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CrewAI Documentation

website·Indexed Apr 17, 2026

https://docs.crewai.com/

Official documentation for CrewAI, a framework for orchestrating autonomous AI agents. Covers core concepts including Agents, Crews, Flows, Tools, Skills, and Knowledge. Includes guides on CLI usage, checkpointing, memory management, LLM configuration, and production architecture. Features tutorials for building first crews and flows, customizing prompts, and implementing collaborative workflows.

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What's inside CrewAI

  1. Overview of CrewAI Cloud Storage Tools

    CrewAI provides a suite of tools that enable agents to interact with cloud services, storage systems, and cloud-based AI platforms. These tools facilitate scalable operations by allowing agents to read/write files to Amazon S3, invoke Amazon Bedrock agents for AI tasks, and retrieve information from Bedrock knowledge bases.

    Common use cases include:

    • File Storage: Storing and retrieving files from cloud storage.
    • Data Backup: Backing up critical data to cloud storage.
    • AI Services: Accessing cloud-based AI models and services.
    • Knowledge Retrieval: Querying cloud-hosted knowledge bases.
    • Scalable Operations: Leveraging cloud infrastructure for processing tasks.

  2. Overview of CrewAI Database and Vector Tools

    CrewAI provides a suite of tools that enable agents to interact with various data sources, including traditional SQL databases, modern vector stores, and data warehouses. These tools facilitate tasks such as data analysis, vector search, ETL operations, and real-time analytics.

    Key capabilities include:

    • Data Analysis: Querying databases for business intelligence and reporting.
    • Vector Search: Finding similar content using semantic embeddings.
    • ETL Operations: Extracting, transforming, and loading data between systems.
    • Real-time Analytics: Accessing live data for decision-making.

    The available tools cover MySQL, PostgreSQL, Snowflake, NL2SQL, Qdrant, Weaviate, MongoDB, and SingleStore.

  3. Overview of CrewAI Integration Tools

    Integration tools enable CrewAI agents to hand off work to external automation platforms and managed AI services. Use these tools when your workflow needs to invoke an existing CrewAI deployment or delegate specialized tasks to providers like Amazon Bedrock. This allows for chaining automations, enterprise hand-offs, hybrid workflows, and managing long-running jobs by polling external services.

  4. Overview of CrewAI Document Processing Tools

    CrewAI's document processing tools enable agents to read, write, and search across various file formats including PDF, JSON, XML, MDX, CSV, DOCX, TXT, and images. These tools allow agents to extract content, import structured data, search large document collections, manage files and directories, and export processed results.

  5. Overview of CrewAI AI/ML Tools and Common Use Cases

    CrewAI provides a suite of tools that integrate with AI and machine learning services to enhance agents with advanced capabilities. These tools enable image generation, vision processing, intelligent code execution, and the construction of knowledge systems.

    Common use cases include:

    • Content Generation: Creating images, text, and multimedia content.
    • Data Analysis: Executing code to analyze complex datasets.
    • Knowledge Systems: Building Retrieval-Augmented Generation (RAG) systems and intelligent databases.
    • Computer Vision: Processing and understanding visual content.
    • AI Safety: Implementing content moderation and safety checks.

  6. Overview of Search and Research Tools for CrewAI Agents

    CrewAI provides a suite of tools that enable agents to perform web searches, research topics, and find information across various platforms including search engines, GitHub, and YouTube. These tools allow agents to conduct market research, content discovery, code research, lead generation, and academic research.

  7. Overview of CrewAI Automation Tools

    CrewAI automation tools enable agents to automate workflows, integrate with external platforms, and connect with third-party services. These tools enhance agent functionality by handling routine tasks, improving efficiency, scalability, and productivity. Common use cases include workflow automation, API integration, data synchronization, process orchestration, and leveraging external services.

  8. Overview of CrewAI Document Processing Tools

    CrewAI's document processing tools enable agents to read, write, and search across various file formats including PDF, JSON, XML, MDX, TXT, CSV, DOCX, and images. These tools allow agents to extract content, parse structured data, search within large document collections, manage file directories, and export processed results. Key capabilities include reading any file type, writing to files, searching PDFs and structured data formats, and extracting text from images using vision-capable LLMs.

  9. Overview of built-in CrewAI tools and their capabilities

    CrewAI provides a suite of built-in tools that enable agents to perform actions like web scraping, file reading, code interpretation, and searching various data sources. All tools include built-in error handling and support caching to improve performance. Key categories include:

    • Web Scraping & Crawling: ApifyActorsTool, BrowserbaseLoadTool, FirecrawlSearchTool, FirecrawlCrawlWebsiteTool, FirecrawlScrapeWebsiteTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool.
    • File & Data Search (RAG): CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, XMLSearchTool, WebsiteSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool, PGSearchTool, GithubSearchTool.
    • Code & Execution: CodeInterpreterTool.
    • API Integrations: DALL-E Tool, Vision Tool, ComposioTool, LlamaIndexTool, EXASearchTool, SerperDevTool.
    • General: FileReadTool, DirectoryReadTool, RagTool.

  10. Overview of SingleStoreSearchTool for CrewAI

    The SingleStoreSearchTool is a CrewAI tool designed to safely execute SELECT and SHOW queries on a SingleStore database. It utilizes connection pooling to manage database connections efficiently. This tool allows AI agents to retrieve data from SingleStore without exposing the underlying database to unsafe operations.

  11. Overview of CrewAI Database and Vector Tools

    CrewAI provides a suite of tools that enable agents to connect to and interact with various data sources, including traditional SQL databases, modern vector stores, and data warehouses. These tools facilitate tasks such as data analysis, vector search, ETL operations, and real-time analytics.

    The available tools include:

    • MySQLTool: Connect to and query MySQL databases with SQL operations.
    • PostgreSQL Search Tool: Search and query PostgreSQL databases efficiently.
    • Snowflake Search Tool: Access Snowflake data warehouse for analytics and reporting.
    • NL2SQLTool: Convert natural language queries to SQL statements automatically.
    • QdrantVectorSearchTool: Search vector embeddings using the Qdrant vector database.
    • WeaviateVectorSearchTool: Perform semantic search with the Weaviate vector database.
    • MongoDBVectorSearchTool: Perform vector similarity search on MongoDB Atlas with indexing helpers.
    • SingleStoreSearchTool: Execute safe SELECT/SHOW queries on SingleStore with pooling and validation.

  12. Overview of CrewAI AMP Triggers and Integration Playbooks

    CrewAI AMP triggers connect automations to real-time events across tools like Gmail, Outlook, Slack, and Salesforce. Instead of polling, triggers listen for changes (new emails, calendar updates, file edits) and immediately launch a crew or flow. Supported integrations include Gmail, Google Calendar, Google Drive, Outlook, OneDrive, Microsoft Teams, HubSpot, Salesforce, Slack, and Zapier. Each integration has specific playbooks for setup and sample workflows.