RAGFlow vs txtai
RAGFlow
ragflow.iotxtai
neuml.github.ioSide by side
| Figure | RAGFlow | txtai |
|---|---|---|
| Price from | $29/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 1 |
| Spec score · Retrieval-Augmented Generation Tools | 8.8 (#3) | 8.3 (#9) |
| License | The public repository is labeled Apache-2.0 licensed. | txtai is open-source under the Apache 2.0 license. |
| Purpose | RAGFlow describes itself as an open-source RAG engine and integrated agent platform for building a context layer for AI agents. | txtai is an all-in-one AI framework for semantic search, LLM orchestration, and language model workflows. |
| Security | By default, RAGFlow validates MCP server URLs and rejects hosts that resolve to loopback, private, link-local, reserved, or other non-public addresses. | The API is open over HTTP by default; the documentation describes HTTPS options, API-token authorization, and a safeopen option enabled by default for API and application runs. |
Plans
RAGFlow
- FreeFree
- FreeFree
- Starter$29 a month
- Pro$129 a month
- EnterprisePriced on request
In their own words
RAGFlow
RAGFlow is an open-source retrieval-augmented generation engine and agent platform for building a context layer for AI agents. Its ingestion pipeline processes multiple data formats into semantic representations for retrieval. Search combines vector search and BM25 with custom scoring and re-ranking, while visual workflows connect retrieval, tools, and MCPs for agent orchestration. Users can connect online, local, and OpenAI-compatible model providers for knowledge bases, chats, search, and agents. Documented connectors include Notion, Google Drive, S3, Microsoft Teams, REST API, and Sitemap. RAGFlow provides HTTP and Python APIs, and a chat assistant can be embedded in another webpage using an iframe. It offers cloud and self-hosted deployment; Docker deployment has a stated minimum of 4 CPU cores, 16 GB RAM, and 50 GB disk. The free plan includes five apps, one team member, 0.1 GB of dataset storage, and 500 monthly credits, but no API key. Starter costs $29 a month and includes an API key.
txtai
txtai is an open-source framework for semantic search, language-model orchestration, and language-model workflows. Its embeddings database brings together sparse and dense vector indexes, graph networks, and relational databases. It supports vector search with SQL, object storage, topic modeling, graph analysis, and multimodal indexing, and can create embeddings from text, documents, audio, images, and video. Pipelines cover prompts, question answering, labeling, transcription, translation, and summarization; workflows combine pipelines with business logic, while agents can coordinate embeddings, pipelines, workflows, and other agents. Web and Model Context Protocol APIs are available, alongside JavaScript, Java, Rust, and Go bindings. txtai can run locally or be deployed on a server, cluster, or serverless environment. Install with pip or use Docker; Python 3.11 or later is supported. The software is free under the Apache 2.0 license. The API is open over HTTP by default, with HTTPS and token authorization options documented.