Reasoning model (GPT-4o, Claude Sonnet) for complex analysis
-- **"Content Editor"** → Creative model (Claude, GPT-4o) for writing quality
-- **"Data Processor"** → Efficient model (GPT-4o-mini, Gemini Flash) for structured tasks
-- **"API Coordinator"** → Function-calling optimized model (GPT-4o, Claude) for tool usage
-
-### b. Backstory as Model Context Amplifier
-
-<Info>
-Strategic backstories multiply your chosen LLM's effectiveness by providing domain-specific context that generic prompting cannot achieve.
-</Info>
-
-A well-crafted backstory transforms your LLM choice from generic capability to specialized expertise. This is especially crucial for cost optimization - a well-contextualized efficient model can outperform a premium model without proper context.
-
-**Context-Driven Performance Example:**
-
-```python
-# Context amplifies model effectiveness
-domain_expert = Agent(
-    role="B2B SaaS Marketing Strategist",
-    goal="Develop comprehensive go-to-market strategies for enterprise software",
-    backstory="""
-    You have 10+ years of experience scaling B2B SaaS companies from Series A to IPO. 
-    You understand the nuances of enterprise sales cycles, the importance of product-market 
-    fit in different verticals, and how to balance growth metrics with unit economics. 
-    You've worked with companies like Salesforce, HubSpot, and emerging unicorns, giving 
-    you perspective on both established and disruptive go-to-market strategies.
-    """,
-    llm=LLM(model="claude-3-5-sonnet", temperature=0.3)  # Balanced creativity with domain knowledge
-)
-
-# This context enables Claude to perform like a domain expert
-# Without it, even it would produce generic marketing advice
-```
-
-**Backstory Elements That Enhance LLM Performance:**
-- **Domain Experience**: "10+ years in enterprise SaaS sales"
-- **Specific Expertise**: "Specializes in technical due diligence for Series B+ rounds"
-- **Working Style**: "Prefers data-driven decisions with clear documentation"
-- **Quality Standards**: "Insists on citing sources and showing analytical work"
-
-### c. Holistic Agent-LLM Optimization
-
-The most effective agent configurations create synergy between role specificity, backstory depth, and LLM selection. Each element reinforces the others to maximize model performance.
-
-**Optimization Framework:**
-
-```python
-# Example: Technical Documentation Agent
-tech_writer = Agent(
-    role="API Documentation Specialist",  # Specific role for clear LLM requirements
-    goal="Create comprehensive, developer-friendly API documentation",
-    backstory="""
-    You're a technical writer with 8+ years documenting REST APIs, GraphQL endpoints, 
-    and SDK integration guides. You've worked with developer tools companies and 
-    understand what developers need: clear examples, comprehensive error handling, 
-    and practical use cases. You prioritize accuracy and usability over marketing fluff.
-    """,
-    llm=LLM(
-        model="claude-3-5-sonnet",  # Excellent for technical writing
-        temperature=0.1  # Low temperature for accuracy
-    ),
-    tools=[code_analyzer_tool, api_scanner_tool],
-    verbose=True 
-)
-```
-
-**Alignment Checklist:**
-- ✅ **Role Specificity**: Clear domain and responsibilities
-- ✅ **LLM Match**: Model strengths align with role requirements 
-- ✅ **Backstory Depth**: Provides domain context the LLM can leverage
-- ✅ **Tool Integration**: Tools support the agent's specialized function
-- ✅ **Parameter Tuning**: Temperature and settings optimize for role needs
-
-The key is creating agents where every configuration choice reinforces your LLM selection strategy, maximizing performance while optimizing costs.
-
-## Practical Implementation Checklist
-
-Rather than repeating the strategic framework, here's a tactical checklist for implementing your LLM selection decisions in CrewAI:
-
-<Steps>
-  <Step title="Audit Your Current Setup" icon="clipboard-check">
-    **What to Review:**
-    - Are all agents using the same LLM by default?
-    - Which agents handle the most complex reasoning tasks?
-    - Which agents primarily do data processing or formatting?
-    - Are any agents heavily tool-dependent?
-    
-    **Action**: Document current agent roles and identify optimization opportunities.
-  </Step>
-  
-  <Step title="Implement Crew-Level Strategy" icon="users-gear">
-    **Set Your Baseline:**
-    ```python
-    # Start with a reliable default for the crew
-    default_crew_llm = LLM(model="gpt-4o-mini")  # Cost-effective baseline
-    
-    crew = Crew(
-        agents=[...],
-        tasks=[...],
-        memory=True
-    )
-    ```
-    
-    **Action**: Establish your crew's default LLM before optimizing individual agents.
-  </Step>
-  
-  <Step title="Optimize High-Impact Agents" icon="star">
-    **Identify and Upgrade Key Agents:**
-    ```python
-    # Manager or coordination agents
-    manager_agent = Agent(
-        role="Project Manager",
-        llm=LLM(model="gemini-2.5-flash-preview-05-20"),  # Premium for coordination
-        # ... rest of config
-    )
-    
-    # Creative or customer-facing agents  
-    content_agent = Agent(
-        role="Content Creator",
-        llm=LLM(model="claude-3-5-sonnet"),  # Best for writing
-        # ... rest of config
-    )
-    ```
-    
-    **Action**: Upgrade 20% of your agents that handle 80% of the complexity.
-  </Step>
-  
-  <Step title="Validate with Enterprise Testing" icon="test-tube">
-    **Once you deploy your agents to production:**
-    - Use [CrewAI Enterprise platform](https://app.crewai.com) to A/B test your model selections
-    - Run multiple iterations with real inputs to measure consistency and performance
-    - Compare cost vs. performance across your optimized setup
-    - Share results with your team for collaborative decision-making
-    
-    **Action**: Replace guesswork with data-driven validation using the testing platform.
-  </Step>
-</Steps>
-
-### When to Use Different Model Types
-
-<Tabs>
-  <Tab title="Reasoning Models">
-    Reasoning models become essential when tasks require genuine multi-step logical thinking, strategic planning, or high-level decision making that benefits from systematic analysis. These models excel when problems need to be broken down into components and analyzed systematically rather than handled through pattern matching or simple instruction following.
-
-    Consider reasoning models for business strategy development, complex data analysis that requires drawing insights from multiple sources, multi-step problem solving where each step depends on previous analysis, and strategic planning tasks that require considering multiple variables and their interactions.
-
-    However, reasoning models often come with higher costs and slower response times, so they're best reserved for tasks where their sophisticated capabilities provide genuine value rather than being used for simple operations that don't require complex reasoning.
-  </Tab>
-  
-  <Tab title="Creative Models">
-    Creative models become valuable when content generation is the primary output and the quality, style, and engagement level of that content directly impact success. These models excel when writing quality and style matter significantly, creative ideation or brainstorming is needed, or brand voice and tone are important considerations.
-
-    Use creative models for blog post writing and article creation, marketing copy that needs to engage and persuade, creative storytelling and narrative development, and brand communications where voice and tone are crucial. These models often understand nuance and context better than general purpose alternatives.
-
-    Creative models may be less suitable for technical or analytical tasks where precision and factual accuracy are more important than engagement and style. They're best used when the creative and communicative aspects of the output are primary success factors.
-  </Tab>
-  
-  <Tab title="Efficient Models">
-    Efficient models are ideal for high-frequency, routine operations where speed and cost optimization are priorities. These models work best when tasks have clear, well-defined parameters and don't require sophisticated reasoning or creative capabilities.
-
-    Consider efficient models for data processing and transformation tasks, simple formatting and organization operations, function calling and tool usage where precision matters more than sophistication, and high-volume operations where cost per operation is a significant factor.
-
-    The key with efficient models is ensuring that their capabilities align with task requirements. They can handle many routine operations effectively but may struggle with tasks requiring nuanced understanding, complex reasoning, or sophisticated content generation.
-  </Tab>
-  
-  <Tab title="Open Source Models">
-    Open source models become attractive when budget constraints are significant, data privacy requirements exist, customization needs are important, or local deployment is required for operational or compliance reasons.
-
-    Consider open source models for internal company tools where data privacy is paramount, privacy-sensitive applications that can't use external APIs, cost-optimized deployments where per-token pricing is prohibitive, and situations requiring custom model modifications or fine-tuning.
-
-    However, open source models require more technical expertise to deploy and maintain effectively. Consider the total cost of ownership including infrastructure, technical overhead, and ongoing maintenance when evaluating open source options.
-  </Tab>
-</Tabs>
-
-## Common CrewAI Model Selection Pitfalls
-
-<AccordionGroup>
-  <Accordion title="The 'One Model Fits All' Trap" icon="triangle-exclamation">
-    **The Problem**: Using the same LLM for all agents in a crew, regardless of their specific roles and responsibilities. This is often the default approach but rarely optimal.
-
-    **Real Example**: Using GPT-4o for both a strategic planning manager and a data extraction agent. The manager needs reasoning capabilities worth the premium cost, but the data extractor could perform just as well with GPT-4o-mini at a fraction of the price.
-
-    **CrewAI Solution**: Leverage agent-specific LLM configuration to match model capabilities with agent roles:
-    ```python
-    # Strategic agent gets premium model
-    manager = Agent(role="Strategy Manager", llm=LLM(model="gpt-4o"))
-    
-    # Processing agent gets efficient model  
-    processor = Agent(role="Data Processor", llm=LLM(model="gpt-4o-mini"))
-    ```
-  </Accordion>
-  
-  <Accordion title="Ignoring Crew-Level vs Agent-Level LLM Hierarchy" icon="shuffle">
-    **The Problem**: Not understanding how CrewAI's LLM hierarchy works - crew LLM, manager LLM, and agent LLM settings can conflict or be poorly coordinated.
-
-    **Real Example**: Setting a crew to use Claude, but having agents configured with GPT models, creating inconsistent behavior and unnecessary model switching overhead.
-
-    **CrewAI Solution**: Plan your LLM hierarchy strategically:
-    ```python
-    crew = Crew(
-        agents=[agent1, agent2],
-        tasks=[task1, task2],
-        manager_llm=LLM(model="gpt-4o"),  # For crew coordination
-        process=Process.hierarchical  # When using manager_llm
-    )
-    
-    # Agents inherit crew LLM unless specifically overridden
-    agent1 = Agent(llm=LLM(model="claude-3-5-sonnet"))  # Override for specific needs
-    ```
-  </Accordion>
-  
-  <Accordion title="Function Calling Model Mismatch" icon="screwdriver-wrench">
-    **The Problem**: Choosing models based on general capabilities while ignoring function calling performance for tool-heavy CrewAI workflows.
-
-    **Real Example**: Selecting a creative-focused model for an agent that primarily needs to call APIs, search tools, or process structured data. The agent struggles with tool parameter extraction and reliable function calls.
-
-    **CrewAI Solution**: Prioritize function calling capabilities for tool-heavy agents:
-    ```python
-    # For agents that use many tools
-    tool_agent = Agent(
-        role="API Integration Specialist",
-        tools=[search_tool, api_tool, data_tool],
-        llm=LLM(model="gpt-4o"),  # Excellent function calling
-        # OR
-        llm=LLM(model="claude-3-5-sonnet")  # Also strong with tools
-    )
-    ```
-  </Accordion>
-  
-  <Accordion title="Premature Optimization Without Testing" icon="gear">
-    **The Problem**: Making complex model selection decisions based on theoretical performance without validating with actual CrewAI workflows and tasks.
-
-    **Real Example**: Implementing elaborate model switching logic based on task types without testing if the performance gains justify the operational complexity.
-
-    **CrewAI Solution**: Start simple, then optimize based on real performance data:
-    ```python
-    # Start with this
-    crew = Crew(agents=[...], tasks=[...], llm=LLM(model="gpt-4o-mini"))
-    
-    # Test performance, then optimize specific agents as needed
-    # Use Enterprise platform testing to validate improvements
-    ```
-  </Accordion>
-
-  <Accordion title="Overlooking Context and Memory Limitations" icon="brain">
-    **The Problem**: Not considering how model context windows interact with CrewAI's memory and context sharing between agents.
-
-    **Real Example**: Using a short-context model for agents that need to maintain conversation history across multiple task iterations, or in crews with extensive agent-to-agent communication.
-
-    **CrewAI Solution**: Match context capabilities to crew communication patterns.
-  </Accordion>
-</AccordionGroup>
-
-## Testing and Iteration Strategy
-
-<Steps>
-  <Step title="Start Simple" icon="play">
-    Begin with reliable, general-purpose models that are well-understood and widely supported. This provides a stable foundation for understanding your specific requirements and performance expectations before optimizing for specialized needs.
-  </Step>
-  <Step title="Measure What Matters" icon="chart-line">
-    Develop metrics that align with your specific use case and business requirements rather than relying solely on general benchmarks. Focus on measuring outcomes that directly impact your success rather than theoretical performance indicators.
-  </Step>
-  <Step title="Iterate Based on Results" icon="arrows-rotate">
-    Make model changes based on observed performance in your specific context rather than theoretical considerations or general recommendations. Real-world performance often differs significantly from benchmark results or general reputation.
-  </Step>
-  <Step title="Consider Total Cost" icon="calculator">
-    Evaluate the complete cost of ownership including model costs, development time, maintenance overhead, and operational complexity. The cheapest model per token may not be the most cost-effective choice when considering all factors.
-  </Step>
-</Steps>
-
-<Tip>
-Focus on understanding your requirements first, then select models that best match those needs. The best LLM choice is the one that consistently delivers the results you need within your operational constraints.
-</Tip>
-
-### Enterprise-Grade Model Validation
-
-For teams serious about optimizing their LLM selection, the **CrewAI Enterprise platform** provides sophisticated testing capabilities that go far beyond basic CLI testing. The platform enables comprehensive model evaluation that helps you make data-driven decisions about your LLM strategy.
-
-<Frame>
-  ![Enterprise Testing Interface](/images/enterprise/enterprise-testing.png)
-</Frame>
-
-**Advanced Testing Features:**
-
-- **Multi-Model Comparison**: Test multiple LLMs simultaneously across the same tasks and inputs. Compare performance between GPT-4o, Claude, Llama, Groq, Cerebras, and other leading models in parallel to identify the best fit for your specific use case.
-
-- **Statistical Rigor**: Configure multiple iterations with consistent inputs to measure reliability and performance variance. This helps identify models that not only perform well but do so consistently across runs.
-
-- **Real-World Validation**: Use your actual crew inputs and scenarios rather than synthetic benchmarks. The platform allows you to test with your specific industry context, company information, and real use cases for more accurate evaluation.
-
-- **Comprehensive Analytics**: Access detailed performance metrics, execution times, and cost analysis across all tested models. This enables data-driven decision making rather than relying on general model reputation or theoretical capabilities.
-
-- **Team Collaboration**: Share testing results and model performance data across your team, enabling collaborative decision-making and consistent model selection strategies across projects.
-
-Go to [app.crewai.com](https://app.crewai.com) to get started!
-
-<Info>
-The Enterprise platform transforms model selection from guesswork into a data-driven process, enabling you to validate the principles in this guide with your actual use cases and requirements.
-</Info>
-
-## Key Principles Summary
-
-<CardGroup cols={2}>
-  <Card title="Task-Driven Selection" icon="bullseye">
-    Choose models based on what the task actually requires, not theoretical capabilities or general reputation.
-  </Card>
-  
-  <Card title="Capability Matching" icon="puzzle-piece">
-    Align model strengths with agent roles and responsibilities for optimal performance.
-  </Card>
-  
-  <Card title="Strategic Consistency" icon="link">
-    Maintain coherent model selection strategy across related components and workflows.
-  </Card>
-  
-  <Card title="Practical Testing" icon="flask">
-    Validate choices through real-world usage rather than benchmarks alone.
-  </Card>
-  
-  <Card title="Iterative Improvement" icon="arrow-up">
-    Start simple and optimize based on actual performance and needs.
-  </Card>
-  
-  <Card title="Operational Balance" icon="scale-balanced">
-    Balance performance requirements with cost and complexity constraints.
-  </Card>
-</CardGroup>
-
-<Check>
-Remember: The best LLM choice is the one that consistently delivers the results you need within your operational constraints. Focus on understanding your requirements first, then select models that best match those needs.
-</Check>
-
-## Current Model Landscape (June 2025)
-
-<Warning>
-**Snapshot in Time**: The following model rankings represent current leaderboard standings as of June 2025, compiled from [LMSys Arena](https://arena.lmsys.org/), [Artificial Analysis](https://artificialanalysis.ai/), and other leading benchmarks. LLM performance, availability, and pricing change rapidly. Always conduct your own evaluations with your specific use cases and data.
-</Warning>
-
-### Leading Models by Category
-
-The tables below show a representative sample of current top-performing models across different categories, with guidance on their suitability for CrewAI agents:
-
-<Note>
-These tables/metrics showcase selected leading models in each category and are not exhaustive. Many excellent models exist beyond those listed here. The goal is to illustrate the types of capabilities to look for rather than provide a complete catalog.
-</Note>
-
-<Tabs>
-  <Tab title="Reasoning & Planning">
-    **Best for Manager LLMs and Complex Analysis**
-    
-    | Model | Intelligence Score | Cost ($/M tokens) | Speed | Best Use in CrewAI |
-    |:------|:------------------|:------------------|:------|:------------------|
-    | **o3** | 70 | $17.50 | Fast | Manager LLM for complex multi-agent coordination |
-    | **Gemini 2.5 Pro** | 69 | $3.44 | Fast | Strategic planning agents, research coordination |
-    | **DeepSeek R1** | 68 | $0.96 | Moderate | Cost-effective reasoning for budget-conscious crews |
-    | **Claude 4 Sonnet** | 53 | $6.00 | Fast | Analysis agents requiring nuanced understanding |
-    | **Qwen3 235B (Reasoning)** | 62 | $2.63 | Moderate | Open-source alternative for reasoning tasks |
-
-    These models excel at multi-step reasoning and are ideal for agents that need to develop strategies, coordinate other agents, or analyze complex information.
-  </Tab>
-  
-  <Tab title="Coding & Technical">
-    **Best for Development and Tool-Heavy Workflows**
-    
-    | Model | Coding Performance | Tool Use Score | Cost ($/M tokens) | Best Use in CrewAI |
-    |:------|:------------------|:---------------|:------------------|:------------------|
-    | **Claude 4 Sonnet** | Excellent | 72.7% | $6.00 | Primary coding agent, technical documentation |
-    | **Claude 4 Opus** | Excellent | 72.5% | $30.00 | Complex software architecture, code review |
-    | **DeepSeek V3** | Very Good | High | $0.48 | Cost-effective coding for routine development |
-    | **Qwen2.5 Coder 32B** | Very Good | Medium | $0.15 | Budget-friendly coding agent |
-    | **Llama 3.1 405B** | Good | 81.1% | $3.50 | Function calling LLM for tool-heavy workflows |
-
-    These models are optimized for code generation, debugging, and technical problem-solving, making them ideal for development-focused crews.
-  </Tab>
-  
-  <Tab title="Speed & Efficiency">
-    **Best for High-Throughput and Real-Time Applications**
-    
-    | Model | Speed (tokens/s) | Latency (TTFT) | Cost ($/M tokens) | Best Use in CrewAI |
-    |:------|:-----------------|:---------------|:------------------|:------------------|
-    | **Llama 4 Scout** | 2,600 | 0.33s | $0.27 | High-volume processing agents |
-    | **Gemini 2.5 Flash** | 376 | 0.30s | $0.26 | Real-time response agents |
-    | **DeepSeek R1 Distill** | 383 | Variable | $0.04 | Cost-optimized high-speed processing |
-    | **Llama 3.3 70B** | 2,500 | 0.52s | $0.60 | Balanced speed and capability |
-    | **Nova Micro** | High | 0.30s | $0.04 | Simple, fast task execution |
-
-    These models prioritize speed and efficiency, perfect for agents handling routine operations or requiring quick responses. **Pro tip**: Pairing these models with fast inference providers like Groq can achieve even better performance, especially for open-source models like Llama.
-  </Tab>
-  
-  <Tab title="Balanced Performance">
-    **Best All-Around Models for General Crews**
-    
-    | Model | Overall Score | Versatility | Cost ($/M tokens) | Best Use in CrewAI |
-    |:------|:--------------|:------------|:------------------|:------------------|
-    | **GPT-4.1** | 53 | Excellent | $3.50 | General-purpose crew LLM |
-    | **Claude 3.7 Sonnet** | 48 | Very Good | $6.00 | Balanced reasoning and creativity |
-    | **Gemini 2.0 Flash** | 48 | Good | $0.17 | Cost-effective general use |
-    | **Llama 4 Maverick** | 51 | Good | $0.37 | Open-source general purpose |
-    | **Qwen3 32B** | 44 | Good | $1.23 | Budget-friendly versatility |
-
-    These models offer good performance across multiple dimensions, suitable for crews with diverse task requirements.
-  </Tab>
-</Tabs>
-
-### Selection Framework for Current Models
-
-<AccordionGroup>
-  <Accordion title="High-Performance Crews" icon="rocket">
-    **When performance is the priority**: Use top-tier models like **o3**, **Gemini 2.5 Pro**, or **Claude 4 Sonnet** for manager LLMs and critical agents. These models excel at complex reasoning and coordination but come with higher costs.
-
-    **Strategy**: Implement a multi-model approach where premium models handle strategic thinking while efficient models handle routine operations.
-  </Accordion>
-  
-  <Accordion title="Cost-Conscious Crews" icon="dollar-sign">
-    **When budget is a primary constraint**: Focus on models like **DeepSeek R1**, **Llama 4 Scout**, or **Gemini 2.0 Flash**. These provide strong performance at significantly lower costs.
-
-    **Strategy**: Use cost-effective models for most agents, reserving premium models only for the most critical decision-making roles.
-  </Accordion>
-  
-  <Accordion title="Specialized Workflows" icon="screwdriver-wrench">
-    **For specific domain expertise**: Choose models optimized for your primary use case. **Claude 4** series for coding, **Gemini 2.5 Pro** for research, **Llama 405B** for function calling.
-
-    **Strategy**: Select models based on your crew's primary function, ensuring the core capability aligns with model strengths.
-  </Accordion>
-  
-  <Accordion title="Enterprise & Privacy" icon="shield">
-    **For data-sensitive operations**: Consider open-source models like **Llama 4** series, **DeepSeek V3**, or **Qwen3** that can be deployed locally while maintaining competitive performance.
-
-    **Strategy**: Deploy open-source models on private infrastructure, accepting potential performance trade-offs for data control.
-  </Accordion>
-  </AccordionGroup>
-
-### Key Considerations for Model Selection
-
-- **Performance Trends**: The current landscape shows strong competition between reasoning-focused models (o3, Gemini 2.5 Pro) and balanced models (Claude 4, GPT-4.1). Specialized models like DeepSeek R1 offer excellent cost-performance ratios.
-
-- **Speed vs. Intelligence Trade-offs**: Models like Llama 4 Scout prioritize speed (2,600 tokens/s) while maintaining reasonable intelligence, whereas models like o3 maximize reasoning capability at the cost of speed and price.
-
-- **Open Source Viability**: The gap between open-source and proprietary models continues to narrow, with models like Llama 4 Maverick and DeepSeek V3 offering competitive performance at attractive price points. Fast inference providers particularly shine with open-source models, often delivering better speed-to-cost ratios than proprietary alternatives.
-
-<Info>
-**Testing is Essential**: Leaderboard rankings provide general guidance, but your specific use case, prompting style, and evaluation criteria may produce different results. Always test candidate models with your actual tasks and data before making final decisions.
-</Info>
-
-### Practical Implementation Strategy
-
-<Steps>
-  <Step title="Start with Proven Models">
-    Begin with well-established models like **GPT-4.1**, **Claude 3.7 Sonnet**, or **Gemini 2.0 Flash** that offer good performance across multiple dimensions and have extensive real-world validation.
-  </Step>
-  
-  <Step title="Identify Specialized Needs">
-    Determine if your crew has specific requirements (coding, reasoning, speed) that would benefit from specialized models like **Claude 4 Sonnet** for development or **o3** for complex analysis. For speed-critical applications, consider fast inference providers like **Groq** alongside model selection.
-  </Step>
-  
-  <Step title="Implement Multi-Model Strategy">
-    Use different models for different agents based on their roles. High-capability models for managers and complex tasks, efficient models for routine operations.
-  </Step>
-  
-  <Step title="Monitor and Optimize">
-    Track performance metrics relevant to your use case and be prepared to adjust model selections as new models are released or pricing changes.
-  </Step>
-</Steps> 

