MAID Project v1.0

The MAID ecosystem orchestrates production-grade load testing, authentication, and AI-assisted analysis. It consists of three Rust microservices:

• maid-core – Goose-inspired load-testing engine with REST + WebSocket interfaces, reporting, and plugin bus connectivity.
• maid-apikeys – Security service for user management, JWT/API-key issuance, RBAC, and audit logging.
• maid-mistral – Lightweight mistral.rs integration that supplies AI-generated Goose configurations and post-run insights.

Each service is packaged as an independent binary within a shared Cargo workspace. Configuration is JSON-first, follows environment-variable overrides, and persists runtime state in SQLite databases under the data/ directory by default.

Getting started

Prerequisites

• Rust toolchain 1.79+ (rustup default stable)
• SQLite 3 (bundled via rusqlite for portability)
• Optional: CUDA 11.8+ / Metal for running mistral.rs against GPU workloads

Building the workspace

cargo build --release

Running the services

Start each service in its own terminal (or supervise via systemd/docker):

# Authentication & RBAC service
cargo run -p maid-apikeys

# Goose load-testing core
cargo run -p maid-core

# mistral.rs AI plugin
cargo run -p maid-mistral

Services bind to production ports by default:

Service	Port	Purpose
maid-apikeys	43119	Auth, API key, RBAC, audit APIs
maid-core	43121	Goose load controller + metrics
maid-mistral	43140	AI-assisted recipe + analysis

Override any configuration via JSON files (config*.json) or environment variables (MAID__*, MAID_APIKEYS__*, MAID_MISTRAL__*). See docs/configuration.md.

Documentation suite

• Architecture overview
• Goose core API reference
• Authentication/API key service
• mistral.rs plugin guide
• Configuration reference

The documentation covers deployment topologies, plugin bus semantics, schema-driven configuration, and dashboard integration guidance.

Testing & quality

Automated integration tests are forthcoming; meanwhile run cargo fmt, cargo clippy --workspace, and targeted smoke runs to validate environments. Each service includes structured logging (tracing) and emits audit events for critical operations.

Contributing

• Follow the security baseline: strong Argon2 password hashing, hashed API keys, JWT with configurable issuer/audience, TLS termination recommended upstream.
• Keep load testing scenarios in JSON Schema to support dashboard form generation.
• Extend plugin capabilities via the bus; register new features in PluginRegistry so downstream services can auto-discover.