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Local transcription, agentic coding worktree management, RAG and memory proxies, TTS and STT servers with smart memory management, MacOS App (OSS Wisprflow), and more!

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최종 버전 다운로드 (.zip)

- marketplace.json
- plugin.json
- README.md
- - check_extras_sync.py
  - check_plugin_skill_sync.py
  - normalize_appcast.py
  - sync_extras.py
  - sync_requirements.py
- - automerge.yml
  - docker.yml
  - docs.yml
  - markdown-code-runner.yml
  - pytest.yml
  - release-drafter.yml
  - release.yml
  - toc.yaml
- logo.svg
- release-drafter.yml
- renovate.json
- docs-review.md
- pr-review.md
- - nemo-whisper.txt
- - .gitkeep
  - audio.txt
  - diarization.txt
  - faster-whisper.txt
  - kokoro.txt
  - llm.txt
  - memory.txt
  - mlx-whisper.txt
  - nemo-whisper.txt
  - piper.txt
  - rag.txt
  - server.txt
  - speed.txt
  - vad.txt
  - vectordb.txt
  - whisper-transformers.txt
  - wyoming.txt
- - __init__.py
  - _voice_agent_common.py
  - assistant.py
  - autocorrect.py
  - chat.py
  - diarize_live_session.py
  - rag_proxy.py
  - speak.py
  - speakers.py
  - transcribe.py
  - transcribe_live.py
  - voice_edit.py
- - __init__.py
  - alignment.py
  - audio.py
  - audio_format.py
  - chroma.py
  - deps.py
  - diarization.py
  - openai_proxy.py
  - process.py
  - reranker.py
  - speaker_identity.py
  - sse.py
  - transcription_logger.py
  - utils.py
  - vad.py
  - watch.py
- - __init__.py
  - cli.py
- - __init__.py
  - _branch_name.py
  - _config.py
  - _output.py
  - cleanup.py
  - cli.py
  - hooks.py
  - launch.py
  - project.py
  - registry.py
  - worktree.py
- - __init__.py
  - common.py
  - extras.py
  - hotkeys.py
  - launchd.py
  - service_config.py
  - services.py
  - systemd.py
- - __init__.py
  - _files.py
  - _filters.py
  - _git.py
  - _indexer.py
  - _ingest.py
  - _persistence.py
  - _prompt.py
  - _retrieval.py
  - _store.py
  - _streaming.py
  - _tasks.py
  - api.py
  - client.py
  - engine.py
  - entities.py
  - models.py
- - __init__.py
  - _indexer.py
  - _indexing.py
  - _prompt.py
  - _retriever.py
  - _store.py
  - _utils.py
  - api.py
  - client.py
  - engine.py
  - models.py
- - __init__.py
  - cli.py
  - common.py
  - model_manager.py
  - model_registry.py
  - streaming.py
- - __init__.py
  - _wyoming_utils.py
  - asr.py
  - llm.py
  - tts.py
  - wake_word.py
- __init__.py
- __main__.py
- _extras.json
- _tools.py
- api.py
- cli.py
- config.py
- config_cmd.py
- constants.py
- docs_gen.py
- example-config.toml
- opts.py
- py.typed
- scripts
- docker-compose.yml
- memory-proxy.Dockerfile
- rag-proxy.Dockerfile
- transcribe-proxy.Dockerfile
- tts.Dockerfile
- whisper.Dockerfile
- - index.md
  - memory.md
  - rag.md
- - assistant.md
  - autocorrect.md
  - chat.md
  - config.md
  - daemon.md
  - dev.md
  - diarize-live-session.md
  - index.md
  - install-extras.md
  - install-hotkeys.md
  - install-services.md
  - memory.md
  - rag-proxy.md
  - speak.md
  - speakers.md
  - start-services.md
  - transcribe-live.md
  - transcribe.md
  - voice-edit.md
- - docker.md
  - index.md
  - linux.md
  - macos-app.md
  - macos.md
  - nixos.md
  - windows.md
- CNAME
- configuration.md
- getting-started.md
- index.md
- iOS_Shortcut_Guide.md
- logo-avatar.svg
- logo-clean.svg
- run_markdown_code_runner.py
- system-integration.md
- - Package.resolved
  - Package.swift
  - README.md
- appcast.xml
- build-macos-app.sh
- test-macos-app-e2e.sh
- - .gitkeep
- - README.md
  - toggle-autocorrect.sh
  - toggle-transcription.sh
  - toggle-voice-edit.sh
- - README.md
  - skhd-config-example
  - toggle-autocorrect.sh
  - toggle-transcription.sh
  - toggle-voice-edit.sh
- - pyproject.toml
  - README.md
  - server.py
  - shell.nix
  - uv.lock
- __init__.py
- run-openwakeword.sh
- setup-linux-hotkeys.sh
- setup-linux.sh
- setup-macos-hotkeys.sh
- setup-macos.sh
- setup-windows.ps1
- start-all-services-windows.ps1
- start-all-services.sh
- - __init__.py
  - test_fix_my_text.py
  - test_interactive.py
  - test_interactive_extra.py
  - test_memory_add.py
  - test_speak.py
  - test_speak_e2e.py
  - test_speakers.py
  - test_transcribe.py
  - test_transcribe_agent.py
  - test_transcribe_e2e.py
  - test_transcribe_live.py
  - test_transcribe_recovery.py
  - test_tts_common.py
  - test_tts_common_extra.py
  - test_voice_agent_common.py
  - test_voice_edit.py
  - test_voice_edit_e2e.py
  - test_wake_word_assistant.py
- - __init__.py
  - test_audio.py
  - test_audio_format.py
  - test_audio_levels.py
  - test_chroma.py
  - test_sse.py
  - test_vad.py
  - test_watch.py
- - __init__.py
  - test_cleanup.py
  - test_cli.py
  - test_coding_agents.py
  - test_editors.py
  - test_hooks.py
  - test_launch.py
  - test_project.py
  - test_terminals.py
  - test_verification.py
  - test_worktree.py
- - __init__.py
  - test_extras.py
  - test_launchd.py
- - __init__.py
  - test_api_health.py
  - test_api_integration_liveish.py
  - test_client.py
  - test_engine.py
  - test_files.py
  - test_filters.py
  - test_git_integration.py
  - test_indexer.py
  - test_memory_integration.py
  - test_proxy_passthrough.py
  - test_store.py
  - test_utils.py
- - __init__.py
  - audio.py
  - llm.py
  - wyoming.py
- - __init__.py
  - test_api.py
  - test_engine.py
  - test_history.py
  - test_indexer.py
  - test_indexing.py
  - test_rag_client.py
  - test_rag_integration_liveish.py
  - test_rag_proxy_passthrough.py
  - test_retriever.py
  - test_store.py
  - test_utils.py
- __init__.py
- conftest.py
- test_alignment.py
- test_api.py
- test_api_integration.py
- test_asr.py
- test_asr_recovery.py
- test_audio_e2e.py
- test_cli.py
- test_config.py
- test_config_cmd.py
- test_daemon.py
- test_diarization.py
- test_diarize_live_session.py
- test_docs_gen.py
- test_env_vars.py
- test_faster_whisper_backend.py
- test_json_output.py
- test_llm.py
- test_llm_gemini.py
- test_macos_app.py
- test_memory_tools.py
- test_mlx_backend.py
- test_nemo_backend.py
- test_normalize_appcast.py
- test_process_manager.py
- test_requires_extras.py
- test_server_streaming.py
- test_server_tts.py
- test_server_tts_wyoming.py
- test_server_whisper.py
- test_server_whisper_wyoming.py
- test_services.py
- test_speaker_identity.py
- test_tools.py
- test_transformers_backend.py
- test_tts.py
- test_utils.py
- test_wake_word.py
- test_wyoming_utils.py
.cursorrules
.dockerignore
.env.example
.gitignore
.jscpd.json
.pre-commit-config.yaml
CLAUDE.md
example.agent-cli-config.toml
justfile
LICENSE
LLAMA_SERVER_USAGE.md
pyproject.toml
README.md
reddit.md
shell.nix
uv.lock
zensical.toml

// repository documentation

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# Agent CLI

`agent-cli` is a collection of **_local-first_**, AI-powered command-line agents that run entirely on your machine.
It provides a suite of powerful tools for voice and text interaction, designed for privacy, offline capability, and seamless integration with system-wide hotkeys and workflows.

> [!TIP]
> **Short aliases available:** You can use `agent` or `ag` instead of `agent-cli` for convenience.

> [!IMPORTANT]
> **Local and Private by Design**
> All agents in this tool are designed to run **100% locally**.
> Your data, whether it's from your clipboard, microphone, or files, is never sent to any cloud API.
> This ensures your privacy and allows the tools to work completely offline.
> You can also optionally configure the agents to use OpenAI/Gemini services.

## Why I built this

I got tired of typing long prompts to LLMs. Speaking is faster, so I built this tool to transcribe my voice directly to the clipboard with a hotkey.

**What it does:**

- Voice transcription to clipboard with system-wide hotkeys (Cmd+Shift+R on macOS)
- Autocorrect any text from your clipboard
- Edit clipboard content with voice commands ("make this more formal")
- Runs locally - no internet required, your audio stays on your machine
- Works with any app that can copy/paste

I use it mostly for the `transcribe` command when working with LLMs. Being able to speak naturally means I can provide more context without the typing fatigue.

Since then I have expanded the tool with many more features, all focused on local-first AI agents that integrate seamlessly with your system.

[![A demo video of Agent-CLI showing local AI voice and text tools on a desktop.](http://img.youtube.com/vi/7sBTCgttH48/0.jpg)](http://www.youtube.com/watch?v=7sBTCgttH48 "Agent-CLI: Local AI Voice & Text Tools on Your Desktop (macOS Demo)")

*See agent-cli in action: [Watch the demo](https://www.youtube.com/watch?v=7sBTCgttH48)*

## Features

- **[`autocorrect`](docs/commands/autocorrect.md)**: Correct grammar and spelling in your text using a local LLM.
- **[`transcribe`](docs/commands/transcribe.md)**: Transcribe audio from your microphone to clipboard.
- **[`speak`](docs/commands/speak.md)**: Convert text to speech using a local TTS engine.
- **[`voice-edit`](docs/commands/voice-edit.md)**: Edit clipboard text with voice commands.
- **[`assistant`](docs/commands/assistant.md)**: Wake word-based voice assistant.
- **[`chat`](docs/commands/chat.md)**: Conversational AI with tool-calling capabilities.
- **[`memory`](docs/commands/memory.md)**: Long-term memory system with `memory proxy` and `memory add`.
- **[`rag-proxy`](docs/commands/rag-proxy.md)**: RAG proxy server for chatting with your documents.
- **[`dev`](docs/commands/dev.md)**: Parallel development with git worktrees and AI coding agents.
- **[`server`](docs/commands/server/index.md)**: Local ASR and TTS servers with dual-protocol (Wyoming & OpenAI-compatible APIs), TTL-based memory management, and multi-platform acceleration. Whisper uses MLX on Apple Silicon or Faster Whisper on Linux/CUDA. TTS supports Kokoro (GPU) or Piper (CPU).
- **[`transcribe-live`](docs/commands/transcribe-live.md)**: Continuous background transcription with VAD. Install with `uv tool install "agent-cli[vad]"`.

