llm-chat

A minimal terminal chat client for OpenAI-compatible endpoints — OpenRouter, OpenClaw, Ollama, vLLM, llama.cpp, LM Studio, and anything else that speaks /chat/completions. Single static binary, no tools, no TUI, and only golang.org/x/term + golang.org/x/sys as dependencies.

Runs on Linux and macOS (other Unixes supported by x/term will likely work too). Windows supports line-based chat and piped input; the raw terminal editor is currently Unix-only. On Windows, end each message with a line containing only . (or pipe input to the program). -key-cmd requires sh on PATH, for example from Git for Windows.

Downloads and containers

GitHub Releases provide Linux, Windows, and macOS archives for amd64 (Intel/AMD) and arm64 (including Apple Silicon), plus SHA256SUMS. macOS archives use Go's darwin OS name; Windows archives contain llm-chat.exe. Binaries are built with CGO disabled. The macOS and Windows binaries are not signed or notarized.

Each successful CI run also uploads these archives as workflow artifacts. Push a v* tag (for example v1.0.0) to publish a GitHub Release; tags containing - become prereleases.

The container is published to ghcr.io/naomiamethyst/llm-chat for linux/amd64 and linux/arm64. latest follows successful builds of main; v* tags retain their exact name, and sha-<short commit> identifies a build. Pull requests build the image without publishing it. Publishing uses the repository's built-in GITHUB_TOKEN with packages: write; no registry secret is needed. For anonymous pulls, set the GHCR package visibility to public after its first publication (GitHub package visibility documentation).

# Interactive chat; credentials are forwarded from your environment.
docker run --rm -it -e OPENROUTER_API_KEY \
  ghcr.io/naomiamethyst/llm-chat:latest -model anthropic/claude-sonnet-4.5

# Pipe input; use -i without -t.
printf 'Hello\n' | docker run --rm -i -e OPENROUTER_API_KEY \
  ghcr.io/naomiamethyst/llm-chat:latest -model anthropic/claude-sonnet-4.5

# Persist /save output in the current directory.
docker run --rm -it --user "$(id -u):$(id -g)" \
  -v "$PWD:/data" -e OPENROUTER_API_KEY \
  ghcr.io/naomiamethyst/llm-chat:latest

The final image starts FROM scratch and contains the static executable, CA certificates for HTTPS, timezone data, and the license. Its working directory is /data. There is no shell or credential helper in the image, so -key-cmd is unavailable; pass keys through environment variables instead. For a local build, run docker build -t llm-chat .. To build both architectures:

docker buildx build --platform linux/amd64,linux/arm64 \
  -t ghcr.io/naomiamethyst/llm-chat:dev .

Build

Requires Go 1.25+ (older toolchains with GOTOOLCHAIN=auto, the default, fetch it automatically):

go build -o llm-chat .

Or install straight from GitHub:

go install github.com/NaomiAmethyst/llm-chat@latest

Usage

# OpenRouter (the default endpoint)
export OPENROUTER_API_KEY=sk-or-...
./llm-chat -model anthropic/claude-sonnet-4.5

# Any other OpenAI-compatible endpoint
./llm-chat -url http://localhost:11434/v1 -model llama3.1

# System prompt inline or from a file
./llm-chat -system "You are terse."
./llm-chat -system @prompts/reviewer.md

# Scripting: pipe stdin, output goes to stdout (see "Non-interactive mode")
./llm-chat "summarize this file:" < notes.txt
git diff | ./llm-chat -system "write a commit message"

Flags

flag	default	meaning
-url	OpenRouter, or the OpenClaw gateway (see below)	API base URL ($LLM_CHAT_BASE_URL)
-key	(env)	API key; falls back to $LLM_CHAT_API_KEY, $OPENCLAW_GATEWAY_TOKEN (gateway only), $OPENROUTER_API_KEY, $OPENAI_API_KEY
-key-cmd	(none)	shell command printing the API key ($LLM_CHAT_KEY_CMD); re-run to pick up a rotated key when a request fails
-key-ttl	0	re-run -key-cmd once the cached key is older than this, e.g. 55m ($LLM_CHAT_KEY_TTL)
-model	first model listed by the endpoint	model id ($LLM_CHAT_MODEL); list with /models
-system	(built-in)	system prompt, @file to load from a file, or none for no system prompt
-temperature	endpoint default	sampling temperature
-max-tokens	endpoint default	max completion tokens
-no-stream	off	disable streaming
-interactive	stdin is a tty; false on Windows	terminal editor session; -interactive=false reads .-terminated messages from stdin
-color	on when interactive	ANSI colors + markdown rendering; -color=false disables all decorative ANSI
-load	(none)	load a conversation at startup (JSON save or copied transcript)

Positional arguments become (or are prepended to) the first user message.

