Open-Source RAG Frameworks

Langflow is a visual builder for developers creating AI agents and retrieval-augmented generation (RAG) applications. You can run it locally or on your own server, with Docker support and desktop apps for Windows and macOS. The open-source software uses the MIT license. A hosted cloud offering provides a separate deployment option.

LightRAG combines knowledge graphs with vector search to answer questions across a document collection. It's a self-hosted Python framework for developers building document assistants, particularly where answers depend on relationships between facts in different files, such as legal or financial material.

Dify is a source-available platform for teams building AI agents and apps on a visual canvas. Its Community Edition runs on your own server with Docker. Dify also offers a hosted cloud service, while Enterprise deployments can run in a VPC or on a self-hosted server. The Community Edition uses a custom Apache 2.0 derivative license.

RAGFlow is an Apache 2.0 licensed RAG engine for teams building AI agents that need to answer questions from their own documents. It can run on a self-hosted server through Docker on Windows, macOS or Linux. A separate hosted cloud service is available.

Bisheng is an open source, self-hosted platform for teams building AI applications around business documents and processes. Its visual workflow editor combines automated tasks with human feedback, including intervention during multi-turn conversations. It's suited to document review, support ticket assistance and report generation that need more control than a single chatbot exchange.

Flowise is a visual builder for AI agents and chatbots that can run locally or on your own server, including through Docker. It's for developers and teams building LLM applications with connected workflow blocks. The project is archived and no longer maintained.

pgai keeps search embeddings in sync with PostgreSQL data for developers building RAG applications and AI agents. It's a Python library with database components and workers you can self-host, including in Docker. The project is archived and no longer maintained or supported. Its code is open source under the PostgreSQL License.

Quivr Core is a Python framework for developers adding document-based AI answers to their own applications. It combines file ingestion with retrieval-augmented generation (RAG), so a model can answer questions using material from your documents. It supports local models through Ollama as well as cloud APIs from OpenAI, Anthropic, and Mistral.

Cognita is a self-hosted RAG framework for developers building applications that answer questions using their own documents. The project is archived and no longer maintained. It combines a browser interface for document Q&A with reusable components built on LangChain and LlamaIndex, under the Apache 2.0 open-source license.

OGX, formerly Llama Stack, is a self-hosted AI application server for developers building chat apps, document search or AI agents. It brings model inference, file storage, vector search and agent orchestration into one process. You can run it on a laptop, in a datacenter or in the cloud. It's open source under MIT.

DB-GPT is a self-hosted AI data assistant for teams analyzing business data and developers building data applications. It turns plain-language requests into SQL queries and Python analysis, then produces charts, dashboards, or HTML reports. You can run it on macOS or Linux, with Docker deployment also supported.

BotSharp is a self-hosted framework for .NET developers building AI agents into business applications. Written in C#, it runs on Windows, Linux and macOS and is open source software under Apache 2.0. Its plugin design lets teams choose their model provider, storage and interface while keeping agent coordination in the same framework.

Kaito manages self-hosted LLM inference, fine-tuning, and document retrieval services in a Kubernetes cluster. It's for teams that want to run models on infrastructure they control while reducing the work of sizing GPU resources and managing model deployments. The project is open source under Apache 2.0.

LangChainGo is a Go implementation of LangChain for developers building LLM applications in their own software. It connects Go programs to model backends, including Ollama for local LLM use and cloud services such as OpenAI and Gemini. It's a library, so its audience is developers who want to build an application rather than use a ready-made chat interface.

Morphik Core is a self-hosted multimodal retrieval engine for developers building AI applications around visually rich documents. It searches diagrams, schematics, charts, and datasheets alongside text, so applications can retrieve information that text extraction alone can miss. You can run it on your own server, including through Docker, or use Morphik's hosted service.

R2R is a self-hosted AI retrieval system for developers building applications that answer questions using their own documents. It combines search, retrieval-augmented generation (RAG) and a reasoning agent behind a REST API. The project is open source under the MIT license and runs as a Python service or in Docker.

LLPhant is an MIT-licensed PHP framework for adding language models, embeddings and vector databases to Symfony and Laravel applications. It requires PHP 8.1 or later and is installed through Composer.