docs/en/learn/overview.mdx

@@ -1,158 +0,0 @@
----
-title: "Overview"
-description: "Learn how to build, customize, and optimize your CrewAI applications with comprehensive guides and tutorials"
-icon: "face-smile"
----
-
-## Learn CrewAI
-
-This section provides comprehensive guides and tutorials to help you master CrewAI, from basic concepts to advanced techniques. Whether you're just getting started or looking to optimize your existing implementations, these resources will guide you through every aspect of building powerful AI agent workflows.
-
-## Getting Started Guides
-
-### Core Concepts
-<CardGroup cols={2}>
-  <Card title="Sequential Process" icon="list-ol" href="/en/learn/sequential-process">
-    Learn how to execute tasks in a sequential order for structured workflows.
-  </Card>
-
-  <Card title="Hierarchical Process" icon="sitemap" href="/en/learn/hierarchical-process">
-    Implement hierarchical task execution with manager agents overseeing workflows.
-  </Card>
-
-  <Card title="Conditional Tasks" icon="code-branch" href="/en/learn/conditional-tasks">
-    Create dynamic workflows with conditional task execution based on outcomes.
-  </Card>
-
-  <Card title="Async Kickoff" icon="bolt" href="/en/learn/kickoff-async">
-    Execute crews asynchronously for improved performance and concurrency.
-  </Card>
-</CardGroup>
-
-### Agent Development
-<CardGroup cols={2}>
-  <Card title="Customizing Agents" icon="user-gear" href="/en/learn/customizing-agents">
-    Learn how to customize agent behavior, roles, and capabilities.
-  </Card>
-
-  <Card title="Coding Agents" icon="code" href="/en/learn/coding-agents">
-    Build agents that can write, execute, and debug code automatically.
-  </Card>
-
-  <Card title="Multimodal Agents" icon="images" href="/en/learn/multimodal-agents">
-    Create agents that can process text, images, and other media types.
-  </Card>
-
-  <Card title="Custom Manager Agent" icon="user-tie" href="/en/learn/custom-manager-agent">
-    Implement custom manager agents for complex hierarchical workflows.
-  </Card>
-</CardGroup>
-
-## Advanced Features
-
-### Workflow Control
-<CardGroup cols={2}>
-  <Card title="Human in the Loop" icon="user-check" href="/en/learn/human-in-the-loop">
-    Integrate human oversight and intervention into agent workflows.
-  </Card>
-
-  <Card title="Human Input on Execution" icon="hand-paper" href="/en/learn/human-input-on-execution">
-    Allow human input during task execution for dynamic decision making.
-  </Card>
-
-  <Card title="Replay Tasks" icon="rotate-left" href="/en/learn/replay-tasks-from-latest-crew-kickoff">
-    Replay and resume tasks from previous crew executions.
-  </Card>
-
-  <Card title="Kickoff for Each" icon="repeat" href="/en/learn/kickoff-for-each">
-    Execute crews multiple times with different inputs efficiently.
-  </Card>
-</CardGroup>
-
-### Customization & Integration
-<CardGroup cols={2}>
-  <Card title="Custom LLM" icon="brain" href="/en/learn/custom-llm">
-    Integrate custom language models and providers with CrewAI.
-  </Card>
-
-  <Card title="LLM Connections" icon="link" href="/en/learn/llm-connections">
-    Configure and manage connections to various LLM providers.
-  </Card>
-
-  <Card title="Create Custom Tools" icon="wrench" href="/en/learn/create-custom-tools">
-    Build custom tools to extend agent capabilities.
-  </Card>
-
-  <Card title="Using Annotations" icon="at" href="/en/learn/using-annotations">
-    Use Python annotations for cleaner, more maintainable code.
-  </Card>
-</CardGroup>
-
-## Specialized Applications
-
-### Content & Media
-<CardGroup cols={2}>
-  <Card title="DALL-E Image Generation" icon="image" href="/en/learn/dalle-image-generation">
-    Generate images using DALL-E integration with your agents.
-  </Card>
-
-  <Card title="Bring Your Own Agent" icon="user-plus" href="/en/learn/bring-your-own-agent">
-    Integrate existing agents and models into CrewAI workflows.
-  </Card>
-</CardGroup>
-
-### Tool Management
-<CardGroup cols={2}>
-  <Card title="Force Tool Output as Result" icon="hammer" href="/en/learn/force-tool-output-as-result">
-    Configure tools to return their output directly as task results.
-  </Card>
-</CardGroup>
-
-## Learning Path Recommendations
-
-### For Beginners
-1. Start with **Sequential Process** to understand basic workflow execution
-2. Learn **Customizing Agents** to create effective agent configurations
-3. Explore **Create Custom Tools** to extend functionality
-4. Try **Human in the Loop** for interactive workflows
-
-### For Intermediate Users
-1. Master **Hierarchical Process** for complex multi-agent systems
-2. Implement **Conditional Tasks** for dynamic workflows
-3. Use **Async Kickoff** for performance optimization
-4. Integrate **Custom LLM** for specialized models
-
-### For Advanced Users
-1. Build **Multimodal Agents** for complex media processing
-2. Create **Custom Manager Agents** for sophisticated orchestration
-3. Implement **Bring Your Own Agent** for hybrid systems
-4. Use **Replay Tasks** for robust error recovery
-
-## Best Practices
-
-### Development
-- **Start Simple**: Begin with basic sequential workflows before adding complexity
-- **Test Incrementally**: Test each component before integrating into larger systems
-- **Use Annotations**: Leverage Python annotations for cleaner, more maintainable code
-- **Custom Tools**: Build reusable tools that can be shared across different agents
-
-### Production
-- **Error Handling**: Implement robust error handling and recovery mechanisms
-- **Performance**: Use async execution and optimize LLM calls for better performance
-- **Monitoring**: Integrate observability tools to track agent performance
-- **Human Oversight**: Include human checkpoints for critical decisions
-
-### Optimization
-- **Resource Management**: Monitor and optimize token usage and API costs
-- **Workflow Design**: Design workflows that minimize unnecessary LLM calls
-- **Tool Efficiency**: Create efficient tools that provide maximum value with minimal overhead
-- **Iterative Improvement**: Use feedback and metrics to continuously improve agent performance
-
-## Getting Help
-
-- **Documentation**: Each guide includes detailed examples and explanations
-- **Community**: Join the [CrewAI Forum](https://community.crewai.com) for discussions and support
-- **Examples**: Check the Examples section for complete working implementations
-- **Support**: Contact [support@crewai.com](mailto:support@crewai.com) for technical assistance
-
-Start with the guides that match your current needs and gradually explore more advanced topics as you become comfortable with the fundamentals.

docs/en/learn/using-annotations.mdx

@@ -1,141 +0,0 @@
----
-title: "Using Annotations in crew.py"
-description: "Learn how to use annotations to properly structure agents, tasks, and components in CrewAI"
-icon: "at"
----
-
-This guide explains how to use annotations to properly reference **agents**, **tasks**, and other components in the `crew.py` file.
-
-## Introduction
-
-Annotations in the CrewAI framework are used to decorate classes and methods, providing metadata and functionality to various components of your crew. These annotations help in organizing and structuring your code, making it more readable and maintainable.
-
-## Available Annotations
-
-The CrewAI framework provides the following annotations:
-
-- `@CrewBase`: Used to decorate the main crew class.
-- `@agent`: Decorates methods that define and return Agent objects.
-- `@task`: Decorates methods that define and return Task objects.
-- `@crew`: Decorates the method that creates and returns the Crew object.
-- `@llm`: Decorates methods that initialize and return Language Model objects.
-- `@tool`: Decorates methods that initialize and return Tool objects.
-- `@callback`: Used for defining callback methods.
-- `@output_json`: Used for methods that output JSON data.
-- `@output_pydantic`: Used for methods that output Pydantic models.
-- `@cache_handler`: Used for defining cache handling methods.
-
-## Usage Examples
-
-Let's go through examples of how to use these annotations:
-
-### 1. Crew Base Class
-
-```python
-@CrewBase
-class LinkedinProfileCrew():
-    """LinkedinProfile crew"""
-    agents_config = 'config/agents.yaml'
-    tasks_config = 'config/tasks.yaml'
-```
-
-The `@CrewBase` annotation is used to decorate the main crew class. This class typically contains configurations and methods for creating agents, tasks, and the crew itself.
-
-### 2. Tool Definition
-
-```python
-@tool
-def myLinkedInProfileTool(self):
-    return LinkedInProfileTool()
-```
-
-The `@tool` annotation is used to decorate methods that return tool objects. These tools can be used by agents to perform specific tasks.
-
-### 3. LLM Definition
-
-```python
-@llm
-def groq_llm(self):
-    api_key = os.getenv('api_key')
-    return ChatGroq(api_key=api_key, temperature=0, model_name="mixtral-8x7b-32768")
-```
-
-The `@llm` annotation is used to decorate methods that initialize and return Language Model objects. These LLMs are used by agents for natural language processing tasks.
-
-### 4. Agent Definition
-
-```python
-@agent
-def researcher(self) -> Agent:
-    return Agent(
-        config=self.agents_config['researcher']
-    )
-```
-
-The `@agent` annotation is used to decorate methods that define and return Agent objects.
-
-### 5. Task Definition
-
-```python
-@task
-def research_task(self) -> Task:
-    return Task(
-        config=self.tasks_config['research_linkedin_task'],
-        agent=self.researcher()
-    )
-```
-
-The `@task` annotation is used to decorate methods that define and return Task objects. These methods specify the task configuration and the agent responsible for the task.
-
-### 6. Crew Creation
-
-```python
-@crew
-def crew(self) -> Crew:
-    """Creates the LinkedinProfile crew"""
-    return Crew(
-        agents=self.agents,
-        tasks=self.tasks,
-        process=Process.sequential,
-        verbose=True
-    )
-``` 
-
-The `@crew` annotation is used to decorate the method that creates and returns the `Crew` object. This method assembles all the components (agents and tasks) into a functional crew.
-
-## YAML Configuration
-
-The agent configurations are typically stored in a YAML file. Here's an example of how the `agents.yaml` file might look for the researcher agent:
-
-```yaml
-researcher:
-    role: >
-        LinkedIn Profile Senior Data Researcher
-    goal: >
-        Uncover detailed LinkedIn profiles based on provided name {name} and domain {domain}
-        Generate a Dall-E image based on domain {domain}
-    backstory: >
-        You're a seasoned researcher with a knack for uncovering the most relevant LinkedIn profiles.
-        Known for your ability to navigate LinkedIn efficiently, you excel at gathering and presenting
-        professional information clearly and concisely.
-    allow_delegation: False
-    verbose: True
-    llm: groq_llm
-    tools:
-        - myLinkedInProfileTool
-        - mySerperDevTool
-        - myDallETool
-```
-
-This YAML configuration corresponds to the researcher agent defined in the `LinkedinProfileCrew` class. The configuration specifies the agent's role, goal, backstory, and other properties such as the LLM and tools it uses.
-
-Note how the `llm` and `tools` in the YAML file correspond to the methods decorated with `@llm` and `@tool` in the Python class.
-
-## Best Practices
-
-- **Consistent Naming**: Use clear and consistent naming conventions for your methods. For example, agent methods could be named after their roles (e.g., researcher, reporting_analyst).
-- **Environment Variables**: Use environment variables for sensitive information like API keys.
-- **Flexibility**: Design your crew to be flexible by allowing easy addition or removal of agents and tasks.
-- **YAML-Code Correspondence**: Ensure that the names and structures in your YAML files correspond correctly to the decorated methods in your Python code.
-
-By following these guidelines and properly using annotations, you can create well-structured and maintainable crews using the CrewAI framework. 

