### Quick Setup Summary

Source: https://docs.crewai.com/v1.15.2/en/observability/neatlogs

A summary of the installation and initialization steps required to start capturing agent runs.

```bash
pip install neatlogs

import neatlogs
neatlogs.init("YOUR_API_KEY")
```

--------------------------------

### LangDB and CrewAI Quick Start

Source: https://docs.crewai.com/v1.15.2/en/observability/langdb

A complete example demonstrating initialization, LLM configuration, and agent execution.

```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)
```

--------------------------------

### Install Linkup SDK

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/linkupsearchtool

Command to install the necessary SDK for the LinkupSearchTool.

```shell
uv add linkup-sdk
```

--------------------------------

### Install Dependencies

Source: https://docs.crewai.com/v1.15.2/en/concepts/flows

Installs the necessary project dependencies.

```bash
crewai install
```

--------------------------------

### Install Daytona Tools

Source: https://docs.crewai.com/v1.15.2/en/tools/ai-ml/daytona

Install the necessary package using pip or uv.

```shell
uv add "crewai-tools[daytona]"
# or
pip install "crewai-tools[daytona]"
```

--------------------------------

### Install Weaviate Client

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/weaviatevectorsearchtool

Install the necessary Weaviate client library to enable tool functionality.

```shell
uv add weaviate-client
```

--------------------------------

### Install SingleStore Tool

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/singlestoresearchtool

Install the necessary package to use the SingleStore search tool.

```shell
uv add crewai-tools[singlestore]
```

--------------------------------

### Install Custom Tool

Source: https://docs.crewai.com/v1.15.2/en/guides/tools/publish-custom-tools

Methods for installing the published tool using pip or uv.

```bash
pip install crewai-geolocate
```

```bash
uv add crewai-geolocate
```

--------------------------------

### Install Patronus Package

Source: https://docs.crewai.com/v1.15.2/en/observability/patronus-evaluation

Install the required Patronus package using uv.

```shell
uv add patronus
```

--------------------------------

### Install Qdrant Client

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/qdrantvectorsearchtool

Install the necessary dependency to use the Qdrant vector search tool.

```bash
uv add qdrant-client
```

--------------------------------

### Install MCP dependencies

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/youai-search

Install the required packages for connecting to the You.com MCP server.

```shell
# For DSL (MCPServerHTTP) — recommended
pip install "mcp>=1.0"

# For MCPServerAdapter — when you need more control
pip install "crewai-tools[mcp]>=0.1"
```

--------------------------------

### Install Scrapfly SDK

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/scrapflyscrapetool

Install the required Scrapfly SDK dependency using uv.

```shell
uv add scrapfly-sdk
```

--------------------------------

### Install Traceloop SDK

Source: https://docs.crewai.com/v1.15.2/en/observability/truefoundry

Install the required SDK via pip to enable tracing capabilities.

```bash
pip install traceloop-sdk
```

--------------------------------

### Install Bedrock Knowledge Base Retriever Tool

Source: https://docs.crewai.com/v1.15.2/en/tools/cloud-storage/bedrockkbretriever

Install the necessary package using uv to enable the tool.

```bash
uv pip install 'crewai[tools]'
```

--------------------------------

### Get Availability Items Parameter

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/google_calendar

Example structure for the items parameter when checking calendar availability.

```json
[
  {
    "id": "calendar_id"
  }
]
```

--------------------------------

### Install Multiple Native Providers

Source: https://docs.crewai.com/v1.15.2/en/learn/litellm-removal-guide

Command to install necessary extras for using multiple native providers simultaneously.

```bash
uv add "crewai[openai,anthropic,gemini]"
```

--------------------------------

### Create and Navigate to React Project

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/react-component-export

Commands to initialize a new React application and enter the project directory.

```bash
npx create-react-app my-crew-app
```

```bash
cd my-crew-app
```

