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Cognee vs Orgabot

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 Orgabot
Detail CogneeOrgabot logo Orgabot
What it doesOpen-source memory platform for AI agents AI agent orchestration platform that models workflow with triggers, actors, stages, gates, permissions, and outcomes
StageNot given Not given
FoundedNot given Not given
Based inDE US
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. 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
  • Role-based organization with humans or AI agents in any position
  • Workflow definitions with triggers, stages, gates, and outcomes
  • Agent-agnostic design supporting Claude, Codex, Gemini, and local models
  • Scoped credentials with least privilege access per stage
  • Deterministic gates for tests, reviews, and approvals
  • Audit trail with hash-chained tamper-evident ledger
  • Local-first deployment with optional cloud hosting
  • Isolated git worktrees for independent work and verification
  • Budget tracking and spend monitoring per organization
  • Separation of duties enforced in code
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
  • Orchestration layer that treats work as executable processes rather than remembered habits or wikis
  • Roles swappable between humans and AI agents without rewriting workflows
  • Credentials and secrets held by infrastructure, never exposed to AI models
  • Autonomous AI decision-making within defined stages, deterministic controls at stage boundaries
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 check24 +1 (up) since last check
Trust Flow (Majestic)147
Citation Flow (Majestic)4331
Referring domains (Majestic)108918

4 details filled in for both companies.

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