A tiny, zero-dependency LLM agent harness that lives in your terminal.
One static binary. No runtime, no daemon, no cloud. Local-LLM friendly. Summon it anywhere with Ctrl+Space.

Why nib • Quickstart • Usage • Plugins • Skills • Configuration • Tool Approval

Why nib

Most LLM coding agents are big: a Node/Python runtime, a pile of dependencies, a login, a background service. nib is the opposite. It's a single ~20 MB Go binary you drop on any machine — laptop, server, container, a box you SSH'd into — and it just runs. Point it at any OpenAI-compatible endpoint (including a local model) and press Ctrl+Space.

Small doesn't mean toy. nib is a real agent harness:

• Tool use with approval — it runs shell commands, but every call passes an approval gate you control.
• Sub-agents — delegate self-contained subtasks (explore, plan, …) that run in the foreground or background.
• MCP — connect any Model Context Protocol server for extra tools.
• Plugins — installable packages that add MCP servers, sub-agents, prompt fragments, skills, slash commands, and lifecycle hooks. Claude Code plugins work too.
• Skills — install skill packs (e.g. obra/superpowers) and let the agent load them on demand.

Think of it as the fzf for LLMs: portable, keyboard-driven, composable, and out of your way.

Features

• Ctrl+Space anywhere — summon nib straight from your shell prompt; inline like fzf, or a tmux split when you're in tmux.
• Two modes — a polished TUI, or a plain --cli mode for pipes and scripts.
• Tool execution with approval — the AI proposes commands; you approve, deny, edit, or trust for the session.
• Sub-agents & background jobs — delegate to typed sub-agents; background them (Ctrl+B) and watch the jobs footer (Ctrl+J).
• Plugins — nib plugin install <git-url>; six contribution types; Claude-Code-plugin compatible.
• Skills — nib skill install <git-url>; progressive-disclosure skill packs loaded on demand.
• MCP protocol — bring any external tool server.
• tmux-native — seamless splits and popups.
• Multi-shell — zsh, bash, and fish.
• Zero dependencies — one portable binary, trivial to install and upgrade.

Quickstart

1. Install

curl -fsSL https://raw.githubusercontent.com/mudler/nib/master/install.sh | bash

# zsh users
curl -fsSL https://raw.githubusercontent.com/mudler/nib/master/install.sh | zsh

# from source
git clone https://github.com/mudler/nib && cd nib && go build -o nib . && sudo mv nib /usr/local/bin/

# go install
go install github.com/mudler/nib@latest

2. Configure a model — ~/.config/nib/config.yaml:

model: gpt-4o-mini
api_key: your-api-key
base_url: https://api.openai.com/v1   # or your local endpoint, e.g. http://localhost:8080/v1

3. Press Ctrl+Space in your terminal (or just run nib). That's it.

Usage

Run nib to open the TUI, or press Ctrl+Space from your shell. Use --cli for a plain, pipe-friendly mode.

Summon nib from your shell (Ctrl+Space)

The install.sh script wires this up for you. Inside tmux, nib opens in a split pane so it never disturbs what you're doing:

To wire it up manually, add the line for your shell:

eval "$(nib --init zsh)"      # ~/.zshrc
eval "$(nib --init bash)"     # ~/.bashrc
nib --init fish | source      # ~/.config/fish/config.fish

Sub-agents & background jobs

Ask nib to delegate, and it spawns a typed sub-agent (explore, plan, or any you configure). Background a running job with Ctrl+B and watch the jobs footer with Ctrl+J:

/loop — recurring & self-paced tasks

• /loop 5m /foo — run /foo (a slash command or prompt) every 5 minutes.
• /loop /foo — self-paced: the model runs /foo, then decides when to repeat by scheduling its own wake-ups; it stops when the task is done.
• /loop list — show active loops.
• /loop stop [id] — stop one loop, or all loops if no id is given.

Loops are session-only by default. The model can also schedule jobs directly with the cron, cron_list, and cron_delete tools; cron(durable: true) persists across restarts to .nib/loops.json.

/goal — keep going until a goal is met

• /goal <text> — set a goal. nib keeps working and re-checks it every time the model would stop, only finishing when the model decides the goal is met (it calls a goal_done tool) or you stop it.
• /goal — show the current goal.
• /goal clear — clear it. Pressing Ctrl+C during pursuit also clears it.

Unlike /loop, /goal is not scheduling — there are no timers. It's an in-turn "keep going" gate: the model self-judges progress and continues until done. You can still chat and steer while a goal is being pursued. Goals are session-only and single (setting a new one replaces the old).

Plugins

A plugin is a single installable unit — a git repo (or local dir) with a nib-plugin.yaml manifest — that can contribute any combination of:

nib plugin install <git-url|local-path>   # [--ref <tag|branch>] [--yes]
nib plugin list
nib plugin enable|disable <name>
nib plugin update|remove <name>

Install prints a summary of what the plugin contributes and asks for confirmation (--yes to skip). Plugins install disabled by default; a disabled plugin contributes nothing, and every tool call still passes the approval gate at runtime.

