Llm App — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Llm App. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 10, 2026 • Sentiment updated: Jun 23, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on Dev.to, 'The era of passive data analytics is over' with discussions around Google Cloud Labs, and on GitHub, 'Per-repo learnings store + DB-persist extraction on PR-merge' is a trending topic

Pros & Cons

What People Love

Dev.to users praise the ease of use of LLM-app, GitHub users appreciate the innovation in AI agents

Common Complaints

High latency in LLM responses, Difficulty in integrating with existing systems

Biggest Positive: AI Innovation

Biggest Negative: LLM Security

Why Llm App Stands Out

Pathway's AI Pipelines stand out from alternatives by providing a simple and unified application logic for back-end, embedding, and retrieval, making it easier to put AI applications into production. The pipeline's ability to connect and sync with various data sources, including file systems, Google Drive, and PostgreSQL, sets it apart from other solutions. Additionally, the repository's focus on real-time data indexing and hybrid search enables high-accuracy RAG and AI enterprise search at scale. By leveraging the Pathway Live Data framework, these pipelines can serve API requests and expose an HTTP API to connect the frontend, making it a valuable solution for developers.

Built With

Build a live document indexing pipeline for RAG — Pathway's AI Pipelines provide ready-to-deploy LLM App Templates that connect and sync with data sources on your file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, and real-time data APIs, Build a multimodal RAG pipeline with GPT-4o — The pathwaycom/llm-app repository offers a template for multimodal RAG using GPT-4o in the parsing stage to index PDFs and other documents from connected data sources, Build an adaptive RAG app — The adaptive_rag template in this repository reduces token cost in RAG up to 4x while maintaining accuracy, Build a private RAG app with Mistral and Ollama — The private_rag template provides a fully private (local) version of the question_answering_rag RAG pipeline using Pathway, Mistral, and Ollama, Build a slides AI search app — The slides_ai_search template performs multi-modal indexing of PowerPoint and PDF and maintains a live index of your slides

Getting Started

  1. Install the required dependencies by running `pip install -r requirements.txt`
  2. Clone the repository using `git clone https://github.com/pathwaycom/llm-app.git`
  3. Navigate to the desired template directory, such as `cd templates/question_answering_rag/`
  4. Configure the data source connections by editing the `config.json` file
  5. Try the demo REST endpoint by running `python app.py` and accessing `http://localhost:8000` to verify it works

About

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

Official site: https://pathway.com/developers/templates/

Category & Tags

Category: social

Tags: chatbot, hugging-face, llm, llm-local, llm-prompting, llm-security, llmops, machine-learning, open-ai, pathway, rag, real-time, retrieval-augmented-generation, vector-database, vector-index

Market Context

Highly competitive with innovators like TanStack and Google Cloud