Cognee vs Kanlet

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Cognee compared with Kanlet
Detail	Cognee	Kanlet
What it does	Open-source memory platform for AI agents	Semantic context layer that helps AI agents understand your business data accurately
Stage	Not given	MVP
Founded	Not given	Not given
Based in	DE	Not given
Pricing model	Not given	Not given
Pricing	Free: $0/month with 1M tokens and 1 workspace (no card required). Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: contact for pricing with bring-your-own-cloud deployment. Open-source engine available free to self-host.	Not given
Key features	Unified vector search, knowledge graphs, and relational storage Automatic knowledge graph generation from data sources Multi-tenant support with isolated workspaces Data source integrations (Slack, Notion, Google Drive, GitHub, etc.) Self-improving memory that learns from use Multiple deployment options (cloud, self-hosted, air-gapped) Agent framework integrations (LangGraph, CrewAI, OpenAI SDK, MCP) Support for multiple vector and graph databases GDPR compliance and encryption at rest and in transit Built-in graph visualization of company brain	Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage Provides Web-UI, MCP server and REST APIs for accessing semantic context Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly Role-level access controls and data classifications including PII, PHI and SPII Only reads metadata without accessing or moving actual data from systems Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
What makes it different	Combines vectors with graph relationships and generated data models for connected context retrieval State of the art performance on BEAM benchmark (79% vs 73.4% at 100k tokens) Keeps memory costs flat as data grows compared to baseline LLM approaches Fully open-source with no database lock-in Updates memory as source data changes, not just static snapshots	Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query Ensures consistent answers across all agents and teams using the same definitions and business logic Provides complete traceability and auditability of AI decisions with documented context and ownership
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	70 no change since last check	8 no change since last check
Trust Flow (Majestic)	14	1
Citation Flow (Majestic)	43	24
Referring domains (Majestic)	1089	29

3 details filled in for both companies.

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