docs/en/mcp/multiple-servers.mdx

@@ -1,64 +0,0 @@
----
-title: Connecting to Multiple MCP Servers
-description: Learn how to use MCPServerAdapter in CrewAI to connect to multiple MCP servers simultaneously and aggregate their tools.
-icon: layer-group
----
-
-## Overview
-
-`MCPServerAdapter` in `crewai-tools` allows you to connect to multiple MCP servers concurrently. This is useful when your agents need to access tools distributed across different services or environments. The adapter aggregates tools from all specified servers, making them available to your CrewAI agents.
-
-## Configuration
-
-To connect to multiple servers, you provide a list of server parameter dictionaries to `MCPServerAdapter`. Each dictionary in the list should define the parameters for one MCP server.
-
-Supported transport types for each server in the list include `stdio`, `sse`, and `streamable-http`.
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters # Needed for Stdio example
-
-# Define parameters for multiple MCP servers
-server_params_list = [
-    # Streamable HTTP Server
-    {
-        "url": "http://localhost:8001/mcp", 
-        "transport": "streamable-http"
-    },
-    # SSE Server
-    {
-        "url": "http://localhost:8000/sse",
-        "transport": "sse"
-    },
-    # StdIO Server
-    StdioServerParameters(
-        command="python3",
-        args=["servers/your_stdio_server.py"],
-        env={"UV_PYTHON": "3.12", **os.environ},
-    )
-]
-
-try:
-    with MCPServerAdapter(server_params_list) as aggregated_tools:
-        print(f"Available aggregated tools: {[tool.name for tool in aggregated_tools]}")
-
-        multi_server_agent = Agent(
-            role="Versatile Assistant",
-            goal="Utilize tools from local Stdio, remote SSE, and remote HTTP MCP servers.",
-            backstory="An AI agent capable of leveraging a diverse set of tools from multiple sources.",
-            tools=aggregated_tools, # All tools are available here
-            verbose=True,
-        )
-
-        ... # Your other agent, tasks, and crew code here
-
-except Exception as e:
-    print(f"Error connecting to or using multiple MCP servers (Managed): {e}")
-    print("Ensure all MCP servers are running and accessible with correct configurations.")
-
-```
-
-## Connection Management
-
-When using the context manager (`with` statement), `MCPServerAdapter` handles the lifecycle (start and stop) of all connections to the configured MCP servers. This simplifies resource management and ensures that all connections are properly closed when the context is exited.

docs/en/mcp/overview.mdx

@@ -1,264 +0,0 @@
----
-title: 'MCP Servers as Tools in CrewAI'
-description: 'Learn how to integrate MCP servers as tools in your CrewAI agents using the `crewai-tools` library.'
-icon: plug
----
-
-## Overview
-
-The [Model Context Protocol](https://modelcontextprotocol.io/introduction) (MCP) provides a standardized way for AI agents to provide context to LLMs by communicating with external services, known as MCP Servers.
-The `crewai-tools` library extends CrewAI's capabilities by allowing you to seamlessly integrate tools from these MCP servers into your agents.
-This gives your crews access to a vast ecosystem of functionalities.
-
-We currently support the following transport mechanisms:
-
-- **Stdio**: for local servers (communication via standard input/output between processes on the same machine)
-- **Server-Sent Events (SSE)**: for remote servers (unidirectional, real-time data streaming from server to client over HTTP)
-- **Streamable HTTP**: for remote servers (flexible, potentially bi-directional communication over HTTP, often utilizing SSE for server-to-client streams)
-
-## Video Tutorial
-Watch this video tutorial for a comprehensive guide on MCP integration with CrewAI:
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/TpQ45lAZh48"
-  title="CrewAI MCP Integration Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-## Installation
-
-Before you start using MCP with `crewai-tools`, you need to install the `mcp` extra `crewai-tools` dependency with the following command:
-
-```shell
-uv pip install 'crewai-tools[mcp]'
-```
-
-## Key Concepts & Getting Started
-
-The `MCPServerAdapter` class from `crewai-tools` is the primary way to connect to an MCP server and make its tools available to your CrewAI agents. It supports different transport mechanisms and simplifies connection management.
-
-Using a Python context manager (`with` statement) is the **recommended approach** for `MCPServerAdapter`. It automatically handles starting and stopping the connection to the MCP server.
-
-## Connection Configuration
-
-The `MCPServerAdapter` supports several configuration options to customize the connection behavior:
-
-- **`connect_timeout`** (optional): Maximum time in seconds to wait for establishing a connection to the MCP server. Defaults to 30 seconds if not specified. This is particularly useful for remote servers that may have variable response times.
-
-```python
-# Example with custom connection timeout
-with MCPServerAdapter(server_params, connect_timeout=60) as tools:
-    # Connection will timeout after 60 seconds if not established
-    pass
-```
-
-```python
-from crewai import Agent
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters # For Stdio Server
-
-# Example server_params (choose one based on your server type):
-# 1. Stdio Server:
-server_params=StdioServerParameters(
-    command="python3",
-    args=["servers/your_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-# 2. SSE Server:
-server_params = {
-    "url": "http://localhost:8000/sse",
-    "transport": "sse"
-}
-
-# 3. Streamable HTTP Server:
-server_params = {
-    "url": "http://localhost:8001/mcp",
-    "transport": "streamable-http"
-}
-
-# Example usage (uncomment and adapt once server_params is set):
-with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=mcp_tools, # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-This general pattern shows how to integrate tools. For specific examples tailored to each transport, refer to the detailed guides below.
-
-## Filtering Tools
-
-There are two ways to filter tools:
-
-1. Accessing a specific tool using dictionary-style indexing.
-2. Pass a list of tool names to the `MCPServerAdapter` constructor.
-
-### Accessing a specific tool using dictionary-style indexing.
-
-```python
-with MCPServerAdapter(server_params, connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=[mcp_tools["tool_name"]], # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-
-### Pass a list of tool names to the `MCPServerAdapter` constructor.
-
-```python
-with MCPServerAdapter(server_params, "tool_name", connect_timeout=60) as mcp_tools:
-    print(f"Available tools: {[tool.name for tool in mcp_tools]}")
-
-    my_agent = Agent(
-        role="MCP Tool User",
-        goal="Utilize tools from an MCP server.",
-        backstory="I can connect to MCP servers and use their tools.",
-        tools=mcp_tools, # Pass the loaded tools to your agent
-        reasoning=True,
-        verbose=True
-    )
-    # ... rest of your crew setup ...
-```
-
-## Using with CrewBase
-
-To use MCPServer tools within a CrewBase class, use the `mcp_tools` method. Server configurations should be provided via the mcp_server_params attribute. You can pass either a single configuration or a list of multiple server configurations.
-
-```python
-@CrewBase
-class CrewWithMCP:
-  # ... define your agents and tasks config file ...
-
-  mcp_server_params = [
-    # Streamable HTTP Server
-    {
-        "url": "http://localhost:8001/mcp",
-        "transport": "streamable-http"
-    },
-    # SSE Server
-    {
-        "url": "http://localhost:8000/sse",
-        "transport": "sse"
-    },
-    # StdIO Server
-    StdioServerParameters(
-        command="python3",
-        args=["servers/your_stdio_server.py"],
-        env={"UV_PYTHON": "3.12", **os.environ},
-    )
-  ]
-
-  @agent
-  def your_agent(self):
-      return Agent(config=self.agents_config["your_agent"], tools=self.get_mcp_tools()) # get all available tools
-
-    # ... rest of your crew setup ...
-```
-
-You can filter which tools are available to your agent by passing a list of tool names to the `get_mcp_tools` method.
-
-```python
-@agent
-def another_agent(self):
-    return Agent(
-      config=self.agents_config["your_agent"],
-      tools=self.get_mcp_tools("tool_1", "tool_2") # get specific tools
-    )
-```
-
-## Explore MCP Integrations
-
-<CardGroup cols={2}>
-  <Card
-    title="Stdio Transport"
-    icon="server"
-    href="/en/mcp/stdio"
-    color="#3B82F6"
-  >
-    Connect to local MCP servers via standard input/output. Ideal for scripts and local executables.
-  </Card>
-  <Card
-    title="SSE Transport"
-    icon="wifi"
-    href="/en/mcp/sse"
-    color="#10B981"
-  >
-    Integrate with remote MCP servers using Server-Sent Events for real-time data streaming.
-  </Card>
-  <Card
-    title="Streamable HTTP Transport"
-    icon="globe"
-    href="/en/mcp/streamable-http"
-    color="#F59E0B"
-  >
-    Utilize flexible Streamable HTTP for robust communication with remote MCP servers.
-  </Card>
-  <Card
-    title="Connecting to Multiple Servers"
-    icon="layer-group"
-    href="/en/mcp/multiple-servers"
-    color="#8B5CF6"
-  >
-    Aggregate tools from several MCP servers simultaneously using a single adapter.
-  </Card>
-  <Card
-    title="Security Considerations"
-    icon="lock"
-    href="/en/mcp/security"
-    color="#EF4444"
-  >
-    Review important security best practices for MCP integration to keep your agents safe.
-  </Card>
-</CardGroup>
-
-Checkout this repository for full demos and examples of MCP integration with CrewAI! 👇
-
-<Card
-title="GitHub Repository"
-icon="github"
-href="https://github.com/tonykipkemboi/crewai-mcp-demo"
-target="_blank"
->
-CrewAI MCP Demo
-</Card>
-
-## Staying Safe with MCP
-<Warning>
-Always ensure that you trust an MCP Server before using it.
-</Warning>
-
-#### Security Warning: DNS Rebinding Attacks
-SSE transports can be vulnerable to DNS rebinding attacks if not properly secured.
-To prevent this:
-
-1. **Always validate Origin headers** on incoming SSE connections to ensure they come from expected sources
-2. **Avoid binding servers to all network interfaces** (0.0.0.0) when running locally - bind only to localhost (127.0.0.1) instead
-3. **Implement proper authentication** for all SSE connections
-
-Without these protections, attackers could use DNS rebinding to interact with local MCP servers from remote websites.
-
-For more details, see the [Anthropic's MCP Transport Security docs](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).
-
-### Limitations
-*   **Supported Primitives**: Currently, `MCPServerAdapter` primarily supports adapting MCP `tools`.
-Other MCP primitives like `prompts` or `resources` are not directly integrated as CrewAI components through this adapter at this time.
-*   **Output Handling**: The adapter typically processes the primary text output from an MCP tool (e.g., `.content[0].text`). Complex or multi-modal outputs might require custom handling if not fitting this pattern.

docs/en/mcp/security.mdx

@@ -1,166 +0,0 @@
----
-title: MCP Security Considerations
-description: Learn about important security best practices when integrating MCP servers with your CrewAI agents.
-icon: lock
----
-
-## Overview
-
-<Warning>
-The most critical aspect of MCP security is **trust**. You should **only** connect your CrewAI agents to MCP servers that you fully trust.
-</Warning>
-
-When integrating external services like MCP (Model Context Protocol) servers into your CrewAI agents, security is paramount. 
-MCP servers can execute code, access data, or interact with other systems based on the tools they expose. 
-It's crucial to understand the implications and follow best practices to protect your applications and data.
-
-### Risks
-
-- Execute arbitrary code on the machine where the agent is running (especially with `Stdio` transport if the server can control the command executed).
-- Expose sensitive data from your agent or its environment.
-- Manipulate your agent's behavior in unintended ways, including making unauthorized API calls on your behalf.
-- Hijack your agent's reasoning process through sophisticated prompt injection techniques (see below).
-
-### 1. Trusting MCP Servers
-
-<Warning>
-**Only connect to MCP servers that you trust.**
-</Warning>
-
-Before configuring `MCPServerAdapter` to connect to an MCP server, ensure you know:
-- **Who operates the server?** Is it a known, reputable service, or an internal server under your control?
-- **What tools does it expose?** Understand the capabilities of the tools. Could they be misused if an attacker gained control or if the server itself is malicious?
-- **What data does it access or process?** Be aware of any sensitive information that might be sent to or handled by the MCP server.
-
-Avoid connecting to unknown or unverified MCP servers, especially if your agents handle sensitive tasks or data.
-
-### 2. Secure Prompt Injection via Tool Metadata: The "Model Control Protocol" Risk
-
-A significant and subtle risk is the potential for prompt injection through tool metadata. Here's how it works:
-
-1.  When your CrewAI agent connects to an MCP server, it typically requests a list of available tools.
-2.  The MCP server responds with metadata for each tool, including its name, description, and parameter descriptions.
-3.  Your agent's underlying Language Model (LLM) uses this metadata to understand how and when to use the tools. This metadata is often incorporated into the LLM's system prompt or context.
-4.  A malicious MCP server can craft its tool metadata (names, descriptions) to include hidden or overt instructions. These instructions can act as a prompt injection, effectively telling your LLM to behave in a certain way, reveal sensitive information, or perform malicious actions.
-
-**Crucially, this attack can occur simply by connecting to a malicious server and listing its tools, even if your agent never explicitly decides to *use* any of those tools.** The mere exposure to the malicious metadata can be enough to compromise the agent's behavior.
-
-**Mitigation:**
-
-*   **Extreme Caution with Untrusted Servers:** Reiterate: *Do not connect to MCP servers you do not fully trust.* The risk of metadata injection makes this paramount.
-
-### Stdio Transport Security
-
-Stdio (Standard Input/Output) transport is typically used for local MCP servers running on the same machine as your CrewAI application.
-
-- **Process Isolation**: While generally safer as it doesn't involve network exposure by default, ensure the script or command run by `StdioServerParameters` is from a trusted source and has appropriate file system permissions. A malicious Stdio server script could still harm your local system.
-- **Input Sanitization**: If your Stdio server script takes complex inputs derived from agent interactions, ensure the script itself sanitizes these inputs to prevent command injection or other vulnerabilities within the script's logic.
-- **Resource Limits**: Be mindful that a local Stdio server process consumes local resources (CPU, memory). Ensure it's well-behaved and won't exhaust system resources.
-
-### Confused Deputy Attacks
-
-The [Confused Deputy Problem](https://en.wikipedia.org/wiki/Confused_deputy_problem) is a classic security vulnerability that can manifest in MCP integrations, especially when an MCP server acts as a proxy to other third-party services (e.g., Google Calendar, GitHub) that use OAuth 2.0 for authorization.
-
-**Scenario:**
-
-1.  An MCP server (let's call it `MCP-Proxy`) allows your agent to interact with `ThirdPartyAPI`.
-2.  `MCP-Proxy` uses its own single, static `client_id` when talking to `ThirdPartyAPI`'s authorization server.
-3.  You, as the user, legitimately authorize `MCP-Proxy` to access `ThirdPartyAPI` on your behalf. During this, `ThirdPartyAPI`'s auth server might set a cookie in your browser indicating your consent for `MCP-Proxy`'s `client_id`.
-4.  An attacker crafts a malicious link. This link initiates an OAuth flow with `MCP-Proxy`, but is designed to trick `ThirdPartyAPI`'s auth server.
-5.  If you click this link, and `ThirdPartyAPI`'s auth server sees your existing consent cookie for `MCP-Proxy`'s `client_id`, it might *skip* asking for your consent again.
-6.  `MCP-Proxy` might then be tricked into forwarding an authorization code (for `ThirdPartyAPI`) to the attacker, or an MCP authorization code that the attacker can use to impersonate you to `MCP-Proxy`.
-
-**Mitigation (Primarily for MCP Server Developers):**
-
-*   MCP proxy servers using static client IDs for downstream services **must** obtain explicit user consent for *each client application or agent* connecting to them *before* initiating an OAuth flow with the third-party service. This means `MCP-Proxy` itself should show a consent screen.
-
-**CrewAI User Implication:**
-
-*   Be cautious if an MCP server redirects you for multiple OAuth authentications, especially if it seems unexpected or if the permissions requested are overly broad.
-*   Prefer MCP servers that clearly delineate their own identity versus the third-party services they might proxy.
-
-### Remote Transport Security (SSE & Streamable HTTP)
-
-When connecting to remote MCP servers via Server-Sent Events (SSE) or Streamable HTTP, standard web security practices are essential.
-
-### SSE Security Considerations
-
-### a. DNS Rebinding Attacks (Especially for SSE)
-
-<Critical>
-**Protect against DNS Rebinding Attacks.**
-</Critical>
-
-DNS rebinding allows an attacker-controlled website to bypass the same-origin policy and make requests to servers on the user's local network (e.g., `localhost`) or intranet. This is particularly risky if you run an MCP server locally (e.g., for development) and an agent in a browser-like environment (though less common for typical CrewAI backend setups) or if the MCP server is on an internal network.
-
-**Mitigation Strategies for MCP Server Implementers:**
-- **Validate `Origin` and `Host` Headers**: MCP servers (especially SSE ones) should validate the `Origin` and/or `Host` HTTP headers to ensure requests are coming from expected domains/clients.
-- **Bind to `localhost` (127.0.0.1)**: When running MCP servers locally for development, bind them to `127.0.0.1` instead of `0.0.0.0`. This prevents them from being accessible from other machines on the network.
-- **Authentication**: Require authentication for all connections to your MCP server if it's not intended for public anonymous access.
-
-### b. Use HTTPS
-
-- **Encrypt Data in Transit**: Always use HTTPS (HTTP Secure) for the URLs of remote MCP servers. This encrypts the communication between your CrewAI application and the MCP server, protecting against eavesdropping and man-in-the-middle attacks. `MCPServerAdapter` will respect the scheme (`http` or `https`) provided in the URL.
-
-### c. Token Passthrough (Anti-Pattern)
-
-This is primarily a concern for MCP server developers but understanding it helps in choosing secure servers.
-
-"Token passthrough" is when an MCP server accepts an access token from your CrewAI agent (which might be a token for a *different* service, say `ServiceA`) and simply passes it through to another downstream API (`ServiceB`) without proper validation. Specifically, `ServiceB` (or the MCP server itself) should only accept tokens that were explicitly issued *for them* (i.e., the 'audience' claim in the token matches the server/service).
-
-**Risks:**
-
-*   Bypasses security controls (like rate limiting or fine-grained permissions) on the MCP server or the downstream API.
-*   Breaks audit trails and accountability.
-*   Allows misuse of stolen tokens.
-
-**Mitigation (For MCP Server Developers):**
-
-*   MCP servers **MUST NOT** accept tokens that were not explicitly issued for them. They must validate the token's audience claim.
-
-**CrewAI User Implication:**
-
-*   While not directly controllable by the user, this highlights the importance of connecting to well-designed MCP servers that adhere to security best practices.
-
-#### Authentication and Authorization
-
-- **Verify Identity**: If the MCP server provides sensitive tools or access to private data, it MUST implement strong authentication mechanisms to verify the identity of the client (your CrewAI application). This could involve API keys, OAuth tokens, or other standard methods.
-- **Principle of Least Privilege**: Ensure the credentials used by `MCPServerAdapter` (if any) have only the necessary permissions to access the required tools.
-
-### d. Input Validation and Sanitization
-
-- **Input Validation is Critical**: MCP servers **must** rigorously validate all inputs received from agents *before* processing them or passing them to tools. This is a primary defense against many common vulnerabilities:
-    - **Command Injection:** If a tool constructs shell commands, SQL queries, or other interpreted language statements based on input, the server must meticulously sanitize this input to prevent malicious commands from being injected and executed.
-    - **Path Traversal:** If a tool accesses files based on input parameters, the server must validate and sanitize these paths to prevent access to unauthorized files or directories (e.g., by blocking `../` sequences).
-    - **Data Type & Range Checks:** Servers must ensure that input data conforms to the expected data types (e.g., string, number, boolean) and falls within acceptable ranges or adheres to defined formats (e.g., regex for URLs).
-    - **JSON Schema Validation:** All tool parameters should be strictly validated against their defined JSON schema. This helps catch malformed requests early.
-- **Client-Side Awareness**: While server-side validation is paramount, as a CrewAI user, be mindful of the data your agents are constructed to send to MCP tools, especially if interacting with less-trusted or new MCP servers.
-
-### e. Rate Limiting and Resource Management
-
-- **Prevent Abuse**: MCP servers should implement rate limiting to prevent abuse, whether intentional (Denial of Service attacks) or unintentional (e.g., a misconfigured agent making too many requests).
-- **Client-Side Retries**: Implement sensible retry logic in your CrewAI tasks if transient network issues or server rate limits are expected, but avoid aggressive retries that could exacerbate server load.
-
-## 4. Secure MCP Server Implementation Advice (For Developers)
-
-If you are developing an MCP server that CrewAI agents might connect to, consider these best practices in addition to the points above:
-
-- **Follow Secure Coding Practices**: Adhere to standard secure coding principles for your chosen language and framework (e.g., OWASP Top 10).
-- **Principle of Least Privilege**: Ensure the process running the MCP server (especially for `Stdio`) has only the minimum necessary permissions. Tools themselves should also operate with the least privilege required to perform their function.
-- **Dependency Management**: Keep all server-side dependencies, including operating system packages, language runtimes, and third-party libraries, up-to-date to patch known vulnerabilities. Use tools to scan for vulnerable dependencies.
-- **Secure Defaults**: Design your server and its tools to be secure by default. For example, features that could be risky should be off by default or require explicit opt-in with clear warnings.
-- **Access Control for Tools**: Implement robust mechanisms to control which authenticated and authorized agents or users can access specific tools, especially those that are powerful, sensitive, or incur costs.
-- **Secure Error Handling**: Servers should not expose detailed internal error messages, stack traces, or debugging information to the client, as these can reveal internal workings or potential vulnerabilities. Log errors comprehensively on the server-side for diagnostics.
-- **Comprehensive Logging and Monitoring**: Implement detailed logging of security-relevant events (e.g., authentication attempts, tool invocations, errors, authorization changes). Monitor these logs for suspicious activity or abuse patterns.
-- **Adherence to MCP Authorization Spec**: If implementing authentication and authorization, strictly follow the [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) and relevant [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700).
-- **Regular Security Audits**: If your MCP server handles sensitive data, performs critical operations, or is publicly exposed, consider periodic security audits by qualified professionals.
-
-## 5. Further Reading
-
-For more detailed information on MCP security, refer to the official documentation:
-- **[MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
-
-By understanding these security considerations and implementing best practices, you can safely leverage the power of MCP servers in your CrewAI projects. 
-These are by no means exhaustive, but they cover the most common and critical security concerns. 
-The threats will continue to evolve, so it's important to stay informed and adapt your security measures accordingly.
-

