deepseek-cli: an Ollama-first coding assistant you install with npm
DeepSeek CLI, a command-line AI coding assistant that leverages the powerful DeepSeek Coder models
At a glance
- What is it?
- The package run-deepseek-cli wraps DeepSeek Coder behind a single `deepseek` command, defaulting to a local Ollama server rather than a hosted API. The local default is the interesting part, and it is also where the hardware cost lands.
- Who is it for?
- Adopt it if you already run Ollama and want a terminal entry point to DeepSeek Coder without a paid API key, and you have at least 8GB of RAM for the recommended 6.7b model. Skip it if your machine cannot hold a useful model in memory, or if you need a tool that edits files itself rather than answering prompts you paste in.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 55 days ago.
- What is it written in?
- Mainly TypeScript, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The problem it addresses is API cost and data leaving your machine
Most command-line coding assistants assume a hosted model behind a key you pay for. This project inverts that. The README describes a CLI whose default mode is a local Ollama server, with the cloud path presented as the optional one. The environment table lists DEEPSEEK_USE_LOCAL with a default of true, and the quickstart labels the local setup as "Recommended - Free & Private". That framing tells you who the project is aimed at: developers who want DeepSeek Coder's completions on a laptop or workstation and would rather not send source files to a third party. The audience is narrow in a useful way. It is not for teams that need a hosted service with an audit trail, and it is not for anyone on a machine that cannot hold a multi-gigabyte model in memory. It is for the individual engineer who already has Ollama running, or is willing to install it, and wants a shell command instead of a browser tab.
How a prompt travels: CLI, then Ollama or the DeepSeek API
The architecture visible in the README is a two-path router. On one side sits a local Ollama daemon addressed through OLLAMA_HOST, which defaults to http://localhost:11434. On the other sits the DeepSeek cloud API, which requires DEEPSEEK_API_KEY and is reached only when DEEPSEEK_USE_LOCAL is set to false. The model selection is a single string, DEEPSEEK_MODEL, defaulting to deepseek-coder:6.7b. In local mode that string is an Ollama model tag; in cloud mode it names a hosted model. The CLI itself is written in TypeScript and published as run-deepseek-cli. What the README does not describe is the request payload, the context-window handling, or how the tool decides which files in a directory to read when you ask it to analyze a codebase. The examples show prompts like "Analyze this codebase and suggest performance optimizations" issued after cd into a repository, which implies some directory awareness, but the mechanism is not documented in the supplied material. Treat the repository-reading behaviour as unverified until you look at the source.
Installing it: npm, Ollama, and one model pull
The quickstart is short. Install Node.js 18 or higher, install Ollama, then run npm install -g run-deepseek-cli. The package name matters here: the npm badge points at run-deepseek-cli, while the binary you invoke is deepseek. Start the Ollama service with ollama serve, then pull one of three models. The README recommends deepseek-coder:6.7b at roughly 4GB on disk and 8GB of RAM, with deepseek-coder:1.3b as the lightweight option at 1GB and 2GB of RAM, and deepseek-coder:33b at 19GB and 32GB of RAM. After that, typing deepseek opens interactive mode. The cloud path skips Ollama entirely: export DEEPSEEK_API_KEY and set DEEPSEEK_USE_LOCAL=false. Configuration can also live in a .env file at the project root, with the same four keys: DEEPSEEK_USE_LOCAL, DEEPSEEK_MODEL, OLLAMA_HOST, and DEEPSEEK_API_KEY. There is also a deepseek setup subcommand that the README says checks for Ollama, starts the service if needed, downloads the specified model, and verifies the result. That is a sensible piece of packaging, because the manual sequence has four steps that are easy to get half-right.
The 33b model is the real system requirement, not the CLI
The CLI is a few megabytes of TypeScript. The model is the deployment. Reading the model table as a hardware specification rather than a feature list changes the calculus: the recommended 6.7b build wants 8GB of RAM, and the 33b build wants 32GB. On a 16GB laptop, the recommended tier fits but leaves little headroom for an editor, a browser, and a container runtime at the same time. The 1.3b tier exists for constrained machines, and the README positions it for "Quick completions, lightweight tasks", which is an honest description of what a 1.3b coder model can do. The failure mode to expect is not a crash but a quality cliff. Prompts that ask for repository-level reasoning, the kind shown in the examples, are the ones most likely to degrade when you drop to the smallest model. There is no benchmark in the README, and I have not run one, so the size-to-quality relationship is something to establish on your own workload before committing to a tier.
What the README does not tell you about context and file access
Several claims in the description are larger than the documentation behind them. "Repository-Level Understanding" and "Analyze and work with large codebases" appear in the feature list, but the README never explains how files are selected, whether there is an ignore file, or what happens when a repository exceeds the model's context window. The supported-language claim of 100+ languages is deferred to docs/supported-languages.md, which is not included in the material I have. The examples are all conversational: you type a prompt, the model answers. Nothing in the README shows the CLI writing a file, applying a patch, or running a command on your behalf. If you are coming from tools that edit your working tree directly, this is a different interaction model, and the README does not promise otherwise. That is the honest boundary of the project as documented: it is a chat interface to a coder model, launched from your shell, with a local-first default. The repository-level framing is aspirational until the file-handling code is inspected.
Compared with a hosted assistant like Claude Code
The obvious alternative is a hosted coding agent such as Claude Code, which the repository's own topic list names alongside claude-ai and cursor. The difference in approach is not the model, it is where inference happens and who pays for it. A hosted agent runs the model on the vendor's hardware, which means no local RAM requirement, a context window you do not have to manage, and a per-token or subscription cost. deepseek-cli runs the model on your machine by default, which means no per-token cost and no source code leaving the host, at the price of buying the RAM and accepting a smaller, slower model than a frontier hosted one. Neither is strictly better. If your work involves large repositories and you want the agent to make edits across many files, the local 6.7b tier is likely the wrong tool, and a hosted agent is the pragmatic choice. If your work is single-file questions, refactors you paste in, and you want it to keep working offline, the local default is the reason to pick this project over the alternatives.
Maintenance, licensing, and what a 0.1.x version implies
The project is MIT licensed, which permits commercial and private use with the usual requirement to carry the licence text. That covers the CLI code. It does not cover the DeepSeek Coder model weights you pull through Ollama, which carry their own licence terms set by their publisher, and it does not cover the DeepSeek cloud API, which has separate terms and pricing. The release history in the supplied material shows a single tagged release, 0.1.1, dated 2025-06-26, described only as "fix". A 0.1.x version number with one patch release is a signal about interface stability: expect the command surface and environment variable names to move. The last push to the default branch is dated 2026-07-23, which suggests the repository is still active, but activity is not the same as a stable contract. The upgrade cost is low in absolute terms, since the install is a global npm package and the configuration is four environment variables, but a rename of DEEPSEEK_MODEL or a change to the default model tag would silently alter behaviour for anyone relying on the defaults. Pin the model explicitly in your .env rather than inheriting the default.
Editorial conclusion
Adopt it if you already run Ollama and want a terminal entry point to DeepSeek Coder without a paid API key, and you have at least 8GB of RAM for the recommended 6.7b model. Skip it if your machine cannot hold a useful model in memory, or if you need a tool that edits files itself rather than answering prompts you paste in. Before installing, confirm the package name on npm is run-deepseek-cli and not deepseek-cli, check that the model tag you intend to pull actually exists in the Ollama library, and read the docs/supported-languages.md file the README links to, since that list is the one claim you cannot verify from the README alone.
Community notes