## Quick Start

### Prefer the macOS menu bar app?

Install the native AgentCLI app with Homebrew:

```bash
brew install --cask basnijholt/tap/agent-cli
```

The app provides global shortcuts, a menu bar UI, automatic local Whisper setup on first transcription, and a private bundled `agent-cli` runtime. See the [macOS app guide](docs/installation/macos-app.md) for setup, updates, and uninstall instructions.

### Just want the CLI tool?

If you already have AI services running (or plan to use OpenAI), simply install:

```bash
# Using uv (recommended)
uv tool install agent-cli

# Using pip
pip install agent-cli
```

Then use it:
```bash
agent-cli autocorrect "this has an eror"
```

### Want automatic setup with everything?

We offer two ways to set up agent-cli with all services:

#### Option A: Using Shell Scripts (Traditional)

```bash
# 1. Clone the repository
git clone https://github.com/basnijholt/agent-cli.git
cd agent-cli

# 2. Run setup (installs all services + agent-cli)
./scripts/setup-macos.sh  # or setup-linux.sh

# 3. Start services
./scripts/start-all-services.sh

# 4. (Optional) Set up system-wide hotkeys
./scripts/setup-macos-hotkeys.sh  # or setup-linux-hotkeys.sh

# 5. Use it!
agent-cli autocorrect "this has an eror"
```

#### Option B: Using CLI Commands (New!)

> [!NOTE]
> `agent-cli` uses `sounddevice` for real-time microphone/voice features.
> On Linux only, you need to install the system-level PortAudio library  (`sudo apt install portaudio19-dev` / your distro's equivalent on Linux) **before** you run `uv tool install agent-cli`.
> On Windows and macOS, this is handled automatically.

```bash
# 1. Install agent-cli
uv tool install agent-cli

# 2. Install all required services
agent-cli install-services

# 3. Start all services
agent-cli start-services

# 4. (Optional) Set up system-wide hotkeys
agent-cli install-hotkeys

# 5. Use it!
agent-cli autocorrect "this has an eror"
```

`install-hotkeys` also installs the required `audio` and `llm` extras if they are missing.

The setup scripts automatically install:
- ✅ Package managers (Homebrew/uv) if needed
- ✅ All AI services (Ollama, Whisper, TTS, etc.)
- ✅ The `agent-cli` tool
- ✅ System dependencies
- ✅ Hotkey managers (if using hotkey scripts)

- [Installation](#installation)
  - [Option 1: CLI Tool Only](#option-1-cli-tool-only)
  - [Option 2: Automated Full Setup](#option-2-automated-full-setup)
    - [Step 1: Clone the Repository](#step-1-clone-the-repository)
    - [Step 2: Run the Setup Script](#step-2-run-the-setup-script)
    - [Step 3: Start All Services](#step-3-start-all-services)
    - [Step 4: Test Your Installation](#step-4-test-your-installation)
- [macOS Menu Bar App](#macos-menu-bar-app)
- [System Integration](#system-integration)
  - [macOS Hotkeys](#macos-hotkeys)
  - [Linux Hotkeys](#linux-hotkeys)
  - [Claude Code Plugin](#claude-code-plugin)
- [Prerequisites](#prerequisites)
  - [What You Need to Install Manually](#what-you-need-to-install-manually)
  - [What the Setup Scripts Install for You](#what-the-setup-scripts-install-for-you)
    - [Core Requirements (Auto-installed)](#core-requirements-auto-installed)
    - [AI Services (Auto-installed and configured)](#ai-services-auto-installed-and-configured)
    - [Alternative Cloud Services (Optional)](#alternative-cloud-services-optional)
    - [Alternative Local LLM Servers](#alternative-local-llm-servers)
- [Usage](#usage)
  - [Installation Commands](#installation-commands)
    - [Installing Optional Extras](#installing-optional-extras)
  - [Configuration](#configuration)
    - [Environment variable overrides](#environment-variable-overrides)
    - [Managing Configuration](#managing-configuration)
    - [Provider Defaults](#provider-defaults)
  - [`autocorrect`](#autocorrect)
  - [`transcribe`](#transcribe)
  - [`transcribe-live`](#transcribe-live)
  - [`speak`](#speak)
  - [`voice-edit`](#voice-edit)
  - [`assistant`](#assistant)
  - [`chat`](#chat)
  - [`rag-proxy`](#rag-proxy)
  - [`memory`](#memory)
    - [`memory proxy`](#memory-proxy)
    - [`memory add`](#memory-add)
- [Development](#development)
  - [Running Tests](#running-tests)
  - [Pre-commit Hooks](#pre-commit-hooks)
- [Contributing](#contributing)
- [License](#license)

</details>

## Installation

### Option 1: CLI Tool Only

If you already have AI services set up or plan to use cloud services (OpenAI/Gemini):

```bash
# Using uv (recommended)
uv tool install agent-cli

# Using pip
pip install agent-cli
```

### Option 2: Automated Full Setup

For a complete local setup with all AI services:

#### Step 1: Clone the Repository

```bash
git clone https://github.com/basnijholt/agent-cli.git
cd agent-cli
```

#### Step 2: Run the Setup Script

| Platform | Setup Command | What It Does | Detailed Guide |
|----------|---------------|--------------|----------------|
| **🍎 macOS** | `./scripts/setup-macos.sh` | Installs Homebrew (if needed), uv, Ollama, all services, and agent-cli | [macOS Guide](docs/installation/macos.md) |
| **🐧 Linux** | `./scripts/setup-linux.sh` | Installs uv, Ollama, all services, and agent-cli | [Linux Guide](docs/installation/linux.md) |
| **❄️ NixOS** | See guide → | Special instructions for NixOS | [NixOS Guide](docs/installation/nixos.md) |
| **🐳 Docker** | See guide → | Container-based setup (slower) | [Docker Guide](docs/installation/docker.md) |

#### Step 3: Start All Services

```bash
./scripts/start-all-services.sh
```

This launches all AI services in a single terminal session using Zellij.

#### Step 4: Test Your Installation

```bash
agent-cli autocorrect "this has an eror"
# Output: this has an error
```

> [!NOTE]
> The setup scripts handle everything automatically. For platform-specific details or troubleshooting, see the [installation guides](docs/installation/).

<details><summary>Development Installation</summary>

For contributing or development:

```bash
git clone https://github.com/basnijholt/agent-cli.git
cd agent-cli
uv sync
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

</details>

## macOS Menu Bar App

AgentCLI is also available as a native macOS menu bar app for voice workflows that should work from any app without keeping a terminal open.

Install it with Homebrew:

```bash
brew install --cask basnijholt/tap/agent-cli
```

The app is a SwiftUI wrapper around the same `agent-cli` commands. It bundles `uv`, installs a private `agent-cli[audio,llm]` runtime in your Application Support folder on first use, and starts the local Whisper daemon automatically the first time you transcribe.

Default shortcuts:

- **`Fn+Space`** - Toggle transcription
- **`Fn`** - Record while held and insert the transcript
- **`Cmd+Shift+A`** - Autocorrect clipboard text
- **`Cmd+Shift+V`** - Start voice edit

You can change shortcuts, enable **Start at Login**, or switch to a user-installed `agent-cli` runtime from the app's **Settings...** screen. See the [macOS app guide](docs/installation/macos-app.md) for details.

## System Integration

Want system-wide hotkeys? You'll need the repository for the setup scripts:

```bash
# If you haven't already cloned it
git clone https://github.com/basnijholt/agent-cli.git
cd agent-cli
```

### macOS Hotkeys

```bash
./scripts/setup-macos-hotkeys.sh
```

This script automatically:
- ✅ Installs Homebrew if not present
- ✅ Installs skhd (hotkey daemon) and terminal-notifier
- ✅ Configures these system-wide hotkeys:
  - **`Cmd+Shift+R`** - Toggle voice transcription
  - **`Cmd+Shift+A`** - Autocorrect clipboard text
  - **`Cmd+Shift+V`** - Voice edit clipboard text

> [!NOTE]
> After setup, you may need to grant Accessibility permissions to skhd in System Settings → Privacy & Security → Accessibility

> [!TIP]
> To keep the recording-status notification visible for the whole flow, open System Settings → Notifications → *terminal-notifier* and set the Alert style to **Persistent** (or choose **Alerts** on older macOS versions).
> Also enable "Allow notification when mirroring or sharing the display".
> The hotkey scripts keep only the recording notification pinned; status and result toasts auto-dismiss.

### Linux Hotkeys

```bash
./scripts/setup-linux-hotkeys.sh
```

This script automatically:
- ✅ Installs notification tools if needed
- ✅ Provides configuration for your desktop environment
- ✅ Sets up these hotkeys:
  - **`Super+Shift+R`** - Toggle voice transcription
  - **`Super+Shift+A`** - Autocorrect clipboard text
  - **`Super+Shift+V`** - Voice edit clipboard text

The script supports Hyprland, GNOME, KDE, Sway, i3, XFCE, and provides instructions for manual configuration on other environments.

### Claude Code Plugin

The [`dev`](docs/commands/dev.md) command is also available as a **Claude Code plugin**, enabling Claude to automatically spawn parallel AI agents in isolated git worktrees when you ask it to work on multiple features.

