qwen-code
Local coding assistant CLI built on Qwen2.5-Coder and Ollama, with tool use, a terminal REPL, and RAG via Qdrant
A local, offline coding assistant CLI powered by Qwen2.5-Coder via Ollama, with agentic tool use, a terminal REPL, and Qdrant-based code search.
qwen-code is a command-line coding assistant that runs entirely on your own machine, with no cloud API required. You interact with it through a terminal, either in an interactive back-and-forth session or by passing a one-off question as a command. It is described as a Claude Code-style tool, meaning it can reason through a task by repeatedly using tools to read files, run shell commands, search your codebase, and look things up before giving you a final answer.
The AI model behind it is Qwen2.5-Coder, which runs locally via Ollama, a tool for running large language models on your own hardware. This means your code never leaves your machine. You can switch between different model sizes mid-session, change the working directory, or reset the conversation using slash commands inside the REPL.
One of its notable features is RAG, which stands for retrieval-augmented generation. This lets you feed your own documents or code into a vector database called Qdrant, then have the assistant search that database when answering questions. It uses a separate embedding model called nomic-embed-text, also run through Ollama, to convert text into a searchable format. Qdrant can be run as a local server, a local file, or entirely in memory depending on your preference.
The tool includes a permission system so that read-only operations like reading files or searching happen automatically, while anything that could change or delete files requires you to confirm with a yes or no prompt before it runs. The project is written in Python.
Where it fits
- Run a Claude Code-style AI coding assistant fully offline with no API key and no code leaving your machine.
- Ask an agent to read files, run shell commands, and search your codebase from an interactive terminal REPL.
- Index your own project or documents into a local vector database so the assistant can answer questions using retrieval-augmented generation.
- Require explicit confirmation before the assistant runs any command that writes, edits, or deletes files.