docs/en/mcp/sse.mdx

@@ -1,150 +0,0 @@
----
-title: SSE Transport
-description: Learn how to connect CrewAI to remote MCP servers using Server-Sent Events (SSE) for real-time communication.
-icon: wifi
----
-
-## Overview
-
-Server-Sent Events (SSE) provide a standard way for a web server to send updates to a client over a single, long-lived HTTP connection. In the context of MCP, SSE is used for remote servers to stream data (like tool responses) to your CrewAI application in real-time.
-
-## Key Concepts
-
-- **Remote Servers**: SSE is suitable for MCP servers hosted remotely.
-- **Unidirectional Stream**: Typically, SSE is a one-way communication channel from server to client.
-- **`MCPServerAdapter` Configuration**: For SSE, you'll provide the server's URL and specify the transport type.
-
-## Connecting via SSE
-
-You can connect to an SSE-based MCP server using two main approaches for managing the connection lifecycle:
-
-### 1. Fully Managed Connection (Recommended)
-
-Using a Python context manager (`with` statement) is the recommended approach. It automatically handles establishing and closing the connection to the SSE MCP server.
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-
-server_params = {
-    "url": "http://localhost:8000/sse", # Replace with your actual SSE server URL
-    "transport": "sse" 
-}
-
-# Using MCPServerAdapter with a context manager
-try:
-    with MCPServerAdapter(server_params) as tools:
-        print(f"Available tools from SSE MCP server: {[tool.name for tool in tools]}")
-
-        # Example: Using a tool from the SSE MCP server
-        sse_agent = Agent(
-            role="Remote Service User",
-            goal="Utilize a tool provided by a remote SSE MCP server.",
-            backstory="An AI agent that connects to external services via SSE.",
-            tools=tools,
-            reasoning=True,
-            verbose=True,
-        )
-
-        sse_task = Task(
-            description="Fetch real-time stock updates for 'AAPL' using an SSE tool.",
-            expected_output="The latest stock price for AAPL.",
-            agent=sse_agent,
-            markdown=True
-        )
-
-        sse_crew = Crew(
-            agents=[sse_agent],
-            tasks=[sse_task],
-            verbose=True,
-            process=Process.sequential
-        )
-        
-        if tools: # Only kickoff if tools were loaded
-            result = sse_crew.kickoff() # Add inputs={'stock_symbol': 'AAPL'} if tool requires it
-            print("\nCrew Task Result (SSE - Managed):\n", result)
-        else:
-            print("Skipping crew kickoff as tools were not loaded (check server connection).")
-
-except Exception as e:
-    print(f"Error connecting to or using SSE MCP server (Managed): {e}")
-    print("Ensure the SSE MCP server is running and accessible at the specified URL.")
-
-```
-
-<Note>
-Replace `"http://localhost:8000/sse"` with the actual URL of your SSE MCP server.
-</Note>
-
-### 2. Manual Connection Lifecycle
-
-If you need finer-grained control, you can manage the `MCPServerAdapter` connection lifecycle manually.
-
-<Info>
-You **MUST** call `mcp_server_adapter.stop()` to ensure the connection is closed and resources are released. Using a `try...finally` block is highly recommended.
-</Info>
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-
-server_params = {
-    "url": "http://localhost:8000/sse", # Replace with your actual SSE server URL
-    "transport": "sse"
-}
-
-mcp_server_adapter = None 
-try:
-    mcp_server_adapter = MCPServerAdapter(server_params)
-    mcp_server_adapter.start()
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual SSE): {[tool.name for tool in tools]}")
-
-    manual_sse_agent = Agent(
-        role="Remote Data Analyst",
-        goal="Analyze data fetched from a remote SSE MCP server using manual connection management.",
-        backstory="An AI skilled in handling SSE connections explicitly.",
-        tools=tools,
-        verbose=True
-    )
-    
-    analysis_task = Task(
-        description="Fetch and analyze the latest user activity trends from the SSE server.",
-        expected_output="A summary report of user activity trends.",
-        agent=manual_sse_agent
-    )
-    
-    analysis_crew = Crew(
-        agents=[manual_sse_agent],
-        tasks=[analysis_task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = analysis_crew.kickoff()
-    print("\nCrew Task Result (SSE - Manual):\n", result)
-
-except Exception as e:
-    print(f"An error occurred during manual SSE MCP integration: {e}")
-    print("Ensure the SSE MCP server is running and accessible.")
-finally:
-    if mcp_server_adapter and mcp_server_adapter.is_connected:
-        print("Stopping SSE MCP server connection (manual)...")
-        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
-    elif mcp_server_adapter:
-        print("SSE MCP server adapter was not connected. No stop needed or start failed.")
-
-```
-
-## Security Considerations for SSE
-
-<Warning>
-**DNS Rebinding Attacks**: SSE transports can be vulnerable to DNS rebinding attacks if the MCP server is not properly secured. This could allow malicious websites to interact with local or intranet-based MCP servers.
-</Warning>
-
-To mitigate this risk:
-- MCP server implementations should **validate `Origin` headers** on incoming SSE connections.
-- When running local SSE MCP servers for development, **bind only to `localhost` (`127.0.0.1`)** rather than all network interfaces (`0.0.0.0`).
-- Implement **proper authentication** for all SSE connections if they expose sensitive tools or data.
-
-For a comprehensive overview of security best practices, please refer to our [Security Considerations](./security.mdx) page and the official [MCP Transport Security documentation](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).

docs/en/mcp/stdio.mdx

@@ -1,134 +0,0 @@
----
-title: Stdio Transport
-description: Learn how to connect CrewAI to local MCP servers using the Stdio (Standard Input/Output) transport mechanism.
-icon: server
----
-
-## Overview
-
-The Stdio (Standard Input/Output) transport is designed for connecting `MCPServerAdapter` to local MCP servers that communicate over their standard input and output streams. This is typically used when the MCP server is a script or executable running on the same machine as your CrewAI application.
-
-## Key Concepts
-
-- **Local Execution**: Stdio transport manages a locally running process for the MCP server.
-- **`StdioServerParameters`**: This class from the `mcp` library is used to configure the command, arguments, and environment variables for launching the Stdio server.
-
-## Connecting via Stdio
-
-You can connect to an Stdio-based MCP server using two main approaches for managing the connection lifecycle:
-
-### 1. Fully Managed Connection (Recommended)
-
-Using a Python context manager (`with` statement) is the recommended approach. It automatically handles starting the MCP server process and stopping it when the context is exited.
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters
-import os
-
-# Create a StdioServerParameters object
-server_params=StdioServerParameters(
-    command="python3", 
-    args=["servers/your_stdio_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-with MCPServerAdapter(server_params) as tools:
-    print(f"Available tools from Stdio MCP server: {[tool.name for tool in tools]}")
-
-    # Example: Using the tools from the Stdio MCP server in a CrewAI Agent
-    research_agent = Agent(
-        role="Local Data Processor",
-        goal="Process data using a local Stdio-based tool.",
-        backstory="An AI that leverages local scripts via MCP for specialized tasks.",
-        tools=tools,
-        reasoning=True,
-        verbose=True,
-    )
-    
-    processing_task = Task(
-        description="Process the input data file 'data.txt' and summarize its contents.",
-        expected_output="A summary of the processed data.",
-        agent=research_agent,
-        markdown=True
-    )
-    
-    data_crew = Crew(
-        agents=[research_agent],
-        tasks=[processing_task],
-        verbose=True,
-        process=Process.sequential 
-    )
-   
-    result = data_crew.kickoff()
-    print("\nCrew Task Result (Stdio - Managed):\n", result)
-
-```
-
-### 2. Manual Connection Lifecycle
-
-If you need finer-grained control over when the Stdio MCP server process is started and stopped, you can manage the `MCPServerAdapter` lifecycle manually.
-
-<Info>
-You **MUST** call `mcp_server_adapter.stop()` to ensure the server process is terminated and resources are released. Using a `try...finally` block is highly recommended.
-</Info>
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-from mcp import StdioServerParameters
-import os
-
-# Create a StdioServerParameters object
-stdio_params=StdioServerParameters(
-    command="python3", 
-    args=["servers/your_stdio_server.py"],
-    env={"UV_PYTHON": "3.12", **os.environ},
-)
-
-mcp_server_adapter = MCPServerAdapter(server_params=stdio_params)
-try:
-    mcp_server_adapter.start()  # Manually start the connection and server process
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual Stdio): {[tool.name for tool in tools]}")
-
-    # Example: Using the tools with your Agent, Task, Crew setup
-    manual_agent = Agent(
-        role="Local Task Executor",
-        goal="Execute a specific local task using a manually managed Stdio tool.",
-        backstory="An AI proficient in controlling local processes via MCP.",
-        tools=tools,
-        verbose=True
-    )
-    
-    manual_task = Task(
-        description="Execute the 'perform_analysis' command via the Stdio tool.",
-        expected_output="Results of the analysis.",
-        agent=manual_agent
-    )
-    
-    manual_crew = Crew(
-        agents=[manual_agent],
-        tasks=[manual_task],
-        verbose=True,
-        process=Process.sequential
-    )
-        
-       
-    result = manual_crew.kickoff() # Actual inputs depend on your tool
-    print("\nCrew Task Result (Stdio - Manual):\n", result)
-            
-except Exception as e:
-    print(f"An error occurred during manual Stdio MCP integration: {e}")
-finally:
-    if mcp_server_adapter and mcp_server_adapter.is_connected: # Check if connected before stopping
-        print("Stopping Stdio MCP server connection (manual)...")
-        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
-    elif mcp_server_adapter: # If adapter exists but not connected (e.g. start failed)
-        print("Stdio MCP server adapter was not connected. No stop needed or start failed.")
-
-```
-
-Remember to replace placeholder paths and commands with your actual Stdio server details. The `env` parameter in `StdioServerParameters` can 
-be used to set environment variables for the server process, which can be useful for configuring its behavior or providing necessary paths (like `PYTHONPATH`).

docs/en/mcp/streamable-http.mdx

@@ -1,135 +0,0 @@
----
-title: Streamable HTTP Transport
-description: Learn how to connect CrewAI to remote MCP servers using the flexible Streamable HTTP transport.
-icon: globe
----
-
-## Overview
-
-Streamable HTTP transport provides a flexible way to connect to remote MCP servers. It's often built upon HTTP and can support various communication patterns, including request-response and streaming, sometimes utilizing Server-Sent Events (SSE) for server-to-client streams within a broader HTTP interaction.
-
-## Key Concepts
-
-- **Remote Servers**: Designed for MCP servers hosted remotely.
-- **Flexibility**: Can support more complex interaction patterns than plain SSE, potentially including bi-directional communication if the server implements it.
-- **`MCPServerAdapter` Configuration**: You'll need to provide the server's base URL for MCP communication and specify `"streamable-http"` as the transport type.
-
-## Connecting via Streamable HTTP
-
-You have two primary methods for managing the connection lifecycle with a Streamable HTTP MCP server:
-
-### 1. Fully Managed Connection (Recommended)
-
-The recommended approach is to use a Python context manager (`with` statement), which handles the connection's setup and teardown automatically.
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-
-server_params = {
-    "url": "http://localhost:8001/mcp", # Replace with your actual Streamable HTTP server URL
-    "transport": "streamable-http"
-}
-
-try:
-    with MCPServerAdapter(server_params) as tools:
-        print(f"Available tools from Streamable HTTP MCP server: {[tool.name for tool in tools]}")
-
-        http_agent = Agent(
-            role="HTTP Service Integrator",
-            goal="Utilize tools from a remote MCP server via Streamable HTTP.",
-            backstory="An AI agent adept at interacting with complex web services.",
-            tools=tools,
-            verbose=True,
-        )
-
-        http_task = Task(
-            description="Perform a complex data query using a tool from the Streamable HTTP server.",
-            expected_output="The result of the complex data query.",
-            agent=http_agent,
-        )
-
-        http_crew = Crew(
-            agents=[http_agent],
-            tasks=[http_task],
-            verbose=True,
-            process=Process.sequential
-        )
-        
-        result = http_crew.kickoff() 
-        print("\nCrew Task Result (Streamable HTTP - Managed):\n", result)
-
-except Exception as e:
-    print(f"Error connecting to or using Streamable HTTP MCP server (Managed): {e}")
-    print("Ensure the Streamable HTTP MCP server is running and accessible at the specified URL.")
-
-```
-**Note:** Replace `"http://localhost:8001/mcp"` with the actual URL of your Streamable HTTP MCP server.
-
-### 2. Manual Connection Lifecycle
-
-For scenarios requiring more explicit control, you can manage the `MCPServerAdapter` connection manually.
-
-<Info>
-It is **critical** to call `mcp_server_adapter.stop()` when you are done to close the connection and free up resources. A `try...finally` block is the safest way to ensure this.
-</Info>
-
-```python
-from crewai import Agent, Task, Crew, Process
-from crewai_tools import MCPServerAdapter
-
-server_params = {
-    "url": "http://localhost:8001/mcp", # Replace with your actual Streamable HTTP server URL
-    "transport": "streamable-http"
-}
-
-mcp_server_adapter = None 
-try:
-    mcp_server_adapter = MCPServerAdapter(server_params)
-    mcp_server_adapter.start()
-    tools = mcp_server_adapter.tools
-    print(f"Available tools (manual Streamable HTTP): {[tool.name for tool in tools]}")
-
-    manual_http_agent = Agent(
-        role="Advanced Web Service User",
-        goal="Interact with an MCP server using manually managed Streamable HTTP connections.",
-        backstory="An AI specialist in fine-tuning HTTP-based service integrations.",
-        tools=tools,
-        verbose=True
-    )
-    
-    data_processing_task = Task(
-        description="Submit data for processing and retrieve results via Streamable HTTP.",
-        expected_output="Processed data or confirmation.",
-        agent=manual_http_agent
-    )
-    
-    data_crew = Crew(
-        agents=[manual_http_agent],
-        tasks=[data_processing_task],
-        verbose=True,
-        process=Process.sequential
-    )
-    
-    result = data_crew.kickoff()
-    print("\nCrew Task Result (Streamable HTTP - Manual):\n", result)
-
-except Exception as e:
-    print(f"An error occurred during manual Streamable HTTP MCP integration: {e}")
-    print("Ensure the Streamable HTTP MCP server is running and accessible.")
-finally:
-    if mcp_server_adapter and mcp_server_adapter.is_connected:
-        print("Stopping Streamable HTTP MCP server connection (manual)...")
-        mcp_server_adapter.stop()  # **Crucial: Ensure stop is called**
-    elif mcp_server_adapter:
-        print("Streamable HTTP MCP server adapter was not connected. No stop needed or start failed.")
-```
-
-## Security Considerations
-
-When using Streamable HTTP transport, general web security best practices are paramount:
-- **Use HTTPS**: Always prefer HTTPS (HTTP Secure) for your MCP server URLs to encrypt data in transit.
-- **Authentication**: Implement robust authentication mechanisms if your MCP server exposes sensitive tools or data.
-- **Input Validation**: Ensure your MCP server validates all incoming requests and parameters.
-
-For a comprehensive guide on securing your MCP integrations, please refer to our [Security Considerations](./security.mdx) page and the official [MCP Transport Security documentation](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).