--------------------------------

### Start Development Server

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/react-component-export

Command to launch the local development server.

```bash
npm start
```

--------------------------------

### GET /getRequiredInputs

Source: https://docs.crewai.com/v1.15.2/en/api-reference/inputs

Retrieves the list of required input parameters needed to start a specific crew execution.

```APIDOC
## GET /getRequiredInputs

### Description
Use this endpoint to discover what inputs you need to provide when starting a crew execution.

### Method
GET

### Endpoint
/getRequiredInputs

### Response
#### Success Response (200)
- **inputs** (array) - Array of required input parameter names

#### Response Example
{
  "inputs": [
    "budget",
    "interests",
    "duration",
    "age"
  ]
}

#### Error Responses
- **401** - Authentication failed - check your bearer token
- **404** - Resource not found
- **500** - Internal server error
```

--------------------------------

### Initiate Crew Assistant Conversation

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/enable-crew-studio

Example prompt to provide to the Crew Assistant when starting a new project in Crew Studio.

```markdown
I need a crew that can research the latest AI developments and create a summary report.
```

--------------------------------

### Initialize WebsiteSearchTool

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/websitesearchtool

Demonstrates how to initialize the tool for general searches or for a specific website.

```python
from crewai_tools import WebsiteSearchTool

# Example of initiating tool that agents can use 
# to search across any discovered websites
tool = WebsiteSearchTool()

# Example of limiting the search to the content of a specific website, 
# so now agents can only search within that website
tool = WebsiteSearchTool(website='https://example.com')
```

--------------------------------

### Deployment Creation Output

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/deploy-to-amp

Example output after successfully creating a deployment.

```shell
Deployment created successfully!
Name: your_project_name
Deployment ID: 01234567-89ab-cdef-0123-456789abcdef
Current Status: Deploy Enqueued
```

--------------------------------

### Agent Setup Prompt

Source: https://docs.crewai.com/index

A prompt template for configuring a development environment for CrewAI, including Python version checks, CLI installation, and project initialization.

```javascript
async (event) => {
    const prompt = `Set up this environment so I can build with CrewAI.

First install the official CrewAI coding-agent skills if this environment supports npx:

npx skills add crewaiinc/skills

If npx is missing or the current agent cannot load skills, do not fail the whole setup. Report the exact issue and continue using the CrewAI docs directly.

Use these CrewAI docs as source of truth before making assumptions:
- https://skills.crewai.com
- https://docs.crewai.com/llms.txt
- https://docs.crewai.com/en/installation
- https://docs.crewai.com/en/guides/coding-tools/build-with-ai

Setup steps:
1. Check python3 --version. CrewAI requires Python >=3.10 and <3.14.
2. Install uv if missing:
curl -LsSf https://astral.sh/uv/install.sh | sh
3. Source the uv environment if needed:
source "$HOME/.local/bin/env"
4. Install the CrewAI CLI:
uv tool install crewai
5. Verify the CLI:
crewai version
crewai create --help
6. Create a project:
CREWAI_DMN=true crewai create
7. After project creation, inspect the generated files before editing.
8. Run:
crewai install
crewai run

Do not hardcode API keys. Use .env.
Do not invent CLI flags. Validate with crewai --help or crewai create --help.
If a command fails, show the exact command and error, explain the likely cause, fix what you can safely fix, and retry once.`;
    const button = event.currentTarget;
    const resetTimeout = button.dataset.resetTimeout;
    if (resetTimeout) {
      window.clearTimeout(Number(resetTimeout));
    }
    try {
      await navigator.clipboard.writeText(prompt);
      button.textContent = "Copied";
    } catch {
      button.textContent = "Copy failed";
    } finally {
      button.dataset.resetTimeout = String(window.setTimeout(() => {
        button.textContent = "Copy agent setup prompt";
        delete button.dataset.resetTimeout;
      }, 1600));
    }
  }
```

