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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
Capabilities (differs)
  • Agent memory
  • RAG pipeline
  • Knowledge graph
  • Vector search
  • Document parsing (structured)
  • Transcription (STT)
  • MCP server
  • RAG pipeline
  • Vector search
  • Embeddings
  • Document parsing (structured)
  • Structured extraction

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

When to pick which

Both cover RAG pipeline, Vector search, and Document parsing (structured).

Pick Graphlit if you need Agent memory, Knowledge graph, and Transcription (STT).

  • Agent memory (primary capability)
  • Knowledge graph (secondary capability)
  • Transcription (STT) (secondary capability)

Pick Ragie if you need MCP server, Embeddings, and Structured extraction.

  • MCP server (secondary capability)
  • Embeddings (secondary capability)
  • Structured extraction (secondary capability)

Ragie leans on Document parsing (structured) as a headline capability; Graphlit treats it as secondary.

  • Document parsing (structured) (primary capability)
Graphlit detailsRagie details