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Cognee vs gitring

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 gitring
Detail Cogneegitring logo gitring
What it doesOpen-source memory platform for AI agents Source control for coding agents with conflicts settled before code exists
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: $0 for up to 3 projects and one person (never charges). Builder: $15 a month or $150 a year. Team: $36 per person a month or $360 per person a year. New accounts start on Team for 14 days free with up to 5 projects.
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
  • Agents declare plans before writing code
  • Written rules automatically settle conflicting plans
  • Integration with Claude Code, Codex, and MCP clients
  • GitHub repository mirroring after each merge
  • Intent-based workflow instead of pull requests
  • Drift detection comparing code changes to declared plans
  • No branch names; work identified by intent number
  • Write rules per folder with CI files people-only
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
  • Conflicts settled before any code is written
  • Agents review intent goals and plans, not line-by-line diffs
  • Works with your existing agents without leaving GitHub
  • Only humans approve merges, never agents or models
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 check12 +2 (up) 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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