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AI Crew for Marketing Strategy

- [CrewAI Framework](#crewai-framework) - [Running the script](#running-the-script) - [Details & Explanation](#details--explanation) - [Contributing](#contributing) - [Support and Contact](#support-and-contact) - [License](#license)

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AI Crew for Matching CVs to Job Proposals

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PDF Knowledge Example

This project demonstrates how to create a Crew of AI agents and tasks using crewAI. It uses a PDF knowledge source to answer user questions based on the content of the PDF. The PDF is loaded from a file and the knowledge source is initialized with it. The project also includes a custom task that uses the knowledge source to answer user questions. You can modify the question in the `main.py` file.

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AI Crew for Recruitment

**DISCALIMER** This example uses cookies to authenticate to LinkedIn, and it's meant only as an example or the selenium tool, using this for real-world applications may violate LinkedIn's terms of service and could lead to your account being banned. We do not endorse or encourage the use of this tool for any real-world applications.

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AI Crew for screenwriting

>>I think that about 70% (or so) people approve of the >>death penalty, even realizing all of its shortcomings. Doesn't this make >>it reasonable? Or are *you* the sole judge of reasonability? >Aside from revenge, what merits do you find in capital punishment?

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starter_template

[More Details about Agent](https://docs.crewai.com/concepts/agents).

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AI Crew for Stock Analysis

- [CrewAI Framework](#crewai-framework) - [Running the script](#running-the-script) - [Details & Explanation](#details--explanation) - [Using GPT 3.5](#using-gpt-35) - [Using Local Models with Ollama](#using-local-models-with-ollama) - [Contributing](#contributing) - [Support and Contact](#support-and-contact) - [License](#license)

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AI Crew for Surprise Travel Planning

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AI Crew for Trip Planning

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Email Auto Responder Flow

Welcome to the Email Auto Responder Flow project, powered by [crewAI](https://crewai.com). This example demonstrates how you can leverage Flows from crewAI to automate the process of checking emails and creating draft responses. By utilizing Flows, the process becomes much simpler and more efficient.

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Lead Score Flow

Welcome to the Lead Score Flow project, powered by [crewAI](https://crewai.com). This example demonstrates how you can leverage Flows from crewAI to automate the process of scoring leads, including data collection, analysis, and scoring. By utilizing Flows, the process becomes much simpler and more efficient.

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Meeting Assistant Flow

Welcome to the Meeting Assistant Flow project, powered by [crewAI](https://crewai.com). This example demonstrates how you can leverage Flows from crewAI to automate the process of managing meetings, including scheduling, note-taking, and follow-up actions. By utilizing Flows, the process becomes much simpler and more efficient.

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Self Evaluation Loop Flow

Welcome to the Self Evaluation Loop Flow project, powered by [crewAI](https://crewai.com). This project showcases a powerful pattern in AI workflows: automatic self-evaluation. By leveraging crewAI's multi-agent system, this flow demonstrates how to set up a Crew that evaluates the responses of other Crews, iterating with feedback to improve results.

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Write a Book Flow

Welcome to the Book Writing Flow, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.

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langchain-ai/open_deep_research구성과 사용법 보기 →

🔬 Open Deep Research

Deep research has broken out as one of the most popular agent applications. This is a simple, configurable, fully open source deep research agent that works across many model providers, search tools, and MCP servers. It's performance is on par with many popular deep research agents ([see Deep Research Bench leaderboard](https://huggingface.co/spaces/Ayanami0730/DeepResearch-Leaderboard)).

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Course Setup

This lesson will cover how to run the code samples of this course.

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Introduction to AI Agents and Agent Use Cases

> _(Click the image above to watch the video for this lesson)_

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Explore AI Agent Frameworks

AI agent frameworks are software platforms designed to simplify the creation, deployment, and management of AI agents. These frameworks provide developers with pre-built components, abstractions, and tools that streamline the development of complex AI systems.

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AI Agentic Design Principles

> _(Click the image above to view video of this lesson)_ # AI Agentic Design Principles

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Tool Use Design Pattern

Tools are interesting because they allow AI agents to have a broader range of capabilities. Instead of the agent having a limited set of actions it can perform, by adding a tool, the agent can now perform a wide range of actions. In this chapter, we will look at the Tool Use Design Pattern, which describes how AI agents can use specific tools to achieve their goals.

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Agentic RAG

This lesson provides a comprehensive overview of Agentic Retrieval-Augmented Generation (Agentic RAG), an emerging AI paradigm where large language models (LLMs) autonomously plan their next steps while pulling information from external sources. Unlike static retrieval-then-read patterns, Agentic RAG involves iterative calls to the LLM, interspersed with tool or function calls and structured outputs. The system evaluates results, refines queries, invokes additional tools if needed, and continues this cycle until a satisfactory solution is achieved.

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Building Trustworthy AI Agents

- How to build and deploy safe and effective AI Agents - Important security considerations when developing AI Agents. - How to maintain data and user privacy when developing AI Agents.

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Planning Design

* Defining a clear overall goal and breaking a complex task into manageable tasks. * Leveraging structured output for more reliable and machine-readable responses. * Applying an event-driven approach to handle dynamic tasks and unexpected inputs.

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Multi-agent design patterns

As soon as you start working on a project that involves multiple agents, you will need to consider the multi-agent design pattern. However, it might not be immediately clear when to switch to multi-agents and what the advantages are.

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Metacognition in AI Agents

> _(Click the image above to view video of this lesson)_ # Metacognition in AI Agents

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AI Agents in Production: Observability & Evaluation

As AI agents move from experimental prototypes to real-world applications, the ability to understand their behavior, monitor their performance, and systematically evaluate their outputs becomes important.

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Using Agentic Protocols (MCP, A2A and NLWeb)

As the use of AI agents grows, so does the need for protocols that ensure standardization, security, and support open innovation. In this lesson, we will cover 3 protocols looking to meet this need - Model Context Protocol (MCP), Agent to Agent (A2A) and Natural Language Web (NLWeb).

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Context Engineering for AI Agents

Understanding the complexity of the application you are building an AI agent for is important to making a reliable one. We need to build AI Agents that effectively manage information to address complex needs beyond prompt engineering.

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Memory for AI Agents

When discussing the unique benefits of creating AI Agents, two things are mainly discussed: the ability to call tools to complete tasks and the ability to improve over time. Memory is at the foundation of creating self-improving agent that can create better experiences for our users.

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