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Graphlit vs Ragie

A side-by-side comparison of Graphlit and Ragie, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

Graphlit

Memory

One API for AI agent memory: ingest, extract, store, retrieve.

View Graphlit

Ragie

Search

Managed RAG-as-a-service — the context engine for AI agents and apps.

View Ragie

At a glance

Feature comparison of Graphlit and Ragie
AttributeGraphlitRagie
Category (differs)MemorySearch
PricingFREEMIUMFREEMIUM
LicenseProprietaryProprietary
DeploymentCloudCloud
PlatformsAPI, WebAPI, Web
Model support (differs)Multi-modelModel-agnostic
Vendor (differs)GraphlitRagie, Corp

The honest brief

Graphlit

Graph-native context layer that links ingested content into an entity graph plus stateful memory, not just vector chunks.

  • One API for ingest, extract, store, retrieve
  • Multimodal (docs, audio, video, images)
  • Graph-based entity linking + hybrid search
  • Event-driven webhooks for reactive agents
  • More infra/overhead than plain RAG
  • Overkill for simple doc Q&A
  • Cloud-only managed service
  • Graph/timeline modeling adds complexity

Ragie

Production RAG over an API — skip building ingestion, connectors, chunking, and hybrid retrieval yourself, then maintaining it.

  • Fully managed, fast to integrate
  • Native connectors (Drive, Notion, etc.)
  • Multimodal parsing (PDF, image, audio, video)
  • Hybrid vector + keyword + summary search
  • MCP server for agentic retrieval
  • Production tier starts at $500/month
  • Proprietary, cloud-only (no self-host)
  • Less control than rolling your own RAG