DoclingvsPhidata

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

Docling

Data & ETL

IBM's document conversion tool for AI pipelines

Full review →Website ↗

Phidata

AI Agent Frameworks

Build AI agents with memory, knowledge, and tools

Full review →Website ↗
FeatureDoclingPhidata
CategoryData & ETLAI Agent Frameworks
PricingFree (open-source)Free (open-source) + Cloud
GitHub Stars
15k
15k
PlatformsLinux, macOS, WindowsLinux, macOS, Windows
Key Features
  • ✓ PDF conversion
  • ✓ Table extraction
  • ✓ OCR
  • ✓ Markdown output
  • ✓ LlamaIndex integration
  • ✓ Agent memory
  • ✓ Knowledge base
  • ✓ Tool use
  • ✓ Structured output
  • ✓ Multi-model
Pros
  • + Excellent PDF parsing
  • + Table extraction
  • + OCR capability
  • + IBM Research quality
  • + LlamaIndex integration
  • + Clean, Pythonic API
  • + Built-in memory and knowledge
  • + Production-focused
  • + Good documentation
  • + Multi-model support
Cons
  • − Heavy dependencies
  • − Can be slow on large docs
  • − Python only
  • − Complex output format
  • − Rebranding confusion (Phidata→Agno)
  • − Smaller community than LangChain
  • − Some features require cloud
  • − Less flexible for custom setups
Tags
documentspdfconversionibm
agentsmemoryknowledgepython

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