AI-Powered Multi-Agent Blog Generation System

Overview

This project automates the creation of high-quality, SEO-optimized blog posts using a collaborative team of AI agents. It leverages the CrewAI framework to orchestrate the workflow between different specialized agents, each contributing to a stage of the content creation process, from research to final review.

Features

• Automated Content Pipeline: End-to-end automation of blog post creation, including research, planning, drafting, SEO, and review.
• Specialized AI Agents: Utilizes a team of distinct AI agents, each with a specific role and expertise.
• CrewAI Framework: Built on CrewAI for robust multi-agent task management and orchestration.
• Configurable Parameters:
  • OpenAI models (e.g., "gpt-4o", "gpt-3.5-turbo") can be set in configs.py.
  • OpenAI API temperature is configurable for content generation.
  • Default search query for the ResearchAgent can be modified.
• Web Research: The ResearchAgent uses Browserless API for scraping web content for up-to-date information.
• HTML Parsing: Uses BeautifulSoup for parsing HTML content during research.
• Error Handling: Includes try-except blocks for API calls and other operations.
• Logging: Comprehensive logging is implemented throughout the application, providing insights into the execution flow and aiding in debugging. Log messages are printed to the console.

Workflow / Agents

The system employs a series of AI agents that work sequentially:

1. Research Agent:

   • Goal: Find trending topics (e.g., HR trends) and collect relevant information.
   • Tools: Uses Browserless API for web scraping and BeautifulSoup for HTML parsing.
   • Output: A summary of key findings based on the configured search query.

2. Content Planning Agent:

   • Goal: Create a structured blog post outline based on the research findings.
   • Output: A detailed outline with headings, subheadings, and key points.

3. Content Generation Agent:

   • Goal: Generate a well-structured and engaging blog post based on the provided outline.
   • Output: A draft of the blog post.

4. SEO Optimization Agent:

   • Goal: Optimize the blog post for search engines.
   • Output: The blog post enhanced with relevant keywords, an optimized title, and a meta description.

5. Review Agent:

   • Goal: Proofread and refine the final content for quality, grammar, coherence, and factual accuracy.
   • Output: The final, polished blog post ready for publishing.

Setup and Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Steps

1. Clone the repository:

   git clone https://github.com/AbHIsHEK4011/Multi-Agent-SEO-Blog-Generator.git

2. Navigate to the project directory:

   cd Multi-Agent-SEO-Blog-Generator

3. Install dependencies: Ensure your requirements.txt file is up-to-date (it should include crewai, openai, python-dotenv, requests, and beautifulsoup4). Then run:

   pip install -r requirements.txt

4. Environment Variables: This project requires API keys for OpenAI and Browserless.

   • Create a file named .env in the root of the project directory.

   • Add your API keys to the .env file. Use the following template:

     OPENAI_API_KEY="your_openai_api_key_here"
     BROWSERLESS_API_KEY="your_browserless_api_key_here"

   • Where to get the keys:

     • OpenAI API Key: Visit platform.openai.com/api-keys.
     • Browserless API Key: Visit www.browserless.io and sign up for an API key.

Configuration

Key operational parameters can be configured by directly editing the configs.py file:

• DEFAULT_OPENAI_MODEL (string): Sets the default OpenAI model to be used by the agents (e.g., "gpt-4o", "gpt-3.5-turbo").
• DEFAULT_OPENAI_TEMPERATURE (float): Controls the creativity of the AI's responses. A value between 0.0 (more deterministic) and 1.0 (more creative, up to 2.0 for some models). Default is 0.7.
• RESEARCH_AGENT_SEARCH_QUERY (string): Defines the default search query that the ResearchAgent will use if not overridden. Default is "latest HR trends 2025".

To change these, open configs.py and modify the variable values directly.

Running the Project

1. Ensure all setup and configuration steps are completed.
2. Run the main script from the project root directory:

   python main.py

3. Output:
   • The generated blog post will be saved to a file named crewai_blog_output.txt in the project's root directory.
   • Execution logs, including agent activities and potential errors, will be printed to the console.

Project Structure

.
├── agents/                     # Contains individual agent definitions
│   ├── research_agent.py
│   ├── content_planning_agent.py
│   ├── content_generation_agent.py
│   ├── seo_optimize_agent.py
│   └── review_agent.py
├── tasks/                      # Contains task definitions for CrewAI
│   ├── research_task.py
│   ├── content_planning_task.py
│   ├── content_generation_task.py
│   ├── seo_optimize_task.py
│   └── review_task.py
├── configs.py                  # Project-wide configurations (API keys loaded via dotenv, model params)
├── utils.py                    # Utility functions (e.g., call_openai_api)
├── main.py                     # Main script to initialize and run the CrewAI crew
├── requirements.txt            # Python dependencies for the project
├── .env                        # For storing API keys (not version controlled)
└── README.md                   # This file

To Do / Potential Improvements

• More Sophisticated Content Extraction: Improve the HTML parsing in ResearchAgent to more accurately extract main content from diverse web page structures.
• Keyword Input for SEO Agent: Allow dynamic input of keywords for the SEOOptimizeAgent, perhaps from user input or an earlier analysis step.
• Fact Verification Agent: Introduce an agent specifically for verifying facts and statistics mentioned in the generated content.
• Image Suggestion/Generation Agent: Add an agent to suggest relevant images or even generate them using models like DALL-E.
• Plagiarism Check: Integrate a plagiarism checker.
• Customizable Output Formats: Allow output in different formats (e.g., Markdown, HTML directly).
• Interactive Mode: Allow users to provide input/feedback at different stages of the workflow.
• File-based Logging: Extend current logging to also write to a dedicated log file (e.g., app.log) in addition to console output for easier post-run analysis.

Contributions

Contributions are welcome! If you have suggestions for improvements or find any bugs, please feel free to open an issue or submit a pull request.

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🚀 Happy Automating!