RAG / enterprise search with live data
Ready-to-run cloud templates for RAG and enterprise search that sync with live data sources via Pathway.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
when you want a simple self-hosted RAG API with local models rather than a customizable template
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
when you want a full RAG engine with UI and agent support
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
when you want a lightweight RAG library to embed in your app
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
when you need graph-based summaries for large-scale RAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
when you want a one-stop RAG framework with easy integration
"RAG-Anything: All-in-One RAG Framework"
when you want a Go-based knowledge platform with agents and wiki
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
when you need an all-in-one embeddable framework for semantic search and LLM workflows
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
when you need a private, storage-efficient RAG for personal devices
[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
when you need a modular Python framework for custom LLM pipelines
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
when you want an agentic data assistant for database-centric use cases
open-source agentic AI data assistant for the next generation of AI + Data products.
when you want a production-ready agentic RAG with REST API
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
when you want a memory layer to replace RAG pipelines
Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
when you want a lightweight enterprise RAG framework with small models
Unified framework for building enterprise RAG pipelines with small, specialized models
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
a narrow PDF-only RAG example; useful only for PDF Q&A
AI PDF chatbot agent built with LangChain & LangGraph
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
an end-user deep research tool, not a RAG template
~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
an end-user deep research tool for private data
Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
an end-user AI chat platform with RAG features
Open Source AI Platform - AI Chat with advanced features that works with every LLM
an end-user document chat web app
An open-source RAG-based tool for chatting with your documents.
an end-user AI platform with UI/API, not template code
Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.
These projects were analysed and named llm-app among their alternatives. The relationship is not symmetric — how llm-app rates them is a separate judgement, made when llm-app is analysed in its own right.
calls llm-app “Choose it when you need ready-to-run templates with live data connectors instead of a self-hosted platform.”
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
calls llm-app “Choose if you need ready-to-run templates with live data synchronization for RAG and AI pipelines.”
Complete API layer for private AI applications on local models: RAG, skills, tools, MCP, text-to-sql, and more. Works with any OpenAI-compatible inference server.
calls llm-app “Use when you need live-data-syncing RAG pipelines with streaming and enterprise connectors; PageIndex is not oriented toward real-time data sync.”
📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
calls llm-app “Choose when you want ready-to-run cloud templates for RAG pipelines that stay in sync with live data sources.”
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
calls llm-app “Choose it when you want deployable pipeline templates with live connectors instead of an API-first retrieval layer.”
Open-source context retrieval layer for AI agents
calls llm-app “Choose when you need ready-to-run RAG pipelines that live-sync with external data sources in a cloud deployment.”
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
calls llm-app “Choose it when you want ready-to-run cloud templates for live-data-synced RAG pipelines instead of an in-process library.”
A modular graph-based Retrieval-Augmented Generation (RAG) system
calls llm-app “Deployment templates for live-data RAG; choose when you want ready-to-run Docker sync rather than building a pipeline.”
"RAG-Anything: All-in-One RAG Framework"
calls llm-app “Choose for ready-made Docker templates for RAG deployment over building your own from R2R.”
SoTA production-ready AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.