docs/en/observability/langdb.mdx

@@ -1,286 +0,0 @@
----
-title: LangDB Integration
-description: Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.
-icon: database
----
-
-# Introduction
-
-[LangDB AI Gateway](https://langdb.ai) provides OpenAI-compatible APIs to connect with multiple Large Language Models and serves as an observability platform that makes it effortless to trace CrewAI workflows end-to-end while providing access to 350+ language models. With a single `init()` call, all agent interactions, task executions, and LLM calls are captured, providing comprehensive observability and production-ready AI infrastructure for your applications.
-
-<Frame caption="LangDB CrewAI Trace Example">
-  <img src="/images/langdb-1.png" alt="LangDB CrewAI trace example" />
-</Frame>
-
-**Checkout:** [View the live trace example](https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22)
-
-## Features
-
-### AI Gateway Capabilities
-- **Access to 350+ LLMs**: Connect to all major language models through a single integration
-- **Virtual Models**: Create custom model configurations with specific parameters and routing rules
-- **Virtual MCP**: Enable compatibility and integration with MCP (Model Context Protocol) systems for enhanced agent communication
-- **Guardrails**: Implement safety measures and compliance controls for agent behavior
-
-### Observability & Tracing
-- **Automatic Tracing**: Single `init()` call captures all CrewAI interactions
-- **End-to-End Visibility**: Monitor agent workflows from start to finish
-- **Tool Usage Tracking**: Track which tools agents use and their outcomes
-- **Model Call Monitoring**: Detailed insights into LLM interactions
-- **Performance Analytics**: Monitor latency, token usage, and costs
-- **Debugging Support**: Step-through execution for troubleshooting
-- **Real-time Monitoring**: Live traces and metrics dashboard
-
-## Setup Instructions
-
-<Steps>
-  <Step title="Install LangDB">
-    Install the LangDB client with CrewAI feature flag:
-    ```bash
-    pip install 'pylangdb[crewai]'
-    ```
-  </Step>
-  <Step title="Set Environment Variables">
-    Configure your LangDB credentials:
-    ```bash
-    export LANGDB_API_KEY="<your_langdb_api_key>"
-    export LANGDB_PROJECT_ID="<your_langdb_project_id>"
-    export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
-    ```
-  </Step>
-  <Step title="Initialize Tracing">
-    Import and initialize LangDB before configuring your CrewAI code:
-    ```python
-    from pylangdb.crewai import init
-    # Initialize LangDB
-    init()
-    ```
-  </Step>
-  <Step title="Configure CrewAI with LangDB">
-    Set up your LLM with LangDB headers:
-    ```python
-    from crewai import Agent, Task, Crew, LLM
-    import os
-
-    # Configure LLM with LangDB headers
-    llm = LLM(
-        model="openai/gpt-4o", # Replace with the model you want to use
-        api_key=os.getenv("LANGDB_API_KEY"),
-        base_url=os.getenv("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.getenv("LANGDB_PROJECT_ID")}
-    )
-    ```
-  </Step>
-</Steps>
-
-## Quick Start Example
-
-Here's a simple example to get you started with LangDB and CrewAI:
-
-```python
-import os
-from pylangdb.crewai import init
-from crewai import Agent, Task, Crew, LLM
-
-# Initialize LangDB before any CrewAI imports
-init()
-
-def create_llm(model):
-    return LLM(
-        model=model,
-        api_key=os.environ.get("LANGDB_API_KEY"),
-        base_url=os.environ.get("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
-    )
-
-# Define your agent
-researcher = Agent(
-    role="Research Specialist",
-    goal="Research topics thoroughly",
-    backstory="Expert researcher with skills in finding information",
-    llm=create_llm("openai/gpt-4o"), # Replace with the model you want to use
-    verbose=True
-)
-
-# Create a task
-task = Task(
-    description="Research the given topic and provide a comprehensive summary",
-    agent=researcher,
-    expected_output="Detailed research summary with key findings"
-)
-
-# Create and run the crew
-crew = Crew(agents=[researcher], tasks=[task])
-result = crew.kickoff()
-print(result)
-```
-
-## Complete Example: Research and Planning Agent
-
-This comprehensive example demonstrates a multi-agent workflow with research and planning capabilities.
-
-### Prerequisites
-
-```bash
-pip install crewai 'pylangdb[crewai]' crewai_tools setuptools python-dotenv
-```
-
-### Environment Setup
-
-```bash
-# LangDB credentials
-export LANGDB_API_KEY="<your_langdb_api_key>"
-export LANGDB_PROJECT_ID="<your_langdb_project_id>"
-export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
-
-# Additional API keys (optional)
-export SERPER_API_KEY="<your_serper_api_key>"  # For web search capabilities
-```
-
-### Complete Implementation
-
-```python
-#!/usr/bin/env python3
-
-import os
-import sys
-from pylangdb.crewai import init
-init()  # Initialize LangDB before any CrewAI imports
-from dotenv import load_dotenv
-from crewai import Agent, Task, Crew, Process, LLM
-from crewai_tools import SerperDevTool
-
-load_dotenv()
-
-def create_llm(model):
-    return LLM(
-        model=model,
-        api_key=os.environ.get("LANGDB_API_KEY"),
-        base_url=os.environ.get("LANGDB_API_BASE_URL"),
-        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
-    )
-
-class ResearchPlanningCrew:
-    def researcher(self) -> Agent:
-        return Agent(
-            role="Research Specialist",
-            goal="Research topics thoroughly and compile comprehensive information",
-            backstory="Expert researcher with skills in finding and analyzing information from various sources",
-            tools=[SerperDevTool()],
-            llm=create_llm("openai/gpt-4o"),
-            verbose=True
-        )
-    
-    def planner(self) -> Agent:
-        return Agent(
-            role="Strategic Planner",
-            goal="Create actionable plans based on research findings",
-            backstory="Strategic planner who breaks down complex challenges into executable plans",
-            reasoning=True,
-            max_reasoning_attempts=3,
-            llm=create_llm("openai/anthropic/claude-3.7-sonnet"),
-            verbose=True
-        )
-    
-    def research_task(self) -> Task:
-        return Task(
-            description="Research the topic thoroughly and compile comprehensive information",
-            agent=self.researcher(),
-            expected_output="Comprehensive research report with key findings and insights"
-        )
-    
-    def planning_task(self) -> Task:
-        return Task(
-            description="Create a strategic plan based on the research findings",
-            agent=self.planner(),
-            expected_output="Strategic execution plan with phases, goals, and actionable steps",
-            context=[self.research_task()]
-        )
-    
-    def crew(self) -> Crew:
-        return Crew(
-            agents=[self.researcher(), self.planner()],
-            tasks=[self.research_task(), self.planning_task()],
-            verbose=True,
-            process=Process.sequential
-        )
-
-def main():
-        topic = sys.argv[1] if len(sys.argv) > 1 else "Artificial Intelligence in Healthcare"
-        
-        crew_instance = ResearchPlanningCrew()
-        
-        # Update task descriptions with the specific topic
-        crew_instance.research_task().description = f"Research {topic} thoroughly and compile comprehensive information"
-    crew_instance.planning_task().description = f"Create a strategic plan for {topic} based on the research findings"
-    
-    result = crew_instance.crew().kickoff()
-    print(result)
-
-if __name__ == "__main__":
-    main()
-```
-
-### Running the Example
-
-```bash
-python main.py "Sustainable Energy Solutions"
-```
-
-## Viewing Traces in LangDB
-
-After running your CrewAI application, you can view detailed traces in the LangDB dashboard:
-
-<Frame caption="LangDB Trace Dashboard">
-  <img src="/images/langdb-2.png" alt="LangDB trace dashboard showing CrewAI workflow" />
-</Frame>
-
-### What You'll See
-
-- **Agent Interactions**: Complete flow of agent conversations and task handoffs
-- **Tool Usage**: Which tools were called, their inputs, and outputs
-- **Model Calls**: Detailed LLM interactions with prompts image.pngand responses
-- **Performance Metrics**: Latency, token usage, and cost tracking
-- **Execution Timeline**: Step-by-step view of the entire workflow
-
-
-## Troubleshooting
-
-### Common Issues
-
-- **No traces appearing**: Ensure `init()` is called before any CrewAI imports
-- **Authentication errors**: Verify your LangDB API key and project ID
-
-
-## Resources
-
-<CardGroup cols={3}>
-  <Card title="LangDB Documentation" icon="book" href="https://docs.langdb.ai">
-    Official LangDB documentation and guides
-  </Card>
-  <Card title="LangDB Guides" icon="graduation-cap" href="https://docs.langdb.ai/guides">
-    Step-by-step tutorials for building AI agents
-  </Card>
-  <Card title="GitHub Examples" icon="github" href="https://github.com/langdb/langdb-samples/tree/main/examples/crewai" >
-    Complete CrewAI integration examples
-  </Card>
-  <Card title="LangDB Dashboard" icon="chart-line" href="https://app.langdb.ai">
-    Access your traces and analytics
-  </Card>
-  <Card title="Model Catalog" icon="list" href="https://app.langdb.ai/models">
-    Browse 350+ available language models
-  </Card>
-  <Card title="Enterprise Features" icon="building" href="https://docs.langdb.ai/enterprise">
-    Self-hosted options and enterprise capabilities
-  </Card>
-</CardGroup>
-
-## Next Steps
-
-This guide covered the basics of integrating LangDB AI Gateway with CrewAI. To further enhance your AI workflows, explore:
-
-- **Virtual Models**: Create custom model configurations with routing strategies
-- **Guardrails & Safety**: Implement content filtering and compliance controls
-- **Production Deployment**: Configure fallbacks, retries, and load balancing
-
-For more advanced features and use cases, visit the [LangDB Documentation](https://docs.langdb.ai) or explore the [Model Catalog](https://app.langdb.ai/models) to discover all available models.

docs/en/observability/maxim.mdx

@@ -1,231 +0,0 @@
----
-title: "Maxim Integration"
-description: "Start Agent monitoring, evaluation, and observability"
-icon: "infinity"
----
-
-# Maxim Overview
-
-Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your CrewAI applications. With Maxim's one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.
-
-## Features
-
-### Prompt Management
-
-Maxim's Prompt Management capabilities enable you to create, organize, and optimize prompts for your CrewAI agents. Rather than hardcoding instructions, leverage Maxim’s SDK to dynamically retrieve and apply version-controlled prompts.
-
-<Tabs>
-  <Tab title="Prompt Playground">
-    Create, refine, experiment and deploy your prompts via the playground. Organize of your prompts using folders and versions, experimenting with the real world cases by linking tools and context, and deploying based on custom logic.
-
-    Easily experiment across models by [**configuring models**](https://www.getmaxim.ai/docs/introduction/quickstart/setting-up-workspace#add-model-api-keys) and selecting the relevant model from the dropdown at the top of the prompt playground.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_playground.png'> </img>
-  </Tab>
-  <Tab title="Prompt Versions">
-    As teams build their AI applications, a big part of experimentation is iterating on the prompt structure. In order to collaborate effectively and organize your changes clearly, Maxim allows prompt versioning and comparison runs across versions.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_versions.png'> </img>
-  </Tab>
-  <Tab title="Prompt Comparisons">
-    Iterating on Prompts as you evolve your AI application would need experiments across models, prompt structures, etc. In order to compare versions and make informed decisions about changes, the comparison playground allows a side by side view of results.
-
-    ## **Why use Prompt comparison?**
-
-    Prompt comparison combines multiple single Prompts into one view, enabling a streamlined approach for various workflows:
-
-    1. **Model comparison**: Evaluate the performance of different models on the same Prompt.
-    2. **Prompt optimization**: Compare different versions of a Prompt to identify the most effective formulation.
-    3. **Cross-Model consistency**: Ensure consistent outputs across various models for the same Prompt.
-    4. **Performance benchmarking**: Analyze metrics like latency, cost, and token count across different models and Prompts.
-  </Tab>
-</Tabs>
-
-### Observability & Evals
-
-Maxim AI provides comprehensive observability & evaluation for your CrewAI agents, helping you understand exactly what's happening during each execution.
-
-<Tabs>
-  <Tab title="Agent Tracing">
-    Track your agent’s complete lifecycle, including tool calls, agent trajectories, and decision flows effortlessly.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_agent_tracking.png'> </img>
-  </Tab>
-  <Tab title="Analytics + Evals">
-    Run detailed evaluations on full traces or individual nodes with support for:
-
-    - Multi-step interactions and granular trace analysis
-    - Session Level Evaluations
-    - Simulations for real-world testing
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_trace_eval.png'> </img>
-
-    <CardGroup cols={3}>
-      <Card title="Auto Evals on Logs" icon="e" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/auto-evaluation">
-        <p>
-        Evaluate captured logs automatically from the UI based on filters and sampling
-
-        </p>
-      </Card>
-      <Card title="Human Evals on Logs" icon="hand" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/human-evaluation">
-        <p>
-        Use human evaluation or rating to assess the quality of your logs and evaluate them.
-
-        </p>
-      </Card>
-      <Card title="Node Level Evals" icon="road" href="https://www.getmaxim.ai/docs/observe/how-to/evaluate-logs/node-level-evaluation">
-        <p>
-        Evaluate any component of your trace or log to gain insights into your agent’s behavior.
-
-        </p>
-      </Card>
-    </CardGroup>
-    ---
-  </Tab>
-  <Tab title="Alerting">
-    Set thresholds on **error**, **cost, token usage, user feedback, latency** and get real-time alerts via Slack or PagerDuty.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_alerts_1.png'> </img>
-  </Tab>
-  <Tab title="Dashboards">
-    Visualize Traces over time, usage metrics, latency & error rates with ease.
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/maxim_dashboard_1.png'> </img>
-  </Tab>
-</Tabs>
-
-## Getting Started
-
-### Prerequisites
-
-
-- Python version \>=3.10
-- A Maxim account ([sign up here](https://getmaxim.ai/))
-- Generate Maxim API Key
-- A CrewAI project
-
-### Installation
-
-Install the Maxim SDK via pip:
-
-```python
-pip install maxim-py
-```
-
-Or add it to your `requirements.txt`:
-
-```
-maxim-py
-```
-### Basic Setup
-
-### 1. Set up environment variables
-
-```python
-### Environment Variables Setup
-
-# Create a `.env` file in your project root:
-
-# Maxim API Configuration
-MAXIM_API_KEY=your_api_key_here
-MAXIM_LOG_REPO_ID=your_repo_id_here
-```
-
-### 2. Import the required packages
-
-```python
-from crewai import Agent, Task, Crew, Process
-from maxim import Maxim
-from maxim.logger.crewai import instrument_crewai
-```
-
-### 3. Initialise Maxim with your API key
-
-
-```python {8}
-# Instrument CrewAI with just one line
-instrument_crewai(Maxim().logger())
-```
-
-### 4. Create and run your CrewAI application as usual
-
-```python
-# Create your agent
-researcher = Agent(
-    role='Senior Research Analyst',
-    goal='Uncover cutting-edge developments in AI',
-    backstory="You are an expert researcher at a tech think tank...",
-    verbose=True,
-    llm=llm
-)
-
-# Define the task
-research_task = Task(
-    description="Research the latest AI advancements...",
-    expected_output="",
-    agent=researcher
-)
-
-# Configure and run the crew
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    verbose=True
-)
-
-try:
-    result = crew.kickoff()
-finally:
-    maxim.cleanup()  # Ensure cleanup happens even if errors occur
-```
-
-
-That's it\! All your CrewAI agent interactions will now be logged and available in your Maxim dashboard.
-
-Check this Google Colab Notebook for a quick reference - [Notebook](https://colab.research.google.com/drive/1ZKIZWsmgQQ46n8TH9zLsT1negKkJA6K8?usp=sharing)
-
-## Viewing Your Traces
-
-After running your CrewAI application:
-
-1. Log in to your [Maxim Dashboard](https://app.getmaxim.ai/login)
-2. Navigate to your repository
-3. View detailed agent traces, including:
-   - Agent conversations
-   - Tool usage patterns
-   - Performance metrics
-   - Cost analytics
-
-    <img src='https://raw.githubusercontent.com/akmadan/crewAI/docs_maxim_observability/docs/images/crewai_traces.gif'> </img>
-
-## Troubleshooting
-
-### Common Issues
-
-- **No traces appearing**: Ensure your API key and repository ID are correct
-- Ensure you've **`called instrument_crewai()`** **_before_** running your crew. This initializes logging hooks correctly.
-- Set `debug=True` in your `instrument_crewai()` call to surface any internal errors:
-
-  ```python
-  instrument_crewai(logger, debug=True)
-  ```
-- Configure your agents with `verbose=True` to capture detailed logs:
-
-  ```python
-  agent = CrewAgent(..., verbose=True)
-  ```
-- Double-check that `instrument_crewai()` is called **before** creating or executing agents. This might be obvious, but it's a common oversight.
-
-## Resources
-
-<CardGroup cols="3">
-  <Card title="CrewAI Docs" icon="book" href="https://docs.crewai.com/">
-    Official CrewAI documentation
-  </Card>
-  <Card title="Maxim Docs" icon="book" href="https://getmaxim.ai/docs">
-    Official Maxim documentation
-  </Card>
-  <Card title="Maxim Github" icon="github" href="https://github.com/maximhq">
-    Maxim Github
-  </Card>
-</CardGroup>