```bash
# Option 1: Install skill directly in your project (recommended)
agent-cli dev install-skill

# Option 2: Install via Claude Code plugin marketplace
claude plugin marketplace add basnijholt/agent-cli
claude plugin install agent-cli-dev@agent-cli
```

Once installed, Claude Code can automatically use this skill when you ask to:
- "Work on these 3 features in parallel"
- "Spawn agents for auth and payments"
- "Delegate this refactoring to a separate agent"

See the [plugin documentation](.claude-plugin/README.md) for more details.

## Prerequisites

### What You Need to Install Manually

The only thing you need to have installed is **Git** to clone this repository. Everything else is handled automatically!

### What the Setup Scripts Install for You

Our installation scripts automatically handle all dependencies:

#### Core Requirements (Auto-installed)
- 🍺 **Homebrew** (macOS) - Installed if not present
- 🐍 **uv** - Python package manager - Installed automatically
- 📋 **Clipboard Tools** - Pre-installed on macOS, handled on Linux

#### AI Services (Auto-installed and configured)

| Service | Purpose | Auto-installed? |
|---------|---------|-----------------|
| **[Ollama](https://ollama.ai/)** | Local LLM for text processing | ✅ Yes, with default model |
| **[Wyoming Faster Whisper](https://github.com/rhasspy/wyoming-faster-whisper)** | Speech-to-text | ✅ Yes, via `uvx` |
| **[`agent-cli server whisper`](docs/commands/server/whisper.md)** | Speech-to-text (alternative) | ✅ Built-in, `pip install "agent-cli[faster-whisper]"` |
| **[Wyoming Piper](https://github.com/rhasspy/wyoming-piper)** | Text-to-speech | ✅ Yes, via `uvx` |
| **[Kokoro-FastAPI](https://github.com/remsky/Kokoro-FastAPI)** | Premium TTS (optional) | ⚙️ Can be added later |
| **[Wyoming openWakeWord](https://github.com/rhasspy/wyoming-openwakeword)** | Wake word detection | ✅ Yes, for `assistant` |

> **Why `agent-cli server whisper`?** The built-in Whisper server offers an OpenAI-compatible API (drop-in replacement), Wyoming protocol for Home Assistant, TTL-based VRAM management (auto-unloads idle models), and auto-selects the optimal backend ([MLX](https://github.com/ml-explore/mlx-examples/tree/main/whisper) on Apple Silicon, [faster-whisper](https://github.com/SYSTRAN/faster-whisper) on Linux/CUDA). Docker images available at `ghcr.io/basnijholt/agent-cli-whisper`.

#### Alternative Cloud Services (Optional)

If you prefer cloud services over local ones:

| Service | Purpose | Setup Required |
|---------|---------|----------------|
| **OpenAI** | LLM, Speech-to-text, TTS | API key in config |
| **Gemini** | LLM alternative | API key in config |

#### Alternative Local LLM Servers

You can also use other OpenAI-compatible local servers:

| Server | Purpose | Setup Required |
|---------|---------|----------------|
| **llama.cpp** | Local LLM inference | Use `--openai-base-url http://localhost:8080/v1` |
| **vLLM** | High-performance LLM serving | Use `--openai-base-url` with server endpoint |
| **Ollama** | Default local LLM | Already configured as default |

## Usage

This package provides multiple command-line tools, each designed for a specific purpose.

### Installation Commands

These commands help you set up `agent-cli` and its required services:

- **`install-services`**: Install all required AI services (Ollama, Whisper, Piper, OpenWakeWord)
- **`install-hotkeys`**: Set up system-wide hotkeys for quick access to agent-cli features
- **`install-extras`**: Install optional Python dependencies (rag, memory, vad, etc.) with pinned versions
- **`start-services`**: Start all services in a Zellij terminal session

All necessary scripts are bundled with the package, so you can run these commands immediately after installing `agent-cli`.

#### Installing Optional Extras

Some features require additional Python dependencies. By default, **agent-cli will auto-install missing extras** when you run a command that needs them. To disable this, set `AGENT_CLI_NO_AUTO_INSTALL=1` or add to your config file:

```toml
[settings]
auto_install_extras = false
```

You can also manually install extras with `install-extras`:

```bash
# List available extras
agent-cli install-extras --list

# Install specific extras
agent-cli install-extras rag memory vad
```

<details>
<summary>See the output of <code>agent-cli install-extras --help</code></summary>

```yaml

Usage: agent-cli install-extras [OPTIONS] [EXTRAS]...

Install optional dependencies with pinned, compatible versions.

Many agent-cli features require optional dependencies. This command installs them with
 version pinning to ensure compatibility. Dependencies persist across uv tool upgrade
 when installed via uv tool.

Available extras:

• audio - Audio recording/playback
  • diarization - Speaker diarization (pyannote.audio)
  • faster-whisper - Whisper ASR via CTranslate2
  • kokoro - Kokoro neural TTS (GPU)
  • llm - LLM framework (pydantic-ai)
  • memory - Long-term memory proxy
  • mlx-whisper - Whisper ASR for Apple Silicon
  • nemo-whisper - Whisper-compatible ASR via NVIDIA NeMo (Parakeet)
  • piper - Piper TTS (CPU)
  • rag - RAG proxy (ChromaDB, embeddings)
  • server - FastAPI server components
  • speed - Audio speed adjustment (audiostretchy)
  • vad - Voice Activity Detection (Silero VAD via ONNX)
  • vectordb - Vector database with embeddings (ChromaDB)
  • whisper-transformers - Whisper ASR via HuggingFace transformers
  • wyoming - Wyoming protocol support

Examples:

agent-cli install-extras rag           # Install RAG dependencies
  agent-cli install-extras memory vad    # Install multiple extras
  agent-cli install-extras --list        # Show available extras
  agent-cli install-extras --all         # Install all extras

╭─ Arguments ────────────────────────────────────────────────────────────────────────────╮
│   extras      [EXTRAS]...  Extras to install: audio, diarization, faster-whisper,      │
│                            kokoro, llm, memory, mlx-whisper, nemo-whisper, piper, rag, │
│                            server, speed, vad, vectordb, whisper-transformers, wyoming │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --list  -l        Show available extras with descriptions (what each one enables)      │
│ --all   -a        Install all available extras at once                                 │
│ --help  -h        Show this message and exit.                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### Configuration

All `agent-cli` commands can be configured using a TOML file.
The configuration file is searched for in the following locations, in order:

1.  CLI flag (`--config` on agent commands, `--path` on `agent-cli config ...`)
2.  `$AGENT_CLI_CONFIG_HOME/config.toml` if set
3.  `./agent-cli-config.toml` (in the current directory)
4.  `$XDG_CONFIG_HOME/agent-cli/config.toml` if `XDG_CONFIG_HOME` is set and non-empty, otherwise `~/.config/agent-cli/config.toml`

You can also specify a path to a configuration file using the `--config` option, e.g., `agent-cli transcribe --config /path/to/your/config.toml`.

Command-line options always take precedence over settings in the configuration file.

#### Environment variable overrides

Use `AGENT_CLI_CONFIG_HOME` to redirect the user-level config to a directory of your choice:

```bash
# use a project-scoped config
AGENT_CLI_CONFIG_HOME=./.agent-cli agent-cli chat ...
```

Use `XDG_CONFIG_HOME` to follow XDG-style config placement when no `AGENT_CLI_CONFIG_HOME` is set:

```bash
# follow XDG (e.g. inside a sandbox or container)
XDG_CONFIG_HOME=/workspace/.config agent-cli chat ...
```

Config paths are resolved when `agent-cli` starts, so set these variables before invoking the command.

#### Managing Configuration

Use the `config` command to manage your configuration files:

```bash
# Create a new config file with all options (commented out as a template)
agent-cli config init

# View your current config (syntax highlighted)
agent-cli config show

# View config as raw text (for copy-paste)
agent-cli config show --raw

# Open config in your editor ($EDITOR, or nano/vim)
agent-cli config edit
```

<details>
<summary>See the output of <code>agent-cli config --help</code></summary>

```yaml

Usage: agent-cli config [OPTIONS] COMMAND [ARGS]...

Manage agent-cli configuration files.

Config files are TOML format and searched in order:

1 $AGENT_CLI_CONFIG_HOME/config.toml (if set)
  2 ./agent-cli-config.toml (project-local)
  3 $XDG_CONFIG_HOME/agent-cli/config.toml (if set)
  4 ~/.config/agent-cli/config.toml (user default)

Settings in [defaults] apply globally.

Use [chat] or [transcribe] for command-specific overrides.

CLI arguments override config file settings.

Set env vars before startup.

Use $AGENT_CLI_CONFIG_HOME or $XDG_CONFIG_HOME to change config path.

```

</details>

An example configuration file is also provided in [`example.agent-cli-config.toml`](./example.agent-cli-config.toml).

#### Provider Defaults

You can choose local or cloud services per capability by setting provider keys in
the `[defaults]` section of your configuration file.

```toml
[defaults]
# llm_provider = "ollama"  # 'ollama', 'openai', or 'gemini'
# asr_provider = "wyoming" # 'wyoming', 'openai', or 'gemini'
# tts_provider = "wyoming" # 'wyoming', 'openai', 'kokoro', or 'gemini'
# openai_api_key = "sk-..."
# gemini_api_key = "..."
```

### `autocorrect`

**Purpose:** Quickly fix spelling and grammar in any text you've copied.

**Workflow:** This is a simple, one-shot command.

1.  It reads text from your system clipboard (or from a direct argument).
2.  It sends the text to your configured LLM provider (default: Ollama) with a prompt to perform only technical corrections.
3.  The corrected text is copied back to your clipboard, replacing the original.

