Hybrid search, graph indexing, and vector-symbolic memory for AI agents
The all-in-one agentic AI solution for your organization
Stage
Startup
Startup
Founded
2026
2024
Based in
US
Not given
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.
Starter (free), Professional ($29/seat/month), Enterprise (contact for pricing)
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
Centralized context repository for unifying work signals across tools
MCP protocol integration with AI assistants and tools
First-class integrations with GitHub, Vercel, Supabase, Railway, Gmail, Notion, Calendly, Codex, Claude Code, and Slack
Per-agent scoped access with full audit trail
Custom vector embeddings for agent memory
Instant agent shutdown from anywhere globally
QUAERO enterprise AI agent governance system
Two-way sync with tools for agent actions and work flows
Support for custom MCP servers and internal tools
Real-time organizational context for agent reasoning
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
AI-first design with structured, queryable context rather than static documentation
Consistent policy and audit rules across all integrations
MCP-native integrations with no bespoke connectors to maintain
warm-rust
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