docs/en/observability/neatlogs.mdx

@@ -1,134 +0,0 @@
----
-title: Neatlogs Integration
-description: Understand, debug, and share your CrewAI agent runs
-icon: magnifying-glass-chart
----
-
-# Introduction
-
-Neatlogs helps you **see what your agent did**, **why**, and **share it**.
-
-It captures every step: thoughts, tool calls, responses, evaluations. No raw logs. Just clear, structured traces. Great for debugging and collaboration.
-
-## Why use Neatlogs?
-
-CrewAI agents use multiple tools and reasoning steps. When something goes wrong, you need context — not just errors.
-
-Neatlogs lets you:
-
-- Follow the full decision path
-- Add feedback directly on steps
-- Chat with the trace using AI assistant
-- Share runs publicly for feedback
-- Turn insights into tasks
-
-All in one place.
-
-Manage your traces effortlessly
-
-![Traces](/images/neatlogs-1.png)
-![Trace Response](/images/neatlogs-2.png)
-
-The best UX to view a CrewAI trace. Post comments anywhere you want. Use AI to debug.
-
-![Trace Details](/images/neatlogs-3.png)
-![Ai Chat Bot With A Trace](/images/neatlogs-4.png)
-![Comments Drawer](/images/neatlogs-5.png)
-
-## Core Features
-
-- **Trace Viewer**: Track thoughts, tools, and decisions in sequence
-- **Inline Comments**: Tag teammates on any trace step
-- **Feedback & Evaluation**: Mark outputs as correct or incorrect
-- **Error Highlighting**: Automatic flagging of API/tool failures
-- **Task Conversion**: Convert comments into assigned tasks
-- **Ask the Trace (AI)**: Chat with your trace using Neatlogs AI bot
-- **Public Sharing**: Publish trace links to your community
-
-## Quick Setup with CrewAI
-
-<Steps>
-  <Step title="Sign Up & Get API Key">
-    Visit [neatlogs.com](https://neatlogs.com/?utm_source=crewAI-docs), create a project, copy the API key.
-  </Step>
-  <Step title="Install SDK">
-    ```bash
-    pip install neatlogs
-    ```
-    (Latest version 0.8.0, Python 3.8+; MIT license)
-  </Step>
-  <Step title="Initialize Neatlogs">
-    Before starting Crew agents, add:
-
-    ```python
-    import neatlogs
-    neatlogs.init("YOUR_PROJECT_API_KEY")
-    ```
-
-    Agents run as usual. Neatlogs captures everything automatically.
-
-  </Step>
-</Steps>
-
-
-
-## Under the Hood
-
-According to GitHub, Neatlogs:
-
-- Captures thoughts, tool calls, responses, errors, and token stats
-- Supports AI-powered task generation and robust evaluation workflows
-
-All with just two lines of code.
-
-
-
-## Watch It Work
-
-### 🔍 Full Demo (4 min)
-
-<iframe
-  width="100%"
-  height="315"
-  src="https://www.youtube.com/embed/8KDme9T2I7Q?si=b8oHteaBwFNs_Duk"
-  title="YouTube video player"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
-
-### ⚙️ CrewAI Integration (30 s)
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.loom.com/embed/9c78b552af43452bb3e4783cb8d91230?sid=e9d7d370-a91a-49b0-809e-2f375d9e801d"
-  title="Loom video player"
-  frameBorder="0"
-  allowFullScreen
-></iframe>
-
-
-
-## Links & Support
-
-- 📘 [Neatlogs Docs](https://docs.neatlogs.com/)
-- 🔐 [Dashboard & API Key](https://app.neatlogs.com/)
-- 🐦 [Follow on Twitter](https://twitter.com/neatlogs)
-- 📧 Contact: hello@neatlogs.com
-- 🛠 [GitHub SDK](https://github.com/NeatLogs/neatlogs)
-
-
-
-## TL;DR
-
-With just:
-
-```bash
-pip install neatlogs
-
-import neatlogs
-neatlogs.init("YOUR_API_KEY")
-
-You can now capture, understand, share, and act on your CrewAI agent runs in seconds.
-No setup overhead. Full trace transparency. Full team collaboration.
-```

docs/en/observability/overview.mdx

@@ -1,119 +0,0 @@
----
-title: "Overview"
-description: "Monitor, evaluate, and optimize your CrewAI agents with comprehensive observability tools"
-icon: "face-smile"
----
-
-## Observability for CrewAI
-
-Observability is crucial for understanding how your CrewAI agents perform, identifying bottlenecks, and ensuring reliable operation in production environments. This section covers various tools and platforms that provide monitoring, evaluation, and optimization capabilities for your agent workflows.
-
-## Why Observability Matters
-
-- **Performance Monitoring**: Track agent execution times, token usage, and resource consumption
-- **Quality Assurance**: Evaluate output quality and consistency across different scenarios
-- **Debugging**: Identify and resolve issues in agent behavior and task execution
-- **Cost Management**: Monitor LLM API usage and associated costs
-- **Continuous Improvement**: Gather insights to optimize agent performance over time
-
-## Available Observability Tools
-
-### Monitoring & Tracing Platforms
-
-<CardGroup cols={2}>
-
-  <Card title="LangDB" icon="database" href="/en/observability/langdb">
-    End-to-end tracing for CrewAI workflows with automatic agent interaction capture.
-  </Card>
-
-  <Card title="OpenLIT" icon="magnifying-glass-chart" href="/en/observability/openlit">
-    OpenTelemetry-native monitoring with cost tracking and performance analytics.
-  </Card>
-
-  <Card title="MLflow" icon="bars-staggered" href="/en/observability/mlflow">
-    Machine learning lifecycle management with tracing and evaluation capabilities.
-  </Card>
-
-  <Card title="Langfuse" icon="link" href="/en/observability/langfuse">
-    LLM engineering platform with detailed tracing and analytics.
-  </Card>
-
-  <Card title="Langtrace" icon="chart-line" href="/en/observability/langtrace">
-    Open-source observability for LLMs and agent frameworks.
-  </Card>
-
-  <Card title="Arize Phoenix" icon="meteor" href="/en/observability/arize-phoenix">
-    AI observability platform for monitoring and troubleshooting.
-  </Card>
-
-  <Card title="Portkey" icon="key" href="/en/observability/portkey">
-    AI gateway with comprehensive monitoring and reliability features.
-  </Card>
-
-  <Card title="Opik" icon="meteor" href="/en/observability/opik">
-    Debug, evaluate, and monitor LLM applications with comprehensive tracing.
-  </Card>
-
-  <Card title="Weave" icon="network-wired" href="/en/observability/weave">
-    Weights & Biases platform for tracking and evaluating AI applications.
-  </Card>
-</CardGroup>
-
-### Evaluation & Quality Assurance
-
-<CardGroup cols={2}>
-  <Card title="Patronus AI" icon="shield-check" href="/en/observability/patronus-evaluation">
-    Comprehensive evaluation platform for LLM outputs and agent behaviors.
-  </Card>
-</CardGroup>
-
-## Key Observability Metrics
-
-### Performance Metrics
-- **Execution Time**: How long agents take to complete tasks
-- **Token Usage**: Input/output tokens consumed by LLM calls
-- **API Latency**: Response times from external services
-- **Success Rate**: Percentage of successfully completed tasks
-
-### Quality Metrics
-- **Output Accuracy**: Correctness of agent responses
-- **Consistency**: Reliability across similar inputs
-- **Relevance**: How well outputs match expected results
-- **Safety**: Compliance with content policies and guidelines
-
-### Cost Metrics
-- **API Costs**: Expenses from LLM provider usage
-- **Resource Utilization**: Compute and memory consumption
-- **Cost per Task**: Economic efficiency of agent operations
-- **Budget Tracking**: Monitoring against spending limits
-
-## Getting Started
-
-1. **Choose Your Tools**: Select observability platforms that match your needs
-2. **Instrument Your Code**: Add monitoring to your CrewAI applications
-3. **Set Up Dashboards**: Configure visualizations for key metrics
-4. **Define Alerts**: Create notifications for important events
-5. **Establish Baselines**: Measure initial performance for comparison
-6. **Iterate and Improve**: Use insights to optimize your agents
-
-## Best Practices
-
-### Development Phase
-- Use detailed tracing to understand agent behavior
-- Implement evaluation metrics early in development
-- Monitor resource usage during testing
-- Set up automated quality checks
-
-### Production Phase
-- Implement comprehensive monitoring and alerting
-- Track performance trends over time
-- Monitor for anomalies and degradation
-- Maintain cost visibility and control
-
-### Continuous Improvement
-- Regular performance reviews and optimization
-- A/B testing of different agent configurations
-- Feedback loops for quality improvement
-- Documentation of lessons learned
-
-Choose the observability tools that best fit your use case, infrastructure, and monitoring requirements to ensure your CrewAI agents perform reliably and efficiently.

