🎙️ MediaMind — Autonomous Media Intelligence Platform

⚡ Multi-Agent AI that turns any podcast, video or transcript into summaries, highlights & social content

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🧩 Tech Badges

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🚀 Live Demo

👉 Try the App Here

🔗 Frontend (Streamlit): https://mediamind-ai.onrender.com/

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📌 Project Overview

MediaMind is a production-grade Autonomous Media Intelligence Platform powered by a multi-agent AI pipeline.

Instead of a single LLM call, it routes every user request through a Supervisor → Specialist agent system — intelligently deciding whether to summarize, extract highlights, or generate social content.

It combines Groq's ultra-fast inference with Hybrid RAG (ChromaDB + BM25), MCP-style tool calling, and real-time YouTube transcript ingestion — all behind a clean, session-aware Streamlit chat UI.

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✨ Key Features

Feature	Description
⚡ Ultra-Fast Inference	Groq LPU running Llama 3.3 70B — sub-2s responses
🧠 Multi-Agent Pipeline	Supervisor routes to Summarize / Highlight / Social agent
📺 YouTube Ingestion	Paste any YouTube URL — transcript fetched, indexed, answered
🔍 Hybrid RAG	ChromaDB vector search (60%) + BM25 keyword search (40%) merged
🔧 MCP Tool Registry	Wikipedia, DuckDuckGo, YouTube Transcript, File Reader — per-agent access control
💬 Multi-Session Chat	Full session history, auto-titles, session switching, export to markdown
💬 Direct Q&A Mode	Ask any question — Q&A Agent answers concisely, no structured reports
🚀 Deployed on Render	Persistent ChromaDB storage — data survives server restarts

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🛠️ Tech Stack

Technology	Purpose
🐍 Python	Core programming
⚡ Groq API	Fast LLM inference (Llama 3.3 70B)
🧠 LangGraph	Agent orchestration (StateGraph)
🔗 LangChain	LLM integration + tool binding
🎨 Streamlit	Frontend UI + multi-session chat
📦 ChromaDB	Vector store (persistent)
📊 BM25 (rank_bm25)	Keyword search for hybrid RAG
🤗 all-MiniLM-L6-v2	Local embeddings — zero API cost
🌐 DuckDuckGo DDGS	Live web search tool
📖 Wikipedia API	Factual enrichment tool
🚀 Render	Cloud deployment

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🏗️ System Architecture

MediaMind
│
├── app.py              # Streamlit UI — multi-session chat, source management
├── agent.py            # LangGraph multi-agent pipeline
├── rag.py              # Hybrid RAG (ChromaDB + BM25)
├── mcp_tools.py        # MCP tool registry (4 tools, per-agent access control)
├── llm.py              # Groq LLM client (3 temperature modes)
├── prompts.py          # All LLM prompts — clean separation of concerns
├── config.py           # Central config — models, RAG params, retry settings
├── requirements.txt
├── .env                # API keys (NOT pushed to GitHub)
└── README.md

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🤖 Agent Pipeline

User Query
    │
    ▼
┌─────────────────────────────────────────────────────┐
│                   Supervisor Node                    │
│          Reads query, decides routing (temp=0.0)     │
└──────┬──────────┬──────────────┬────────────────────┘
       │          │              │                │
       ▼          ▼              ▼                ▼
┌──────────┐ ┌──────────┐ ┌──────────┐  ┌─────────────┐
│Summarize │ │Highlight │ │  Social  │  │   Q&A Agent │
│  Agent   │ │  Agent   │ │  Agent   │  │  (NEW ✨)   │
└────┬─────┘ └────┬─────┘ └────┬─────┘  └──────┬──────┘
     │            │            │               │
     ▼            ▼            ▼               ▼
 Wikipedia    Wikipedia    Web Search      Wikipedia
 Web Search   Web Search    (only)        Web Search
     │            │            │               │
     ▼            ▼            ▼               ▼
  Groq 0.3    Groq 0.0     Groq 0.75       Groq 0.3
  (balanced)  (precise)   (creative)      (balanced)
     │            │            │               │
     └────────────┴────────────┴───────────────┘
                              │
                              ▼
                    Final Response → Chat UI

🤖 Agent Routing Logic

Query type	Example	Routes to
Wants a summary / overview	"summarize this video"	summarize_agent
Wants highlights / key moments	"what are the key points?"	highlight_agent
Wants social media content	"write a LinkedIn post"	social_agent
Asks a direct question	"what does X mean?" / "who is Y?"	qa_agent ✨

How the supervisor decides: If the query contains question words — what, why, how, who, when, explain, define — it always routes to qa_agent. The Q&A Agent answers in 2–5 sentences, grounded in the transcript, with no structured reports or bullet points.

