tmuxai

TmuxAI

Your intelligent pair programmer directly within your tmux sessions.



_{by Boring Dystopia Development}

   


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Table of Contents

• About The Project
  • Human-Inspired Interface
• Installation
  • Quick Install
  • Manual Download
  • Install from Main
• Post-Installation Setup
• TmuxAI Layout
• Observe Mode
• Prepare Mode
• Watch Mode
  • Activating Watch Mode
  • Example Use Cases
• Knowledge Base
  • Creating Knowledge Bases
  • Using Knowledge Bases
  • Auto-Loading Knowledge Bases
• Skills
  • Enabling Skills
  • Creating Skills
  • Using Skills
  • Auto-Match
  • Budget Controls
• Model Configuration
  • Setting Up Multiple Models
  • Switching Between Models
• Squashing
  • What is Squashing?
  • Manual Squashing
• Multiline Input
• Web Search & Fetch
• MCP Server Tools
• Core Commands
• Command-Line Usage
• Configuration
  • Status Line Customization
  • Environment Variables
  • Session-Specific Configuration
• Contributing
• License

About The Project

TmuxAI is an intelligent terminal assistant that lives inside your tmux sessions. Unlike other CLI AI tools, TmuxAI observes and understands the content of your tmux panes, providing assistance without requiring you to change your workflow or interrupt your terminal sessions.

Think of TmuxAI as a pair programmer that sits beside you, watching your terminal environment exactly as you see it. It can understand what you're working on across multiple panes, help solve problems and execute commands on your behalf in a dedicated execution pane.

Human-Inspired Interface

TmuxAI's design philosophy mirrors the way humans collaborate at the terminal. Just as a colleague sitting next to you would observe your screen, understand context from what's visible, and help accordingly, TmuxAI:

1. Observes: Reads the visible content in all your panes
2. Communicates: Uses a dedicated chat pane for interaction
3. Acts: Can execute commands in a separate execution pane (with your permission)

This approach provides powerful AI assistance while respecting your existing workflow and maintaining the familiar terminal environment you're already comfortable with.

Installation

TmuxAI requires only tmux to be installed on your system. It's designed to work on Unix-based operating systems including Linux and macOS.

Quick Install

The fastest way to install TmuxAI is using the installation script:

# install tmux if not already installed
curl -fsSL https://get.tmuxai.dev | bash

This installs TmuxAI to /usr/local/bin/tmuxai by default. If you need to install to a different location or want to see what the script does before running it, you can view the source at get.tmuxai.dev.

Manual Download

You can also download pre-built binaries from the GitHub releases page.

After downloading, make the binary executable and move it to a directory in your PATH:

chmod +x ./tmuxai
sudo mv ./tmuxai /usr/local/bin/

Install from Main

To install the latest development version directly from the main branch:

go install github.com/alvinunreal/tmuxai@main

Note: The main branch contains the latest features and fixes but may be less stable than official releases.

Post-Installation Setup

TmuxAI reads its configuration from ~/.config/tmuxai/config.yaml. To get running, create the file with a model entry that points at the provider you use.

1. Create the config path

   mkdir -p ~/.config/tmuxai
   vim ~/.config/tmuxai/config.yaml

2. Add a minimal config

   models:
     primary:
       provider: openrouter  # openrouter, requesty, openai or azure
       model: anthropic/claude-haiku-4.5
       api_key: sk-your-api-key

   Swap the provider name and fill in the model/API key required by your account.

3. Start TmuxAI

   tmuxai

See Model Configuration for more details.

TmuxAI Layout

TmuxAI is designed to operate within a single tmux window, with one instance of TmuxAI running per window and organizes your workspace using the following pane structure:

1. Chat Pane: This is where you interact with the AI. It features a REPL-like interface with syntax highlighting, auto-completion, and readline shortcuts.

2. Exec Pane: TmuxAI selects (or creates) a pane where commands can be executed. You can also force a specific exec pane with --exec-pane.

3. Read-Only Panes: All other panes in the current window serve as additional context. TmuxAI can read their content but does not interact with them.

Observe Mode

TmuxAI sent the first ping command and is waiting for the countdown to check for the next step

TmuxAI operates by default in "observe mode". Here's how the interaction flow works:

1. User types a message in the Chat Pane.

2. TmuxAI captures context from all visible panes in your current tmux window (excluding the Chat Pane itself). This includes:

   • Current command with arguments
   • Detected shell type
   • User's operating system
   • Current content of each pane

3. TmuxAI processes your request by sending user's message, the current pane context, and chat history to the AI.

4. The AI responds with information, which may include a suggested command to run.

5. If a command is suggested, TmuxAI will:

   • Check if the command matches whitelist or blacklist patterns
   • Ask for your confirmation (unless the command is whitelisted). The confirmation prompt includes a risk indicator (✓ safe, ? unknown, ! danger) for guidance only - always review commands carefully as the risk scoring is not exhaustive and should not be relied upon for security decisions
   • Execute the command in the designated Exec Pane if approved
   • Wait for the wait_interval (default: 5 seconds) (You can pause/resume the countdown with space or enter to stop the countdown)
   • Capture the new output from all panes
   • Send the updated context back to the AI to continue helping you

6. The conversation continues until your task is complete.

Prepare Mode

TmuxAI customized the pane prompt and sent the first ping command. Instead of the countdown, it's waiting for command completion

Prepare mode is an optional feature that enhances TmuxAI's ability to work with your terminal by customizing your shell prompt and tracking command execution with better precision. This enhancement eliminates the need for arbitrary wait intervals and provides the AI with more detailed information about your commands and their results.

