Hybrid search, graph indexing, and vector-symbolic memory for AI agents
Memory that reasons
Stage
Startup
Not given
Founded
2026
Not given
Based in
US
US
Pricing model
Open source
Freemium
Pricing
Open 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