Open-Source AI · LLM / RAG framework

RAGFlow vs Langfuse

RAGFlow vs Langfuse compared for 2026 — features, license, ease of use, performance and which one to choose. Deep-document-understanding RAG vs See what your LLM app actually did.

Updated regularly · curated by OpenSourceAI.tech

Choose RAGFlow for RAG over messy, complex documents. Choose Langfuse for debugging and monitoring LLM apps in production.

RAGFlow vs Langfuse at a glance

Spec	RAGFlow	Langfuse
Category	LLM / RAG framework	LLM / RAG framework
Type	RAG engine	LLM observability
License	Apache-2.0	MIT
Runs locally	Self-hosted	Yes
Primary language	Python	TypeScript
Ease of use	Intermediate	Intermediate
Best for	RAG over messy, complex documents	debugging and monitoring LLM apps in production
GitHub stars	85.2k	31.3k

How RAGFlow and Langfuse score

🤝 Too close to call — RAGFlow and Langfuse land within a hair (4.5 vs 4.5 / 5). Pick on fit, not on score.

Criterion	RAGFlow	Langfuse
Popularity	4.5	4.0
Maintenance	5.0	5.0
Ease of use	3.5	3.5
Privacy	4.5	5.0
License freedom	5.0	5.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

RAGFlow

RAG engine · Apache-2.0

RAGFlow is an open-source RAG engine built on deep document understanding, extracting clean structure from complex files to give LLMs grounded, cited answers.

Strong document layout understanding
Grounded answers with citations
Self-hostable web UI

See the RAGFlow page →

Langfuse

LLM observability · MIT

Langfuse traces every LLM call, tool use and cost in your application, with prompt management and evaluation built in — self-hostable.

Full tracing of chains and agents
Cost and latency tracking
Self-hosted, MIT licensed

See the Langfuse page →

Key differences

RAGFlow is rAG engine, while Langfuse is lLM observability. Their licenses differ (Apache-2.0 vs MIT), which matters if you ship a commercial product. They also differ in how they run (Self-hosted vs Yes). In short, RAGFlow fits RAG over messy, complex documents, and Langfuse fits debugging and monitoring LLM apps in production.

Which should you choose?

Choose RAGFlow for RAG over messy, complex documents. Choose Langfuse for debugging and monitoring LLM apps in production.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is RAGFlow or Langfuse easier to use?

Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.

Are RAGFlow and Langfuse free?

RAGFlow is free and open source (Apache-2.0), and Langfuse is free and open source (MIT). Neither charges for the core software.

Can I run RAGFlow and Langfuse locally?

RAGFlow: self-hosted · Langfuse: yes. Both can be used without sending your data to a third-party cloud where their setup allows.

RAGFlow vs Langfuse — which should I pick in 2026?

Choose RAGFlow for RAG over messy, complex documents. Choose Langfuse for debugging and monitoring LLM apps in production.

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