智能数据库引擎

Full suite (requires ANTHROPIC_API_KEY or OPENAI_API_KEY)

python3 benchmarks/run_all.py

Installation

Docker (fastest)

docker run -p 6677:6677 \
  -e MENTEDB_LLM_PROVIDER=openai \
  -e MENTEDB_LLM_API_KEY=sk-... \
  -v mentedb-data:/data \
  ghcr.io/nambok/mentedb:latest

or: pipx install mentedb

```

Production deployment

export MENTEDB_JWT_SECRET="your-secret-here" export MENTEDB_ADMIN_KEY="your-admin-key" export MENTEDB_LLM_PROVIDER="openai" export MENTEDB_LLM_API_KEY="sk-..."

mentedb-server --require-auth --data-dir /var/mentedb/data ```

Docker

```bash

Or build from source

docker build -t mentedb . docker run -p 6677:6677 \ -e MENTEDB_JWT_SECRET=your-secret \ -v mentedb-data:/data \ mentedb

Or with docker-compose:

Building

cargo build              # Build all crates
cargo test               # Run 477+ tests
cargo clippy             # Lint
cargo bench              # Benchmarks
cargo doc --open         # Documentation

Quick Start

process_turn — one call does everything:

```python from mentedb import MenteDB

db = MenteDB("./my-agent-memory") db.configure_llm(provider="anthropic", api_key="sk-...")

result = db.process_turn( user_message="I switched from PostgreSQL to SQLite for side projects", assistant_response="Got it, I'll suggest SQLite going forward.", turn_id=0, )

MQL Examples

-- Vector similarity search
RECALL memories NEAR [0.12, 0.45, 0.78, 0.33] LIMIT 10

-- Boolean filters with OR and NOT
RECALL memories WHERE type = episodic AND (tag = "backend" OR tag = "frontend") LIMIT 5
RECALL memories WHERE NOT tag = "archived" ORDER BY salience DESC

-- Content similarity
RECALL memories WHERE content ~> "database migration strategies" LIMIT 10

-- Graph traversal
TRAVERSE 550e8400-e29b-41d4-a716-446655440000 DEPTH 3 WHERE edge_type = caused

-- Consolidation
CONSOLIDATE WHERE type = episodic AND accessed < "2024-01-01"

Integration Options

Configuration

All cognitive features are enabled by default. Toggle individually:

use mentedb::{MenteDb, CognitiveConfig};

let config = CognitiveConfig {
    write_inference: true,        // auto-edges, contradiction detection
    decay_on_recall: true,        // time-based salience decay
    pain_tracking: true,          // recurring failure warnings
    interference_detection: true, // confusable memory detection
    phantom_tracking: true,       // missing knowledge gap detection
    speculative_cache: true,      // predictive context pre-assembly
    archival_evaluation: true,    // memory lifecycle management
    ..Default::default()
};
let db = MenteDb::open_with_config("./memory", config)?;

LLM Extraction Configuration

Configure the extraction pipeline via environment variables:

Semantic search, auto-linking, and contradiction detection all depend on real embeddings. The Docker image ships with local Candle embeddings; a plain cargo install mentedb-server falls back to non-semantic hash embeddings and warns loudly at startup — build with --features local-embeddings for full quality.

Python SDK

pip install mentedb

On Debian and Ubuntu systems pip refuses system wide installs (PEP 668). Use a virtual environment or pipx:

```bash python3 -m venv .venv && .venv/bin/pip install mentedb

TypeScript SDK

npm install mentedb

2. REST API

```bash

4. SDKs

Python: pip install mentedb ```python from mentedb import MenteDB

db = MenteDB("./agent-memory") result = db.process_turn( user_message="I switched to Vim", assistant_response="Got it!", turn_id=0, )

Integrations

pip install mentedb-langchain  # LangChain memory provider
pip install mentedb-crewai     # CrewAI memory provider

Candle (Local) vs OpenAI Embedding Quality

Candle provides good quality for zero-config local use. OpenAI offers higher accuracy for production workloads. Run python3 benchmarks/candle_vs_openai.py with OPENAI_API_KEY set to get a head-to-head comparison.