Cognee vs Graphlit
A side-by-side comparison of Cognee and Graphlit, two Memory tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Cognee | Graphlit |
|---|---|---|
| Category | Memory | Memory |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Open core | Proprietary |
| Deployment (differs) | Hybrid | Cloud |
| Platforms (differs) | API | API, Web |
| Model support (differs) | BYO key / model | Multi-model |
| Vendor (differs) | Cognee | Graphlit |
| Capabilities (differs) |
|
|
The honest brief
Cognee
Builds an LLM-derived knowledge graph alongside embeddings, so recall follows relationships, not just vector similarity.
- Self-hostable Python SDK
- Recall follows concept relationships
- Bring your own LLM/embedding provider
- Newer, smaller ecosystem
- Cognify pipeline adds LLM cost
- Self-host setup overhead
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
When to pick which
Both cover Agent memory, Knowledge graph, RAG pipeline, and Vector search.
Pick Cognee if you need Embeddings.
- Embeddings (secondary capability)
Pick Graphlit if you need Document parsing (structured) and Transcription (STT).
- Document parsing (structured) (secondary capability)
- Transcription (STT) (secondary capability)
Graphlit leans on RAG pipeline as a headline capability; Cognee treats it as secondary.
- RAG pipeline (primary capability)
They also differ on:
- License
- Open core · Proprietary
- Deployment
- Hybrid · Cloud
- Platforms
- API · API, Web
- Model support
- BYO key / model · Multi-model