RAGFlow vs Supermemory

RAGFlow and Supermemory are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

RAGFlow

Open-source RAG engine with deep document understanding, hybrid search, and agentic workflow orchestration.

Supermemory

Supermemory wraps memory, retrieval, user profiling, data connectors, and document extraction into one API so your agent doesn't reassemble context from scratch on every request. The retrieval layer claims sub-300ms latency using hybrid search with reranking, and the memory layer maintains a knowledge graph that merges contradictions and evolves facts over time rather than appending chunks blindly. Connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 sync automatically — no ETL pipeline to maintain. The core memory engine is proprietary and hosted-only; self-hosting requires an enterprise agreement, so teams with strict data residency requirements hit a wall before they ship.

Attribute	RAGFlow	Supermemory
Pricing	Paid	Paid
Price	$29/mo	$0 - $399+/mo
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	No
Platforms	Docker, Kubernetes, Linux, macOS, cloud (cloud.ragflow.io)	Cloud-hosted (SaaS); MCP server; Browser plugins (Chrome); IDE integrations (Claude Code, Cursor, VS Code)
Released	2024-04	2024
Pros	Deep document understanding and structure recognition reduce noise and hallucinations Unified agentic platform—RAG, tools, and MCPs in one orchestration layer Fully open source, self-hostable, and enterprise-ready deployment options Rich visual UI with workflow builder, citation tracking, and chunking visualization Active community and rapid iteration; frequent feature and model updates	Knowledge graph memory that merges and contradicts facts across sessions, which means your agent doesn't tell a user something they already corrected two conversations ago. Sub-300ms hybrid search with reranking baked into the retrieval layer, so you avoid building and tuning a separate retrieval pipeline to hit production latency targets. Persistent user profiles that carry preference, behavior, and identity context across sessions, which means a support agent or personalized chatbot doesn't reset its understanding of the user on every ticket. Real-time connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 with automatic sync, so your agent's memory reflects live changes in the tools your users actually work in — no manual import jobs to maintain. Multi-format extraction for PDFs, web pages, images, and audio consolidated into one provider, which means you don't wire together separate parsing services before you can ingest mixed document types.
Cons	Complex stack requiring Docker, Elasticsearch or Infinity, MySQL, MinIO, Redis—steep DevOps overhead Slower time-to-value for prototyping compared to managed SaaS alternatives Documentation and community libraries smaller than mature frameworks like LangChain	The core memory engine is not self-hostable without an enterprise agreement — teams with data residency requirements or strict policies against sending user memory to a third-party managed service cannot deploy this in production without negotiating a contract first, and most either wait on procurement or replace the memory layer with a self-managed vector store. The knowledge graph and memory update logic are proprietary and closed; when retrieval behaves unexpectedly — returning stale facts or failing to surface a contradiction — there is no source code to inspect. Teams debugging production retrieval issues work from API responses and vendor support, not from the system itself. The free tier is capped at defined token and query limits, meaning a team validating the tool at scale will exhaust the free tier before they have enough production data to make a confident architecture decision — at which point cost exposure begins before the build is complete. Agent frameworks that manage their own memory or context windows require explicit integration work to hand off to Supermemory rather than their native store; teams already deep in a framework with memory primitives — LangGraph, for example — often find the integration layer adds complexity that exceeds the benefit for their specific architecture and abandon Supermemory in favor of the framework's native memory tooling.

Bottom line

Only RAGFlow can be self-hosted; RAGFlow runs on Docker, Kubernetes, Linux, macOS, cloud (cloud.ragflow.io); Supermemory on Cloud-hosted (SaaS); MCP server; Browser plugins (Chrome); IDE integrations (Claude Code, Cursor, VS Code). Pick the difference that actually blocks you.

Frequently asked questions

What is the difference between RAGFlow and Supermemory?

RAGFlow is Paid and open source, while Supermemory is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is RAGFlow better than Supermemory?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.