jarv 0.3.0

A simple OpenAI-powered CLI agent

jarv

An OpenAI-powered CLI agent that can run shell commands, fan out work to parallel subagents, and keep track of conversation history across terminal sessions.

jarv                                    # start an interactive session
jarv whats the meaning of life?         # one-shot question
jarv commit all these files             # let it run commands to do the job
jarv refactor the auth module           # complex tasks get split across subagents

Install

Requires Python 3.10+ and an OpenAI API key.

pip install jarv
jarv /set api_key YOUR_OPENAI_API_KEY

The API key can also be set via the OPENAI_API_KEY environment variable.

To upgrade:

jarv /update

Usage

One-shot mode

Pass a prompt as arguments. Jarv answers (running commands if needed) and exits.

jarv what process is using port 8080?
jarv find all TODO comments in src/

Heads-up mode

Run jarv with no arguments to enter an interactive prompt loop.

jarv> what files changed today?
jarv> now run the tests
jarv> /history
jarv> /new

• Type a prompt and press Enter.
• Slash commands start with / — type /help to list them.
• Exit with exit, quit, /exit, or Ctrl+C.

Flags

Flags override config values for a single run and work in both one-shot and heads-up mode.

Flag	Short	Description
--model MODEL	-m	Override the model (e.g. gpt-4o)
--effort EFFORT	-e	Override reasoning effort (low / medium / high)
--timeout SECONDS		Override command timeout in seconds
--system PROMPT	-s	Override the system prompt
--new		Start a fresh session (ignore prior history, but still save)
--incognito		Don't load or save session history
--version		Print the version and exit

jarv -m gpt-4o "summarise this repo"
jarv --effort high "refactor the auth module"
jarv --new "start fresh without prior context"
jarv --incognito "one-off task, leave no trace"
jarv --timeout 120 --system "You are a poet" "write me a haiku"

How it works

Jarv wraps the OpenAI Responses API with a tool-calling agent loop. The model can call three tools:

Tool	Purpose
run_command	Execute a shell command and return stdout, stderr, and exit code
spawn	Fan out work to parallel subagents, each with their own tool access
read_artifact	Retrieve the full output of a completed subagent

On Windows, commands run through PowerShell. On other platforms, they run through the system shell.

Subagent orchestration

When the model calls spawn, Jarv runs N child agents in parallel. Each child operates independently — running commands, reasoning through subtasks — and terminates by calling finish with a detailed report and a short summary. The parent agent can then read any child's full output via read_artifact.

• Parallel by default — all children in a spawn call run concurrently in a thread pool.
• Artifacts — each child's output is stored as a named artifact. The parent (or siblings that declare a dependency) can fetch the full content.
• Recursive — children can themselves spawn further children, up to max_subagent_depth levels deep (default 4). Children are sterile by default; the parent must explicitly allow further spawning.
• Scoped per query — the artifact store resets with each new top-level prompt.

The terminal shows a live progress panel as children run, with a green checkmark or red cross as each finishes.

Commands

Command	Description
/help	Show all commands
/about	Detailed info and examples
/set <key> <value>	Set a config value
/unset <key>	Reset a config key to default
/config	Show current settings
/new	Start a fresh session on the next prompt
/archive	Archive session history and artifacts
/sessions	Browse sessions (interactive when in a TTY)
/sessions <id>	Load a specific session by ID prefix
/load	Load the most recently used session
/load <id>	Load a specific session
/history	Show recent conversation history
/undo [n]	Remove last n exchanges (default 1)
/redo [n]	Restore last n undone exchanges (default 1)
/usage	Show token usage, cost, and context breakdown
/update	Update Jarv to the latest version

All commands work both as jarv /command (one-shot) and inside heads-up mode.

Sessions

Each terminal is automatically bound to its own session. Jarv identifies terminals using environment variables (WT_SESSION, TERM_SESSION_ID, TMUX, STY) with a parent-process fallback, so history persists across runs in the same terminal.

• /new starts a fresh session on the next prompt without archiving the current session.
• /sessions opens an interactive browser (arrow keys to navigate, Enter to load, a to archive, d to delete, p to preview, Tab to switch views, Ctrl+F to search).
• /undo and /redo let you step through recent exchanges.

Config

Settings live in ~/.jarv/config.json (created on first run). Edit the file directly or use /set and /unset.

Key	Default	Description
api_key	""	OpenAI API key. Falls back to OPENAI_API_KEY env var if empty.
model	"gpt-5.4-mini"	Model name passed to the API.
reasoning_effort	""	Reasoning effort level. Leave empty to disable.
max_history	40	Number of recent messages kept as context.
command_timeout	60	Seconds before a shell command is killed.
max_subagent_depth	4	Maximum nesting depth for spawned subagents.
subagent_thread_pool_max_workers	8	Max parallel subagents per spawn call.
check_updates	true	Background update check on startup (non-blocking, throttled to once per 24h).
system_prompt	"You are Jarv..."	System instructions sent with each request.

Local files

All state is stored in ~/.jarv/ (on Windows, %USERPROFILE%\.jarv\):

~/.jarv/
├── config.json                      # settings and optional API key
├── sessions.json                    # terminal → session mappings
├── sessions/
│   ├── history-<hash>.json          # conversation history
│   ├── artifacts-<hash>.json        # subagent artifacts
│   ├── usage-<hash>.json            # token usage totals
│   └── redo-<hash>.json             # undo/redo stack
└── archive/                         # archived sessions

Dependencies

Package	Role
openai	Responses API client
rich	Terminal styling, live rendering, markdown
litellm	Token counting, model pricing, context window metadata

License

MIT