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Vectorize vs iLang Inc.

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Vectorize compared with iLang Inc.
DetailVectorize logo VectorizeiLang Inc. logo iLang Inc.
What it doesAgent memory that learns from experience The native language of AI; in human terms, a protocol.
StageStartup Startup
Founded2024 2026
Based inUS CA
Pricing modelFreemium Open source
PricingSelf-hosted: Free, MIT licensed. Hindsight Cloud: Pay-as-you-go with free credits to start. Retain (store memories) $10.00 per million tokens, Recall (retrieve memories) $0.75 per million tokens, Reflect (synthesize patterns) $0.05 per call, Iris Extract (structured extraction) $7.50 per million tok Not given
Key features
  • Per-user persistent memory with preferences, history and decisions kept separate
  • Cross-session persistence allows context to survive weeks between interactions
  • Fast parallel retrieval in under 100ms through four strategies running simultaneously
  • Works with any LLM without losing learned context when swapping models
  • Learns from mistakes and corrects behavior on repeated problems
  • Automatic pattern detection synthesizes individual facts into consolidated knowledge
  • Conflict detection preserves both old and new states with timestamps when facts change
  • Integrates with 20+ coding agents including Claude Code, Cursor, and Copilot
  • AI-native communication protocol with 88 verbs and two syntaxes
  • AI See: give any AI model vision by pasting i.ilang.ai URLs
  • Zero installation required, pure text format
  • Works across ChatGPT, Claude, Gemini, DeepSeek, Qwen and other models
  • Loader available via pip and npm for automatic protocol injection
  • Prompt compression: 67% reduction in tokens for typical instructions
What makes it different
  • 94.6% retrieval accuracy on LongMemEval-S, beating next-best system at 74.0%
  • MIT licensed open source with no usage limits, telemetry or restrictions
  • Reflection layer automatically builds judgment and mental models over time without manual tagging
  • Entity resolution handles hundreds of entities with 99.5% query reduction
  • First protocol to formally map Greek mathematical symbols as primitive verbs for AI-to-AI communication
  • Computable vector space for AI judgment using 11 dimensions and fuzzy mathematics instead of binary classification
  • Open source and MIT licensed with no royalties or approval required
  • Operates at the instruction and behavioral layer, complementary to MCP and A2A protocols
  • Tested and benchmarked with public conformance results and DOI-registered research
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)68 +1 (up) since last check38 +1 (up) since last check
Trust Flow (Majestic)187
Citation Flow (Majestic)4727
Referring domains (Majestic)137866

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