AI Agents with LangChain, CrewAI and Llama 3: Build an AI Tweet Writing App | Step-by-Step Tutorial

Introduction

In this tutorial, we will explore how to use AI agents with LangChain and Llama 3 to build a functional application for writing and editing tweets. The app will utilize AI to fetch data from specified URLs, conduct research, and eventually generate tweets based on the gathered information. Let's dive in!

Understanding AI Agents

AI agents are autonomous entities that perform specific tasks, make decisions, and can communicate with other agents. These agents can be thought of as units assigned to individual tasks—upon receiving a task, they utilize functions and tools to complete it.

Moreover, teams of agents can work together, allowing for a comprehensive approach to problem-solving—breaking down complex tasks into simpler, actionable components. As highlighted by Andrew Ng, utilizing agentic design patterns often results in improved performance, with benchmarks showing agent-based methods outperforming non-agent approaches.

The Role of Observability in AI Agents

When developing AI agent applications, it's essential to have observability features in place. Observability allows developers to track the actions and decisions made by the agents. CrewAI provides tools for logging events and prompts, making it easier to debug and understand the agents' workings.

Application Architecture

We will create an application structure that includes a user input system for topics and URLs, a scraper agent to gather webpage contents, a research report generator, a tweet writing agent, and an editing agent to draft multiple tweet versions. The architecture can be summarized as follows:

1. User Input: The user enters a topic, URLs, and suggestions.
2. Scraper Agent: Gathers HTML content from the provided URLs.
3. Research Agent: Analyzes the scraped content and generates a research report.
4. Writer Agent: Creates a tweet based on the report.
5. Editor Agent: Produces three different versions of the original tweet and saves them.

Coding the Application

To build this application, we will set up the project in Python using the Poetry package manager. We will need to create several files and directories, configure our models, and write the core logic for each agent.

Key Steps in Implementation:

1. Setting Up the Project: Create a project directory, add a data directory for storing tweets, and configure a pyproject.toml file for dependencies.
2. Creating Agents: Define agents for scraping, research, writing, and editing tweets, employing tools provided by CrewAI.
3. Building Tasks: Implement tasks that each agent will execute, such as scraping URLs, writing reports, and generating tweets.
4. Adding Observability: Integrate callbacks to log prompts and events for easier debugging.
5. Running the Application: Execute the application and observe how each agent processes input and produces output.

With the setup complete, we can initiate the project by running it through the command line. Each step will generate logs in our workspace, providing insights into the internal operations of each agent.

Results and Output

After executing the project, we can expect to see a markdown file with generated tweets and different variations. It is essential to review the logs to ensure the agents worked correctly and to troubleshoot any issues.

Conclusion

In conclusion, we've designed a tweet-writing assistant that leverages AI agents in a sophisticated yet practical manner. The use of LangChain, CrewAI, and Llama 3 allows us to create a robust platform for automated tweet generation, enabling users to produce content efficiently.

Thank you for joining this tutorial! If you found it helpful, consider subscribing to the channel. Feel free to join our Discord community linked in the description.

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Keywords

AI agents, LangChain, CrewAI, Llama 3, tweet writing app, automated content generation, web scraping, research report generation, observability, programming tutorial

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FAQ

Q1: What is an AI agent?
A1: AI agents are autonomous units that can perform tasks, make decisions, and communicate with other agents. They can be used together to complete complex workflows.

Q2: What libraries are used in this tutorial?
A2: The tutorial uses LangChain, CrewAI, and Llama 3 to build the tweet writing application.

Q3: How does the application fetch data from URLs?
A3: The application uses a scraper agent to extract HTML content from the specified URLs.

Q4: What are the roles of different agents in the application?
A4: The scraper agent gathers content, the research agent analyzes it, the writer agent generates a tweet, and the editor agent produces multiple variations of that tweet.

Q5: How can I debug the AI agents?
A5: Observability features, such as logging events and prompts, are integrated to help track the agents' actions and facilitate debugging.