Leon

Leon is a modular, self‑hosted personal assistant built to run on your own infrastructure. From a technical standpoint it is an event‑driven NLP pipeline that receives user input, applies a hierarchy of models (simple classifiers, transformer‑based LLMs, and rule‑based engines), then dispatches the result to a skill or an external service. The core engine is written in Go, chosen for its lightweight runtime, native concurrency support, and excellent cross‑platform binary distribution. This makes Leon well suited for containerized deployments on Kubernetes, Docker Swarm, or even bare‑metal servers.

Architecture

• Runtime: Go 1.22+, leveraging goroutines for parallel skill execution and async I/O.
• Core Modules:
  • NLP Engine: A hybrid stack that combines rule‑based intent parsing, a simple keyword classifier, and optional transformer models (e.g., HuggingFace bert-base-uncased) for deep semantic understanding.
  • Skill Registry: A pluggable system where each skill is a Go package implementing the Skill interface. Skills can be loaded at runtime from a local directory or fetched via HTTP.
  • TTS/STT: Optional integration points for cloud providers (Google, AWS, IBM Watson) or offline engines (CMU Flite, Coqui STT). These are exposed as adapters implementing a common interface.
• Data Store: Uses SQLite for lightweight persistence (skill metadata, conversation history) but can be swapped out for PostgreSQL or MySQL via a simple abstraction layer.
• API Layer: A RESTful API (JSON over HTTP) for external control, plus a WebSocket endpoint for real‑time streaming of speech recognition and synthesis events.

Core Capabilities

• Intent Dispatching: A priority queue that routes user utterances to the most appropriate skill based on confidence scores.
• Skill Development SDK: Exposes a concise Go interface (Handle(ctx, request)) and command‑line tools to scaffold new skills.
• Webhook Integration: Skills can expose HTTP endpoints that Leon calls, enabling two‑way communication with third‑party services.
• Extensible NLP Pipelines: Developers can inject custom tokenizers, embeddings, or even plug in their own transformer models via a simple configuration file.
• Observability: Built‑in logging (structured JSON), metrics via Prometheus exporter, and tracing hooks for OpenTelemetry.

Deployment & Infrastructure

Leon ships as a single static binary, making it ideal for containerization. Docker images are available on Docker Hub (leon-ai/leon) and can be run with minimal environment variables:

• LEON_CONFIG_PATH – path to the YAML config file.
• LEON_SKILL_DIR – directory containing compiled skill binaries.

For production, a typical deployment includes:

1. Kubernetes: Deploy as a StatefulSet with persistent volume for SQLite, expose a LoadBalancer or Ingress.
2. Horizontal Scaling: Multiple replicas can be run; the state is sharded by SQLite, so for heavy workloads consider a shared Postgres backend.
3. CI/CD: Automated tests and linting are enforced via GitHub Actions, ensuring new skills maintain interface contracts.

Integration & Extensibility

• Skill Registry Platform: Future plans include a marketplace akin to npm, where developers publish and version skills.
• Custom Webhooks: Any skill can expose an HTTP API; Leon will forward events, allowing integration with Slack, Discord, or proprietary dashboards.
• Scripting Hooks: A lightweight Lua interpreter can be embedded for rapid prototyping of intent handlers without recompiling Go code.
• Model Plug‑in: Replace the default transformer with a custom ONNX or TensorRT model by implementing the Inference interface.

Developer Experience

• Documentation: The official docs (docs.getleon.ai) provide a comprehensive guide to the SDK, skill lifecycle, and deployment patterns. The API reference is auto‑generated from Go doc comments.
• Community: Active Discord channel, regular roadmap updates, and a welcoming contribution guide lower the barrier to entry.
• Testing: Skills can be unit‑tested with Go’s standard testing framework; integration tests run against a mock Leon instance via the API.

Use Cases

• Home Automation: A skill that talks to Home Assistant or MQTT brokers, enabling voice‑controlled lights and thermostats.
• Enterprise Chatbot: Deploy Leon behind a corporate intranet to answer FAQs, pull from internal knowledge bases, and trigger CI pipelines.
• Accessibility Tool: Combine the TTS/STT stack to create a hands‑free interface for users with motor impairments.
• Research Prototype: Plug in experimental NLP models (e.g., a custom BERT fine‑tuned on domain data) and evaluate performance in a real‑world assistant setting.

Advantages

• Performance: Go’s compiled binaries and efficient goroutine scheduling deliver low latency, essential for real‑time speech interactions.
• Flexibility: The skill registry and adapter patterns mean developers can swap out components (e.g., switch from Google TTS to an offline engine) without touching the core.
• Licensing: MIT license removes any commercial restrictions, encouraging enterprise adoption and internal tooling.
• Self‑Hosting: Full data control aligns with privacy regulations; no third‑party cloud