**How to Use It:** This tool is ideal for integrating with a system-wide hotkey.

- **From Clipboard**: `agent-cli autocorrect`
- **From Argument**: `agent-cli autocorrect "this text has an eror"`

<details>
<summary>See the output of <code>agent-cli autocorrect --help</code></summary>

```yaml

Usage: agent-cli autocorrect [OPTIONS] [TEXT]

Fix grammar, spelling, and punctuation using an LLM.

Reads text from clipboard (or argument), sends to LLM for correction, and copies the
 result back to clipboard. Only makes technical corrections without changing meaning or
 tone.

Workflow:

1 Read text from clipboard (or TEXT argument)
  2 Send to LLM for grammar/spelling/punctuation fixes
  3 Copy corrected text to clipboard (unless --json)
  4 Display result

Examples:

# Correct text from clipboard (default)
  agent-cli autocorrect

# Correct specific text
  agent-cli autocorrect "this is incorect"

# Use OpenAI instead of local Ollama
  agent-cli autocorrect --llm-provider openai

# Get JSON output for scripting (disables clipboard)
  agent-cli autocorrect --json

╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│   text      [TEXT]  Text to correct. If omitted, reads from system clipboard.          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --help  -h        Show this message and exit.                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Provider Selection ───────────────────────────────────────────────────────────────────╮
│ --llm-provider        TEXT  The LLM provider to use ('ollama', 'openai', 'gemini').    │
│                             [env var: LLM_PROVIDER]                                    │
│                             [default: ollama]                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Ollama ──────────────────────────────────────────────────────────────────────────╮
│ --llm-ollama-model        TEXT  The Ollama model to use. Default is gemma3:4b.         │
│                                 [env var: LLM_OLLAMA_MODEL]                            │
│                                 [default: gemma3:4b]                                   │
│ --llm-ollama-host         TEXT  The Ollama server host. Default is                     │
│                                 http://localhost:11434.                                │
│                                 [env var: LLM_OLLAMA_HOST]                             │
│                                 [default: http://localhost:11434]                      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: OpenAI-compatible ───────────────────────────────────────────────────────────────╮
│ --llm-openai-model        TEXT  The OpenAI model to use for LLM tasks.                 │
│                                 [env var: LLM_OPENAI_MODEL]                            │
│                                 [default: gpt-5-mini]                                  │
│ --openai-api-key          TEXT  Your OpenAI API key. Can also be set with the          │
│                                 OPENAI_API_KEY environment variable.                   │
│                                 [env var: OPENAI_API_KEY]                              │
│ --openai-base-url         TEXT  Custom base URL for OpenAI-compatible API (e.g., for   │
│                                 llama-server: http://localhost:8080/v1).               │
│                                 [env var: OPENAI_BASE_URL]                             │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Gemini ──────────────────────────────────────────────────────────────────────────╮
│ --llm-gemini-model        TEXT  The Gemini model to use for LLM tasks.                 │
│                                 [env var: LLM_GEMINI_MODEL]                            │
│                                 [default: gemini-3-flash-preview]                      │
│ --gemini-api-key          TEXT  Your Gemini API key. Can also be set with the          │
│                                 GEMINI_API_KEY environment variable.                   │
│                                 [env var: GEMINI_API_KEY]                              │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --log-level           [debug|info|warning|error]  Set logging level.                   │
│                                                   [env var: LOG_LEVEL]                 │
│                                                   [default: warning]                   │
│ --log-file            TEXT                        Path to a file to write logs to.     │
│ --quiet       -q                                  Suppress console output from rich.   │
│ --json                                            Output result as JSON (implies       │
│                                                   --quiet and --no-clipboard).         │
│ --config              TEXT                        Path to a TOML configuration file.   │
│ --print-args                                      Print the command line arguments,    │
│                                                   including variables taken from the   │
│                                                   configuration file.                  │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### `transcribe`

**Purpose:** A simple tool to turn your speech into text.

**Workflow:** This agent listens to your microphone and converts your speech to text in real-time.

1.  Run the command. It will start listening immediately.
2.  Speak into your microphone.
3.  Press `Ctrl+C` to stop recording.
4.  The transcribed text is copied to your clipboard.
5.  Optionally, use the `--llm` flag to have an Ollama model clean up the raw transcript (fixing punctuation, etc.).

**How to Use It:**

- **Simple Transcription**: `agent-cli transcribe --input-device-index 1`
- **With LLM Cleanup**: `agent-cli transcribe --input-device-index 1 --llm`

<details>
<summary>See the output of <code>agent-cli transcribe --help</code></summary>

```yaml

Usage: agent-cli transcribe [OPTIONS]

Record audio from microphone and transcribe to text.

Records until you press Ctrl+C (or send SIGINT), then transcribes using your configured
 ASR provider. The transcript is copied to the clipboard by default.

With --llm: Passes the raw transcript through an LLM to clean up speech recognition
 errors, add punctuation, remove filler words, and improve readability.

With --toggle: Bind to a hotkey for push-to-talk. First call starts recording, second
 call stops and transcribes.

Examples:

• Record and transcribe: agent-cli transcribe
  • With LLM cleanup: agent-cli transcribe --llm
  • Re-transcribe last recording: agent-cli transcribe --last-recording 1
  • Remember unknown voices: agent-cli transcribe --diarize --remember-unknown-speakers
  • Name a remembered voice profile: agent-cli speakers rename UNKNOWN_001 Alice

╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --help -h Show this message and exit. │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM Configuration ────────────────────────────────────────────────────────────────────╮
│ --extra-instructions TEXT Extra ASR context where supported, and LLM │
│ cleanup instructions when --llm is enabled. │
│ The NeMo backend ignores ASR text prompts. │
│ --llm --no-llm Clean up transcript with LLM: fix errors, │
│ add punctuation, remove filler words. Uses │
│ --extra-instructions if set (via CLI or │
│ config file). Not compatible with --diarize. │
│ [default: no-llm] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Recovery ───────────────────────────────────────────────────────────────────────╮
│ --from-file PATH Transcribe from audio file instead │
│ of microphone. Supports wav, mp3, │
│ m4a, ogg, flac, aac, webm. │
│ Requires ffmpeg for non-WAV │
│ formats with Wyoming. │
│ --last-recording INTEGER Re-transcribe a saved recording │
│ (1=most recent, 2=second-to-last, │
│ etc). Useful after connection │
│ failures or to retry with │
│ different options. │
│ [default: 0] │
│ --save-recording --no-save-recording Save recordings to │
│ ~/.cache/agent-cli/ for │
│ --last-recording recovery. │
│ [default: save-recording] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Provider Selection ───────────────────────────────────────────────────────────────────╮
│ --asr-provider TEXT The ASR provider to use ('wyoming', 'openai', 'gemini'). │
│ [env var: ASR_PROVIDER] │
│ [default: wyoming] │
│ --llm-provider TEXT The LLM provider to use ('ollama', 'openai', 'gemini'). │
│ [env var: LLM_PROVIDER] │
│ [default: ollama] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input ──────────────────────────────────────────────────────────────────────────╮
│ --input-device-index INTEGER Audio input device index (see --list-devices). │
│ Uses system default if omitted. │
│ --input-device-name TEXT Select input device by name substring (e.g., │
│ MacBook or USB). │
│ --list-devices List available audio devices with their indices │
│ and exit. │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Wyoming ─────────────────────────────────────────────────────────────────╮
│ --asr-wyoming-ip TEXT Wyoming ASR server IP address. │
│ [env var: ASR_WYOMING_IP] │
│ [default: localhost] │
│ --asr-wyoming-port INTEGER Wyoming ASR server port. │
│ [env var: ASR_WYOMING_PORT] │
│ [default: 10300] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: OpenAI-compatible ───────────────────────────────────────────────────────╮
│ --asr-openai-model TEXT The OpenAI model to use for ASR (transcription). │
│ [env var: ASR_OPENAI_MODEL] │
│ [default: whisper-1] │
│ --asr-openai-base-url TEXT Custom base URL for OpenAI-compatible ASR API │
│ (e.g., for custom Whisper server: │
│ http://localhost:9898). │
│ [env var: ASR_OPENAI_BASE_URL] │
│ --asr-openai-prompt TEXT Custom prompt to guide transcription (optional). │
│ [env var: ASR_OPENAI_PROMPT] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Gemini ──────────────────────────────────────────────────────────────────╮
│ --asr-gemini-model TEXT The Gemini model to use for ASR (transcription). │
│ [env var: ASR_GEMINI_MODEL] │
│ [default: gemini-3-flash-preview] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Ollama ──────────────────────────────────────────────────────────────────────────╮
│ --llm-ollama-model TEXT The Ollama model to use. Default is gemma3:4b. │
│ [env var: LLM_OLLAMA_MODEL] │
│ [default: gemma3:4b] │
│ --llm-ollama-host TEXT The Ollama server host. Default is │
│ http://localhost:11434. │
│ [env var: LLM_OLLAMA_HOST] │
│ [default: http://localhost:11434] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: OpenAI-compatible ───────────────────────────────────────────────────────────────╮
│ --llm-openai-model TEXT The OpenAI model to use for LLM tasks. │
│ [env var: LLM_OPENAI_MODEL] │
│ [default: gpt-5-mini] │
│ --openai-api-key TEXT Your OpenAI API key. Can also be set with the │
│ OPENAI_API_KEY environment variable. │
│ [env var: OPENAI_API_KEY] │
│ --openai-base-url TEXT Custom base URL for OpenAI-compatible API (e.g., for │
│ llama-server: http://localhost:8080/v1). │
│ [env var: OPENAI_BASE_URL] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Gemini ──────────────────────────────────────────────────────────────────────────╮
│ --llm-gemini-model TEXT The Gemini model to use for LLM tasks. │
│ [env var: LLM_GEMINI_MODEL] │
│ [default: gemini-3-flash-preview] │
│ --gemini-api-key TEXT Your Gemini API key. Can also be set with the │
│ GEMINI_API_KEY environment variable. │
│ [env var: GEMINI_API_KEY] │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Process Management ───────────────────────────────────────────────────────────────────╮
│ --start Start this command if it is not already running. │
│ --stop Stop any running instance of this command. │
│ --status Check if an instance is currently running. │
│ --toggle Start if not running, stop if running. Ideal for hotkey │
│ binding. │
│ --wait-for-start When stopping, wait briefly for a just-launched process to │
│ write its PID. │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --clipboard --no-clipboard Copy result to │
│ clipboard. │
│ [default: clipboard] │
│ --log-level [debug|info|warning| Set logging level. │
│ error] [env var: LOG_LEVEL] │
│ [default: warning] │
│ --log-file TEXT Path to a file to │
│ write logs to. │
│ --quiet -q Suppress console │
│ output from rich. │
│ --json Output result as JSON │
│ (implies --quiet and │
│ --no-clipboard). │
│ --config TEXT Path to a TOML │
│ configuration file. │
│ --print-args Print the command │
│ line arguments, │
│ including variables │
│ taken from the │
│ configuration file. │
│ --transcription-log PATH Append transcripts to │
│ JSONL file │
│ (timestamp, hostname, │
│ model, raw/processed │
│ text). Recent entries │
│ provide context for │
│ LLM cleanup. │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Live Preview ─────────────────────────────────────────────────────────────────────────╮
│ --live-preview-log PATH Write rolling live transcription │
│ preview events to JSONL while │
│ recording. │
│ --live-preview-interval FLOAT Seconds between live preview │
│ retranscriptions. │
│ [default: 2.0] │
│ --live-preview-window FLOAT Seconds of recent audio to include │
│ in each live preview │
│ retranscription. │
│ [default: 15.0] │
│ --live-preview-console,--live-previ… Print rolling live transcription │
│ preview updates to the terminal │
│ while recording. │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Diarization ──────────────────────────────────────────────────────────────────────────╮
│ --diarize --no-diarize Enable speaker │
│ diarization │
│ (requires │
│ pyannote-audio). │
│ Install with: pip │
│ install │
│ agent-cli[diarizat… │
│ [default: │
│ no-diarize] │
│ --diarize-format [inline|json] Output format for │
│ diarization │
│ ('inline' for │
│ [Speaker N]: text, │
│ 'json' for │
│ structured output). │
│ [default: inline] │
│ --hf-token TEXT HuggingFace token │
│ for pyannote │
│ models. Required │
│ for diarization. │
│ Token must have │
│ 'Read access to │
│ contents of all │
│ public gated repos │
│ you can access' │
│ permission. Accept │
│ licenses at: │
│ https://hf.co/pyan… │
│ https://hf.co/pyan… │
│ https://hf.co/pyan… │
│ [env var: HF_TOKEN] │
│ --min-speakers INTEGER Minimum number of │
│ speakers (optional │
│ hint for │
│ diarization). │
│ --max-speakers INTEGER Maximum number of │
│ speakers (optional │
│ hint for │
│ diarization). │
│ --align-words --no-align-words Use wav2vec2 forced │
│ alignment for │
│ word-level speaker │
│ assignment (more │
│ accurate but │
│ slower). │
│ [default: │
│ no-align-words] │
│ --align-language TEXT Language code for │
│ word alignment │
│ model (e.g., 'en', │
│ 'fr', 'de', 'es', │
│ 'it'). │
│ [default: en] │
│ --enroll-speakers TEXT Enroll current │
│ speaker labels or │
│ remembered profile │
│ IDs into persistent │
│ voice profiles, │
│ e.g. │
│ SPEAKER_00=Alice or │
│ UNKNOWN_001=Alice. │
│ For simple renames, │
│ use agent-cli │
│ speakers rename. │
│ --identify-speakers --no-identify-spe… Match diarized │
│ speakers against │
│ persistent voice │
│ profiles when │
│ profiles exist. │
│ [default: │
│ identify-speakers] │
│ --remember-unknown-… --no-remember-unk… Persist unmatched │
│ speaker embeddings │
│ as stable │
│ UNKNOWN_### voice │
│ profiles. │
│ [default: │
│ no-remember-unknow… │
│ --speaker-profiles-… PATH JSON file storing │
│ persistent speaker │
│ voice embeddings. │
│ [default: │
│ /home/runner/.conf… │
│ --speaker-match-thr… FLOAT RANGE Cosine-similarity │
│ [0.0<=x<=1.0] threshold for │
│ matching diarized │
│ speakers to stored │
│ profiles. │
│ [default: 0.7] │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### `transcribe-live`

