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Cognee vs Levain Labs

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 Levain Labs
Detail CogneeLevain Labs logo Levain Labs
What it doesOpen-source memory platform for AI agents The AI backend that gets better with every run
StageNot given Startup
FoundedNot given Not given
Based inDE Not given
Pricing modelNot given Not given
PricingFree: $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. Free: 500 credits/month, one workspace. Team: $49/month, 3,000 to 80,000 credits/month (fee scales with credits chosen). Enterprise: Custom pricing with volume-discounted usage, customer workspaces via API, pooled credits with spend limits per customer.
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
  • Agent delivery via app, Slack, Claude, ChatGPT or events
  • Automatic agent versioning from underperforming runs
  • Stack review and consulting services
  • Organization API with webhooks and delivery history
  • SSO and SAML on Enterprise plan
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
  • Outcome-driven improvement: runs are scored against your goals and underperforming versions automatically improve
  • Hands-on engineering: optional stack reviews and agent builds for customers who want expert implementation
Free planNot given Not given
Open sourceNot given Not given
PlatformsNot given Not given
Public APINot given Not given
Community votes00
DR (Domain Rating by Ahrefs)70 no change since last checkNot checked yet no change since last check
Trust Flow (Majestic)140
Citation Flow (Majestic)430
Referring domains (Majestic)10890

4 details filled in for both companies.

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