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warm-rust vs Perseus Computing

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 Perseus Computing
Detailwarm-rust logo warm-rustPerseus Computing logo Perseus Computing
What it doesHybrid search, graph indexing, and vector-symbolic memory for AI agents High-assurance infrastructure for AI agents
StageStartup Not given
Founded2026 Not given
Based inUS US
Pricing modelOpen source Open source
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. Not given
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
  • Harpe: chooses what an AI agent reads under a token budget with consistent results
  • Kibisis: keeps agent memory on your hardware with AES-256-GCM encryption
  • Argus: append-only record that detects edits with cryptographic verification
  • No network code by default for local and disconnected use
  • MIT License with published code, benchmarks and raw results
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
  • Measured empirical validation across 6,111 questions outperforming full-context models
  • Zero plaintext on disk with encrypted vaults matching plaintext accuracy
  • 78.3% fewer tokens sent to the model with a 1,200-token budget per turn
  • Preserves conversational chronology and relational qualifiers under token budgets
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 check42 +1 (up) since last check
Trust Flow (Majestic)123
Citation Flow (Majestic)2424
Referring domains (Majestic)5662

5 details filled in for both companies.

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