**Purpose:** A continuous background transcription service that automatically detects and transcribes speech.

**Workflow:** Runs as a daemon, listening to your microphone and automatically segmenting speech using voice activity detection (VAD).

1. Run the command. It starts listening immediately.
2. Speak naturally - the daemon detects when you start and stop speaking.
3. Each speech segment is automatically transcribed and logged.
4. Optionally, audio is saved as MP3 files for later reference.
5. Press `Ctrl+C` to stop the daemon.

**Installation:** Requires the `vad` extra:
```bash
uv tool install "agent-cli[vad]"
```

**How to Use It:**

- **Basic Daemon**: `agent-cli transcribe-live`
- **With Custom Role**: `agent-cli transcribe-live --role meeting`
- **With LLM Cleanup**: `agent-cli transcribe-live --llm`
- **Custom Silence Threshold**: `agent-cli transcribe-live --silence-threshold 1.5`

**Output Files:**

- **Transcription Log**: `~/.config/agent-cli/transcriptions.jsonl` (JSON Lines format)
- **Audio Files**: `~/.config/agent-cli/audio/YYYY/MM/DD/*.mp3`

<details>
<summary>See the output of <code>agent-cli transcribe-live --help</code></summary>

```yaml

Usage: agent-cli transcribe-live [OPTIONS]

Continuous live transcription using Silero VAD for speech detection.

Unlike transcribe (single recording session), this runs indefinitely and automatically
 detects speech segments using Voice Activity Detection (VAD). Each detected segment is
 transcribed and logged with timestamps.

How it works:

1 Listens continuously to microphone input
  2 Silero VAD detects when you start/stop speaking
  3 After --silence-threshold seconds of silence, the segment is finalized
  4 Segment is transcribed (and optionally cleaned by LLM with --llm)
  5 Results are appended to the JSONL log file
  6 Audio is saved as MP3 if --save-audio is enabled (requires ffmpeg)

Use cases: Meeting transcription, note-taking, voice journaling, accessibility.

Examples:

agent-cli transcribe-live
  agent-cli transcribe-live --role meeting --silence-threshold 1.5
  agent-cli transcribe-live --llm --clipboard --role notes
  agent-cli transcribe-live --transcription-log ~/meeting.jsonl --no-save-audio
  agent-cli transcribe-live --asr-provider openai --llm-provider gemini --llm

Tips:

• Use --role to tag entries (e.g., speaker1, meeting, personal)
  • Adjust --vad-threshold if detection is too sensitive (increase) or missing speech
    (decrease)
  • Use --stop to cleanly terminate a running process
  • With --llm, transcripts are cleaned up (punctuation, filler words removed)

╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --role               -r                     TEXT   Label for log entries. Use to       │
│                                                    distinguish speakers or contexts in │
│                                                    logs.                               │
│                                                    [default: user]                     │
│ --silence-threshold  -s                     FLOAT  Seconds of silence after speech to  │
│                                                    finalize a segment. Increase for    │
│                                                    slower speakers.                    │
│                                                    [default: 1.0]                      │
│ --min-segment        -m                     FLOAT  Minimum seconds of speech required  │
│                                                    before a segment is processed.      │
│                                                    Filters brief sounds.               │
│                                                    [default: 0.25]                     │
│ --vad-threshold                             FLOAT  Silero VAD confidence threshold     │
│                                                    (0.0-1.0). Higher values require    │
│                                                    clearer speech; lower values are    │
│                                                    more sensitive to quiet/distant     │
│                                                    voices.                             │
│                                                    [default: 0.3]                      │
│ --save-audio             --no-save-audio           Save each speech segment as MP3.    │
│                                                    Requires ffmpeg to be installed.    │
│                                                    [default: save-audio]               │
│ --audio-dir                                 PATH   Base directory for MP3 files. Files │
│                                                    are organized by date:              │
│                                                    YYYY/MM/DD/HHMMSS_mmm.mp3. Default: │
│                                                    ~/.config/agent-cli/audio.          │
│ --transcription-log  -t                     PATH   JSONL file for transcript logging   │
│                                                    (one JSON object per line with      │
│                                                    timestamp, role, raw/processed      │
│                                                    text, audio path). Default:         │
│                                                    ~/.config/agent-cli/transcriptions… │
│ --clipboard              --no-clipboard            Copy each completed transcription   │
│                                                    to clipboard (overwrites previous). │
│                                                    Useful with --llm to get cleaned    │
│                                                    text.                               │
│                                                    [default: no-clipboard]             │
│ --help               -h                            Show this message and exit.         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Provider Selection ───────────────────────────────────────────────────────────────────╮
│ --asr-provider        TEXT  The ASR provider to use ('wyoming', 'openai', 'gemini').   │
│                             [env var: ASR_PROVIDER]                                    │
│                             [default: wyoming]                                         │
│ --llm-provider        TEXT  The LLM provider to use ('ollama', 'openai', 'gemini').    │
│                             [env var: LLM_PROVIDER]                                    │
│                             [default: ollama]                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input ──────────────────────────────────────────────────────────────────────────╮
│ --input-device-index        INTEGER  Audio input device index (see --list-devices).    │
│                                      Uses system default if omitted.                   │
│ --input-device-name         TEXT     Select input device by name substring (e.g.,      │
│                                      MacBook or USB).                                  │
│ --list-devices                       List available audio devices with their indices   │
│                                      and exit.                                         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Wyoming ─────────────────────────────────────────────────────────────────╮
│ --asr-wyoming-ip          TEXT     Wyoming ASR server IP address.                      │
│                                    [env var: ASR_WYOMING_IP]                           │
│                                    [default: localhost]                                │
│ --asr-wyoming-port        INTEGER  Wyoming ASR server port.                            │
│                                    [env var: ASR_WYOMING_PORT]                         │
│                                    [default: 10300]                                    │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: OpenAI-compatible ───────────────────────────────────────────────────────╮
│ --asr-openai-model           TEXT  The OpenAI model to use for ASR (transcription).    │
│                                    [env var: ASR_OPENAI_MODEL]                         │
│                                    [default: whisper-1]                                │
│ --asr-openai-base-url        TEXT  Custom base URL for OpenAI-compatible ASR API       │
│                                    (e.g., for custom Whisper server:                   │
│                                    http://localhost:9898).                             │
│                                    [env var: ASR_OPENAI_BASE_URL]                      │
│ --asr-openai-prompt          TEXT  Custom prompt to guide transcription (optional).    │
│                                    [env var: ASR_OPENAI_PROMPT]                        │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Gemini ──────────────────────────────────────────────────────────────────╮
│ --asr-gemini-model        TEXT  The Gemini model to use for ASR (transcription).       │
│                                 [env var: ASR_GEMINI_MODEL]                            │
│                                 [default: gemini-3-flash-preview]                      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Ollama ──────────────────────────────────────────────────────────────────────────╮
│ --llm-ollama-model        TEXT  The Ollama model to use. Default is gemma3:4b.         │
│                                 [env var: LLM_OLLAMA_MODEL]                            │
│                                 [default: gemma3:4b]                                   │
│ --llm-ollama-host         TEXT  The Ollama server host. Default is                     │
│                                 http://localhost:11434.                                │
│                                 [env var: LLM_OLLAMA_HOST]                             │
│                                 [default: http://localhost:11434]                      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: OpenAI-compatible ───────────────────────────────────────────────────────────────╮
│ --llm-openai-model        TEXT  The OpenAI model to use for LLM tasks.                 │
│                                 [env var: LLM_OPENAI_MODEL]                            │
│                                 [default: gpt-5-mini]                                  │
│ --openai-api-key          TEXT  Your OpenAI API key. Can also be set with the          │
│                                 OPENAI_API_KEY environment variable.                   │
│                                 [env var: OPENAI_API_KEY]                              │
│ --openai-base-url         TEXT  Custom base URL for OpenAI-compatible API (e.g., for   │
│                                 llama-server: http://localhost:8080/v1).               │
│                                 [env var: OPENAI_BASE_URL]                             │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Gemini ──────────────────────────────────────────────────────────────────────────╮
│ --llm-gemini-model        TEXT  The Gemini model to use for LLM tasks.                 │
│                                 [env var: LLM_GEMINI_MODEL]                            │
│                                 [default: gemini-3-flash-preview]                      │
│ --gemini-api-key          TEXT  Your Gemini API key. Can also be set with the          │
│                                 GEMINI_API_KEY environment variable.                   │
│                                 [env var: GEMINI_API_KEY]                              │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM Configuration ────────────────────────────────────────────────────────────────────╮
│ --llm    --no-llm      Clean up transcript with LLM: fix errors, add punctuation,      │
│                        remove filler words. Uses --extra-instructions if set (via CLI  │
│                        or config file). Not compatible with --diarize.                 │
│                        [default: no-llm]                                               │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Process Management ───────────────────────────────────────────────────────────────────╮
│ --stop            Stop any running instance of this command.                           │
│ --status          Check if an instance is currently running.                           │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --log-level           [debug|info|warning|error]  Set logging level.                   │
│                                                   [env var: LOG_LEVEL]                 │
│                                                   [default: warning]                   │
│ --log-file            TEXT                        Path to a file to write logs to.     │
│ --quiet       -q                                  Suppress console output from rich.   │
│ --config              TEXT                        Path to a TOML configuration file.   │
│ --print-args                                      Print the command line arguments,    │
│                                                   including variables taken from the   │
│                                                   configuration file.                  │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### `speak`

**Purpose:** Reads any text out loud.

**Workflow:** A straightforward text-to-speech utility.

1.  It takes text from a command-line argument or your clipboard.
2.  It sends the text to a Wyoming TTS server (like Piper).
3.  The generated audio is played through your default speakers.

**How to Use It:**

- **Speak from Argument**: `agent-cli speak "Hello, world!"`
- **Speak from Clipboard**: `agent-cli speak`
- **Save to File**: `agent-cli speak "Hello" --save-file hello.wav`

<details>
<summary>See the output of <code>agent-cli speak --help</code></summary>

