Cognee uses LanceDB for per-workspace isolated vector storage in its AI agent memory platform
Agent teams struggle with stateless context and homegrown RAG stacks that require juggling separate graph stores, vector stores, and ad hoc rules, raising reliability risks and slowing iteration. Cognee specifically needed a vector store that matched its isolation model, supported per-workspace development, and avoided the complex orchestration that traditional vector databases require.
Adopting LanceDB accelerated Cognee's development cycle and improved reliability, enabling faster CI, cleaner multi-tenant setups, and more predictable performance, while also improving retrieval accuracy on multi-hop reasoning tasks.
Frequently asked questions
What did this team achieve with this AI workflow?
Adopting LanceDB accelerated Cognee's development cycle and improved reliability, enabling faster CI, cleaner multi-tenant setups, and more predictable performance, while also improving retrieval accuracy on multi-hop…
What tools did this team use?
LanceDB, Kuzu, PostgreSQL.
What results were reported?
Development cycle velocity: accelerated Cognee's development cycle and improved reliability; CI speed and multi-tenant reliability: faster CI, cleaner multi-tenant setups, and more predictable performance; Multi-hop retrieval accuracy: improved retrieval accuracy, particularly on multi-hop reasoning tasks; Vector store isolation ease: effortless, truly isolated vector stores per user and per test (source-reported, not independently verified).
How is this back office ops AI workflow structured?
Add data sources → Extract raw data → Cognify to knowledge graph → Load to vector and graph stores → Multi-step query retrieval → Memify knowledge refresh.