Cognee vs Dosu

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 Dosu
Detail	Cognee	Dosu
What it does	Open-source memory platform for AI agents	Knowledge infrastructure for agents and teams
Stage	Not given	Not given
Founded	Not given	Not given
Based in	DE	US
Pricing model	Not given	Freemium
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.	Free plan with 200 credits per month for public repositories and core features; Pro plan at $16/month ($192 billed annually) with 4000 credits per month and private repository support; Enterprise plan with custom pricing and credits as needed
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	Connect agents with MCP Server for automatic documentation capture Self-documenting pull requests Sync existing documentation from Notion, Confluence, Coda, or repositories Generate documentation on demand Custom documentation templates Shared knowledge layer for team-wide agent learning Integration with Slack and MS Teams for context requests Analytics dashboards and observability
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	Reduces agent token consumption by 50% through shared knowledge caching Automatically maintains up-to-date documentation as code changes Knowledge stays in human-readable Markdown format and is portable Improves agent consistency across runs with shared knowledge layer SOC 2 Type II certified for enterprise security compliance
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	66 no change since last check
Trust Flow (Majestic)	14	16
Citation Flow (Majestic)	43	37
Referring domains (Majestic)	1089	432

5 details filled in for both companies.

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