Cognee vs ContextBolt

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 ContextBolt
Detail	Cognee	ContextBolt
What it does	Open-source memory platform for AI agents	Tools that give your AI context
Stage	Not given	Startup
Founded	Not given	2026
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.	ContextBolt SEO: 7-day free trial, then $35/month (1,000 monthly credits; top-ups from $9). ContextBolt Bookmarks: Free up to 150 bookmarks, Pro from $6/month. Context Board: 3-day free trial, then $6/month at launch price.
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	AI visibility tracking for ChatGPT and Google AI answers Connected Google Search Console with zero credits Web and social media bookmark management from X, Reddit and LinkedIn Auto-syncing bookmarks across platforms Searchable bookmark library Private SEO Dashboard that fills automatically Cross-session memory for research Works with Claude, Cursor, Codex and other MCP-compatible tools Bookmark export as CSV, JSON or XLSX on Pro 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	MCP-native design means nothing new to learn, with your AI as the main interface Research stays searchable across sessions, not lost after each one
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	46 no change since last check
Trust Flow (Majestic)	14	9
Citation Flow (Majestic)	43	35
Referring domains (Majestic)	1089	258

4 details filled in for both companies.

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