RAG Frameworks, Parsers and Vector Databases

XERJ is a local search engine for developers building AI agents that need to retrieve code, documents or stored knowledge without reading whole files into their context. It runs on a laptop or self-hosted server.

OpenRAG is a self-hosted platform for searching documents and asking questions about them through an AI chat interface. It brings Langflow, OpenSearch, and Docling together for developers and teams that want a document knowledge base with control over its retrieval workflows. It's open source under Apache 2.0 and supports self-managed deployment with Docker or Podman.

Open WebUI is a self-hosted chat workspace for individuals and teams using local LLMs and cloud models. It can work entirely offline with local models.

LiteRT-LM is Google's open-source framework for developers building apps that run language models on the user's device. It uses LiteRT for local inference across mobile devices, desktops and browsers, with GPU and NPU acceleration. The project uses the Apache 2.0 license.

vllm-mlx is a local LLM inference server for developers who want to connect AI apps and coding assistants to models running on an Apple Silicon Mac. One process exposes both OpenAI-compatible and Anthropic-compatible APIs. It's open source under Apache 2.0 and runs on macOS with at least 8 GB of unified memory.

FalkorDB is a self-hosted graph database for developers building GraphRAG applications and AI agents that retrieve connected facts. It stores entities, relationships and their attributes, with Cypher queries and vector search for finding relevant information. Docker runs the database on your own hardware; a separate hosted cloud service is also available.

Neo4j LLM Graph Builder converts documents and web content into knowledge graphs for people building searchable collections or AI applications with Neo4j. It uses an LLM to identify entities, relationships, and their properties.

chromem-go is a vector database for Go developers adding semantic search or retrieval augmented generation (RAG) to an application. It runs inside the app, so it doesn't need a separate database server.

DocsGPT helps teams search company documents and build AI assistants that answer from those materials. It can run inside your network with local models, including an air-gapped deployment where documents and questions stay within that network.

MindsDB Query Engine gives developers building AI agents one SQL interface for business data and document search. It runs as a standalone server and can also provide data access to MindsHub agents.

Haystack is a Python framework for developers building AI agents, document search and applications that answer questions using their own data. Its pipelines give teams control over the information a model receives and the steps that produce an answer.

ThinkRAG answers questions about your own documents using a knowledge base on your laptop. It's for researchers, students and other knowledge workers who want to find information across their files, with particular attention to Chinese text.

funes gives coding agents a searchable memory of past work. It's for developers who want earlier decisions, reasoning and findings to remain available when they start another session or switch agents.

FlagEmbedding is a Python toolkit for developers building semantic search and retrieval-augmented generation (RAG). It runs embedding models to find relevant text and rerankers to refine the order of retrieved results.

Gensim is a free, open-source Python library for developers and researchers working with large text collections on their own machines or servers. It trains models that capture topics and semantic relationships, then uses those representations to find related documents. It runs on Linux, Windows and macOS under the GNU Lesser General Public License v2.1 (LGPL-2.1).

Quivr is a content search and monitoring engine for developers building applications around incoming material and topics they want to follow over time. It's at an evaluation stage, and the project advises against production use because its API may change without notice.

ExtractThinker is a Python library for developers who want to extract structured data from documents using models running locally through Ollama. It also supports OpenAI, Anthropic and Groq. With a cloud provider, extraction sends document content to that service, requires provider credentials and may incur charges.

DB-GPT is a self-hosted AI data assistant for teams analyzing business data and developers building data applications. It connects plain-language questions to database queries and code-based analysis.

LangChain is an open-source framework for developers building AI agents and LLM applications, including apps that run models locally through Ollama. Its Python ChatOllama integration connects to open-weight models such as gpt-oss. You write the application in code, while Ollama handles local inference.

MonkeyOCR is a local AI document parser for developers and researchers who need to extract content from English and Chinese PDFs or images. It recognizes page structure and reading order alongside the content itself.

FAISS is a vector search and clustering library for developers building applications that compare embeddings or other vector data. It runs on your own machine or server and lets you balance search accuracy against speed and memory use.

Ragpi is a self-hosted AI assistant API for teams that want to answer technical questions using their own project material. It lets developers add documentation-based support to a product, website or community.

olmOCR converts scanned and image-based documents into text for researchers and developers preparing LLM datasets. It handles pages whose layout makes ordinary text extraction difficult.

Gitingest prepares code context for developers who want to give an LLM a repository or local directory. It gathers the project structure and file contents into a text digest instead of requiring you to assemble files by hand.

LazyLLM is a Python framework for developers building applications with multiple AI agents. It brings application workflows, model training and evaluation together so teams can refine a prototype using task-specific data.

ColPali is a document retrieval library for developers and researchers building search or retrieval-augmented generation systems. It matches text queries to page images, using visual content alongside text. The colpali-engine package is deprecated, and maintainers recommend Sentence Transformers for new projects and production use.

Flexible GraphRAG is a self-hosted platform for teams building document search and AI chat that use both text and relationships between entities. It extracts knowledge graphs from documents and combines graph retrieval with vector similarity and full-text search. You can run it standalone or in Docker. It's open source under Apache 2.0.

Vearch is a self-hosted, distributed vector database for developers building AI applications that search embedding vectors. It combines similarity search with filters on stored fields, so an application can retrieve related content while applying criteria beyond vector similarity. It's open source under Apache 2.0 and can run on your servers through Docker, Docker Compose, or Kubernetes.

kotaemon is an open source document chat app for people who want to ask questions across their files and inspect the evidence behind an answer. Developers can also use it to build custom document question-answering applications.

MaxKB is an AI agent platform for organizations building support bots, internal knowledge assistants and business automation. It combines answers based on company documents with workflows that can act through connected tools.

Infinity Embeddings is a self-hosted model server for developers building semantic search and retrieval-augmented generation applications. It runs retrieval models on your own hardware and can serve several models together.

BotSharp is a C# framework for .NET developers building AI assistants into business applications. It coordinates agents with different responsibilities and keeps track of conversation state.

PipesHub connects company knowledge to AI search and agents for teams whose files and conversations sit across business apps. It can also supply that context to coding assistants and custom applications.

Data-Juicer is a Python framework for researchers and teams preparing datasets for AI training, evaluation, agents or document retrieval. It processes data on your own machine or across a Ray cluster.

AutoRAG Agent searches documents and connected archives where they already live, then reads the relevant sources to produce answers with file, page or line references. It runs on macOS, Windows and Linux and suits people researching information spread across local files, messages and notes.

RAGatouille is a Python library for developers building retrieval-augmented generation (RAG) applications with ColBERT. It builds and searches document indexes on your own hardware or server, with compressed indexes stored on disk that your application can query directly.