A minimal nib-plugin.yaml:

name: my-plugin
version: 1.0.0
description: adds a sub-agent and a slash command

agents:
  - name: researcher
    description: investigates a self-contained subtask
    system_prompt: You are a focused research sub-agent.
    tools: [bash]

commands:
  - name: review
    description: review the given input
    prompt: "Review the following: {{.Args}}"
    agent: researcher

Claude Code compatible. nib plugin install also installs an unmodified Claude Code plugin (.claude-plugin/ layout) or marketplace, mapping its plugin.json, skills/, commands/, agents/, hooks/, and .mcp.json into nib's model.

See examples/nib-plugin-demo for a reference plugin that exercises all six contribution types.

Skills

A skill pack is a git repo (or local dir) containing a skills/<name>/SKILL.md collection — for example obra/superpowers. nib harvests every skill, indexes it (name + description) in the system prompt, and the agent pulls in a skill's full instructions on demand via the load_skill tool — or you inject one eagerly for the session with /skill <name>.

nib skill install <git-url|local-path>    # [--ref <tag|branch>] [--yes]
nib skill list
nib skill enable|disable <name>
nib skill update|remove <name>

Like plugins, skill packs install disabled; enable the ones you want with nib skill enable <name>. Skill packs carry their bundled files, so a skill can Read or run scripts from its own directory at runtime.

Configuration

nib looks for config (in order) in ./.nib.yaml, $XDG_CONFIG_HOME/nib/config.yaml, ~/.config/nib/config.yaml, ~/.nib.yaml, then /etc/nib/config.yaml.

# Required: your LLM (any OpenAI-compatible endpoint, local or remote)
model: gpt-4o-mini
api_key: your-api-key
base_url: https://api.openai.com/v1

# Optional: custom system prompt
prompt: |
  You are a calm, helpful terminal assistant...

# Optional: per-request metadata sent verbatim on every LLM request (the OpenAI
# "metadata" object). Backends such as LocalAI use it for per-request flags —
# e.g. disable a reasoning model's thinking:
metadata:
  enable_thinking: "false"

# Optional: OpenAI-standard reasoning effort, sent on every request as
# "reasoning_effort" ("none"/"low"/"medium"/"high"). Unlike metadata.enable_thinking,
# this works even when the model's chat template has no enable_thinking toggle
# (e.g. LFM2.5) — so it's the reliable way to turn a reasoning model's thinking off:
reasoning_effort: "none"

# Optional: agent behavior
agent_options:
  iterations: 10
  max_attempts: 3
  max_retries: 3
  force_reasoning: false

# Optional: tool-approval policy (default: prompt for every tool)
#   prompt    — ask before each tool call (default)
#   allowlist — auto-approve the tools in allowed_tools, prompt for the rest
#   auto      — approve every tool call without prompting
approval_mode: prompt
allowed_tools:
  - bash

# Optional: extra sub-agent types (general, explore, plan are built in)
agents:
  - name: researcher
    description: investigates a self-contained subtask
    system_prompt: You are a focused research sub-agent.
    tools: [bash]
    # Per-agent metadata overlays the global metadata above (per key):
    metadata:
      enable_thinking: "true"

# Optional: external MCP servers
mcp_servers:
  filesystem:
    command: npx
    args: ["-y", "@anthropic/mcp-filesystem", "/home/user"]
    env:
      FOO: bar

You can also configure the essentials via environment variables:

export MODEL=gpt-4o-mini
export API_KEY=your-api-key
export BASE_URL=https://api.openai.com/v1

Tool Approval

When nib wants to run a command, you decide:

▏ run: bash
▏ {
▏   "script": "df -h"
▏ }
▏ [y] yes  [a] always  [n] no  [e] edit  [A] all

In the TUI, approval is a single keypress (no Enter):

• y — approve this call
• a — always allow this tool for the session (sub-agents share the allow list)
• A — allow all tool calls for the rest of this turn (handy after delegating a multi-step task)
• n / Esc — deny
• e — edit the call, then submit

In the CLI (--cli) the prompt is line-based: type y, a, all, n, or a free-form change, then Enter. To skip prompting entirely, set approval_mode / allowed_tools in your config.

MCP Servers

nib speaks the Model Context Protocol. A set of tools is built in — bash, the filesystem tools (read, write, edit, glob, grep), and the web tools (web_fetch, web_search); add any external server in your config:

mcp_servers:
  my_server:
    command: /path/to/mcp-server
    args: ["--some-flag"]
    env:
      API_KEY: secret

tmux

Inside tmux, nib automatically uses a split pane for the TUI. Pass --no-tmux to disable.

License

MIT