If -model and $LLM_CHAT_MODEL are both unset, the endpoint's /models list is fetched at startup and the first model it returns becomes the default (a warning is printed if the list can't be fetched — pick one with /model).

OpenClaw: if $OPENCLAW_GATEWAY_TOKEN is set, the default endpoint becomes the local OpenClaw gateway (http://127.0.0.1:18789/v1) with the token as the API key — so export OPENCLAW_GATEWAY_TOKEN=… followed by a bare ./llm-chat just works. The token is only used as a key when talking to the gateway; an explicit -url/-key always wins.

Keys from a command

-key-cmd takes a shell command that prints the API key, so short-lived or vaulted credentials never have to sit in the environment:

./llm-chat -key-cmd 'pass show openrouter/api-key'
./llm-chat -key-cmd 'op read op://vault/openrouter/credential'
./llm-chat -key-cmd 'gcloud auth print-access-token'

It runs once at startup, and again whenever a request fails: if the command then prints a different key, that request is retried once with it, which covers tokens that expire mid-session. If the key comes back unchanged the error is reported as-is rather than re-sending pointlessly, and a request that was cancelled or had already printed part of an answer is never retried. /models lookups refresh and retry the same way.

-key-ttl adds the proactive half: set it below the credential's lifetime and the key is renewed before a request that would otherwise fail, so an expiring token costs no failed round trip at all.

./llm-chat -key-cmd 'gcloud auth print-access-token' -key-ttl 55m

The two work together — the TTL keeps the key fresh, and the failure- triggered refresh remains the backstop for a credential revoked early. With no TTL (the default) the helper runs only at startup and after a failure.

The command runs through sh -c with $LLM_CHAT_ENDPOINT set to the endpoint the key is for (useful when one command serves several endpoints — after an endpoint switch, the first failed request refreshes the key with the new value set). Only the first line of its output is used, since helpers commonly print the secret first and metadata after. It inherits stdin, so a helper may prompt for a passphrase or a hardware-key touch; it has two minutes to finish. A failure at startup is fatal; a failure later is a warning and the original API error stands. -key-cmd takes precedence over -key and over the environment.

Non-interactive mode

With -interactive=false (the default when stdin is not a tty, and always on Windows), the terminal editor is not used. Instead stdin carries one or more messages, each ended by a line containing only . — history is kept between them, so this is a real multi-turn conversation. EOF sends any pending text as the final message, so a plain echo hi | llm-chat still works. Responses go to stdout with nothing else added; pass -color if you want ANSI markdown highlighting in the piped output (emitted line-by-line, with no cursor-movement codes).

printf 'summarize the plan\n.\nnow list the risks\n.\n' | ./llm-chat

Commands

command	effect
/model [id]	show or switch the model (mid-conversation is fine)
/models [filter]	list the endpoint's models, optionally substring-filtered
/system [text|@file|clear|default]	show, set, load, remove, or restore the system prompt
/clear	wipe conversation history (keeps the system prompt)
/tokens	show context size and session token totals
/undo	remove the last exchange
/retry	resend the last user message (e.g. after an error or /model switch)
/save [file]	save the conversation — markdown, or JSON if the name ends in .json
/load [file]	load a conversation (JSON save or copied transcript); no file = paste it at the prompt
/quit	exit (Ctrl+D also works)

Input editor

The prompt is a multi-line editor:

• Enter inserts a newline; Ctrl+D sends the message (on an empty prompt it exits). A single-line /command runs on Enter directly.
• Arrow keys move the cursor, including up/down across lines (and across wrapped rows); Backspace and Delete work as usual.
• Home/End (or Ctrl+A/Ctrl+E) jump within the line; Ctrl+K/Ctrl+U kill to line end/start; Ctrl+W deletes the previous word; Ctrl+C clears the input; Ctrl+L redraws the screen.
• Pasting multi-line text inserts it literally, ready for editing before you send.
• Start a message with // to send a literal message beginning with /.

Your message is composed on its own line(s) below the you ❯ marker, with no continuation prefixes, so finished messages can be copied cleanly from the scrollback. On submit a status line is printed, and another follows the response:

you ❯
tell me about foo
[sent: 2026-08-13 22:37:05; 33 bytes]

◆ anthropic/claude-sonnet-4.5
...response...
[recv: 2026-08-13 22:37:12; 6.8s; 1,204 in / 87 out; session 5,410 in / 322 out]

────────────────────────────────────────

The dim horizontal rule closes each AI turn before the next prompt.

