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Build an AI Social Media Content Generator in 20 Minutes | AI Agents with LangGraph and Llama 3.1

Education

Introduction

In this article, we'll explore how to create a powerful social media content generator using Llama 3.1 and the LangGraph API. Our aim is to transform technical content into engaging posts suitable for Twitter and LinkedIn through an AI agent system. Let's dive in!

Getting Started

If you wish to follow along, there’s a comprehensive text tutorial available for M Expert Pro subscribers within the Bootcamp section. Under the projects tab, you can find the project labeled "Social Media Content with Agents," which includes the full text tutorial, links to the Google Colab notebook, and all the source code utilized in this guide. If you enjoy this content, please consider supporting my work by subscribing to M Expert Pro. Thank you!

Overview of the Implementation

Here’s a simplified diagram of how our execution flow (FL) works:

  1. We start with raw text which we will pass into our system.
  2. This text will first be processed by an "editor" agent, which rewrites the content into a coherent text suitable for further refinement.
  3. It then branches into two separate writers: one focusing on LinkedIn and the other on Twitter.
  4. After these writers complete their drafts, the output is passed to a "supervisor" or "watcher." The supervisor decides if the drafts are complete or if they need further critique.
  5. If the drafts require adjustments, they'll be sent to a LinkedIn critique and a Tweet critique for feedback.
  6. The writers will update their drafts based on the critiques, and this feedback loop will continue until both posts meet the necessary criteria.

This system adopts a looping structure that allows for parallel execution of multiple branches, significantly boosting performance compared to sequential processing.

Prerequisites and Setup

To set up our AI content generator, we need two primary dependencies:

  • The LangChain and Gro API libraries
  • The latest version of LangGraph, specifically using the Llama 3.1 model with 70 billion parameters.

Before diving into the code, remember to upgrade pip to ensure you have the latest dependencies.

Code Implementation

Here's an overview of the key steps involved in our implementation:

  1. Initialization: We’ll import all necessary libraries and set up a random seed for reproducibility. The Llama model is configured with a temperature setting of zero for consistent outputs.

  2. API Key: Store the API key securely, which can be retrieved from console.gro.com.

  3. Prompts Setup: Define specific prompts for the editors, tweet writers, and LinkedIn writers. Each prompt is tailored to extract the right format and structure based on the platform's nuances.

  4. Agent Definitions: Define the editor, Tweet writer, LinkedIn writer, critiques, and supervisor. This involves implementing logic to handle feedback and manage the content lifecycle effectively.

  5. Conditional Execution Logic: Use a supervisory node to handle the conditions for continuing execution based on the number of drafts created.

  6. Compiling and Executing the Graph: Finally, compile the Llama 3.1 model and graph, set up the initial input, and execute the flow, which can produce outputs in approximately 15 seconds.

Example Outputs

Upon execution, the edited text transforms into drafts for both Twitter and LinkedIn. Here’s an overview of what our output might look like:

  • Edited Text: "Exciting news! Introducing Mr. Small, a groundbreaking model designed to enhance your AI experience."

  • Twitter Drafts:

    1. "Introducing Mr. Small—a 22 billion parameter model that balances cost and performance!"
    2. "Imagine achieving state-of-the-art performance without breaking the bank—Meet Mr. Small!"
    3. "Unlock AI performance without the high costs—Discover Mr. Small, delivering improved human alignment."

  • LinkedIn Drafts:

    1. "Revolutionizing AI with Mr. Small—a cost-effective solution balancing performance and flexibility."
    2. "Unlocking AI potential with Mr. Small, our latest model that sets a new standard for efficiency and capability."

Conclusion

This tutorial showcased how to convert technical content into impactful social media posts for both Twitter and LinkedIn using Llama 3.1 and AI agents. If you're interested in building a user-friendly application around this concept, feel free to reach out for guidance.

Thank you for reading! If you found value in this article, please like, share, and subscribe. Join the community by accessing the Discord channel linked below, and consider supporting my work through M Expert Pro.

Keywords

  • AI Content Generator
  • LangGraph
  • Llama 3.1
  • Social Media Posts
  • Twitter
  • LinkedIn
  • AI Agents
  • Raw Text Conversion
  • Feedback Loop
  • Parallel Execution

FAQ

Q: What is the purpose of the AI social media content generator?
A: The generator is designed to convert technical content into engaging social media posts for platforms such as Twitter and LinkedIn using AI agents for optimization and feedback.

Q: What are the main dependencies required for this project?
A: The main dependencies are the LangChain and Gro API libraries, as well as the latest version of LangGraph that uses the Llama 3.1 model.

Q: How long does it take to execute the entire graph?
A: The complete execution of the graph generally takes about 15 seconds, depending on the speed of the Gro API.

Q: Can the prompts for the editor and writers be customized?
A: Yes, the prompts can be tailored to fit the desired tone, structure, and elements that align with the intended audience for each social media platform.

Q: Is this content generator suitable for commercial use?
A: While the model can be experimented with under a free tier for educational purposes, commercial use would be subject to licensing and compliance with the model's deployment terms.