LangChain is an MIT-licensed open-source framework for developers building AI agents and applications powered by LLMs. It provides a shared interface for models, tools and data connections, so developers can change providers or test workflows without rebuilding the whole application.

Spring AI is an open-source Java framework for developers adding AI to Spring applications. It connects application data and APIs to models through a common interface, with Ollama support for local LLM use and integrations with cloud providers such as OpenAI, Anthropic and Amazon Bedrock. The framework runs within your application; your choice of model provider determines whether model requests stay local or go to a cloud service.

kotaemon is a self-hosted document chat app for people who want to ask questions across their files and check where the answers came from. It runs in a browser on Windows, macOS or Linux, with Docker also supported. The project uses the Apache 2.0 license.

DocsGPT is an MIT-licensed, open-source platform for teams that want AI search, assistants and agents over their own documents. It can run on your servers with local models, including fully air-gapped deployments where documents and questions stay inside your network. Answers include the source title and page number so readers can check the evidence.

PrivateGPT is a self-hosted API layer for developers building AI applications around local models. It adds document retrieval, database access and agent tools to an existing model server. Local workflows can work offline and keep data within your environment; web search and connections to online providers need internet access.

txtai is a Python framework for developers building search applications, chat with their data, and AI agents on their own hardware or servers. Its embeddings database combines sparse and dense vector search with graphs and relational data, so the same system can find related content and supply context to language models. It's open source under Apache 2.0.

DSPy is a Python framework for developers building AI applications whose tasks need clear inputs, predictable output types, and measurable results. You define what a language model should produce, then compose those tasks into a larger program. It's open source under the MIT license.

GraphRAG builds a knowledge graph from text so an LLM can answer questions that depend on connections across documents or themes across a whole collection. It's for developers and researchers working with private datasets, such as business documents, proprietary research, or communications.

LlamaIndex is an MIT-licensed Python framework for developers building AI agents and apps that answer questions using their own data. It connects documents and other sources to language models, then helps an app find the relevant material when a user asks something. Its open source framework can work with models served through Ollama.

FastGPT is a self-hosted AI agent builder for teams that want assistants to answer questions using company documents and carry out business workflows. Its visual editor connects model calls, knowledge retrieval and tools into applications for customer support, internal knowledge search and document review. You can run the platform on your own server through Docker or use the vendor's hosted service.

Cognee gives AI agents persistent memory across sessions, connecting documents, code, and conversations in a searchable knowledge graph. It's for developers who want agents to retain project context and teams whose knowledge sits across tickets, discussions, and repositories. The Python package is open source under Apache 2.0.

LangChain4j is an Apache 2.0 open-source Java library for developers building chatbots, assistants and AI agents in JVM applications. It connects application code to local LLM backends such as Ollama as well as cloud providers such as OpenAI and Google Vertex AI. Where model requests go depends on the backend you choose.

RubyLLM is an MIT-licensed AI framework for developers building Ruby and Rails applications with local or hosted models. Its shared API lets an application switch between Ollama, cloud providers such as Anthropic and OpenAI, and OpenAI-compatible endpoints without rewriting its model integration. The framework runs in your application; model processing happens at the local or hosted backend you choose.

MaxKB is a self-hosted AI agent platform for organizations building customer support bots, internal knowledge assistants and business automation. It combines answers grounded in company documents with workflows that can call functions and MCP tools. You can run it on your own server through Docker and use it through a browser.

PaperQA2 is an open source Python research assistant for people who need answers grounded in a collection of scientific papers. It searches documents on your machine and writes answers with in-text citations, including page references. Researchers can use it to summarize findings or check for contradictions across papers, while developers can build it into their own research tools.

Kiln is a desktop workbench for teams building AI applications on macOS, Windows and Linux. It keeps a task and its dataset together across evaluation, prompt optimization, RAG and fine-tuning, so teams can compare changes against the same examples. Engineers, data scientists, QA staff and subject matter experts can contribute through the app.

Haystack is a Python framework for developers building self-hosted AI agents, document search, and apps that answer questions using their own data. Its modular pipelines let teams control which information reaches a model and inspect how retrieval, memory, tools, and generation contribute to an answer. It's open source under Apache 2.0.