--------------------------------

### Coding Agent Setup Prompt

Source: https://docs.crewai.com/v1.15.2/en/installation

A comprehensive prompt for configuring coding agents like Claude Code or Cursor to work with CrewAI. It includes steps for installing dependencies, setting up the CLI, and initializing a project.

```bash
Set up this environment so I can build with CrewAI.

First install the official CrewAI coding-agent skills if this environment supports npx:

npx skills add crewaiinc/skills

If npx is missing or the current agent cannot load skills, do not fail the whole setup. Report the exact issue and continue using the CrewAI docs directly.

Use these CrewAI docs as source of truth before making assumptions:
- https://skills.crewai.com
- https://docs.crewai.com/llms.txt
- https://docs.crewai.com/en/installation
- https://docs.crewai.com/en/guides/coding-tools/build-with-ai

Setup steps:
1. Check python3 --version. CrewAI requires Python >=3.10 and <3.14.
2. Install uv if missing:
curl -LsSf https://astral.sh/uv/install.sh | sh
3. Source the uv environment if needed:
source "$HOME/.local/bin/env"
4. Install the CrewAI CLI:
uv tool install crewai
5. Verify the CLI:
crewai version
crewai create --help
6. Create a project:
CREWAI_DMN=true crewai create
7. After project creation, inspect the generated files before editing.
8. Run:
crewai install
crewai run

Do not hardcode API keys. Use .env.
Do not invent CLI flags. Validate with crewai --help or crewai create --help.
If a command fails, show the exact command and error, explain the likely cause, fix what you can safely fix, and retry once.
```

--------------------------------

### Instantiate an Agent in Python

Source: https://docs.crewai.com/v1.15.2/en/concepts/agents

Comprehensive example showing all available parameters for the Agent class.

```python
from crewai import Agent
from crewai_tools import SerperDevTool

# Create an agent with all available parameters
agent = Agent(
    role="Senior Data Scientist",
    goal="Analyze and interpret complex datasets to provide actionable insights",
    backstory="With over 10 years of experience in data science and machine learning, "
              "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
    verbose=False,  # Default: False
    allow_delegation=False,  # Default: False
    max_iter=20,  # Default: 20 iterations
    max_rpm=None,  # Optional: Rate limit for API calls
    max_execution_time=None,  # Optional: Maximum execution time in seconds
    max_retry_limit=2,  # Default: 2 retries on error
    allow_code_execution=False,  # Default: False
    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
    system_template=None,  # Optional: Custom system prompt template
    prompt_template=None,  # Optional: Custom prompt template
    response_template=None,  # Optional: Custom response template
    step_callback=None,  # Optional: Callback function for monitoring
)
```

--------------------------------

### Basic Google Drive Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/google_drive

Initializes an agent with full Google Drive capabilities by including 'google_drive' in the apps list.

```python
from crewai import Agent, Task, Crew

# Create an agent with Google Drive capabilities
drive_agent = Agent(
    role="File Manager",
    goal="Manage files and folders in Google Drive efficiently",
    backstory="An AI assistant specialized in document and file management.",
    apps=['google_drive']  # All Google Drive actions will be available
)

# Task to organize files
organize_files_task = Task(
    description="List all files in the root directory and organize them into appropriate folders",
    agent=drive_agent,
    expected_output="Summary of files organized with folder structure"
)

# Run the task
crew = Crew(
    agents=[drive_agent],
    tasks=[organize_files_task]
)

crew.kickoff()
```

--------------------------------

### Interact directly with an agent using kickoff()

Source: https://docs.crewai.com/v1.15.2/en/concepts/agents

Demonstrates initializing an agent and executing a direct query using the kickoff method.

```python
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)
```

--------------------------------

### Install crewai_tools

Source: https://docs.crewai.com/v1.15.2/en/tools/file-document/docxsearchtool

Install the necessary packages to use the DOCXSearchTool.

```shell
uv pip install docx2txt 'crewai[tools]'
```

--------------------------------

### Install CrewAI

Source: https://docs.crewai.com/v1.15.2/en/concepts/cli

Install the library to enable CLI functionality.

```shell
pip install crewai
```

--------------------------------

### Initialize Tool with Predefined Parameters

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/scrapeelementfromwebsitetool

Configure the tool with a specific website URL and CSS selector upon initialization.

```python
# Initialize the tool with predefined parameters
scrape_tool = ScrapeElementFromWebsiteTool(
    website_url="https://www.example.com",
    css_element=".main-content"
)
```

--------------------------------

### Install SeleniumScrapingTool dependencies

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/seleniumscrapingtool

Install the necessary packages to use the SeleniumScrapingTool.

```shell
pip install 'crewai[tools]'
uv add selenium webdriver-manager
```

--------------------------------

### Basic Shopify Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/shopify

Initializes an agent with full Shopify capabilities and executes a customer creation task.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['shopify']  # All Shopify actions will be available
)

# 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()
```

--------------------------------

### Initialize and Use SerpApiGoogleShoppingTool

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/serpapi-googleshoppingtool

Example showing how to instantiate the tool and assign it to a CrewAI agent for searching products.

