leon-ai/leon
Leon
Leon is a framework for building personal AI assistants that integrates large language models with local tool execution and persistent memory. It functions as an agentic workflow orchestrator and modular skill engine, enabling the creation of autonomous assistants capable of planning and executing multi-step tasks.
The system features a retrieval-augmented generation memory architecture that indexes conversation history and user facts for context-aware grounding. It utilizes a modular skill system to interact with external binaries and APIs, supported by a loop that handles tool calling, schema validation, and failure recovery.
The project covers several broad capability areas, including voice interaction through speech-to-text and text-to-speech synthesis, natural language understanding for intent parsing, and a dynamic persona engine that adapts communication tone. It also includes administrative interfaces for assistant information management and security layers for HTTP API and client socket access.
The application is provided as a dockerized AI server to ensure consistent deployment and hosting.
Features
- Agentic Loops - Orchestrates autonomous cycles of observing, reasoning, and acting to execute multi-step tasks via a loop.
- Agentic Workflow Automations - Builds multi-step autonomous plans that execute external tools and binaries to complete complex goals.
- Long-term Memory Stores - Organizes user information into persistent and discussion stores to separate long-term facts from history.
- Hybrid Short-and-Long Term Memory - Organizes data into persistent, daily, and discussion stores to balance long-term facts with immediate context.
