MenteDB

The Mind Database for AI Agents

MenteDB is a purpose built database engine for AI agent memory. Not a wrapper around existing databases, but a ground up Rust storage engine that understands how AI/LLMs consume data.

mente (Spanish): mind, intellect

Why MenteDB?

Every database ever built assumes the consumer can compensate for bad data organization. AI can't. A transformer gets ONE SHOT, a single context window, a single forward pass. MenteDB is a cognition preparation engine that delivers perfectly organized knowledge because the consumer has no ability to reorganize it.

What Makes MenteDB Different

Core Features

• Unified Memory Representation Embeddings + graph + temporal + attributes in one primitive
• Attention Optimized Context Assembly Respects the U curve (critical data at start/end)
• Belief Propagation When facts change, downstream beliefs are flagged for re evaluation
• Delta Aware Serving Only sends what changed since last turn (40-60% token savings)
• Cognitive Memory Tiers Working, Episodic, Semantic, Procedural, Archival
• Token Efficient Serialization 3x more information per token vs JSON
• Memory Spaces True multi agent isolation with hardware accelerated protection
• MQL Mente Query Language designed for memory retrieval

Performance Targets (10M memories)

Architecture

graph TD
    subgraph API["API Layer"]
        MQL["MQL Parser"]
        QE["Query Planner"]
        GRPC["gRPC / REST"]
    end

    subgraph Cognitive["Cognitive Engine"]
        CTX["Context Assembly<br/>U curve, delta aware"]
        STREAM["Stream Cognition<br/>live push corrections"]
        SPEC["Speculative Pre Assembly<br/>predict next context"]
        PAIN["Pain Signals<br/>mistake aversion"]
        PHANTOM["Phantom Memories<br/>knowledge gap tracking"]
        TRAJ["Trajectory Tracker<br/>thought continuation"]
    end

    subgraph Intelligence["Inference Layer"]
        BP["Belief Propagation"]
        WI["Write Time Inference"]
        INTERF["Interference Detection"]
    end

    subgraph Index["Index Layer"]
        HNSW["HNSW Vector Index"]
        ROAR["Roaring Bitmap Tags"]
        TEMP["Temporal Index"]
        SAL["Salience Index"]
    end

    subgraph Graph["Knowledge Graph"]
        CSR["CSR/CSC Storage"]
        TRAV["BFS / DFS Traversal"]
        CONTRA["Contradiction Detection"]
    end

    subgraph Storage["Storage Engine"]
        BUF["Buffer Pool<br/>CLOCK eviction"]
        WAL["Write Ahead Log<br/>LZ4, CRC32"]
        PAGE["Page Manager<br/>16KB pages"]
    end

    subgraph OS["OS / Hardware"]
        URING["io_uring"]
        MMAP["mmap + hugepages"]
        DIO["Direct I/O"]
        MPK["Memory Protection Keys"]
    end

    MQL --> QE
    GRPC --> QE
    QE --> CTX
    QE --> Index
    QE --> Graph

    CTX --> STREAM
    CTX --> SPEC
    CTX --> PAIN
    CTX --> PHANTOM
    CTX --> TRAJ

    WI --> Graph
    WI --> Index
    BP --> Graph
    INTERF --> Index

    Index --> Storage
    Graph --> Storage

    BUF --> PAGE
    WAL --> PAGE
    PAGE --> OS

Crates

MenteDB is organized as a Cargo workspace with 10 crates:

Configuration

MenteDB is configured via a JSON file. All sections are optional; omitted fields use sensible defaults.

{
  "storage": {
    "data_dir": "/var/mentedb/data",
    "buffer_pool_size": 1024,
    "page_size": 16384
  },
  "index": {
    "hnsw_m": 16,
    "hnsw_ef_construction": 200,
    "hnsw_ef_search": 50
  },
  "context": {
    "default_token_budget": 4096,
    "token_multiplier": 1.3,
    "zone_system_pct": 0.10,
    "zone_critical_pct": 0.25,
    "zone_primary_pct": 0.35,
    "zone_supporting_pct": 0.20,
    "zone_reference_pct": 0.10
  },
  "cognitive": {
    "contradiction_threshold": 0.95,
    "related_threshold_min": 0.6,
    "related_threshold_max": 0.85,
    "interference_threshold": 0.8,
    "speculative_cache_size": 10,
    "speculative_hit_threshold": 0.5,
    "max_trajectory_turns": 100,
    "max_pain_warnings": 5,
    "max_phantom_warnings": 5
  },
  "consolidation": {
    "decay_half_life_hours": 168.0,
    "min_salience": 0.01,
    "archival_min_age_days": 30,
    "archival_max_salience": 0.05
  },
  "server": {
    "host": "0.0.0.0",
    "port": 6677
  }
}

Load configuration in Rust:

use mentedb_core::MenteConfig;
let config = MenteConfig::from_file(std::path::Path::new("mentedb.json")).unwrap();

MQL Examples

MQL (Mente Query Language) is the native query language for memory retrieval.

-- Vector similarity search: find the 10 memories closest to an embedding
RECALL memories NEAR [0.12, 0.45, 0.78, 0.33] LIMIT 10

-- Filtered recall: episodic memories tagged "backend"
RECALL memories WHERE type = episodic AND tag = "backend" LIMIT 5

-- Content similarity: fuzzy match on content text
RECALL memories WHERE content ~> "database migration strategies" LIMIT 10

-- Graph traversal: walk causal edges up to depth 3
TRAVERSE 550e8400-e29b-41d4-a716-446655440000 DEPTH 3 WHERE edge_type = caused

-- Consolidation: archive stale episodic memories
CONSOLIDATE WHERE type = episodic AND accessed < "2024-01-01"

Benchmarks

Run cargo bench to benchmark storage writes, HNSW search, context assembly, and MQL parsing.

Building

cargo build
cargo test

License

Apache 2.0, see LICENSE for details.