This project aims to compare different Retrieval-Augmented Generation (RAG) frameworks in terms of speed and performance.

Agentic RAG using Crew AI

Automate complex business workflows with our Multi-AI-Agent Systems using crewAI. This framework leverages autonomous, role-specific AI agents to collaboratively perform multi-step tasks, enhancing efficiency and accuracy across various domains. Ideal for applications in resume tailoring, website design, research, customer support, and more.

An AI-powered resume tailoring system that automatically optimizes your resume for specific job posting.

Supercharge your AI workflows by combining Anyparser’s advanced content extraction with Crew AI. With this integration, you can effortlessly leverage Anyparser’s document processing and data extraction tools within your Crew AI applications.

A Blog Agent with CrewAI is an AI-powered team that automates blog creation. It includes agents for research, writing, editing, and publishing—working together for efficient content generation. 🚀

A modular, agent-based Retrieval-Augmented Generation (RAG) system that answers questions about The Great Gatsby using the CrewAI framework, integrated with DeepEval for answer evaluation.

This system uses CrewAI's multi-agent architecture to transform YouTube videos into concise, professional summaries with minimal user input

An automated blog writing system that leverages CrewAI to create high-quality, well-researched blog posts. The project implements a multi-agent workflow for researching topics, generating content, and publishing blog posts with minimal human intervention.

Agentic AI refers to artificial intelligence systems designed with autonomous decision-making capabilities, goal-directed behavior, and the ability to interact dynamically with their environment or other agents. For example :- MCP, Phidata, Agno, LangChain, LangGraph, LangSmith, CrewAi .