AutoCoder Nano is an AI-powered coding assistant that leverages large language models (LLMs) for code generation, understanding, and modification. It provides both a command-line interface (CLI) and a web-based gateway server for interactive coding support. The system enables users to generate code, edit files, and answer questions about their codebase through natural language interactions with LLMs. The architecture is modular, consisting of a core LLM client for provider interactions, an agent-based system for task execution, context management for conversation history and file indexing, a retrieval-augmented generation (RAG) mechanism for relevant code retrieval, and a code editing module using SEARCH/REPLACE blocks. Additional components include a skill-based extension system, file locking utilities, and configuration management. The system supports multiple LLM providers (OpenAI, Volcengine, etc.) and integrates with Git for version control operations. Key entry points are the CLI (`auto_coder_nano.py`) and the gateway server (`auto_coder_gateway.py`). Data flows involve user input processed by the agent system, which coordinates LLM interactions, tool usage (file operations, web search, RAG), and context updates. External dependencies include FastAPI, DuckDB with VSS extension, HuggingFace tokenizers, and various utility libraries. The project demonstrates active maintenance with recent commits and exhibits strong engineering practices in its modular design and reusable patterns.