Creating AI Agents and Tools for Newsletter Automation

AI agents coordinate to create a newsletter by researching the web via EXA to summarize and populate an HTML template.

### Creating a Team of AI Agents to Generate Newsletters Using CreAI and EXA
* 📰 Learn how to use CreAI to **coordinate a team of AI agents** that can automatically **research and summarize the latest news on any topic** to create newsletters.
* 🤖 CreAI is a framework that allows you to orchestrate AI agents with different expertise to **solve problems collaboratively**.
* 🔎 EXA is a semantic search engine that searches by meaning, making it more suitable for language models that **think by meaning** rather than keywords.
* 🐍 Create a Conda environment and install **CreAI** to get started with the project.
* 📝 Plan your automation by defining **inputs, tasks, agents, and tools** before writing the prompts.
* 🧠 Define the **role, goal, and backstory** for each agent based on the tasks they will perform.
* 🛠 Create tools using EXA's API to give your agents the ability to **search the web** and **access external services**.

### Building an Automated Newsletter Generation System with Langchain and AI Language Models
* 🤖 The system consists of agents that perform different tasks: **Researcher** (queries the internet for relevant news), **Editor** (enhances the news titles and provides context), and **Generator** (creates the final newsletter).
* ⚙ Each task is implemented as a method within the `Crew` class, and the agents communicate with each other to **pass information and results**.
* 🔍 The Researcher task searches the internet for news related to the specified topic and retrieves the **titles, summaries, and URLs** of the articles.
* ✏ ️ The Editor task enhances the news titles for **better engagement**, provides **context for each story**, and **reorders the stories**.
* 📰 The final task is the Generator, which **fills an HTML template** with the information provided by the Editor and **returns the complete newsletter**.
* 🔑 The system uses the Langchain framework to interact with AI language models, and the default model is **GPT-4**.
* 🗝 To use different language models, such as Anthropic's Clot and Gro, the corresponding Langchain integrations are **installed**, and the models are **initialized** within the `Crew` class.
* 📈 The system also includes logging functionality, with each task output saved to a **separate file** in the `logs` directory.
* 🌐 The next step is to build a graphical user interface (GUI) for the system to make it more **user-friendly and accessible**.

CrewAI + Exa: Generate a Newsletter with Research Agents (Part 1)

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Alejandro AO - Software & Ai

AI agents can now be coordinated to research the internet and summarize the most recent news on any topic, replacing the need for a manual marketing team.

The process involves using the Crei framework to create a virtual environment, install the necessary dependencies, and generate a project structure.

Planning the agents and tasks is crucial before populating them, in order to ensure the success of the automation.

CreAI is a framework for orchestrating AI autonomous agents.

The planning for this kind of automation involves defining inputs and outputs, specifying the tasks involved, identifying the agents responsible for the tasks, and determining the tools needed for the agents to complete the tasks.

The agents, powered by AI, can recursively call a language model and ask each other questions, while also requiring access to external services for more sophisticated tasks.

Writing prompts for GPT automation involves brainstorming ideas, optimizing them through trial and error, and providing clear descriptions and rules for the tasks.

Using one-shot or few-shot examples in the prompts can help ensure consistent results.

It's also important to use markdown markup to help the language model identify different parts of the prompt.

The process of writing prompts for GPT automation is lengthy and requires a lot of trial and error to get them right.

Creating Prompts for Newsletter Generation Crew

A step-by-step guide to creating prompts for the researcher, editor, and designer in the newsletter generation crew.

The prompts include the role, goal, and backstory for each agent, with a focus on thinking like a manager to define the employee's background.

The process involves populating a class for the crew, assigning configurations from yaml files, and setting up the tools for each agent.

Setting Up Agents, Tasks, and Tools for AI Project

The configuration involves creating agents with specific tasks and setting up tools for their use.

The "HTML composer" agent is restricted from seeking help, aiming to minimize resource usage.

Three tasks, "research task," "edit task," and "newsletter task," are assigned to different agents for completion.

The next step is to establish a research toolset using EXA, a semantic search engine, to enable the "research agent" to find relevant information for the project's needs.

Improving Search Results with Semantic AI Technology

Semantic search engines like EXA provide more relevant results by focusing on the meaning of the query.

EXA's API offers 3,000 free requests per month, making it a cost-effective option.

By utilizing EXA's semantic search capabilities, the research agent can enhance the quality of its internet searches.

A tool is created to retrieve search results with the option to include HTML text and specify the maximum characters.

Another tool is developed to find similar articles to a given URL, using the EXA API and a run method to execute the search.

Lastly, a tool is built to get the contents of a specific article by taking the article ID as input and using the EXA API to retrieve the content, with the option to input a list of article IDs.

Testing the Functionality of News Research Tools

The user tests three different tools for retrieving and analyzing news content.

The first tool successfully retrieves the latest news on the USA stock market.

The second tool, "find similar," effectively identifies articles similar to a given URL.

The third tool, "get contents," accurately extracts and prints the contents of a specific article URL from the EXA index.

The process involves setting up inputs for the newsletter topic, personal message, and HTML template, then running the automation using Poetry scripts.

The automation uses AI to gather content, enhance titles, provide context, and reorder stories before filling out the HTML template.

Creating a Newsletter Generation Automation

The automation successfully generates a newsletter with stock market updates and relevant stories.

Additional features were added to save the newsletter file automatically and to use multiple language models.

The next step involved building a graphical user interface for the application.

Installing and Initializing Anthropic and Groc Language Models

To implement Anthropic and Groc language models, the integrations need to be installed and API keys added to the environment.

The language models are then initialized using specific parameters, such as the model name and maximum tokens.

Anthropic is a paid model, while Groc has a free version with rate limits.

Logging can be added to each step of the automation to track the output of each language model.

The automation successfully stores output files for each task in a designated "logs" folder.

It retrieves information on the real estate market in San Francisco and generates a newsletter in HTML format.

Some potential issues with the number of returned stories from the editor are mentioned, but overall, the automation is deemed successful.