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Agentic AI Projects

This repository contains a collection of Agentic AI workflows built using structured multi-agent architectures. These projects explore how LLM planning, tool usage, reflection loops, evaluation feedback, and continuous improvement can be designed in real-world automated systems.


Project Philosophy

Agentic AI goes beyond simple prompt-based responses. These projects follow a systematic agent design workflow:

Stage Purpose
Initial Planning & Task Decomposition Break down a user problem into logical steps
Reflective Design Think before acting – evaluate steps and plan improvements
Tool Use (Functions & External APIs) Use tools such as databases, file systems, web search, and compute functions
Evals & Error Analysis Use feedback from LLM self-evaluation and/or external evaluators
Iterative Refinement Improve responses using refinement loops
Impact Measurement Show how structured planning improves output quality and reliability

Project Index

S.No Project Name Description Key Concepts
1 Interactive Chart Generator Generates visual insights from CSV files and refines based on feedback File Parsing, Visualization, Reflection
2 SQL Query Generation Agent Validates SQL queries from DB schemas with reflection loops DB Tools, Planning
3 Tool Toggle Agent Chooses and toggles tools based on task goals Decision Routing
4 Content Writing Agentic AI Generates and refines content using constructive feedback Reflection, Evals
5 Research Agentic AI Researches topics using external tools and citation refinement Web Tools, Self-Correction
6 Startup Builder Analyses and idea communicates with 6 different agents and gives the market analysis, gap analysis, gap, scope, mvp features Multi-Agent Workflow
7 Portfolio Generator Analyzes the resume, parses it, generates a portfolio website based on it Resume Parsing, Website Generation
8 Stock Data Visualizer Retreives Stock information using Yahoo finance and plots a graph based on the data. Refines the charts. Uses 5 different agents Stock Data, Visualization, Reflection

🔐 Environment Variables (.env Setup)

Before running any project in this repository, create a .env file in the project root directory and add the following API keys:

OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
TAVILY_API_KEY=your_tavily_api_key
DLAI_TAVILY_BASE_URL=https://api.tavily.com

Tech Stack

  • Python
  • AISuite
  • LLM APIs
  • Plotly / Matplotlib
  • SQLite/Postgres
  • Docker (optional)

Repo Layout

agenticAIProjects/

  • charts_agent
  • sql_agent
  • tool_toggle_agent
  • content_writer_agent
  • research_agent

Contributions

Pull requests are welcome.


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small-scale projects using agentic AI workflows

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