```yaml

Usage: agent-cli speak [OPTIONS] [TEXT]

Convert text to speech and play audio through speakers.

By default, synthesized audio plays immediately. Use --save-file to save to a WAV file
 instead (skips playback).

Text can be provided as an argument or read from clipboard automatically.

Examples:

Speak text directly: agent-cli speak "Hello, world!"

Speak clipboard contents: agent-cli speak

Save to file instead of playing: agent-cli speak "Hello" --save-file greeting.wav

Use OpenAI-compatible TTS: agent-cli speak "Hello" --tts-provider openai

╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│   text      [TEXT]  Text to synthesize. If not provided, reads from clipboard.         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --help  -h        Show this message and exit.                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Provider Selection ───────────────────────────────────────────────────────────────────╮
│ --tts-provider        TEXT  The TTS provider to use ('wyoming', 'openai', 'kokoro',    │
│                             'gemini').                                                 │
│                             [env var: TTS_PROVIDER]                                    │
│                             [default: wyoming]                                         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output ─────────────────────────────────────────────────────────────────────────╮
│ --output-device-index        INTEGER  Audio output device index (see --list-devices    │
│                                       for available devices).                          │
│ --output-device-name         TEXT     Partial match on device name (e.g., 'speakers',  │
│                                       'headphones').                                   │
│ --tts-speed                  FLOAT    Speech speed multiplier (1.0 = normal, 2.0 =     │
│                                       twice as fast, 0.5 = half speed).                │
│                                       [default: 1.0]                                   │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Wyoming ────────────────────────────────────────────────────────────────╮
│ --tts-wyoming-ip              TEXT     Wyoming TTS server IP address.                  │
│                                        [default: localhost]                            │
│ --tts-wyoming-port            INTEGER  Wyoming TTS server port.                        │
│                                        [default: 10200]                                │
│ --tts-wyoming-voice           TEXT     Voice name to use for Wyoming TTS (e.g.,        │
│                                        'en_US-lessac-medium').                         │
│ --tts-wyoming-language        TEXT     Language for Wyoming TTS (e.g., 'en_US').       │
│ --tts-wyoming-speaker         TEXT     Speaker name for Wyoming TTS voice.             │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: OpenAI-compatible ──────────────────────────────────────────────────────╮
│ --tts-openai-model           TEXT  The OpenAI model to use for TTS.                    │
│                                    [default: tts-1]                                    │
│ --tts-openai-voice           TEXT  Voice for OpenAI TTS (alloy, echo, fable, onyx,     │
│                                    nova, shimmer).                                     │
│                                    [default: alloy]                                    │
│ --tts-openai-base-url        TEXT  Custom base URL for OpenAI-compatible TTS API       │
│                                    (e.g., http://localhost:8000/v1 for a proxy).       │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Kokoro ─────────────────────────────────────────────────────────────────╮
│ --tts-kokoro-model        TEXT  The Kokoro model to use for TTS.                       │
│                                 [default: kokoro]                                      │
│ --tts-kokoro-voice        TEXT  The voice to use for Kokoro TTS.                       │
│                                 [default: af_sky]                                      │
│ --tts-kokoro-host         TEXT  The base URL for the Kokoro API.                       │
│                                 [default: http://localhost:8880/v1]                    │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Gemini ─────────────────────────────────────────────────────────────────╮
│ --tts-gemini-model        TEXT  The Gemini model to use for TTS.                       │
│                                 [default: gemini-2.5-flash-preview-tts]                │
│ --tts-gemini-voice        TEXT  The voice to use for Gemini TTS (e.g., 'Kore', 'Puck', │
│                                 'Charon', 'Fenrir').                                   │
│                                 [default: Kore]                                        │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Gemini ──────────────────────────────────────────────────────────────────────────╮
│ --gemini-api-key        TEXT  Your Gemini API key. Can also be set with the            │
│                               GEMINI_API_KEY environment variable.                     │
│                               [env var: GEMINI_API_KEY]                                │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input ──────────────────────────────────────────────────────────────────────────╮
│ --list-devices          List available audio devices with their indices and exit.      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --save-file           PATH                        Save audio to WAV file instead of    │
│                                                   playing through speakers.            │
│ --log-level           [debug|info|warning|error]  Set logging level.                   │
│                                                   [env var: LOG_LEVEL]                 │
│                                                   [default: warning]                   │
│ --log-file            TEXT                        Path to a file to write logs to.     │
│ --quiet       -q                                  Suppress console output from rich.   │
│ --json                                            Output result as JSON (implies       │
│                                                   --quiet and --no-clipboard).         │
│ --config              TEXT                        Path to a TOML configuration file.   │
│ --print-args                                      Print the command line arguments,    │
│                                                   including variables taken from the   │
│                                                   configuration file.                  │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Process Management ───────────────────────────────────────────────────────────────────╮
│ --stop            Stop any running instance of this command.                           │
│ --status          Check if an instance is currently running.                           │
│ --toggle          Start if not running, stop if running. Ideal for hotkey binding.     │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### `voice-edit`

**Purpose:** A powerful clipboard assistant that you command with your voice.

**Workflow:** This agent is designed for a hotkey-driven workflow to act on text you've already copied.

1.  Copy a block of text to your clipboard (e.g., an email draft).
2.  Press a hotkey to run `agent-cli voice-edit &` in the background. The agent is now listening.
3.  Speak a command, such as "Make this more formal" or "Summarize the key points."
4.  Press the same hotkey again, which should trigger `agent-cli voice-edit --stop`.
5.  The agent transcribes your command, sends it along with the original clipboard text to the LLM, and the LLM performs the action.
6.  The result is copied back to your clipboard. If `--tts` is enabled, it will also speak the result.

**How to Use It:** The power of this tool is unlocked with a hotkey manager like Keyboard Maestro (macOS) or AutoHotkey (Windows). See the docstring in `agent_cli/agents/voice_edit.py` for a detailed Keyboard Maestro setup guide.

<details>
<summary>See the output of <code>agent-cli voice-edit --help</code></summary>

```yaml

Usage: agent-cli voice-edit [OPTIONS]

Edit or query clipboard text using voice commands.

Workflow: Captures clipboard text → records your voice command → transcribes it → sends
 both to an LLM → copies result back to clipboard.

Use this for hands-free text editing (e.g., "make this more formal") or asking questions
 about clipboard content (e.g., "summarize this").

Typical hotkey integration: Run voice-edit & on keypress to start recording, then send
 SIGINT (via --stop) on second keypress to process.

Examples:

• Basic usage: agent-cli voice-edit
  • With TTS response: agent-cli voice-edit --tts
  • Toggle on/off: agent-cli voice-edit --toggle
  • List audio devices: agent-cli voice-edit --list-devices

```

</details>

### `assistant`

**Purpose:** A hands-free voice assistant that starts and stops recording based on a wake word.

**Workflow:** This agent continuously listens for a wake word (e.g., "Hey Nabu").

1.  Run the `assistant` command. It will start listening for the wake word.
2.  Say the wake word to start recording.
3.  Speak your command or question.
4.  Say the wake word again to stop recording.
5.  The agent transcribes your speech, sends it to the LLM, and gets a response.
6.  The agent speaks the response back to you and then immediately starts listening for the wake word again.