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🔍 Hybrid RAG Pipeline

User Query
    │
    ├──────────────────────────────┐
    ▼                              ▼
ChromaDB Vector Search         BM25 Keyword Search
(semantic similarity)          (exact term matching)
all-MiniLM-L6-v2 embeddings    rank_bm25 BM25Okapi
Top-4 chunks (60% weight)      Top-4 chunks (40% weight)
    │                              │
    └──────────┬───────────────────┘
               ▼
       Merge + Deduplicate
    (vector results get priority)
               │
               ▼
       Top-4 chunks → context string → Agent

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🔧 MCP Tool Registry

Tool	Description	Agent Access
youtube_transcript	Fetches full transcript from YouTube URL	Research agent
web_search	Live DuckDuckGo search for news & trends	All agents
wikipedia_search	Factual background on people & topics	Summarize, Highlight
read_file	Reads local .txt / .srt / .md transcript	Research agent

Each agent gets only the tools it needs — social agent gets web search only, summarize and highlight agents get Wikipedia + web search. This is deliberate architecture, not default behaviour.

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⚙️ Installation Guide

1️⃣ Clone Repository

git clone https://github.com/hari9618/mediamind
cd mediamind

2️⃣ Create Virtual Environment

python -m venv venv
source venv/bin/activate   # Mac/Linux
venv\Scripts\activate      # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Setup Environment Variables

Create a .env file:

GROQ_API_KEY=your_groq_api_key_here

Get your free Groq API key at console.groq.com

5️⃣ Run the App

streamlit run app.py

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🧠 How It Works

1️⃣  User sends a query (or pastes a YouTube URL)
        │
2️⃣  YouTube URL detected? → Fetch transcript → Clear ChromaDB → Re-index
        │
3️⃣  Hybrid RAG retrieval → ChromaDB semantic + BM25 keyword → Top-4 chunks
        │
4️⃣  Supervisor reads query → Routes to Summarize / Highlight / Social / Q&A agent
        │        (question words detected? → qa_agent for direct concise answer)
        │
5️⃣  Agent calls MCP tools (Wikipedia, DuckDuckGo) for real-world enrichment
        │
6️⃣  Agent formats prompt: RAG context + tool results + user query → Groq LLM
        │
7️⃣  Response rendered in chat — markdown or styled highlight cards

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📷 Application Preview

(Add your screenshot here)

<img width="951" height="446" alt="Screenshot 2026-05-09 170729" src="https://github.com/user-attachments/assets/978fbee0-d71f-4b39-9519-98e0de61ecab" />

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📚 What I Learned

✔ LangGraph StateGraph — building real state machines with typed state and conditional edges
✔ Hybrid RAG Engineering — combining vector + keyword search with weighted merging
✔ MCP Tool Architecture — per-agent access control, tool binding, ToolMessage conversations
✔ Multi-Session State Management — Streamlit session_state design for complex apps
✔ Production RAG Deployment — PersistentClient ChromaDB, real-time re-indexing
✔ LLM Temperature Strategy — precise / balanced / creative modes for different task types
✔ YouTube API Integration — youtube-transcript-api v1.x, URL parsing, live ingestion
✔ Intelligent Task Routing — keyword-based intent detection to separate Q&A from generation tasks

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🎯 Future Improvements

🔹 Speaker diarization — identify who said what in transcripts
🔹 Multi-turn Q&A — follow-up questions that remember previous answers in session
🔹 Multi-document RAG — index multiple videos/files simultaneously
🔹 Audio file support — direct .mp3/.wav upload with Whisper transcription
🔹 Scheduled indexing — auto-index new episodes from RSS feeds
🔹 Shareable sessions — export and share full conversation threads

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👨‍💻 Author

Hari Krishna T
AI Engineer | Multi-Agent Systems Builder | Gen AI Developer

🔗 GitHub: github.com/hari9618
🔗 LinkedIn: linkedin.com/in/hari-krishna-thota-06b850275

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⭐ Support

If you like this project:

⭐ Star the repository
📢 Share with others
🍴 Fork and build on top of it

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📢 Tags

AI Multi-Agent LangGraph LangChain Groq RAG ChromaDB BM25 Streamlit YouTube MCP Python Generative AI LLM Render