```python
from crewai import Agent, Task, Crew
from crewai_tools import SerpApiGoogleShoppingTool

tool = SerpApiGoogleShoppingTool()

agent = Agent(
    role="Shopping Researcher",
    goal="Find relevant products",
    backstory="Expert in product search",
    tools=[tool],
    verbose=True,
)

task = Task(
    description="Search Google Shopping for 'wireless noise-canceling headphones'",
    expected_output="Top relevant products with titles and links",
    agent=agent,
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
```

--------------------------------

### Install Hyperbrowser SDK

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/hyperbrowserloadtool

Install the necessary SDK to use the HyperbrowserLoadTool.

```shell
uv add hyperbrowser
```

--------------------------------

### Basic GitHub Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/github

Initializes an agent with full GitHub capabilities by setting the apps parameter to ['github'].

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['github']  # All Github actions will be available
)

# 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()
```

--------------------------------

### Basic Asana Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/asana

Initializes an agent with full access to all Asana tools by including 'asana' in the apps list.

```python
from crewai import Agent, Task, Crew

# 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.",
    apps=['asana']  # All Asana actions will be available
)

# 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()
```

--------------------------------

### Install LlamaIndex

Source: https://docs.crewai.com/v1.15.2/en/tools/ai-ml/llamaindextool

Install the necessary LlamaIndex package via uv.

```shell
uv add llama-index
```

--------------------------------

### Install React Dependencies

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/react-component-export

Command to install the required react-dom package.

```bash
npm install react-dom
```

--------------------------------

### Basic ClickUp Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/clickup

Initializes an agent with full ClickUp capabilities to manage tasks and projects.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['clickup']  # All Clickup actions will be available
)

# 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()
```

--------------------------------

### Install Scrapegraph Python Client

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/scrapegraphscrapetool

Install the necessary dependency to use the ScrapegraphScrapeTool.

```shell
uv add scrapegraph-py
```

--------------------------------

### Example Planning Output

Source: https://docs.crewai.com/v1.15.2/en/concepts/planning

Sample output generated by the AgentPlanner showing the step-by-step logic for task execution.

```markdown
[2024-07-15 16:49:11][INFO]: Planning the crew execution
**Step-by-Step Plan for Task Execution**

**Task Number 1: Conduct a thorough research about AI LLMs**

**Agent:** AI LLMs Senior Data Researcher

**Agent Goal:** Uncover cutting-edge developments in AI LLMs

**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs

**Task Tools:** None specified

**Agent Tools:** None specified

**Step-by-Step Plan:**

1. **Define Research Scope:**

   - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.

2. **Identify Reliable Sources:**

   - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).

3. **Collect Data:**

   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.

4. **Analyze Findings:**

   - Read and summarize the key points from each source.
   - Highlight new techniques, models, and applications introduced in the past year.

5. **Organize Information:**

   - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
   - Ensure each bullet point is concise but informative.

6. **Create the List:**

   - Compile the 10 most relevant pieces of information into a bullet point list.
   - Review the list to ensure clarity and relevance.

**Expected Output:**

A list with 10 bullet points of the most relevant information about AI LLMs.

---

**Task Number 2: Review the context you got and expand each topic into a full section for a report**

**Agent:** AI LLMs Reporting Analyst

**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings

**Task Expected Output:** A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'

**Task Tools:** None specified

**Agent Tools:** None specified

**Step-by-Step Plan:**

1. **Review the Bullet Points:**
   - Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.

2. **Outline the Report:**
   - Create an outline with each bullet point as a main section heading.
   - Plan sub-sections under each main heading to cover different aspects of the topic.

3. **Research Further Details:**
   - For each bullet point, conduct additional research if necessary to gather more detailed information.
   - Look for case studies, examples, and statistical data to support each section.

4. **Write Detailed Sections:**
   - Expand each bullet point into a comprehensive section.
   - Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
   - Use markdown formatting for headings, subheadings, lists, and emphasis.

