LangChain Enterprise AI Workbench

Enterprise GenAI orchestration platform: multi-agent AI, RAG, local LLM support, MLOps tooling. Next.js/TypeScript frontend; demo data and core logic run client-side.

Live: GitHub Pages

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Key Features

Multi-Agent AI

• Agent routing (Researcher, Coder, Analyst) with task delegation
• Context sharing and coordinated execution
• Streaming responses

RAG Pipeline

• Hybrid search (semantic + keyword/BM25)
• Chunking and metadata filtering
• Private data support

Local LLM

• LLaMA, Mistral, GPT4All, etc.
• Local inference; optional cloud toggle
• On-prem option for sensitive data

LLM Fine-Tuning

• LoRA, QLoRA, PEFT
• Pipeline from data to deployment

MLOps

• Model registry (versioning, lifecycle)
• A/B testing (t-test, chi-square, Mann-Whitney; sample size)
• Experiment tracking (MLflow-like; params/metrics)
• Model monitoring (drift, performance)

Analytics

• Dashboards (Recharts)
• Data profiling
• Statistical tests, time series

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Stack

• Frontend: Next.js 14 (App Router), TypeScript 5.3, React 18.3, Tailwind, Framer Motion, Recharts
• Data: Client-side generation, localStorage persistence, cross-tab sync
• No backend required for core demo features

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Quick Start

Prerequisites: Node.js 18+, npm or yarn

git clone <repository-url>
cd LangChain_Enterprise_Dashboard
npm install
npm run dev

App: http://localhost:3000

Build: npm run build then npm start

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Docs

• Features: Multi-agent (/multi-agent), RAG (/rag), A/B testing (/ab-testing), Experiments (/experiments), Registry (/registry)
• What’s real vs simulated: docs/WHAT_IS_REAL.md
• Full index: docs/README.md

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Use Cases

• Enterprise GenAI apps (secure, scalable)
• R&D and prototyping
• Data analysis and dashboards
• MLOps lifecycle
• Local/private LLM deployment

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Project Structure

├── app/                 # Next.js app
├── components/          # React + page components
├── lib/                 # ab-testing, experiment-tracking, demo-data-generator, persistence
└── public/

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License

MIT — see LICENSE.

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Author

Drake Talley