Open-source Unstructured alternatives

RAGFlow is an open-source engine for retrieval-augmented generation (RAG). It combines RAG with agent capabilities to give large language models a context layer built from your own documents, so applications can answer from real data. It is released under the Apache-2.0 license and, per the repository, is currently at a 1.0.0 release candidate.

It extracts knowledge from unstructured documents with complicated formats using deep document understanding, and offers pre-built agent templates. Recent updates mentioned in the README include website ingestion through sitemaps, Google BigQuery data sources with incremental sync, knowledge compilation that generates wikis, graphs, trees and mind maps, and agentic RAG with multiple thinking modes.

You can try the managed cloud service or deploy it locally by following the local deployment guide. The project provides documentation, a roadmap and a Discord community. It suits developers and enterprises building document question answering and knowledge-based assistants who want control over their data pipeline.

Instill Core is an end-to-end platform for orchestrating data, pipelines and models when building AI-first applications. It is meant for developers and data teams who would otherwise stitch together separate tools for ETL, document processing, model serving and retrieval. The project positions itself as a complete solution for unstructured data, covering ETL, AI readiness, open-source LLM hosting and retrieval-augmented generation (RAG).

The platform is organized around four ideas. Pipelines let you build AI-backed APIs or automated workflows, components are the connectable building blocks that make up those pipelines, artifacts turn unstructured data such as documents, images, audio and video into AI-ready formats, and models can be deployed and monitored without managing GPU infrastructure by hand. The README links cookbooks for parsing PDFs to Markdown, generating structured output from LLMs, web scraping with search, and image segmentation.

Instill Core can be installed locally by following the documented steps, so teams can run it on their own hardware. The codebase combines Python, Go and TypeScript. Its repository metadata lists the license as Other rather than a standard open-source license, so check the terms in the repository before depending on it commercially.