A blazing fast, single-binary CLI for chatting with any OpenAI-compatible LLM from your terminal. No runtime dependencies. No Docker. No Node. Just a 10 MB binary that works on any Linux machine.
Heavily inspired by simonw/LLM but but built for people who want something simpler.
$ llm -s "What command shall I use to see all files and folders including hidden ones"
ls -la
macOS and Windows releases coming soon.
wget -qO- https://raw.githubusercontent.com/Glaicer/llm-cli/main/install.sh | bash
That's it. The llm binary is dropped into ~/.local/bin. Make sure it's in your PATH:
export PATH="$HOME/.local/bin:$PATH"
Single shot — one question, one answer, done:
llm -s "Explain the difference between TCP and UDP"
Interactive mode — multi-turn conversation with full context memory:
$ llm
>> What command lists all files
ls
>> And hidden ones
ls -la
>> exit
You can also seed the conversation with an initial prompt:
llm "What is the capital of France"
REPL commands: exit, quit, Ctrl-D, or Ctrl-C to leave.
On first run, llm walks you through setup interactively — base URL, model, and API key. The config is saved to:
~/.config/llm-cli/config.toml
Edit it directly to change settings:
base_url = "https://api.openai.com/v1"
api_key = "sk-..."
model = "gpt-5.4-mini"
system_instruction = "You are an AI CLI assistant. Your answers should be short and concise. If the user asks for command, output just command without any comments."
| Option | Description |
|---|---|
base_url | Any OpenAI-compatible API endpoint (OpenAI, Ollama, LM Studio, vLLM, etc.) |
api_key | Bearer token for authentication |
model | Model ID accepted by your endpoint (e.g. gpt-4o-mini, claude-opus-4) |
system_instruction | System prompt prepended to every conversation |
Environment overrides — set these to override the config file without editing it:
LLM_CLI_API_KEY=sk-...
LLM_CLI_BASE_URL=
LLM_CLI_MODEL=
Remove the binary:
rm ~/.local/bin/llm
Remove the config:
rm -r ~/.config/llm-cli
git clone repo
cd llm-cli
cargo build
cargo test
The binary is built at target/debug/llm. For a release build:
cargo build --release
MIT
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