back_office_ops · saas · workflow
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Add data sources
Cognee accepts files, APIs, and databases as input sources to begin the memory pipeline.
Tools used
LanceDBKuzuPostgreSQL
Outcome
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.
Results
Volumeimproved retrieval accuracy, particularly on multi-hop reasoning tasks
Grounding & classification
Source type: vendor customer story
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agentic workflowdata extractionknowledge searchragknowledge basenamed customersource backedtools describedvendor confirmedworkflow describedsoftwareaccuracy improvementcycle time reductionemployee productivityvendor customer storyback office opsdata sync enrichmentrag answering