5. **Review and Edit:**
   - Proofread the report for clarity, coherence, and correctness.
   - Make sure the report flows logically from one section to the next.
   - Format the report according to markdown standards.

6. **Finalize the Report:**
   - Ensure the report is complete with all sections expanded and detailed.
   - Double-check formatting and make any necessary adjustments.

**Expected Output:**
A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
```

--------------------------------

### Basic Google Docs Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/google_docs

Initializes an agent with general Google Docs capabilities to create and manage documents.

```python
from crewai import Agent, Task, Crew

# Create an agent with Google Docs capabilities
docs_agent = Agent(
    role="Document Creator",
    goal="Create and manage Google Docs documents efficiently",
    backstory="An AI assistant specialized in Google Docs document creation and editing.",
    apps=['google_docs']  # All Google Docs actions will be available
)

# Task to create a new document
create_doc_task = Task(
    description="Create a new Google Document titled 'Project Status Report'",
    agent=docs_agent,
    expected_output="New Google Document 'Project Status Report' created successfully"
)

# Run the task
crew = Crew(
    agents=[docs_agent],
    tasks=[create_doc_task]
)

crew.kickoff()
```

--------------------------------

### Install Stagehand Python SDK

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/stagehandtool

Install the required dependency to use the StagehandTool.

```bash
pip install stagehand-py
```

--------------------------------

### Install Dependencies

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/scrapeelementfromwebsitetool

Install the necessary packages to enable web scraping functionality.

```shell
uv add requests beautifulsoup4
```

--------------------------------

### Initialize ExaSearchTool with highlights

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/exasearchtool

Configure the tool to return token-efficient excerpts and integrate it into a CrewAI Agent.

```python
# Get token-efficient excerpts most relevant to the query
exa_tool = ExaSearchTool(
    highlights=True,
    type="auto",
)

# Use it in an agent
agent = Agent(
    role="Researcher",
    goal="Answer questions with current web data",
    tools=[exa_tool]
)
```

--------------------------------

### Install Snowflake dependencies

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/snowflakesearchtool

Commands to install the necessary packages for using the SnowflakeSearchTool.

```shell
uv add cryptography snowflake-connector-python snowflake-sqlalchemy
```

```shell
uv sync --extra snowflake
```

--------------------------------

### Basic Notion Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/notion

Initializes an agent with full Notion capabilities to manage workspace users.

```python
from crewai import Agent, Task, Crew

# Create an agent with Notion capabilities
notion_agent = Agent(
    role="Workspace Manager",
    goal="Manage workspace users and facilitate collaboration through comments",
    backstory="An AI assistant specialized in user management and team collaboration.",
    apps=['notion']  # All Notion actions will be available
)

# Task to list workspace users
user_management_task = Task(
    description="List all users in the workspace and provide a summary of team members",
    agent=notion_agent,
    expected_output="Complete list of workspace users with their details"
)

# Run the task
crew = Crew(
    agents=[notion_agent],
    tasks=[user_management_task]
)

crew.kickoff()
```

--------------------------------

### Initialize ImageFile from various sources

Source: https://docs.crewai.com/v1.15.2/en/concepts/files

Demonstrates creating an ImageFile instance from a local file path, a URL, or raw byte data.

```python
from crewai_files import ImageFile

image = ImageFile(source="./images/chart.png")
```

```python
from crewai_files import ImageFile

image = ImageFile(source="https://example.com/image.png")
```

```python
from crewai_files import ImageFile, FileBytes

image_bytes = download_image_from_api()
image = ImageFile(source=FileBytes(data=image_bytes, filename="downloaded.png"))
image = ImageFile(source=image_bytes)
```

--------------------------------

### Basic SharePoint Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/microsoft_sharepoint

Initializes an agent with full SharePoint access to list sites and organize content.

```python
from crewai import Agent, Task, Crew

# Create an agent with SharePoint capabilities
sharepoint_agent = Agent(
    role="SharePoint Manager",
    goal="Manage SharePoint sites, lists, and documents efficiently",
    backstory="An AI assistant specialized in SharePoint content management and collaboration.",
    apps=['microsoft_sharepoint']  # All SharePoint actions will be available
)

# Task to organize SharePoint content
content_organization_task = Task(
    description="List all accessible SharePoint sites and organize content by department",
    agent=sharepoint_agent,
    expected_output="SharePoint sites listed and content organized by department"
)

# Run the task
crew = Crew(
    agents=[sharepoint_agent],
    tasks=[content_organization_task]
)

crew.kickoff()
```

--------------------------------

### Install MultiOn Package

Source: https://docs.crewai.com/v1.15.2/en/tools/automation/multiontool

Install the necessary MultiOn package via uv.