docs/en/observability/portkey.mdx

@@ -1,824 +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%" />
-
-
-
-## Introduction
-
-Portkey enhances CrewAI with production-readiness features, turning your experimental agent crews into robust systems by providing:
-
-- **Complete observability** of every agent step, tool use, and interaction
-- **Built-in reliability** with fallbacks, retries, and load balancing
-- **Cost tracking and optimization** to manage your AI spend
-- **Access to 200+ LLMs** through a single integration
-- **Guardrails** to keep agent behavior safe and compliant
-- **Version-controlled prompts** for consistent agent performance
-
-
-### Installation & Setup
-
-<Steps>
-<Step title="Install the required packages">
-```bash
-pip install -U crewai portkey-ai
-```
-</Step>
-
-<Step title="Generate API Key" icon="lock">
-Create a Portkey API key with optional budget/rate limits from the [Portkey dashboard](https://app.portkey.ai/). You can also attach configurations for reliability, caching, and more to this key. More on this later.
-</Step>
-
-<Step title="Configure CrewAI with Portkey">
-The integration is simple - you just need to update the LLM configuration in your CrewAI setup:
-
-```python
-from crewai import LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Create an LLM instance with Portkey integration
-gpt_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",  # We are using a Virtual key, so this is a placeholder
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_LLM_VIRTUAL_KEY",
-        trace_id="unique-trace-id",               # Optional, for request tracing
-    )
-)
-
-#Use them in your Crew Agents like this:
-
-	@agent
-	def lead_market_analyst(self) -> Agent:
-		return Agent(
-			config=self.agents_config['lead_market_analyst'],
-			verbose=True,
-			memory=False,
-			llm=gpt_llm
-		)
-
-```
-
-<Info>
-**What are Virtual Keys?** Virtual keys in Portkey securely store your LLM provider API keys (OpenAI, Anthropic, etc.) in an encrypted vault. They allow for easier key rotation and budget management. [Learn more about virtual keys here](https://portkey.ai/docs/product/ai-gateway/virtual-keys).
-</Info>
-</Step>
-</Steps>
-
-## Production Features
-
-### 1. Enhanced Observability
-
-Portkey provides comprehensive observability for your CrewAI agents, helping you understand exactly what's happening during each execution.
-
-<Tabs>
-  <Tab title="Traces">
-<Frame>
-    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Product%2011.1.webp"/>
-</Frame>
-
-Traces provide a hierarchical view of your crew's execution, showing the sequence of LLM calls, tool invocations, and state transitions.
-
-```python
-# Add trace_id to enable hierarchical tracing in Portkey
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        trace_id="unique-session-id"  # Add unique trace ID
-    )
-)
-```
-  </Tab>
-
-  <Tab title="Logs">
-<Frame>
-    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs%20Metadata.png"/>
-</Frame>
-
-Portkey logs every interaction with LLMs, including:
-
-- Complete request and response payloads
-- Latency and token usage metrics
-- Cost calculations
-- Tool calls and function executions
-
-All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific crew runs.
-  </Tab>
-
-  <Tab title="Metrics & Dashboards">
-<Frame>
-    <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Dashboard.png"/>
-</Frame>
-
-Portkey provides built-in dashboards that help you:
-
-- Track cost and token usage across all crew runs
-- Analyze performance metrics like latency and success rates
-- Identify bottlenecks in your agent workflows
-- Compare different crew configurations and LLMs
-
-You can filter and segment all metrics by custom metadata to analyze specific crew types, user groups, or use cases.
-  </Tab>
-
-  <Tab title="Metadata Filtering">
-<Frame>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Metadata%20Filters%20from%20CrewAI.png" alt="Analytics with metadata filters" />
-</Frame>
-
-Add custom metadata to your CrewAI LLM configuration to enable powerful filtering and segmentation:
-
-```python
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        metadata={
-            "crew_type": "research_crew",
-            "environment": "production",
-            "_user": "user_123",   # Special _user field for user analytics
-            "request_source": "mobile_app"
-        }
-    )
-)
-```
-
-This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific crew runs, users, or environments.
-  </Tab>
-</Tabs>
-
-### 2. Reliability - Keep Your Crews Running Smoothly
-
-When running crews in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey's reliability features ensure your agents keep running smoothly even when problems occur.
-
-It's simple to enable fallback in your CrewAI setup by using a Portkey Config:
-
-```python
-from crewai import LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Create LLM with fallback configuration
-portkey_llm = LLM(
-    model="gpt-4o",
-    max_tokens=1000,
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        config={
-            "strategy": {
-                "mode": "fallback"
-            },
-            "targets": [
-                {
-                    "provider": "openai",
-                    "api_key": "YOUR_OPENAI_API_KEY",
-                    "override_params": {"model": "gpt-4o"}
-                },
-                {
-                    "provider": "anthropic",
-                    "api_key": "YOUR_ANTHROPIC_API_KEY",
-                    "override_params": {"model": "claude-3-opus-20240229"}
-                }
-            ]
-        }
-    )
-)
-
-# Use this LLM configuration with your agents
-```
-
-This configuration will automatically try Claude if the GPT-4o request fails, ensuring your crew can continue operating.
-
-<CardGroup cols="2">
-  <Card title="Automatic Retries" icon="rotate" href="https://portkey.ai/docs/product/ai-gateway/automatic-retries">
-    Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.
-  </Card>
-  <Card title="Request Timeouts" icon="clock" href="https://portkey.ai/docs/product/ai-gateway/request-timeouts">
-    Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.
-  </Card>
-  <Card title="Conditional Routing" icon="route" href="https://portkey.ai/docs/product/ai-gateway/conditional-routing">
-    Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.
-  </Card>
-  <Card title="Fallbacks" icon="shield" href="https://portkey.ai/docs/product/ai-gateway/fallbacks">
-    Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.
-  </Card>
-  <Card title="Load Balancing" icon="scale-balanced" href="https://portkey.ai/docs/product/ai-gateway/load-balancing">
-    Spread requests across multiple API keys or providers. Great for high-volume crew operations and staying within rate limits.
-  </Card>
-</CardGroup>
-
-### 3. Prompting in CrewAI
-
-Portkey's Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your CrewAI agents. Instead of hardcoding prompts or instructions, use Portkey's prompt rendering API to dynamically fetch and apply your versioned prompts.
-
-<Frame caption="Manage prompts in Portkey's Prompt Library">
-![Prompt Playground Interface](https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs.webp)
-</Frame>
-
-<Tabs>
-  <Tab title="Prompt Playground">
-Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It's where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:
-
-1. Iteratively develop prompts before using them in your agents
-2. Test prompts with different variables and models
-3. Compare outputs between different prompt versions
-4. Collaborate with team members on prompt development
-
-This visual environment makes it easier to craft effective prompts for each step in your CrewAI agents' workflow.
-  </Tab>
-
-  <Tab title="Using Prompt Templates">
-The Prompt Render API retrieves your prompt templates with all parameters configured:
-
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL, Portkey
-
-# Initialize Portkey admin client
-portkey_admin = Portkey(api_key="YOUR_PORTKEY_API_KEY")
-
-# Retrieve prompt using the render API
-prompt_data = portkey_client.prompts.render(
-    prompt_id="YOUR_PROMPT_ID",
-    variables={
-        "agent_role": "Senior Research Scientist",
-    }
-)
-
-backstory_agent_prompt=prompt_data.data.messages[0]["content"]
-
-
-# Set up LLM with Portkey integration
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY"
-    )
-)
-
-# Create agent using the rendered prompt
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory=backstory_agent,  # Use the rendered prompt
-    verbose=True,
-    llm=portkey_llm
-)
-```
-  </Tab>
-
-  <Tab title="Prompt Versioning">
-You can:
-- Create multiple versions of the same prompt
-- Compare performance between versions
-- Roll back to previous versions if needed
-- Specify which version to use in your code:
-
-```python
-# Use a specific prompt version
-prompt_data = portkey_admin.prompts.render(
-    prompt_id="YOUR_PROMPT_ID@version_number",
-    variables={
-        "agent_role": "Senior Research Scientist",
-        "agent_goal": "Discover groundbreaking insights"
-    }
-)
-```
-  </Tab>
-
-  <Tab title="Mustache Templating for variables">
-Portkey prompts use Mustache-style templating for easy variable substitution:
-
-```
-You are a {{agent_role}} with expertise in {{domain}}.
-
-Your mission is to {{agent_goal}} by leveraging your knowledge
-and experience in the field.
-
-Always maintain a {{tone}} tone and focus on providing {{focus_area}}.
-```
-
-When rendering, simply pass the variables:
-
-```python
-prompt_data = portkey_admin.prompts.render(
-    prompt_id="YOUR_PROMPT_ID",
-    variables={
-        "agent_role": "Senior Research Scientist",
-        "domain": "artificial intelligence",
-        "agent_goal": "discover groundbreaking insights",
-        "tone": "professional",
-        "focus_area": "practical applications"
-    }
-)
-```
-  </Tab>
-</Tabs>
-
-<Card title="Prompt Engineering Studio" icon="wand-magic-sparkles" href="https://portkey.ai/docs/product/prompt-library">
-  Learn more about Portkey's prompt management features
-</Card>
-
-### 4. Guardrails for Safe Crews
-
-Guardrails ensure your CrewAI agents operate safely and respond appropriately in all situations.
-
-**Why Use Guardrails?**
-
-CrewAI agents can experience various failure modes:
-- Generating harmful or inappropriate content
-- Leaking sensitive information like PII
-- Hallucinating incorrect information
-- Generating outputs in incorrect formats
-
-Portkey's guardrails add protections for both inputs and outputs.
-
-**Implementing Guardrails**
-
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Create LLM with guardrails
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        config={
-            "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
-            "output_guardrails": ["guardrails-id-zzz"]
-        }
-    )
-)
-
-# Create agent with guardrailed LLM
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=portkey_llm
-)
-```
-
-Portkey's guardrails can:
-- Detect and redact PII in both inputs and outputs
-- Filter harmful or inappropriate content
-- Validate response formats against schemas
-- Check for hallucinations against ground truth
-- Apply custom business logic and rules
-
-<Card title="Learn More About Guardrails" icon="shield-check" href="https://portkey.ai/docs/product/guardrails">
-  Explore Portkey's guardrail features to enhance agent safety
-</Card>
-
-### 5. User Tracking with Metadata
-
-Track individual users through your CrewAI agents using Portkey's metadata system.
-
-**What is Metadata in Portkey?**
-
-Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special `_user` field is specifically designed for user tracking.
-
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Configure LLM with user tracking
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        metadata={
-            "_user": "user_123",  # Special _user field for user analytics
-            "user_tier": "premium",
-            "user_company": "Acme Corp",
-            "session_id": "abc-123"
-        }
-    )
-)
-
-# Create agent with tracked LLM
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=portkey_llm
-)
-```
-
-**Filter Analytics by User**
-
-With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:
-
-<Frame caption="Filter analytics by user">
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Metadata%20Filters%20from%20CrewAI.png"/>
-</Frame>
-
-This enables:
-- Per-user cost tracking and budgeting
-- Personalized user analytics
-- Team or organization-level metrics
-- Environment-specific monitoring (staging vs. production)
-
-<Card title="Learn More About Metadata" icon="tags" href="https://portkey.ai/docs/product/observability/metadata">
-  Explore how to use custom metadata to enhance your analytics
-</Card>
-
-### 6. Caching for Efficient Crews
-
-Implement caching to make your CrewAI agents more efficient and cost-effective:
-
-<Tabs>
-  <Tab title="Simple Caching">
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Configure LLM with simple caching
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        config={
-            "cache": {
-                "mode": "simple"
-            }
-        }
-    )
-)
-
-# Create agent with cached LLM
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=portkey_llm
-)
-```
-
-Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.
-  </Tab>
-
-  <Tab title="Semantic Caching">
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Configure LLM with semantic caching
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY",
-        config={
-            "cache": {
-                "mode": "semantic"
-            }
-        }
-    )
-)
-
-# Create agent with semantically cached LLM
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=portkey_llm
-)
-```
-
-Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.
-  </Tab>
-</Tabs>
-
-### 7. Model Interoperability
-
-CrewAI supports multiple LLM providers, and Portkey extends this capability by providing access to over 200 LLMs through a unified interface. You can easily switch between different models without changing your core agent logic:
-
-```python
-from crewai import Agent, LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-# Set up LLMs with different providers
-openai_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_OPENAI_VIRTUAL_KEY"
-    )
-)
-
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    max_tokens=1000,
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY"
-    )
-)
-
-# Choose which LLM to use for each agent based on your needs
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=openai_llm  # Use anthropic_llm for Anthropic
-)
-```
-
-Portkey provides access to LLMs from providers including:
-
-- OpenAI (GPT-4o, GPT-4 Turbo, etc.)
-- Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
-- Mistral AI (Mistral Large, Mistral Medium, etc.)
-- Google Vertex AI (Gemini 1.5 Pro, etc.)
-- Cohere (Command, Command-R, etc.)
-- AWS Bedrock (Claude, Titan, etc.)
-- Local/Private Models
-
-<Card title="Supported Providers" icon="server" href="https://portkey.ai/docs/integrations/llms">
-  See the full list of LLM providers supported by Portkey
-</Card>
-
-## Set Up Enterprise Governance for CrewAI
-
-**Why Enterprise Governance?**
-If you are using CrewAI inside your organization, you need to consider several governance aspects:
-- **Cost Management**: Controlling and tracking AI spending across teams
-- **Access Control**: Managing which teams can use specific models
-- **Usage Analytics**: Understanding how AI is being used across the organization
-- **Security & Compliance**: Maintaining enterprise security standards
-- **Reliability**: Ensuring consistent service across all users
-
-Portkey adds a comprehensive governance layer to address these enterprise needs. Let's implement these controls step by step.
-
-<Steps>
-<Step title="Create Virtual Key">
-Virtual Keys are Portkey's secure way to manage your LLM provider API keys. They provide essential controls like:
-- Budget limits for API usage
-- Rate limiting capabilities
-- Secure API key storage
-
-To create a virtual key:
-Go to [Virtual Keys](https://app.portkey.ai/virtual-keys) in the Portkey App. Save and copy the virtual key ID
-
-<Frame>
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Virtual%20Key%20from%20Portkey%20Docs.png" width="500"/>
-</Frame>
-
-<Note>
-Save your virtual key ID - you'll need it for the next step.
-</Note>
-</Step>
-
-<Step title="Create Default Config">
-Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries.
-
-To create your config:
-1. Go to [Configs](https://app.portkey.ai/configs) in Portkey dashboard
-2. Create new config with:
-    ```json
-    {
-        "virtual_key": "YOUR_VIRTUAL_KEY_FROM_STEP1",
-       	"override_params": {
-          "model": "gpt-4o" // Your preferred model name
-        }
-    }
-    ```
-3. Save and note the Config name for the next step
-
-<Frame>
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20Portkey%20Docs%20Config.png" width="500"/>
-
-</Frame>
-</Step>
-
-<Step title="Configure Portkey API Key">
-Now create a Portkey API key and attach the config you created in Step 2:
-
-1. Go to [API Keys](https://app.portkey.ai/api-keys) in Portkey and Create new API key
-2. Select your config from `Step 2`
-3. Generate and save your API key
-
-<Frame>
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/CrewAI%20API%20Key.png" width="500"/>
-
-</Frame>
-</Step>
-
-<Step title="Connect to CrewAI">
-After setting up your Portkey API key with the attached config, connect it to your CrewAI agents:
-
-```python
-from crewai import Agent, LLM
-from portkey_ai import PORTKEY_GATEWAY_URL
-
-# Configure LLM with your API key
-portkey_llm = LLM(
-    model="gpt-4o",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="YOUR_PORTKEY_API_KEY"
-)
-
-# Create agent with Portkey-enabled LLM
-researcher = Agent(
-    role="Senior Research Scientist",
-    goal="Discover groundbreaking insights about the assigned topic",
-    backstory="You are an expert researcher with deep domain knowledge.",
-    verbose=True,
-    llm=portkey_llm
-)
-```
-</Step>
-</Steps>
-
-<AccordionGroup>
-  <Accordion title="Step 1: Implement Budget Controls & Rate Limits">
-### Step 1: Implement Budget Controls & Rate Limits
-
-Virtual Keys enable granular control over LLM access at the team/department level. This helps you:
-- Set up [budget limits](https://portkey.ai/docs/product/ai-gateway/virtual-keys/budget-limits)
-- Prevent unexpected usage spikes using Rate limits
-- Track departmental spending
-
-#### Setting Up Department-Specific Controls:
-1. Navigate to [Virtual Keys](https://app.portkey.ai/virtual-keys) in Portkey dashboard
-2. Create new Virtual Key for each department with budget limits and rate limits
-3. Configure department-specific limits
-
-<Frame>
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/refs/heads/main/Virtual%20Key%20from%20Portkey%20Docs.png" width="500"/>
-</Frame>
-  </Accordion>
-
-  <Accordion title="Step 2: Define Model Access Rules">
-### Step 2: Define Model Access Rules
-
-As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:
-
-#### Access Control Features:
-- **Model Restrictions**: Limit access to specific models
-- **Data Protection**: Implement guardrails for sensitive data
-- **Reliability Controls**: Add fallbacks and retry logic
-
-#### Example Configuration:
-Here's a basic configuration to route requests to OpenAI, specifically using GPT-4o:
-
-```json
-{
-	"strategy": {
-		"mode": "single"
-	},
-	"targets": [
-		{
-			"virtual_key": "YOUR_OPENAI_VIRTUAL_KEY",
-			"override_params": {
-				"model": "gpt-4o"
-			}
-		}
-	]
-}
-```
-
-  Create your config on the [Configs page](https://app.portkey.ai/configs) in your Portkey dashboard.
-
-    <Note>
-    Configs can be updated anytime to adjust controls without affecting running applications.
-    </Note>
-  </Accordion>
-
-  <Accordion title="Step 3: Implement Access Controls">
-    ### Step 3: Implement Access Controls
-
-    Create User-specific API keys that automatically:
-    - Track usage per user/team with the help of virtual keys
-    - Apply appropriate configs to route requests
-    - Collect relevant metadata to filter logs
-    - Enforce access permissions
-
-    Create API keys through:
-    - [Portkey App](https://app.portkey.ai/)
-    - [API Key Management API](/en/api-reference/admin-api/control-plane/api-keys/create-api-key)
-
-    Example using Python SDK:
-    ```python
-    from portkey_ai import Portkey
-
-    portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")
-
-    api_key = portkey.api_keys.create(
-        name="engineering-team",
-        type="organisation",
-        workspace_id="YOUR_WORKSPACE_ID",
-        defaults={
-            "config_id": "your-config-id",
-            "metadata": {
-                "environment": "production",
-                "department": "engineering"
-            }
-        },
-        scopes=["logs.view", "configs.read"]
-    )
-    ```
-
-    For detailed key management instructions, see our [API Keys documentation](/en/api-reference/admin-api/control-plane/api-keys/create-api-key).
-  </Accordion>
-
-  <Accordion title="Step 4: Deploy & Monitor">
-    ### Step 4: Deploy & Monitor
-    After distributing API keys to your team members, your enterprise-ready CrewAI setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls.
-
-    Monitor usage in Portkey dashboard:
-    - Cost tracking by department
-    - Model usage patterns
-    - Request volumes
-    - Error rates
-  </Accordion>
-
-</AccordionGroup>
-
-<Note>
-### Enterprise Features Now Available
-**Your CrewAI integration now has:**
-- Departmental budget controls
-- Model access governance
-- Usage tracking & attribution
-- Security guardrails
-- Reliability features
-</Note>
-
-## Frequently Asked Questions
-
-<AccordionGroup>
-  <Accordion title="How does Portkey enhance CrewAI?">
-    Portkey adds production-readiness to CrewAI through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 200+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.
-  </Accordion>
-
-  <Accordion title="Can I use Portkey with existing CrewAI applications?">
-    Yes! Portkey integrates seamlessly with existing CrewAI applications. You just need to update your LLM configuration code with the Portkey-enabled version. The rest of your agent and crew code remains unchanged.
-  </Accordion>
-
-  <Accordion title="Does Portkey work with all CrewAI features?">
-    Portkey supports all CrewAI features, including agents, tools, human-in-the-loop workflows, and all task process types (sequential, hierarchical, etc.). It adds observability and reliability without limiting any of the framework's functionality.
-  </Accordion>
-
-  <Accordion title="Can I track usage across multiple agents in a crew?">
-    Yes, Portkey allows you to use a consistent `trace_id` across multiple agents in a crew to track the entire workflow. This is especially useful for complex crews where you want to understand the full execution path across multiple agents.
-  </Accordion>
-
-  <Accordion title="How do I filter logs and traces for specific crew runs?">
-    Portkey allows you to add custom metadata to your LLM configuration, which you can then use for filtering. Add fields like `crew_name`, `crew_type`, or `session_id` to easily find and analyze specific crew executions.
-  </Accordion>
-
-  <Accordion title="Can I use my own API keys with Portkey?">
-    Yes! Portkey uses your own API keys for the various LLM providers. It securely stores them as virtual keys, allowing you to easily manage and rotate keys without changing your code.
-  </Accordion>
-
-</AccordionGroup>
-
-## Resources
-
-<CardGroup cols="3">
-  <Card title="CrewAI Docs" icon="book" href="https://docs.crewai.com/">
-    <p>Official CrewAI documentation</p>
-  </Card>
-  <Card title="Book a Demo" icon="calendar" href="https://calendly.com/portkey-ai">
-    <p>Get personalized guidance on implementing this integration</p>
-  </Card>
-</CardGroup>

docs/en/observability/truefoundry.mdx

@@ -1,146 +0,0 @@
----
-title: TrueFoundry Integration
-icon: chart-line
----
-
-TrueFoundry provides an enterprise-ready [AI Gateway](https://www.truefoundry.com/ai-gateway) which can integrate with agentic frameworks like CrewAI and provides governance and observability for your AI Applications. TrueFoundry AI Gateway serves as a unified interface for LLM access, providing:
-
-- **Unified API Access**: Connect to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) through one API
-- **Low Latency**: Sub-3ms internal latency with intelligent routing and load balancing  
-- **Enterprise Security**: SOC 2, HIPAA, GDPR compliance with RBAC and audit logging
-- **Quota and cost management**: Token-based quotas, rate limiting, and comprehensive usage tracking
-- **Observability**: Full request/response logging, metrics, and traces with customizable retention
-
-## How TrueFoundry Integrates with CrewAI
-
-
-### Installation & Setup
-
-<Steps>
-<Step title="Install CrewAI">
-```bash
-pip install crewai
-```
-</Step>
-
-<Step title="Get TrueFoundry Access Token">
-1. Sign up for a [TrueFoundry account](https://www.truefoundry.com/register)
-2. Follow the steps here in [Quick start](https://docs.truefoundry.com/gateway/quick-start)
-</Step>
-
-<Step title="Configure CrewAI with TrueFoundry">
-![TrueFoundry Code Configuration](/images/new-code-snippet.png)
-
-```python
-from crewai import LLM
-
-# Create an LLM instance with TrueFoundry AI Gateway
-truefoundry_llm = LLM(
-    model="openai-main/gpt-4o",  # Similarly, you can call any model from any provider
-    base_url="your_truefoundry_gateway_base_url",
-    api_key="your_truefoundry_api_key"
-)
-
-# Use in your CrewAI agents
-from crewai import Agent
-
-@agent
-def researcher(self) -> Agent:
-    return Agent(
-        config=self.agents_config['researcher'],
-        llm=truefoundry_llm,
-        verbose=True
-    )
-```
-</Step>
-</Steps>
-
-### Complete CrewAI Example
-
-```python
-from crewai import Agent, Task, Crew, LLM
-
-# Configure LLM with TrueFoundry
-llm = LLM(
-    model="openai-main/gpt-4o",
-    base_url="your_truefoundry_gateway_base_url", 
-    api_key="your_truefoundry_api_key"
-)
-
-# Create agents
-researcher = Agent(
-    role='Research Analyst',
-    goal='Conduct detailed market research',
-    backstory='Expert market analyst with attention to detail',
-    llm=llm,
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer', 
-    goal='Create comprehensive reports',
-    backstory='Experienced technical writer',
-    llm=llm,
-    verbose=True
-)
-
-# Create tasks
-research_task = Task(
-    description='Research AI market trends for 2024',
-    agent=researcher,
-    expected_output='Comprehensive research summary'
-)
-
-writing_task = Task(
-    description='Create a market research report',
-    agent=writer,
-    expected_output='Well-structured report with insights',
-    context=[research_task]
-)
-
-# Create and execute crew
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, writing_task],
-    verbose=True
-)
-
-result = crew.kickoff()
-```
-
-### Observability and Governance
-
-Monitor your CrewAI agents through TrueFoundry's metrics tab:
-![TrueFoundry metrics](/images/gateway-metrics.png)
-
-With Truefoundry's AI gateway, you can monitor and analyze:
-
-- **Performance Metrics**: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
-- **Cost and Token Usage**: Gain visibility into your application's costs with detailed breakdowns of input/output tokens and the associated expenses for each model
-- **Usage Patterns**: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
-- **Rate limit and Load balancing**: You can set up rate limiting, load balancing and fallback for your models
-
-## Tracing
-
-For a more detailed understanding on tracing, please see [getting-started-tracing](https://docs.truefoundry.com/docs/tracing/tracing-getting-started).For tracing, you can add the Traceloop SDK: 
-For tracing, you can add the Traceloop SDK: 
-
-```bash
-pip install traceloop-sdk
-```
-
-```python
-from traceloop.sdk import Traceloop
-
-# Initialize enhanced tracing
-Traceloop.init(
-    api_endpoint="https://your-truefoundry-endpoint/api/tracing",
-    headers={
-        "Authorization": f"Bearer {your_truefoundry_pat_token}",
-        "TFY-Tracing-Project": "your_project_name",
-    },
-)
-```
-
-This provides additional trace correlation across your entire CrewAI workflow.
-![TrueFoundry CrewAI Tracing](/images/tracing_crewai.png)

docs/en/tools/ai-ml/overview.mdx

@@ -1,64 +0,0 @@
----
-title: "Overview"
-description: "Leverage AI services, generate images, process vision, and build intelligent systems"
-icon: "face-smile"
----
-
-These tools integrate with AI and machine learning services to enhance your agents with advanced capabilities like image generation, vision processing, and intelligent code execution.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="DALL-E Tool" icon="image" href="/en/tools/ai-ml/dalletool">
-    Generate AI images using OpenAI's DALL-E model.
-  </Card>
-
-  <Card title="Vision Tool" icon="eye" href="/en/tools/ai-ml/visiontool">
-    Process and analyze images with computer vision capabilities.
-  </Card>
-
-  <Card title="AI Mind Tool" icon="brain" href="/en/tools/ai-ml/aimindtool">
-    Advanced AI reasoning and decision-making capabilities.
-  </Card>
-
-  <Card title="LlamaIndex Tool" icon="llama" href="/en/tools/ai-ml/llamaindextool">
-    Build knowledge bases and retrieval systems with LlamaIndex.
-  </Card>
-
-  <Card title="LangChain Tool" icon="link" href="/en/tools/ai-ml/langchaintool">
-    Integrate with LangChain for complex AI workflows.
-  </Card>
-
-  <Card title="RAG Tool" icon="database" href="/en/tools/ai-ml/ragtool">
-    Implement Retrieval-Augmented Generation systems.
-  </Card>
-
-  <Card title="Code Interpreter Tool" icon="code" href="/en/tools/ai-ml/codeinterpretertool">
-    Execute Python code and perform data analysis.
-  </Card>
-
-
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Content Generation**: Create images, text, and multimedia content
-- **Data Analysis**: Execute code and analyze complex datasets
-- **Knowledge Systems**: Build RAG systems and intelligent databases
-- **Computer Vision**: Process and understand visual content
-- **AI Safety**: Implement content moderation and safety checks
-
-```python
-from crewai_tools import DallETool, VisionTool, CodeInterpreterTool
-
-# Create AI tools
-image_generator = DallETool()
-vision_processor = VisionTool()
-code_executor = CodeInterpreterTool()
-
-# Add to your agent
-agent = Agent(
-    role="AI Specialist",
-    tools=[image_generator, vision_processor, code_executor],
-    goal="Create and analyze content using AI capabilities"
-)

