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warm-rust vs Cogni

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

warm-rust compared with Cogni
Detailwarm-rust logo warm-rust Cogni
What it doesHybrid search, graph indexing, and vector-symbolic memory for AI agents Memory that reasons
StageStartup Not given
Founded2026 Not given
Based inUS US
Pricing modelOpen source Freemium
PricingOpen source under Apache-2.0 license. WARM Cloud is in development with usage-based pricing for sync, backup and verification; details to be published at launch. Free plan at $0 USD; Pro plan at $5 USD per month.
Key features
  • Hybrid search combining BM25 lexical and vector scoring
  • Vertex graph vector index with 2.29-2.75x throughput vs hnswlib
  • Vector-symbolic fact memory with holographic reduced representations
  • AES-256-GCM encryption at rest with Argon2id key derivation
  • Signed audit log with Ed25519 and W3C DID identity
  • Secure purge that overwrites deleted data with zeros
  • Crash-safe append-only log with checksumming
  • Native Rust API with Python HTTP client and Node.js bindings
  • MCP server integration for editors and AI agents
  • WARM Cloud sync, backup and verification without server access to keys or documents
  • Entity-graph spreading activation for cross-vocabulary retrieval
  • Works with any LLM model
  • No LLM or GPU in retrieval path
  • Point-in-time and windowed recall
  • Timestamped memories
  • Fixed recall cost as memory grows
What makes it different
  • Runs entirely locally in your process with no network calls or telemetry
  • Encryption at rest built into the open-source engine itself, not an enterprise add-on
  • Significantly faster vector search than hnswlib with graph-indexed vertex retrieval
  • Signed provenance and verified audit trail using C2PA, COSE and SCITT standards
  • Embedded library model eliminates dependency on external services or vendor lock-in
  • Reaches answers across unrelated documents that share no keywords, where vector search scores zero
  • Deterministic retrieval with no model in the path, so results never vary between queries
  • Constant per-query cost regardless of memory size, even at 32,000 words
  • No infrastructure to run—no vector database, cluster, or GPU needed
  • Works with any model, so small local models gain the most from better retrieval
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)26 no change since last check4 no change since last check
Trust Flow (Majestic)122
Citation Flow (Majestic)2421
Referring domains (Majestic)5634

6 details filled in for both companies.

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