While a response is streaming, Ctrl+C interrupts it without ending the session; the partial reply stays in history.

Saving & loading conversations

/save chat.json writes the full conversation state — endpoint, model, system prompt, and every message with its timestamp — as JSON; any other file name writes a human-readable markdown transcript. Restore a JSON save with /load chat.json, or at startup with -load chat.json (works in non-interactive mode too, so a script can continue a saved conversation).

A JSON save restores the endpoint it was recorded with, so a conversation held against a local endpoint is never replayed to a different one by accident. An explicit -url (or $LLM_CHAT_BASE_URL) always wins, and the API key is re-resolved for whichever endpoint ends up in use — the OpenClaw gateway token is only ever sent to the gateway. Since a save file names the endpoint it will be sent to, treat conversation files like any other config you execute: load ones you trust. Pasted transcripts record no endpoint and can never redirect one.

/load also understands a transcript copied straight from the terminal: it rebuilds messages from the you ❯ / ◆ model markers and takes timestamps from the [sent: …] / [recv: …] lines (banner, rules, and status noise are ignored; a copied system ❯ block restores the system prompt). Run /load with no argument to paste a transcript directly at a paste ❯ prompt, ending with Ctrl+D. Loading replaces the current history.

The horizontal rule after each AI turn also gives copied transcripts an unambiguous shape for the parser.

Markdown rendering

Responses are highlighted with ANSI colors as they stream: headers, bold, italic, __underline__ (rendered underlined), inline code, list/quote markers, and fenced code blocks with light generic syntax highlighting (comments, strings, numbers, common keywords). All markdown characters are kept — markers are just drawn in dark gray — so text copied from the terminal is the model's verbatim output. Piped output is plain by default; pass -color to get the same highlighting line-by-line without cursor codes.

System prompt & placeholders

The active system prompt (fully expanded) is printed under a system ❯ header at the top of every interactive session, so you always see exactly what the model was given. If no -system is given, a generic default is used: "You are a helpful AI chat assistant running in llm-chat, a simple terminal harness. The model powering you is {model}. This session began on {date} at {time}. …" — continuing with an explanation of the per-message timestamp prefixes; plus, when color is on, a terse description of the markdown the harness renders; when color is off, a request for plain-text prose; and, in interactive sessions, a note that a human is typing live at a terminal. Pass -system none (or /system clear) for no system prompt at all, and /system default to restore the built default.

Any system prompt (yours included) may use the placeholders {model}, {endpoint}, {date}, and {time}. They are expanded once — at session start and again only when core state changes (/system, /model, /load) — never per message, so the request prefix stays byte-identical across turns and remains cacheable by providers with prompt caching. Fresh times come from the per-message [YYYY-MM-DD HH:MM:SS] prefixes instead. /system with no argument shows the raw template and the expansion currently in use.

Timestamps

Every message sent to the model carries a [YYYY-MM-DD HH:MM:SS] prefix (applied on the wire only — display, history, and transcripts stay clean). The default system prompt tells the model about these prefixes and instructs it not to emit timestamps of its own; if you supply a custom system prompt, the prefixes are still applied, so mention them yourself if it matters. The [sent: …] / [recv: …] status lines show each turn's wall-clock timestamps (plus message bytes and response duration), and /save transcripts timestamp every message.

Token counter

The [recv: …] line after each response shows that turn's prompt/completion tokens and the session's running totals, e.g. 1,204 in / 87 out; session 5,410 in / 322 out. Counts come from the endpoint's reported usage (stream_options.include_usage is requested when streaming); if the endpoint reports none, a ~-prefixed estimate (~4 chars/token) is used instead. /tokens shows the current context size and session totals on demand.

Notes

• The system prompt is fully under your control: nothing is prepended or appended to it (per-message timestamp prefixes are the only content the harness adds), and no requests are made beyond /chat/completions and /models.
• Reasoning-model "thinking" deltas (OpenRouter's delta.reasoning) are shown dimmed, with every line prefixed by a ┊ marker, and are never added to history — /load skips those lines too, so copying a transcript back in restores the answer without the reasoning.
• Text from the endpoint is stripped of control characters before it reaches the terminal in every output mode, so a hostile or malfunctioning endpoint cannot repaint the screen, set the window title, or drive the clipboard through escape sequences.
• Do not wrap it in rlwrap — the built-in editor needs the terminal in raw mode and provides its own line editing.
• Stock macOS Terminal.app does not render the ANSI italic attribute (*italic* spans lose their slant there); iTerm2, Ghostty, kitty, etc. render everything.

License

Copyright © 2026 Naomi Persephone Amethyst naomi@amethyst.name

GPL-3.0 — see LICENSE.