**How to Use It:**

- **Start the agent**: `agent-cli assistant --wake-word "ok_nabu" --input-device-index 1`
- **With TTS**: `agent-cli assistant --wake-word "ok_nabu" --tts --tts-wyoming-voice "en_US-lessac-medium"`

<details>
<summary>See the output of <code>agent-cli assistant --help</code></summary>

```yaml

Usage: agent-cli assistant [OPTIONS]

Hands-free voice assistant using wake word detection.

Continuously listens for a wake word, then records your speech until you say the wake
 word again. The recording is transcribed and sent to an LLM for a conversational
 response, optionally spoken back via TTS.

Conversation flow:

1 Say wake word → starts recording
  2 Speak your question/command
  3 Say wake word again → stops recording and processes

The assistant runs in a loop, ready for the next command after each response. Stop with
 Ctrl+C or --stop.

Requirements:

• Wyoming wake word server (e.g., wyoming-openwakeword on port 10400)
  • Wyoming ASR server (e.g., wyoming-whisper on port 10300)
  • Optional: TTS server for spoken responses (enable with --tts)

Example: assistant --wake-word ok_nabu --tts --input-device-name USB

╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --help  -h        Show this message and exit.                                          │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Provider Selection ───────────────────────────────────────────────────────────────────╮
│ --asr-provider        TEXT  The ASR provider to use ('wyoming', 'openai', 'gemini').   │
│                             [env var: ASR_PROVIDER]                                    │
│                             [default: wyoming]                                         │
│ --llm-provider        TEXT  The LLM provider to use ('ollama', 'openai', 'gemini').    │
│                             [env var: LLM_PROVIDER]                                    │
│                             [default: ollama]                                          │
│ --tts-provider        TEXT  The TTS provider to use ('wyoming', 'openai', 'kokoro',    │
│                             'gemini').                                                 │
│                             [env var: TTS_PROVIDER]                                    │
│                             [default: wyoming]                                         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Wake Word ────────────────────────────────────────────────────────────────────────────╮
│ --wake-server-ip          TEXT     Wyoming wake word server IP (requires               │
│                                    wyoming-openwakeword or similar).                   │
│                                    [default: localhost]                                │
│ --wake-server-port        INTEGER  Wyoming wake word server port.                      │
│                                    [default: 10400]                                    │
│ --wake-word               TEXT     Wake word to detect. Common options: ok_nabu,       │
│                                    hey_jarvis, alexa. Must match a model loaded in     │
│                                    your wake word server.                              │
│                                    [default: ok_nabu]                                  │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input ──────────────────────────────────────────────────────────────────────────╮
│ --input-device-index        INTEGER  Audio input device index (see --list-devices).    │
│                                      Uses system default if omitted.                   │
│ --input-device-name         TEXT     Select input device by name substring (e.g.,      │
│                                      MacBook or USB).                                  │
│ --list-devices                       List available audio devices with their indices   │
│                                      and exit.                                         │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Wyoming ─────────────────────────────────────────────────────────────────╮
│ --asr-wyoming-ip          TEXT     Wyoming ASR server IP address.                      │
│                                    [env var: ASR_WYOMING_IP]                           │
│                                    [default: localhost]                                │
│ --asr-wyoming-port        INTEGER  Wyoming ASR server port.                            │
│                                    [env var: ASR_WYOMING_PORT]                         │
│                                    [default: 10300]                                    │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: OpenAI-compatible ───────────────────────────────────────────────────────╮
│ --asr-openai-model        TEXT  The OpenAI model to use for ASR (transcription).       │
│                                 [env var: ASR_OPENAI_MODEL]                            │
│                                 [default: whisper-1]                                   │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Input: Gemini ──────────────────────────────────────────────────────────────────╮
│ --asr-gemini-model        TEXT  The Gemini model to use for ASR (transcription).       │
│                                 [env var: ASR_GEMINI_MODEL]                            │
│                                 [default: gemini-3-flash-preview]                      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Ollama ──────────────────────────────────────────────────────────────────────────╮
│ --llm-ollama-model        TEXT  The Ollama model to use. Default is gemma3:4b.         │
│                                 [env var: LLM_OLLAMA_MODEL]                            │
│                                 [default: gemma3:4b]                                   │
│ --llm-ollama-host         TEXT  The Ollama server host. Default is                     │
│                                 http://localhost:11434.                                │
│                                 [env var: LLM_OLLAMA_HOST]                             │
│                                 [default: http://localhost:11434]                      │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: OpenAI-compatible ───────────────────────────────────────────────────────────────╮
│ --llm-openai-model        TEXT  The OpenAI model to use for LLM tasks.                 │
│                                 [env var: LLM_OPENAI_MODEL]                            │
│                                 [default: gpt-5-mini]                                  │
│ --openai-api-key          TEXT  Your OpenAI API key. Can also be set with the          │
│                                 OPENAI_API_KEY environment variable.                   │
│                                 [env var: OPENAI_API_KEY]                              │
│ --openai-base-url         TEXT  Custom base URL for OpenAI-compatible API (e.g., for   │
│                                 llama-server: http://localhost:8080/v1).               │
│                                 [env var: OPENAI_BASE_URL]                             │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ LLM: Gemini ──────────────────────────────────────────────────────────────────────────╮
│ --llm-gemini-model        TEXT  The Gemini model to use for LLM tasks.                 │
│                                 [env var: LLM_GEMINI_MODEL]                            │
│                                 [default: gemini-3-flash-preview]                      │
│ --gemini-api-key          TEXT  Your Gemini API key. Can also be set with the          │
│                                 GEMINI_API_KEY environment variable.                   │
│                                 [env var: GEMINI_API_KEY]                              │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output ─────────────────────────────────────────────────────────────────────────╮
│ --tts                    --no-tts             Enable text-to-speech for responses.     │
│                                               [default: no-tts]                        │
│ --output-device-index                INTEGER  Audio output device index (see           │
│                                               --list-devices for available devices).   │
│ --output-device-name                 TEXT     Partial match on device name (e.g.,      │
│                                               'speakers', 'headphones').               │
│ --tts-speed                          FLOAT    Speech speed multiplier (1.0 = normal,   │
│                                               2.0 = twice as fast, 0.5 = half speed).  │
│                                               [default: 1.0]                           │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Wyoming ────────────────────────────────────────────────────────────────╮
│ --tts-wyoming-ip              TEXT     Wyoming TTS server IP address.                  │
│                                        [default: localhost]                            │
│ --tts-wyoming-port            INTEGER  Wyoming TTS server port.                        │
│                                        [default: 10200]                                │
│ --tts-wyoming-voice           TEXT     Voice name to use for Wyoming TTS (e.g.,        │
│                                        'en_US-lessac-medium').                         │
│ --tts-wyoming-language        TEXT     Language for Wyoming TTS (e.g., 'en_US').       │
│ --tts-wyoming-speaker         TEXT     Speaker name for Wyoming TTS voice.             │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: OpenAI-compatible ──────────────────────────────────────────────────────╮
│ --tts-openai-model           TEXT  The OpenAI model to use for TTS.                    │
│                                    [default: tts-1]                                    │
│ --tts-openai-voice           TEXT  Voice for OpenAI TTS (alloy, echo, fable, onyx,     │
│                                    nova, shimmer).                                     │
│                                    [default: alloy]                                    │
│ --tts-openai-base-url        TEXT  Custom base URL for OpenAI-compatible TTS API       │
│                                    (e.g., http://localhost:8000/v1 for a proxy).       │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Kokoro ─────────────────────────────────────────────────────────────────╮
│ --tts-kokoro-model        TEXT  The Kokoro model to use for TTS.                       │
│                                 [default: kokoro]                                      │
│ --tts-kokoro-voice        TEXT  The voice to use for Kokoro TTS.                       │
│                                 [default: af_sky]                                      │
│ --tts-kokoro-host         TEXT  The base URL for the Kokoro API.                       │
│                                 [default: http://localhost:8880/v1]                    │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Audio Output: Gemini ─────────────────────────────────────────────────────────────────╮
│ --tts-gemini-model        TEXT  The Gemini model to use for TTS.                       │
│                                 [default: gemini-2.5-flash-preview-tts]                │
│ --tts-gemini-voice        TEXT  The voice to use for Gemini TTS (e.g., 'Kore', 'Puck', │
│                                 'Charon', 'Fenrir').                                   │
│                                 [default: Kore]                                        │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Process Management ───────────────────────────────────────────────────────────────────╮
│ --stop            Stop any running instance of this command.                           │
│ --status          Check if an instance is currently running.                           │
│ --toggle          Start if not running, stop if running. Ideal for hotkey binding.     │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --save-file                         PATH                      Save audio to WAV file   │
│                                                               instead of playing       │
│                                                               through speakers.        │
│ --clipboard       --no-clipboard                              Copy result to           │
│                                                               clipboard.               │
│                                                               [default: clipboard]     │
│ --log-level                         [debug|info|warning|erro  Set logging level.       │
│                                     r]                        [env var: LOG_LEVEL]     │
│                                                               [default: warning]       │
│ --log-file                          TEXT                      Path to a file to write  │
│                                                               logs to.                 │
│ --quiet       -q                                              Suppress console output  │
│                                                               from rich.               │
│ --config                            TEXT                      Path to a TOML           │
│                                                               configuration file.      │
│ --print-args                                                  Print the command line   │
│                                                               arguments, including     │
│                                                               variables taken from the │
│                                                               configuration file.      │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

### `chat`

**Purpose:** A full-featured, conversational AI assistant that can interact with your system.

**Workflow:** This is a persistent, conversational agent that you can have a conversation with.

1.  Run the `chat` command. It will start listening for your voice.
2.  Speak your command or question (e.g., "What's in my current directory?").
3.  The agent transcribes your speech, sends it to the LLM, and gets a response. The LLM can use tools like `read_file` or `execute_code` to answer your question.
4.  The agent speaks the response back to you and then immediately starts listening for your next command.
5.  The conversation continues in this loop. Conversation history is saved between sessions.

**Interaction Model:**

- **To Interrupt**: Press `Ctrl+C` **once** to stop the agent from either listening or speaking, and it will immediately return to a listening state for a new command. This is useful if it misunderstands you or you want to speak again quickly.
- **To Exit**: Press `Ctrl+C` **twice in a row** to terminate the application.

**How to Use It:**

- **Start the agent**: `agent-cli chat --input-device-index 1 --tts`
- **Have a conversation**:
  - _You_: "Read the pyproject.toml file and tell me the project version."
  - _AI_: (Reads file) "The project version is 0.1.0."
  - _You_: "Thanks!"

<details>
<summary>See the output of <code>agent-cli chat --help</code></summary>

```yaml

Usage: agent-cli chat [OPTIONS]

Voice-based conversational chat agent with memory and tools.

Runs an interactive loop: listen → transcribe → LLM → speak response. Conversation
 history is persisted and included as context for continuity.

Built-in tools (LLM uses automatically when relevant):

• add_memory/search_memory/update_memory - persistent long-term memory
  • duckduckgo_search - web search for current information
  • read_file/execute_code - file access and shell commands

Process management: Use --toggle to start/stop via hotkey (bind to a keyboard shortcut),
 --stop to terminate, or --status to check state.

