Haystack vs RAGFlow
A side-by-side comparison of Haystack and RAGFlow, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Haystack | RAGFlow |
|---|---|---|
| Category (differs) | Orchestration | Search |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Open core |
| Deployment (differs) | — | Hybrid |
| Platforms (differs) | API | Web, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | deepset | InfiniFlow Inc. |
| Capabilities (differs) |
|
|
The honest brief
Haystack
More production-oriented and explicitly composable than lighter agent libraries: typed pipelines give direct control over retrieval, routing, and memory.
- Composable, typed pipeline architecture
- Model- and vector-store agnostic
- Strong production tooling
- Mature docs and integrations
- Pipeline model has a learning curve
- Python-only
- Heavier than minimal agent libraries
RAGFlow
DeepDoc parsing turns messy PDFs, tables, and scans into citation-backed chunks—grounding answers better than naive text-splitting RAG stacks.
- Apache-2.0, fully self-hostable
- Deep document, table, and scan parsing
- Hallucination-resistant grounded QA
- Hybrid vector + full-text search
- Built-in agent orchestration
- Heavier setup than hosted RAG APIs
- Cloud tiers cap apps and storage
- Resource-intensive to self-host
When to pick which
Both cover RAG pipeline, Chat with documents, and Vector search.
Pick Haystack if you need Agent framework and Tool / function calling.
- Agent framework (primary capability)
- Tool / function calling (secondary capability)
Pick RAGFlow if you need Multi-agent orchestration, Cited answers, and Document parsing (structured).
- Multi-agent orchestration (secondary capability)
- Cited answers (secondary capability)
- Document parsing (structured) (primary capability)
They also differ on:
- Pricing
- FREE · FREEMIUM
- License
- Open source · Open core
- Platforms
- API · Web, API