Cognee vs Switch Transformers

Cognee turns documents, chats, tickets, and API data into graph memory language model agents can recall across sessions. It builds linked entity graphs from that context so agents stop restarting from scratch each run. You can connect Slack, GitHub, or Linear so coding agents and support bots share one permission-aware company brain.

Plain RAG returns similar text chunks. Cognee pairs vector search with knowledge graphs and auto-generated ontologies, so recall pulls connected entities and cited facts rather than isolated snippets. The SDK centers on four verbs, remember, recall, forget, and improve, and the same surface ships over HTTP and MCP for Claude Code, Codex, and OpenClaw.

Platform teams use it for coding agent memory, GraphRAG pipelines, deal intelligence, and customer-facing agents that need grounded answers. Run it with pip locally, self-host in Docker or on-prem, or move to Cognee Cloud when you want managed scale. The project reports 30.4k GitHub stars and 5M+ SDK runs per month, with production deployments at Bayer and Knowunity.

Switch Transformers introduce a sparse Mixture of Experts architecture that routes each input to a single expert, reducing communication overhead while scaling to trillion-parameter language models with constant compute cost. The paper from Google researchers William Fedus, Barret Zoph, and Noam Shazeer simplifies MoE routing, improves training stability, and reports up to 7x faster pre-training than dense T5 models on the same compute budget.

The approach builds on the T5 architecture and supports multilingual training across 101 languages. Switch Transformers also enable training with bfloat16 precision for faster, more stable large-scale runs. The work targets researchers and engineers who need to scale NLP models without proportional increases in hardware cost.

Published on arXiv as a research paper, Switch Transformers documents methods for efficient sparse activation rather than a commercial SaaS product. The paper and PDF are freely available for download and citation.