LLM Framework & App Architecture: How the Popular LLM Stack Works Inside
Every LLM application ends up building the same layers: something that turns your data into context (RAG pipelines, indexes, retrievers), something that orchestrates model calls (chains, programs, agents), and something that faces the user (chat UIs, APIs). The projects below are the widely-deployed open source implementations of that stack — frameworks like DSPy and Langchain-Chatchat, interfaces like Open WebUI and NextChat, and retrieval systems like PageIndex.
Each page is an architectural analysis generated from the actual source code — not the docs, not the marketing. Module maps, execution flows, and diagrams that show how these systems really route a prompt from user input to model call and back.
LLM Frameworks & Apps: full architecture breakdowns
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PageIndex Architecture — Python
vectorless, reasoning-based RAG -
nanochat Architecture — Python, FastAPI
LLM Training Program by Andrej Karpathy -
dspy Architecture — Python
Programming model for LLM pipelines -
Langchain-Chatchat Architecture — Python
Enterprise-grade LLM knowledge base and chatbot system -
llama-gpt Architecture — Unknown
Self-hosted ChatGPT-style interface -
open-webui Architecture — Docker, JavaScript, Node.js
ChatGPT-like interface for local and deployed LLMs -
NextChat Architecture — Docker, JavaScript, Next.js
LLM Chat Application
Patterns across the LLM stack
The retrieval pipeline
Ingest, chunk, embed, index, retrieve — the RAG pipeline is the backbone of most LLM apps. The interesting differences are in indexing strategy: vector databases in Langchain-Chatchat versus PageIndex's vector-less, tree-based retrieval.
Orchestration as a first-class layer
Whether it's DSPy compiling declarative programs into prompts or a chat app managing conversation state, every project separates 'deciding what to ask the model' from 'asking the model'. That separation is the framework.
Model abstraction
Self-hosted or API, one model or many — each project has an adapter layer that normalizes providers behind a single interface. How thick that layer is tells you who the project is really built for.
Streaming-first interfaces
Chat UIs like Open WebUI and NextChat are architected around token streaming end to end — from provider response through backend relay to frontend render. It shapes their entire request path.
More AI system architectures
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mindsdb Architecture — Python
AutoML system that integrates ML into SQL workflows -
AI-For-Beginners Architecture — Python
Collection of ready-made AI projects and notebooks
Frequently asked questions
What does an LLM framework actually do?
It manages everything around the model call: assembling context, orchestrating multi-step flows, retrying and parsing outputs, and abstracting model providers. The model does the generation; the framework does the plumbing. The analyses on this page map that plumbing module by module.
How does a RAG pipeline work architecturally?
Documents are ingested, split into chunks, embedded, and stored in an index. At query time the system retrieves the most relevant chunks and injects them into the model's context. Langchain-Chatchat implements the classic vector-database version; PageIndex replaces embeddings with tree-structured reasoning-based retrieval.
What is the difference between an LLM framework and an LLM app?
A framework (DSPy, Langchain-Chatchat's core) provides building blocks for developers to compose; an app (Open WebUI, NextChat) ships a complete user-facing product with UI, auth, and persistence. Architecturally, apps embed a small framework inside — you can see that boundary clearly in the module maps.
Can I get this analysis for my own codebase?
Yes — these pages are generated by Revibe's codebase analysis. Upload a repository or import it from GitHub and you get the same architecture diagrams, module maps, and execution flows for your own project.
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