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Takibi Base vs Cognee

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

Takibi Base compared with Cognee
DetailTakibi Base logo Takibi Base Cognee
What it doesThe shared knowledge base for AI agents Open-source memory platform for AI agents
StageNot given Not given
FoundedNot given Not given
Based inNot given DE
Pricing modelPaid Not given
PricingEmber at €18/month for 2 Profiles and 250 asks per day, or Campfire at €52/month for 8 Profiles and 800 asks per day. 7-day trial available; card required at checkout. 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.
Key features
  • Organize documents into projects and folders
  • Control agent access with Profiles
  • Return exact passages with citations
  • Evidence support scoring
  • Agent notes and discoveries
  • Support for PDF, Word and Markdown documents
  • Task assignment and workflow
  • Export in Open Knowledge Format
  • CLI and API access
  • 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
What makes it different
  • Returns exact passages from documents instead of generating answers
  • Provides evidence support scores with each result
  • Flexible document access control per agent
  • Export your knowledge base and documents
  • 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
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)40 +20 (up) since last check70 no change since last check
Trust Flow (Majestic)2214
Citation Flow (Majestic)2143
Referring domains (Majestic)801089

4 details filled in for both companies.

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