A stateful, multi-usecase Agentic AI system built using LangGraph, integrating chat, tool-use, web search, AI news summarization, and document-grounded RAG (Retrieval-Augmented Generation).

The project focuses on workflow orchestration, state management, and grounded generation, rather than just LLM prompting.

• Basic Chatbot
  LLM-powered conversational assistant.

• Chatbot with Web Search
  Tool-augmented agent using Tavily search, orchestrated via LangGraph tool nodes.

• AI News Summarizer
  Fetches latest AI news and generates structured markdown summaries.

• Retrieval-Augmented Generation (RAG)
  Upload documents (.txt, .pdf) and ask questions grounded strictly in document content using FAISS vector search.

The system is built as explicit LangGraph workflows:

User Input
↓
LangGraph Router
↓
| Chatbot | Web Agent | AI News | RAG |

• Basic Chatbot using Groq LLM
• Tool-augmented chatbot with Tavily web search
• AI news summarizer pipeline
• RAG with FAISS for document-grounded Q&A

LangGraph • LangChain • FAISS • Groq • Tavily • Streamlit • Python

A stateful, multi-usecase Agentic AI system built using LangGraph, integrating chat, tool-use, web search, AI news summarization, and document-grounded RAG (Retrieval-Augmented Generation).

The project focuses on workflow orchestration, state management, and grounded generation, rather than just LLM prompting.

• Basic Chatbot
  LLM-powered conversational assistant.

• Chatbot with Web Search
  Tool-augmented agent using Tavily search, orchestrated via LangGraph tool nodes.

• AI News Summarizer
  Fetches latest AI news and generates structured markdown summaries.

• Retrieval-Augmented Generation (RAG)
  Upload documents (.txt, .pdf) and ask questions grounded strictly in document content using FAISS vector search.

The system is built as explicit LangGraph workflows:

User Input
↓
LangGraph Router
↓
| Chatbot | Web Agent | AI News | RAG |

• Basic Chatbot using Groq LLM
• Tool-augmented chatbot with Tavily web search
• AI news summarizer pipeline
• RAG with FAISS for document-grounded Q&A

LangGraph • LangChain • FAISS • Groq • Tavily • Streamlit • Python

A stateful, multi-usecase Agentic AI system built using LangGraph, integrating chat, tool-use, web search, AI news summarization, and document-grounded RAG (Retrieval-Augmented Generation).

The project focuses on workflow orchestration, state management, and grounded generation, rather than just LLM prompting.

• Basic Chatbot
  LLM-powered conversational assistant.

• Chatbot with Web Search
  Tool-augmented agent using Tavily search, orchestrated via LangGraph tool nodes.

• AI News Summarizer
  Fetches latest AI news and generates structured markdown summaries.

• Retrieval-Augmented Generation (RAG)
  Upload documents (.txt, .pdf) and ask questions grounded strictly in document content using FAISS vector search.

The system is built as explicit LangGraph workflows:

User Input
↓
LangGraph Router
↓
| Chatbot | Web Agent | AI News | RAG |

• Basic Chatbot using Groq LLM
• Tool-augmented chatbot with Tavily web search
• AI news summarizer pipeline
• RAG with FAISS for document-grounded Q&A

LangGraph • LangChain • FAISS • Groq • Tavily • Streamlit • Python

MIT