docs/en/tools/automation/overview.mdx

@@ -1,59 +0,0 @@
----
-title: "Overview"
-description: "Automate workflows and integrate with external platforms and services"
-icon: "face-smile"
----
-
-These tools enable your agents to automate workflows, integrate with external platforms, and connect with various third-party services for enhanced functionality.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="Apify Actor Tool" icon="spider" href="/en/tools/automation/apifyactorstool">
-    Run Apify actors for web scraping and automation tasks.
-  </Card>
-
-  <Card title="Composio Tool" icon="puzzle-piece" href="/en/tools/automation/composiotool">
-    Integrate with hundreds of apps and services through Composio.
-  </Card>
-
-  <Card title="Multion Tool" icon="window-restore" href="/en/tools/automation/multiontool">
-    Automate browser interactions and web-based workflows.
-  </Card>
-
-  <Card title="Zapier Actions Adapter" icon="bolt" href="/en/tools/automation/zapieractionstool">
-    Expose Zapier Actions as CrewAI tools for automation across thousands of apps.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Workflow Automation**: Automate repetitive tasks and processes
-- **API Integration**: Connect with external APIs and services
-- **Data Synchronization**: Sync data between different platforms
-- **Process Orchestration**: Coordinate complex multi-step workflows
-- **Third-party Services**: Leverage external tools and platforms
-
-```python
-from crewai_tools import ApifyActorTool, ComposioTool, MultiOnTool
-
-# Create automation tools
-apify_automation = ApifyActorTool()
-platform_integration = ComposioTool()
-browser_automation = MultiOnTool()
-
-# Add to your agent
-agent = Agent(
-    role="Automation Specialist",
-    tools=[apify_automation, platform_integration, browser_automation],
-    goal="Automate workflows and integrate systems"
-)
-```
-
-## **Integration Benefits**
-
-- **Efficiency**: Reduce manual work through automation
-- **Scalability**: Handle increased workloads automatically
-- **Reliability**: Consistent execution of workflows
-- **Connectivity**: Bridge different systems and platforms
-- **Productivity**: Focus on high-value tasks while automation handles routine work

docs/en/tools/automation/zapieractionstool.mdx

@@ -1,58 +0,0 @@
----
-title: Zapier Actions Tool
-description: The `ZapierActionsAdapter` exposes Zapier actions as CrewAI tools for automation.
-icon: bolt
----
-
-# `ZapierActionsAdapter`
-
-## Description
-
-Use the Zapier adapter to list and call Zapier actions as CrewAI tools. This enables agents to trigger automations across thousands of apps.
-
-## Installation
-
-This adapter is included with `crewai-tools`. No extra install required.
-
-## Environment Variables
-
-- `ZAPIER_API_KEY` (required): Zapier API key. Get one from the Zapier Actions dashboard at https://actions.zapier.com/ (create an account, then generate an API key). You can also pass `zapier_api_key` directly when constructing the adapter.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools.adapters.zapier_adapter import ZapierActionsAdapter
-
-adapter = ZapierActionsAdapter(api_key="your_zapier_api_key")
-tools = adapter.tools()
-
-agent = Agent(
-    role="Automator",
-    goal="Execute Zapier actions",
-    backstory="Automation specialist",
-    tools=tools,
-    verbose=True,
-)
-
-task = Task(
-    description="Create a new Google Sheet and add a row using Zapier actions",
-    expected_output="Confirmation with created resource IDs",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes & limits
-
-- The adapter lists available actions for your key and creates `BaseTool` wrappers dynamically.
-- Handle action‑specific required fields in your task instructions or tool call.
-- Rate limits depend on your Zapier plan; see the Zapier Actions docs.
-
-## Notes
-
-- The adapter fetches available actions and generates `BaseTool` wrappers dynamically.
-
-

docs/en/tools/cloud-storage/overview.mdx

@@ -1,50 +0,0 @@
----
-title: "Overview"
-description: "Interact with cloud services, storage systems, and cloud-based AI platforms"
-icon: "face-smile"
----
-
-These tools enable your agents to interact with cloud services, access cloud storage, and leverage cloud-based AI platforms for scalable operations.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="S3 Reader Tool" icon="cloud" href="/en/tools/cloud-storage/s3readertool">
-    Read files and data from Amazon S3 buckets.
-  </Card>
-
-  <Card title="S3 Writer Tool" icon="cloud-arrow-up" href="/en/tools/cloud-storage/s3writertool">
-    Write and upload files to Amazon S3 storage.
-  </Card>
-
-  <Card title="Bedrock Invoke Agent" icon="aws" href="/en/tools/cloud-storage/bedrockinvokeagenttool">
-    Invoke Amazon Bedrock agents for AI-powered tasks.
-  </Card>
-
-  <Card title="Bedrock KB Retriever" icon="database" href="/en/tools/cloud-storage/bedrockkbretriever">
-    Retrieve information from Amazon Bedrock knowledge bases.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **File Storage**: Store and retrieve files from cloud storage systems
-- **Data Backup**: Backup important data to cloud storage
-- **AI Services**: Access cloud-based AI models and services
-- **Knowledge Retrieval**: Query cloud-hosted knowledge bases
-- **Scalable Operations**: Leverage cloud infrastructure for processing
-
-```python
-from crewai_tools import S3ReaderTool, S3WriterTool, BedrockInvokeAgentTool
-
-# Create cloud tools
-s3_reader = S3ReaderTool()
-s3_writer = S3WriterTool()
-bedrock_agent = BedrockInvokeAgentTool()
-
-# Add to your agent
-agent = Agent(
-    role="Cloud Operations Specialist",
-    tools=[s3_reader, s3_writer, bedrock_agent],
-    goal="Manage cloud resources and AI services"
-)

docs/en/tools/database-data/mongodbvectorsearchtool.mdx

@@ -1,168 +0,0 @@
----
-title: MongoDB Vector Search Tool
-description: The `MongoDBVectorSearchTool` performs vector search on MongoDB Atlas with optional indexing helpers.
-icon: "leaf"
----
-
-# `MongoDBVectorSearchTool`
-
-## Description
-
-Perform vector similarity queries on MongoDB Atlas collections. Supports index creation helpers and bulk insert of embedded texts.
-
-MongoDB Atlas supports native vector search. Learn more:
-https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/
-
-## Installation
-
-Install with the MongoDB extra:
-
-```shell
-pip install crewai-tools[mongodb]
-```
-
-or
-
-```shell
-uv add crewai-tools --extra mongodb
-```
-
-## Parameters
-
-### Initialization
-
-- `connection_string` (str, required)
-- `database_name` (str, required)
-- `collection_name` (str, required)
-- `vector_index_name` (str, default `vector_index`)
-- `text_key` (str, default `text`)
-- `embedding_key` (str, default `embedding`)
-- `dimensions` (int, default `1536`)
-
-### Run Parameters
-
-- `query` (str, required): Natural language query to embed and search.
-
-## Quick start
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-  connection_string="mongodb+srv://...",
-  database_name="mydb",
-  collection_name="docs",
-)
-
-print(tool.run(query="how to create vector index"))
-```
-
-## Index creation helpers
-
-Use `create_vector_search_index(...)` to provision an Atlas Vector Search index with the correct dimensions and similarity.
-
-## Common issues
-
-- Authentication failures: ensure your Atlas IP Access List allows your runner and the connection string includes credentials.
-- Index not found: create the vector index first; name must match `vector_index_name`.
-- Dimensions mismatch: align embedding model dimensions with `dimensions`.
-
-## More examples
-
-### Basic initialization
-
-```python Code
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-```
-
-### Custom query configuration
-
-```python Code
-from crewai_tools import MongoDBVectorSearchConfig, MongoDBVectorSearchTool
-
-query_config = MongoDBVectorSearchConfig(limit=10, oversampling_factor=2)
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-    query_config=query_config,
-    vector_index_name="my_vector_index",
-)
-
-rag_agent = Agent(
-    name="rag_agent",
-    role="You are a helpful assistant that can answer questions with the help of the MongoDBVectorSearchTool.",
-    goal="...",
-    backstory="...",
-    tools=[tool],
-)
-```
-
-### Preloading the database and creating the index
-
-```python Code
-import os
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    database_name="example_database",
-    collection_name="example_collection",
-    connection_string="<your_mongodb_connection_string>",
-)
-
-# Load text content from a local folder and add to MongoDB
-texts = []
-for fname in os.listdir("knowledge"):
-    path = os.path.join("knowledge", fname)
-    if os.path.isfile(path):
-        with open(path, "r", encoding="utf-8") as f:
-            texts.append(f.read())
-
-tool.add_texts(texts)
-
-# Create the Atlas Vector Search index (e.g., 3072 dims for text-embedding-3-large)
-tool.create_vector_search_index(dimensions=3072)
-```
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import MongoDBVectorSearchTool
-
-tool = MongoDBVectorSearchTool(
-    connection_string="mongodb+srv://...",
-    database_name="mydb",
-    collection_name="docs",
-)
-
-agent = Agent(
-    role="RAG Agent",
-    goal="Answer using MongoDB vector search",
-    backstory="Knowledge retrieval specialist",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Find relevant content for 'indexing guidance'",
-    expected_output="A concise answer citing the most relevant matches",
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-

docs/en/tools/database-data/overview.mdx

@@ -1,65 +0,0 @@
----
-title: "Overview"
-description: "Connect to databases, vector stores, and data warehouses for comprehensive data access"
-icon: "face-smile"
----
-
-These tools enable your agents to interact with various database systems, from traditional SQL databases to modern vector stores and data warehouses.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="MySQL Tool" icon="database" href="/en/tools/database-data/mysqltool">
-    Connect to and query MySQL databases with SQL operations.
-  </Card>
-
-  <Card title="PostgreSQL Search" icon="elephant" href="/en/tools/database-data/pgsearchtool">
-    Search and query PostgreSQL databases efficiently.
-  </Card>
-
-  <Card title="Snowflake Search" icon="snowflake" href="/en/tools/database-data/snowflakesearchtool">
-    Access Snowflake data warehouse for analytics and reporting.
-  </Card>
-
-  <Card title="NL2SQL Tool" icon="language" href="/en/tools/database-data/nl2sqltool">
-    Convert natural language queries to SQL statements automatically.
-  </Card>
-
-  <Card title="Qdrant Vector Search" icon="vector-square" href="/en/tools/database-data/qdrantvectorsearchtool">
-    Search vector embeddings using Qdrant vector database.
-  </Card>
-
-  <Card title="Weaviate Vector Search" icon="network-wired" href="/en/tools/database-data/weaviatevectorsearchtool">
-    Perform semantic search with Weaviate vector database.
-  </Card>
-
-  <Card title="MongoDB Vector Search" icon="leaf" href="/en/tools/database-data/mongodbvectorsearchtool">
-    Vector similarity search on MongoDB Atlas with indexing helpers.
-  </Card>
-
-  <Card title="SingleStore Search" icon="database" href="/en/tools/database-data/singlestoresearchtool">
-    Safe SELECT/SHOW queries on SingleStore with pooling and validation.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Data Analysis**: Query databases for business intelligence and reporting
-- **Vector Search**: Find similar content using semantic embeddings
-- **ETL Operations**: Extract, transform, and load data between systems
-- **Real-time Analytics**: Access live data for decision making
-
-```python
-from crewai_tools import MySQLTool, QdrantVectorSearchTool, NL2SQLTool
-
-# Create database tools
-mysql_db = MySQLTool()
-vector_search = QdrantVectorSearchTool()
-nl_to_sql = NL2SQLTool()
-
-# Add to your agent
-agent = Agent(
-    role="Data Analyst",
-    tools=[mysql_db, vector_search, nl_to_sql],
-    goal="Extract insights from various data sources"
-)

docs/en/tools/database-data/singlestoresearchtool.mdx

@@ -1,61 +0,0 @@
----
-title: SingleStore Search Tool
-description: The `SingleStoreSearchTool` safely executes SELECT/SHOW queries on SingleStore with pooling.
-icon: circle
----
-
-# `SingleStoreSearchTool`
-
-## Description
-
-Execute read‑only queries (`SELECT`/`SHOW`) against SingleStore with connection pooling and input validation.
-
-## Installation
-
-```shell
-uv add crewai-tools[singlestore]
-```
-
-## Environment Variables
-
-Variables like `SINGLESTOREDB_HOST`, `SINGLESTOREDB_USER`, `SINGLESTOREDB_PASSWORD`, etc., can be used, or `SINGLESTOREDB_URL` as a single DSN.
-
-Generate the API key from the SingleStore dashboard, [docs here](https://docs.singlestore.com/cloud/reference/management-api/#generate-an-api-key).
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import SingleStoreSearchTool
-
-tool = SingleStoreSearchTool(
-    tables=["products"], 
-    host="host", 
-    user="user", 
-    password="pass", 
-    database="db",
-)
-
-agent = Agent(
-    role="Analyst", 
-    goal="Query SingleStore", 
-    tools=[tool], 
-    verbose=True,
-)
-
-task = Task(
-    description="List 5 products", 
-    expected_output="5 rows as JSON/text", 
-    agent=agent,
-)
-
-crew = Crew(
-    agents=[agent], 
-    tasks=[task],
-    verbose=True,
-)
-
-result = crew.kickoff()
-```
-
-

docs/en/tools/file-document/ocrtool.mdx

@@ -1,89 +0,0 @@
----
-title: OCR Tool
-description: The `OCRTool` extracts text from local images or image URLs using an LLM with vision.
-icon: image
----
-
-# `OCRTool`
-
-## Description
-
-Extract text from images (local path or URL). Uses a vision‑capable LLM via CrewAI’s LLM interface.
-
-## Installation
-
-No extra install beyond `crewai-tools`. Ensure your selected LLM supports vision.
-
-## Parameters
-
-### Run Parameters
-
-- `image_path_url` (str, required): Local image path or HTTP(S) URL.
-
-## Examples
-
-### Direct usage
-
-```python Code
-from crewai_tools import OCRTool
-
-print(OCRTool().run(image_path_url="/tmp/receipt.png"))
-```
-
-### With an agent
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-ocr = OCRTool()
-
-agent = Agent(
-    role="OCR", 
-    goal="Extract text", 
-    tools=[ocr],
-)
-
-task = Task(
-    description="Extract text from https://example.com/invoice.jpg", 
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-## Notes
-
-- Ensure the selected LLM supports image inputs.
-- For large images, consider downscaling to reduce token usage.
- - You can pass a specific LLM instance to the tool (e.g., `LLM(model="gpt-4o")`) if needed, matching the README guidance.
-
-## Example
-
-```python Code
-from crewai import Agent, Task, Crew
-from crewai_tools import OCRTool
-
-tool = OCRTool()
-
-agent = Agent(
-    role="OCR Specialist",
-    goal="Extract text from images",
-    backstory="Vision‑enabled analyst",
-    tools=[tool],
-    verbose=True,
-)
-
-task = Task(
-    description="Extract text from https://example.com/receipt.png",
-    expected_output="All detected text in plain text",
-    agent=agent,
-)
-
-crew = Crew(agents=[agent], tasks=[task])
-result = crew.kickoff()
-```
-
-

docs/en/tools/file-document/overview.mdx

@@ -1,96 +0,0 @@
----
-title: "Overview"
-description: "Read, write, and search through various file formats with CrewAI's document processing tools"
-icon: "face-smile"
----
-
-These tools enable your agents to work with various file formats and document types. From reading PDFs to processing JSON data, these tools handle all your document processing needs.
-
-## **Available Tools**
-
-<CardGroup cols={2}>
-  <Card title="File Read Tool" icon="folders" href="/en/tools/file-document/filereadtool">
-    Read content from any file type including text, markdown, and more.
-  </Card>
-
-  <Card title="File Write Tool" icon="file-pen" href="/en/tools/file-document/filewritetool">
-    Write content to files, create new documents, and save processed data.
-  </Card>
-
-  <Card title="PDF Search Tool" icon="file-pdf" href="/en/tools/file-document/pdfsearchtool">
-    Search and extract text content from PDF documents efficiently.
-  </Card>
-
-  <Card title="DOCX Search Tool" icon="file-word" href="/en/tools/file-document/docxsearchtool">
-    Search through Microsoft Word documents and extract relevant content.
-  </Card>
-
-  <Card title="JSON Search Tool" icon="brackets-curly" href="/en/tools/file-document/jsonsearchtool">
-    Parse and search through JSON files with advanced query capabilities.
-  </Card>
-
-  <Card title="CSV Search Tool" icon="table" href="/en/tools/file-document/csvsearchtool">
-    Process and search through CSV files, extract specific rows and columns.
-  </Card>
-
-  <Card title="XML Search Tool" icon="code" href="/en/tools/file-document/xmlsearchtool">
-    Parse XML files and search for specific elements and attributes.
-  </Card>
-
-  <Card title="MDX Search Tool" icon="markdown" href="/en/tools/file-document/mdxsearchtool">
-    Search through MDX files and extract content from documentation.
-  </Card>
-
-  <Card title="TXT Search Tool" icon="file-lines" href="/en/tools/file-document/txtsearchtool">
-    Search through plain text files with pattern matching capabilities.
-  </Card>
-
-  <Card title="Directory Search Tool" icon="folder-open" href="/en/tools/file-document/directorysearchtool">
-    Search for files and folders within directory structures.
-  </Card>
-
-  <Card title="Directory Read Tool" icon="folder" href="/en/tools/file-document/directoryreadtool">
-    Read and list directory contents, file structures, and metadata.
-  </Card>
-
-  <Card title="OCR Tool" icon="image" href="/en/tools/file-document/ocrtool">
-    Extract text from images (local files or URLs) using a vision‑capable LLM.
-  </Card>
-
-  <Card title="PDF Text Writing Tool" icon="file-pdf" href="/en/tools/file-document/pdf-text-writing-tool">
-    Write text at specific coordinates in PDFs, with optional custom fonts.
-  </Card>
-</CardGroup>
-
-## **Common Use Cases**
-
-- **Document Processing**: Extract and analyze content from various file formats
-- **Data Import**: Read structured data from CSV, JSON, and XML files
-- **Content Search**: Find specific information within large document collections
-- **File Management**: Organize and manipulate files and directories
-- **Data Export**: Save processed results to various file formats
-
-## **Quick Start Example**
-
-```python
-from crewai_tools import FileReadTool, PDFSearchTool, JSONSearchTool
-
-# Create tools
-file_reader = FileReadTool()
-pdf_searcher = PDFSearchTool()
-json_processor = JSONSearchTool()
-
-# Add to your agent
-agent = Agent(
-    role="Document Analyst",
-    tools=[file_reader, pdf_searcher, json_processor],
-    goal="Process and analyze various document types"
-)
-```
-
-## **Tips for Document Processing**
-
-- **File Permissions**: Ensure your agent has proper read/write permissions
-- **Large Files**: Consider chunking for very large documents
-- **Format Support**: Check tool documentation for supported file formats
-- **Error Handling**: Implement proper error handling for corrupted or inaccessible files