CueMap

CueMap is a high-performance temporal-associative memory store built in Rust, designed to give AI agents durable, inspectable context recall. It turns natural-language queries into deterministic cues, intersects the strongest evidence, and returns source-backed memories—without relying on external model calls or opaque vector search by default. The project is developed by cuemap-dev and is currently at v0.7.2, released in August 2026.

What It Is

CueMap is a memory layer for AI agents and long-running assistants that need to recall information across sessions without hallucinating or losing provenance. Unlike pure vector databases, CueMap's default recall path is deterministic and ontology-free: it extracts normalized lexical cues, structural facets (dates, numbers, entities, source metadata), and recency/salience signals at write time, then resolves queries through the same deterministic normalization at read time. An optional bundled local MiniLM-L3-v2 encoder adds semantic reranking in hybrid mode without opening external network calls.

Architecture and Core Algorithm

CueMap implements what it calls a Continuous Gradient Algorithm with five stages:

• Intersection (Context Filter): triangulates relevant memories by overlapping cues
• Structural Extraction: emits deterministic cues for observable evidence such as dates, numbers, lists, and surface entities
• Recency & Salience: balances fresh data with high-signal events via an adaptive impact scoring module
• Reinforcement: frequently accessed memories gain signal strength, remaining accessible as they age
• Sparse Recall: uses normalized lexical cues, structural facets, recency, salience, and bounded deterministic reranking

The engine is built on Axum for async HTTP, DashMap + aHash for lock-free concurrent access, and Zstd + ChaCha20-Poly1305 for compressed, encrypted-at-rest persistence. Snapshots are stored as zstd-compressed JSON .bin files and restored automatically on startup.

Performance at Scale

The v0.7.2 release benchmarks, run on a MacBook Pro M-series with 64GB RAM against 1M Wikipedia-derived memories, show:

• Lexical recall: 2.63 ms average, 3.72 ms P95
• Hybrid recall (with bundled local encoder): 8.36 ms average, 10.67 ms P95
• Lexical write: 3.33 ms average
• Hybrid write: 11.28 ms average
• Zero external model calls on the hot path

On retrieval benchmarks, the project reports 96.2% Hit@20 on LongMemEval (470 non-abstention questions, hybrid recall), 96.1% Hit@20 on LoCoMo, and 80.3% Hit@20 on BEAM at 1M scale. These are raw retrieval scores before an answer model interprets results.

Integration and Deployment Paths

CueMap offers four access paths that all talk to the same local Rust process:

• CLI: cuemap add and cuemap recall from any terminal
• Python SDK: pip install cuemap
• TypeScript SDK: npm install cuemap
• MCP Server: npx -y cuemap-mcp@0.7.2 — connects coding agents (Claude Code, Cursor, VS Code, OpenAI Codex, Google Antigravity, OpenCode) to the local engine
• HTTP API: OpenAPI 3.1 contract at /openapi.yml
• Docker: cuemap/engine image with bind-mounted data directory

Configuration uses a layered TOML system (CLI args > env vars > server_config.toml > defaults). Cloud backup is supported for AWS S3, Google Cloud Storage, and Azure Blob Storage. Authentication uses API key headers; encryption-at-rest uses ChaCha20-Poly1305 with PBKDF2 key derivation.

Licensing and Source Availability

CueMap is released under the Business Source License 1.1 (BSL-1.1), licensed by CueMap/Kaan Demirel. The BSL allows full use for development, testing, and self-hosting, but prohibits offering the engine as a competing managed database service. The Change Date is 2030-03-02, after which the license converts to Apache 2.0. Commercial licensing for closed-source SaaS or managed-service use is available by contacting the team directly.