Examples:

Use OpenAI-compatible providers for speech and LLM, with TTS enabled:

agent-cli chat --asr-provider openai --llm-provider openai --tts

Start in background mode (toggle on/off with hotkey):

agent-cli chat --toggle

Use local Ollama LLM with Wyoming ASR:

agent-cli chat --llm-provider ollama

```

</details>

### `rag-proxy`

**Purpose:** Enables "Chat with your Data" by running a local proxy server that injects document context into LLM requests.

**Workflow:**

1.  Start the server, pointing it to your documents folder and your local LLM (e.g., Ollama or llama.cpp) or OpenAI.
2.  The server watches the folder and automatically indexes any text/markdown/PDF files into a local ChromaDB vector store, skipping paths matched by `.gitignore` files in the docs folder and, when inside a git repo, its parent directories up to the repo root.
3.  Point any OpenAI-compatible client (including `agent-cli chat`) to this server's URL.
4.  When you ask a question, the server retrieves relevant document chunks, adds them to the prompt, and forwards it to the LLM.

**How to Use It:**

- **Install RAG deps first**: `pip install "agent-cli[rag]"` (or, from the repo, `uv sync --extra rag`)
- **Note on ignored files**: `.gitignore` rules in your docs folder are respected during indexing; if the docs folder is inside a git repo, parent `.gitignore` files up to the repo root are also applied. Use `!pattern` entries to re-include paths when needed
- **Start Server (Local LLM)**: `agent-cli rag-proxy --docs-folder ~/Documents/Notes --openai-base-url http://localhost:11434/v1 --port 8000`
- **Start Server (OpenAI)**: `agent-cli rag-proxy --docs-folder ~/Documents/Notes --openai-api-key sk-...`
- **Use with Agent-CLI**: `agent-cli chat --openai-base-url http://localhost:8000/v1 --llm-provider openai`

<details>
<summary>See the output of <code>agent-cli rag-proxy --help</code></summary>

```yaml

Usage: agent-cli rag-proxy [OPTIONS]

Start a RAG proxy server that enables "chat with your documents".

Watches a folder for documents, indexes them into a vector store, and provides an
 OpenAI-compatible API at /v1/chat/completions. When you send a chat request, the server
 retrieves relevant document chunks and injects them as context before forwarding to your
 LLM backend.

Quick start:

• agent-cli rag-proxy — Start with defaults (./rag_docs, OpenAI-compatible API)
  • agent-cli rag-proxy --docs-folder ~/notes — Index your notes folder

How it works:

1 Documents in --docs-folder are chunked, embedded, and stored in ChromaDB
  2 Paths matched by .gitignore files in the docs folder and, when inside a git repo, its
    parents up to the repo root are skipped
  3 A file watcher auto-reindexes when files change
  4 Chat requests trigger a semantic search for relevant chunks
  5 Retrieved context is injected into the prompt before forwarding to the LLM
  6 Responses include a rag_sources field listing which documents were used

Supported file formats:

Text: .txt, .md, .json, .py, .js, .ts, .yaml, .toml, .rst, etc. Rich documents (via
 MarkItDown): .pdf, .docx, .pptx, .xlsx, .html, .csv

API endpoints:

• POST /v1/chat/completions — Main chat endpoint (OpenAI-compatible)
  • GET /health — Health check with configuration info
  • GET /files — List indexed files with chunk counts
  • POST /reindex — Trigger manual reindex
  • All other paths are proxied to the LLM backend

Per-request overrides (in JSON body):

• rag_top_k: Override --limit for this request
  • rag_enable_tools: Override --rag-tools for this request

```

</details>

### `memory`

The `memory proxy` command is the core feature—a middleware server that gives any OpenAI-compatible app long-term memory. Additional subcommands (`memory add`, etc.) help manage the memory store directly.

#### `memory proxy`

**Purpose:** Adds long-term conversational memory (self-hosted) to any OpenAI-compatible client.

**Key Features:**

- **Simple Markdown Files:** Your memories are stored as human-readable Markdown files, serving as the ultimate source of truth.
- **Automatic Version Control:** Built-in Git integration automatically commits changes, giving you a full history of your memory's evolution.
- **Lightweight & Local:** Minimal dependencies and runs entirely on your machine.
- **Proxy Middleware:** Works transparently with any OpenAI-compatible `/chat/completions` endpoint (OpenAI, Ollama, vLLM).

**Workflow:**

- Stores a per-conversation memory collection in Chroma with the same embedding settings as `rag-proxy`, reranked with a cross-encoder.
- For each turn, retrieves the top-k relevant memories (conversation + global) plus a rolling summary and augments the prompt.
- After each reply, extracts salient facts and refreshes the running summary (disable with `--no-summarization`).
- Enforces a per-conversation cap (`--max-entries`, default 500) and evicts oldest memories first.

**How to Use It:**

- **Install memory deps first**: `pip install "agent-cli[memory]"` (or, from the repo, `uv sync --extra memory`)
- **Start Server (Local LLM/OpenAI-compatible)**: `agent-cli memory proxy --memory-path ./memory_db --openai-base-url http://localhost:11434/v1 --embedding-model embeddinggemma:300m`
- **Use with Agent-CLI**: `agent-cli chat --openai-base-url http://localhost:8100/v1 --llm-provider openai`

<details>
<summary>See the output of <code>agent-cli memory proxy --help</code></summary>

```yaml

Usage: agent-cli memory proxy [OPTIONS]

Start the memory-backed chat proxy server.

This server acts as a middleware between your chat client (e.g., a web UI, CLI, or IDE
 plugin) and an OpenAI-compatible LLM provider (e.g., OpenAI, Ollama, vLLM).

Key Features:

• Simple Markdown Files: Memories are stored as human-readable Markdown files, serving
    as the ultimate source of truth.
  • Automatic Version Control: Built-in Git integration automatically commits changes,
    providing a full history of memory evolution.
  • Lightweight & Local: Minimal dependencies and runs entirely on your machine.
  • Proxy Middleware: Works transparently with any OpenAI-compatible /chat/completions
    endpoint.

How it works:

1 Intercepts POST /v1/chat/completions requests.
  2 Retrieves relevant memories (facts, previous conversations) from a local vector
    database (ChromaDB) based on the user's query.
  3 Injects these memories into the system prompt.
  4 Forwards the augmented request to the real LLM (--openai-base-url).
  5 Extracts new facts from the conversation in the background and updates the long-term
    memory store (including handling contradictions).

Example:

# Start proxy pointing to local Ollama
  agent-cli memory proxy --openai-base-url http://localhost:11434/v1

# Then configure your chat client to use http://localhost:8100/v1
  # as its OpenAI base URL. All requests flow through the memory proxy.

Per-request overrides: Clients can include these fields in the request body: memory_id
 (conversation ID), memory_top_k, memory_recency_weight, memory_score_threshold.

```

</details>

#### `memory add`

**Purpose:** Directly add memories to the store without LLM extraction. Useful for bulk imports or seeding memories.

**How to Use It:**

```bash
# Add single memories as arguments
agent-cli memory add "User likes coffee" "User lives in Amsterdam"

# Read from JSON file
agent-cli memory add -f memories.json

# Read from stdin (plain text, one per line)
echo "User prefers dark mode" | agent-cli memory add -f -

# Read JSON from stdin
echo '["Fact one", "Fact two"]' | agent-cli memory add -f -

# Specify conversation ID
agent-cli memory add -c work "Project deadline is Friday"
```

<details>
<summary>See the output of <code>agent-cli memory add --help</code></summary>

```yaml

Usage: agent-cli memory add [OPTIONS] [MEMORIES]...

Add memories directly without LLM extraction.

This writes facts directly to the memory store, bypassing the LLM-based fact extraction.
 Useful for bulk imports or seeding memories.

The memory proxy file watcher (if running) will auto-index new files. Otherwise, they'll
 be indexed on next memory proxy startup.

Examples::

# Add single memories as arguments
  agent-cli memory add "User likes coffee" "User lives in Amsterdam"

# Read from JSON file
  agent-cli memory add -f memories.json

# Read from stdin (plain text, one per line)
  echo "User prefers dark mode" | agent-cli memory add -f -

# Read JSON from stdin
  echo '["Fact one", "Fact two"]' | agent-cli memory add -f -

# Specify conversation ID
  agent-cli memory add -c work "Project deadline is Friday"

╭─ Arguments ────────────────────────────────────────────────────────────────────────────╮
│   memories      [MEMORIES]...  Memories to add. Each argument becomes one fact.        │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ──────────────────────────────────────────────────────────────────────────────╮
│ --file             -f                         PATH  Read memories from file. Use '-'   │
│                                                     for stdin. Supports JSON array,    │
│                                                     JSON object with 'memories' key,   │
│                                                     or plain text (one per line).      │
│ --conversation-id  -c                         TEXT  Conversation namespace for these   │
│                                                     memories. Memories are retrieved   │
│                                                     per-conversation unless shared     │
│                                                     globally.                          │
│                                                     [default: default]                 │
│ --memory-path                                 PATH  Directory for memory storage (same │
│                                                     as memory proxy --memory-path).    │
│                                                     [default: ./memory_db]             │
│ --git-versioning       --no-git-versioning          Auto-commit changes to git for     │
│                                                     version history.                   │
│                                                     [default: git-versioning]          │
│ --help             -h                               Show this message and exit.        │
╰────────────────────────────────────────────────────────────────────────────────────────╯
╭─ General Options ──────────────────────────────────────────────────────────────────────╮
│ --quiet       -q            Suppress console output from rich.                         │
│ --config              TEXT  Path to a TOML configuration file.                         │
│ --print-args                Print the command line arguments, including variables      │
│                             taken from the configuration file.                         │
╰────────────────────────────────────────────────────────────────────────────────────────╯

```

</details>

## Development

### Running Tests

The project uses `pytest` for testing. To run tests using `uv`:

```bash
uv run pytest
```

### Pre-commit Hooks

This project uses pre-commit hooks (ruff for linting and formatting, mypy for type checking) to maintain code quality. To set them up:

1. Install pre-commit:

```bash
   pip install pre-commit
   ```

2. Install the hooks:

```bash
   pre-commit install
   ```

Now, the hooks will run automatically before each commit.

## Contributing

Contributions are welcome! If you find a bug or have a feature request, please open an issue. If you'd like to contribute code, please fork the repository and submit a pull request.

## License

This project is licensed under the MIT License - see the `LICENSE` file for details.

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