DB-GPT alternatives: 6 tools compared (October 2026)

LightRAG is an open-source RAG system that extracts entities and relationships from documents into a knowledge graph and uses both the graph and vector search to answer questions. It targets developers who need answers that connect facts across many documents.

Key functions

• Builds a knowledge graph of entities and relations from documents automatically
• Combines graph and vector retrieval, with a reranker as default for mixed queries
• Parses multimodal content through MinerU or Docling

Compared with DB-GPT

• Strongest elsewhere: Hong Kong.

Check before buying: Building the graph sends every document through a language model, so indexing costs grow with the volume of text; test on a sample first. Choose a model provider whose data terms fit your documents, or run open models locally. There is no vendor support contract, so plan who maintains the installation and its storage backends.

MinerU is an open-source toolkit that converts PDFs, scanned images and Office documents into Markdown, JSON and other formats that language models can use. It suits teams building RAG or agent systems on large, mixed document collections.

Key functions

• Four parsing tiers from fast preview to accurate analysis of difficult layouts
• OCR and a visual language model for scans and photos of documents
• Local document library with search and stable citation locators for agents

Compared with DB-GPT

• Strongest elsewhere: China.

Check before buying: Read the MinerU Open Source License: it is based on Apache 2.0 but adds conditions, so have legal review it before commercial use. If you use the hosted service at mineru.net, clarify where documents are processed and on what terms, since we found no prices or data terms on its homepage in September 2026. Self-hosting needs Python 3.10 to 3.14 and GPU capacity for the higher tiers.

ScrapeGraphAI is an open-source Python library that uses language models and graph-based pipelines to extract data from websites and local documents. You state what you need in plain language.

Key functions

• SmartScraperGraph extracts information from a page based on a natural-language prompt
• Works on websites and local XML, HTML, JSON and Markdown files
• Integrations with LangChain, LlamaIndex, CrewAI and an MCP server

Compared with DB-GPT

• Strongest elsewhere: United States.

Check before buying: Each extraction calls a language model, so costs and data flows depend on the model provider you choose; with a cloud model, page content leaves your systems. LLM extraction can return wrong or missing fields, so validate samples before relying on the output. Self-hosting means your team maintains the library and Playwright. The separately sold managed API offers features for bypassing bot protection, with stealth rendering and anti-bot handling included. Using them can breach the target sites' terms of use and is legally risky in Germany, for example under Section 202a of the German Criminal Code (StGB) or database rights. Have their use legally reviewed before deployment.

Crawl4AI is an open-source Python web crawler that converts websites into clean Markdown for retrieval systems, AI agents and data pipelines. It runs as a library, in Docker or as a hosted API.

Key functions

• Outputs LLM-ready Markdown from crawled pages
• Hosted API extracts data with a language model, no LLM key of your own needed (POST /extract)
• MCP endpoint connects Claude Code, Codex, Cursor and OpenCode

Check before buying: Self-hosting means your team operates browsers, proxies and updates; the maintainer is an individual account, so plan for your own maintenance if the project slows down. For the hosted cloud, check where data is processed and whether a DPA is available before sending company data. Confirm that crawling your target sites is permitted. The tool offers features for bypassing bot protection: a stealth mode, an undetected-browser adapter for sites that detect automation, and automatic handling of bot walls in the cloud. Using them can breach the target sites' terms of use and is legally risky in Germany, for example under Section 202a of the German Criminal Code (StGB) or database rights. Have their use legally reviewed before deployment.

Docling is an open-source library that parses PDFs, Office files, images, audio and more into a structured format for generative AI. Teams use it as the first step of a RAG pipeline, especially when documents must stay on their own machines.

Key functions

• Detects page layout, reading order, table structure, formulas and code in PDFs
• OCR for scanned PDFs and images
• GraniteDocling visual language model and chart understanding

Check before buying: Docling is a Python library that your team installs, operates and updates; Python 3.10 or newer is required. OCR and visual models need more compute, so test throughput on your document volume. Some announced features such as metadata extraction are listed as coming soon and are not available yet.

Wren AI is an open-source text-to-SQL engine that answers questions about company databases in plain language. A semantic layer describes tables and metrics so that the generated SQL follows business definitions. It suits data teams that want to run the engine themselves.

Key functions

• Turns natural language questions into SQL through a semantic layer
• LLM-agnostic, you can connect your own models
• MCP server and CLI for AI agents

Compared with DB-GPT

• Publishes a price; DB-GPT quotes on request.
• Strongest elsewhere: United States.

Check before buying: Running the open-source edition means your team handles hosting, updates and model costs. Decide which language model receives table names and questions, since schema information leaves the database. The license file notes that future modules may use AGPL-3.0, so check the license of each component you deploy.