```shell
uv add multion
```

--------------------------------

### Install OpenLIT SDK

Source: https://docs.crewai.com/v1.15.2/en/observability/openlit

Install the OpenLIT Python package via pip.

```shell
pip install openlit
```

--------------------------------

### Basic Box Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/box

Initializes an agent with full Box capabilities by including 'box' in the apps list.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['box']  # All Box actions will be available
)

# 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()
```

--------------------------------

### Install Required Packages

Source: https://docs.crewai.com/v1.15.2/en/observability/weave

Install the necessary dependencies for CrewAI and Weave integration.

```shell
pip install crewai weave
```

--------------------------------

### Initialize and Use NL2SQLTool

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/nl2sqltool

Configure the tool with a database URI and assign it to an agent.

```python
from crewai_tools import NL2SQLTool

# psycopg2 was installed to run this example with PostgreSQL
nl2sql = NL2SQLTool(db_uri="postgresql://example@localhost:5432/test_db")

@agent
def researcher(self) -> Agent:
    return Agent(
        config=self.agents_config["researcher"],
        allow_delegation=False,
        tools=[nl2sql]
    )
```

--------------------------------

### Install Neatlogs SDK

Source: https://docs.crewai.com/v1.15.2/en/observability/neatlogs

Install the required Python package for Neatlogs integration.

```bash
pip install neatlogs
```

--------------------------------

### Basic Stripe Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/stripe

Initializes an agent with Stripe capabilities to perform basic customer creation tasks.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['stripe']  # All Stripe actions will be available
)

# 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()
```

--------------------------------

### Install Dependencies

Source: https://docs.crewai.com/v1.15.2/en/observability/langfuse

Install the necessary packages including langfuse, openlit, and crewai.

```python
%pip install langfuse openlit crewai crewai_tools
```

--------------------------------

### Install Galileo dependencies

Source: https://docs.crewai.com/v1.15.2/en/observability/galileo

Install the necessary Galileo package using uv.

```bash
uv add galileo 
```

--------------------------------

### Basic HubSpot Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/hubspot

Initializes an agent with full HubSpot capabilities by including 'hubspot' in the apps list.

```python
from crewai import Agent, Task, Crew

# 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.",
    apps=['hubspot']  # All HubSpot actions will be available
)

# 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()
```

--------------------------------

### Install CrewAI CLI

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/deploy-to-amp

Install the CrewAI CLI with necessary tools dependencies.

```bash
pip install crewai[tools]
```

--------------------------------

### Initialize and Use LinkupSearchTool

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/linkupsearchtool

Example showing how to initialize the tool with an API key and assign it to a CrewAI agent.

```python
from crewai_tools import LinkupSearchTool
from crewai import Agent
import os

# Initialize the tool with your API key
linkup_tool = LinkupSearchTool(api_key=os.getenv("LINKUP_API_KEY"))

# Define an agent that uses the tool
@agent
def researcher(self) -> Agent:
    '''
    This agent uses the LinkupSearchTool to retrieve contextual information
    from the Linkup API.
    '''
    return Agent(
        config=self.agents_config["researcher"],
        tools=[linkup_tool]
    )
```

--------------------------------

### Initialize and Use SingleStoreSearchTool

Source: https://docs.crewai.com/v1.15.2/en/tools/database-data/singlestoresearchtool

Configure the tool with database credentials and integrate it into a CrewAI agent.

```python
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()
```

--------------------------------

### Install Oxylabs tools

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/oxylabsscraperstool

Install the necessary packages to use Oxylabs scrapers with CrewAI.

