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Cognee vs RailCall

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 RailCall
Detail CogneeRailCall logo RailCall
What it doesOpen-source memory platform for AI agents AI runs where you own it
StageNot given Not given
FoundedNot given Not given
Based inDE Not given
Pricing modelNot given Freemium
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. RailCall local runtime: free. RailHub Team: $5 per seat per month. Cloud agents: $5 per trained agent per month. Advanced thinking (overflow): metered against a budget you set, with 500 flows per month included. Storage on hub: $0.25 per GB-month with 5 GB free.
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
  • Studio visual workflow builder with 1,000+ nodes
  • CLI and terminal-first interface
  • Ed25519-signed tamper-evident receipts
  • Human approval airlock for all actions
  • Autonomous agents that run 24/7
  • Capacity routing across organization machines
  • MCP integration with 33 tools
  • Multi-level human approval workflows
  • Offline receipt verification
  • Company-wide stop button
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
  • Entire local runtime free with MIT license, no login required
  • Cryptographically signed receipts verifiable offline
  • 80% reduction in AI costs compared to top models
  • No execution counters or machine count limits
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 check26 no change since last check
Trust Flow (Majestic)143
Citation Flow (Majestic)4313
Referring domains (Majestic)108935

4 details filled in for both companies.

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