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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 590 |
| 2026-08-01 | 590 |
| 2026-08-02 | 590 |
| 2026-08-06 | 590 |
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growth rate 0.00%/day
# Multi-Modal Researcher This project is a simple research and podcast generation workflow that uses LangGraph with the unique capabilities of Google's Gemini 2.5 model family. It combines three useful features of the Gemini 2.5 model family. You can pass a research topic and, optionally, a YouTube video URL. The system will then perform research on the topic using search, analyze the video, combine the insights, and generate a report with citations as well as a short podcast on the topic for you. It takes advantage of a few of Gemini's native capabilities: - 🎥 [Video understanding and native YouTube tool](https://developers.googleblog.com/en/gemini-2-5-video-understanding/): Integrated processing of YouTube videos - 🔍 [Google search tool](https://developers.googleblog.com/en/gemini-2-5-thinking-model-updates/): Native Google Search tool integration with real-time web results - 🎙️ [Multi-speaker text-to-speech](https://ai.google.dev/gemini-api/docs/speech-generation): Generate natural conversations with distinct speaker voices  ## Quick Start ### Prerequisites - Python 3.11+ - [uv](https://docs.astral.sh/uv/) package manager - Google Gemini API key ### Setup 1. **Clone and navigate to the project**: ```bash git clone https://github.com/langchain-ai/multi-modal-researcher cd mutli-modal-researcher ``` 2. **Set up environment variables**: ```bash cp .env.example .env ``` Edit `.env` and [add your Google Gemini API key](https://ai.google.dev/gemini-api/docs/api-key): ```bash GEMINI_API_KEY=your_api_key_here ``` 3. **Run the development server**: ```bash # Install uv package manager curl -LsSf https://astral.sh/uv/install.sh | sh # Install dependencies and start the LangGraph server uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev --allow-blocking ``` 4. **Access the application**: LangGraph will open in your browser. ```bash ╦ ┌─┐┌┐┌┌─┐╔═╗┬─┐┌─┐┌─┐┬ ┬ ║ ├─┤││││ ┬║ ╦├┬┘├─┤├─┘├─┤ ╩═╝┴ ┴┘└┘└─┘╚═╝┴└─┴ ┴┴ ┴ ┴ - 🚀 API: http://127.0.0.1:2024 - 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024 - 📚 API Docs: http://127.0.0.1:2024/docs ``` 5. Pass a `topic` and optionally a `video_url`. Example: * `topic`: Give me an overview of the idea that LLMs are like a new kind of operating system. * `video_url`: https://youtu.be/LCEmiRjPEtQ?si=raeMN2Roy5pESNG2 <img width="1604" alt="Screenshot 2025-06-24 at 5 13 31 PM" src="https://github.com/user-attachments/assets/6407e802-8932-4cfb-bdf9-5af96050ee1f" /> Result: [🔍 See the example report](./example/report/karpathy_os.md) [▶️ Download the example podcast](./example/audio/karpathy_os.wav) ## Architecture The system implements a LangGraph workflow with the following nodes: 1. **Search Research Node**: Performs web search using Gemini's Google Search integration 2. **Analyze Video Node**: Analyzes YouTube videos when provided (conditional) 3. **Create Report Node**: Synthesizes findings into a comprehensive markdown report 4. **Create Podcast Node**: Generates a 2-speaker podcast discussion with TTS audio ### Workflow ``` START → search_research → [analyze_video?] → create_report → create_podcast → END ``` The workflow conditionally includes video analysis if a YouTube URL is provided, otherwise proceeds directly to report generation. ### Output The system generates: - **Research Report**: Comprehensive markdown report with executive summary and sources - **Podcast Script**: Natural dialogue between Dr. Sarah (expert) and Mike (interviewer) - **Audio File**: Multi-speaker TTS audio file (`research_podcast_*.wav`) ## Configuration The system supports runtime configuration through the `Configuration` class: ### Model Settings - `search_model`: Model for web search (default: "gemini-2.5-flash") - `synthesis_model`: Model for report synthesis (default: "gemini-2.5-flash") - `video_model`:
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Lance Martin
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c27d079ae61e6f18, llm:Repository name 'multi-modal-researcher' under org 'langchain-ai' suggests a LangChain-related project focused on multimodal research tools; no README or topics provided.
matched fp:c27d079ae61e6f18, llm:Repository name 'multi-modal-researcher' under org 'langchain-ai' suggests a LangChain-related project focused on multimodal research tools; no README or topics provided.
matched fp:c27d079ae61e6f18, llm:Repository name 'multi-modal-researcher' under org 'langchain-ai' suggests a LangChain-related project focused on multimodal research tools; no README or topics provided.