```shell
pip install 'crewai[tools]' oxylabs
```

--------------------------------

### Basic Linear Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/linear

Initializes an agent with full access to Linear capabilities to manage issues.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['linear']  # All Linear actions will be available
)

# 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()
```

--------------------------------

### Initialize and Configure RagTool

Source: https://docs.crewai.com/v1.15.2/en/tools/ai-ml/ragtool

Demonstrates how to instantiate the RagTool, add data sources, and assign it to an agent.

```python
from crewai_tools import RagTool

# Create a RAG tool with default settings
rag_tool = RagTool()

# Add content from a file
rag_tool.add(data_type="file", path="path/to/your/document.pdf")

# Add content from a web page
rag_tool.add(data_type="web_page", url="https://example.com")

# Define an agent with the RagTool
@agent
def knowledge_expert(self) -> Agent:
	'''
	This agent uses the RagTool to answer questions about the knowledge base.
	'''
	return Agent(
		config=self.agents_config["knowledge_expert"],
		allow_delegation=False,
		tools=[rag_tool]
	)
```

--------------------------------

### Install Browserbase SDK and CrewAI Tools

Source: https://docs.crewai.com/v1.15.2/en/tools/web-scraping/browserbaseloadtool

Install the necessary packages to use the BrowserbaseLoadTool.

```shell
pip install browserbase 'crewai[tools]'
```

--------------------------------

### Basic Gmail Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/gmail

Initializes an agent with full Gmail capabilities by including 'gmail' in the apps list.

```python
from crewai import Agent, Task, Crew

# Create an agent with Gmail capabilities
gmail_agent = Agent(
    role="Email Manager",
    goal="Manage email communications and messages efficiently",
    backstory="An AI assistant specialized in email management and communication.",
    apps=['gmail']  # All Gmail actions will be available
)

# 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()
```

--------------------------------

### Set Up Environment Variables

Source: https://docs.crewai.com/v1.15.2/en/observability/langfuse

Configure the required API keys and host settings for Langfuse and OpenAI.

```python
import os
 
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..." 
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
 
 
# Your OpenAI key
os.environ["OPENAI_API_KEY"] = "sk-proj-..."
```

--------------------------------

### Install Databricks Tool

Source: https://docs.crewai.com/v1.15.2/en/tools/search-research/databricks-query-tool

Install the necessary dependencies for the Databricks tool using uv.

```shell
uv add crewai-tools[databricks-sdk]
```

--------------------------------

### Install S3ReaderTool dependencies

Source: https://docs.crewai.com/v1.15.2/en/tools/cloud-storage/s3readertool

Install the required boto3 library to enable S3 interaction.

```shell
uv add boto3
```

--------------------------------

### Project Entry Points

Source: https://docs.crewai.com/v1.15.2/en/enterprise/guides/prepare-for-deployment

Define how to execute the project, whether via CLI or Python entry points.

```bash
crewai run
```

```python
# src/my_crew/main.py
from my_crew.crew import MyCrew

def run():
    """Run the crew."""
    inputs = {'topic': 'AI in Healthcare'}
    result = MyCrew().crew().kickoff(inputs=inputs)
    return result

if __name__ == "__main__":
    run()
```

```python
# src/my_flow/main.py
from crewai.flow import Flow, listen, start
from my_flow.crews.poem_crew.poem_crew import PoemCrew

class MyFlow(Flow):
    @start()
    def begin(self):
        # Flow logic here
        result = PoemCrew().crew().kickoff(inputs={...})
        return result

def kickoff():
    """Run the flow."""
    MyFlow().kickoff()

if __name__ == "__main__":
    kickoff()
```

--------------------------------

### Install E2B tools

Source: https://docs.crewai.com/v1.15.2/en/tools/ai-ml/e2bsandboxtools

Install the necessary dependencies for using E2B tools with CrewAI.

```shell
uv add "crewai-tools[e2b]"
```

--------------------------------

### Basic Jira Agent Setup

Source: https://docs.crewai.com/v1.15.2/en/enterprise/integrations/jira

Initializes an agent with Jira capabilities and executes a bug report creation task.

```python
from crewai import Agent, Task, Crew
from crewai import Agent, Task, Crew

# 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.",
    apps=['jira']  # All Jira actions will be available
)

# 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()
```

--------------------------------

### Install Minds SDK

Source: https://docs.crewai.com/v1.15.2/en/tools/ai-ml/aimindtool

Install the required Minds SDK package via shell.

```shell
uv add minds-sdk
```