When you enable Prepare Mode, TmuxAI will:

1. Detects your current shell in the execution pane (supports bash, zsh, and fish)
2. Customizes your shell prompt to include special markers that TmuxAI can recognize
3. Will track command execution history including exit codes, and per-command outputs
4. Will detect command completion instead of using fixed wait time intervals

To activate Prepare Mode, simply use:

TmuxAI » /prepare

By default, TmuxAI will attempt to detect the shell running in the execution pane. If you need to specify the shell manually, you can provide it as an argument:

TmuxAI » /prepare bash

Prepared Fish Example:

$ function fish_prompt; set -l s $status; printf '%s@%s:%s[%s][%d]» ' $USER (hostname -s) (prompt_pwd) (date +"%H:%M") $s; end
username@hostname:~/r/tmuxai[21:05][0]»

Watch Mode

TmuxAI watching user shell commands and better alternatives

Watch Mode transforms TmuxAI into a proactive assistant that continuously monitors your terminal activity and provides suggestions based on what you're doing.

Activating Watch Mode

To enable Watch Mode, use the /watch command followed by a description of what you want TmuxAI to look for:

TmuxAI » /watch spot and suggest more efficient alternatives to my shell commands

When activated, TmuxAI will:

1. Start capturing the content of all panes in your current tmux window at regular intervals (wait_interval configuration)
2. Analyze content based on your specified watch goal and provide suggestions when appropriate

Example Use Cases

Watch Mode could be valuable for scenarios such as:

• Learning shell efficiency: Get suggestions for more concise commands as you work

  TmuxAI » /watch spot and suggest more efficient alternatives to my shell commands
  

• Detecting common errors: Receive warnings about potential issues or mistakes

  TmuxAI » /watch flag commands that could expose sensitive data or weaken system security
  

• Log Monitoring and Error Detection: Have TmuxAI monitor log files or terminal output for errors

  TmuxAI » /watch monitor log output for errors, warnings, or critical issues and suggest fixes
  

Squashing

As you work with TmuxAI, your conversation history grows, adding to the context provided to the AI model with each interaction. Different AI models have different context size limits and pricing structures based on token usage. To manage this, TmuxAI implements a simple context management feature called "squashing."

What is Squashing?

Squashing is TmuxAI's built-in mechanism for summarizing chat history to manage token usage.

When your context grows too large, TmuxAI condenses previous messages into a more compact summary.

You can check your current context utilization at any time using the /info command:

TmuxAI » /info

Context
────────

Messages            15
Context Size~       82500 tokens
                    ████████░░ 82.5%
Max Size            100000 tokens

This example shows that the context is at 82.5% capacity (82,500 tokens out of 100,000). When the context size reaches 80% of the configured maximum (max_context_size in your config), TmuxAI automatically triggers squashing.

Manual Squashing

If you'd like to manage your context before reaching the automatic threshold, you can trigger squashing manually with the /squash command:

TmuxAI » /squash

Multiline Input

For longer or more complex prompts, you can open your current input in an external text editor. This is similar to how bash allows editing commands with Ctrl+X Ctrl+E.

Keyboard Shortcuts:

• Ctrl+O - Open current prompt in external editor (works on all platforms)
• Alt+E - Alternative binding (may not work on macOS due to Option key behavior)

When triggered, TmuxAI will:

1. Open your $EDITOR (falls back to vim if not set) with the current prompt content
2. Wait for you to edit, save, and close the editor
3. Replace the prompt with the edited content

This is useful for:

• Writing multi-line prompts or detailed instructions
• Editing long commands more comfortably
• Pasting and formatting complex content

Knowledge Base

The Knowledge Base feature allows you to create pre-defined context files in markdown format that can be loaded into TmuxAI's conversation context. This is useful for sharing common patterns, workflows, or project-specific information with the AI across sessions.

Creating Knowledge Bases

Knowledge bases are text files stored in ~/.config/tmuxai/kb/. To create one:

1. Create the knowledge base directory if it doesn't exist:

   mkdir -p ~/.config/tmuxai/kb

2. Create a file with your knowledge base content:

   cat > ~/.config/tmuxai/kb/docker-workflows << 'EOF'
   # Docker Workflows
   
   ## Common Commands
   - Always use `docker compose` (not `docker-compose`)
   - Prefer named volumes over bind mounts for databases
   - Use `.env` files for environment-specific configuration
   
   ## Project Structure
   - Development: `docker compose -f docker-compose.dev.yml up`
   - Production: `docker compose -f docker-compose.prod.yml up -d`
   EOF

Using Knowledge Bases

Once created, you can load knowledge bases into your TmuxAI session:

# List available knowledge bases
TmuxAI » /kb
Available knowledge bases:
  [ ] docker-workflows
  [ ] git-conventions
  [ ] testing-procedures

# Load a knowledge base
TmuxAI » /kb load docker-workflows
✓ Loaded knowledge base: docker-workflows (850 tokens)

# List again to see loaded status
TmuxAI » /kb
Available knowledge bases:
  [✓] docker-workflows (850 tokens)
  [ ] git-conventions
  [ ] testing-procedures

Loaded: 1 KB(s), 850 tokens

# Unload a knowledge base
TmuxAI » /kb unload docker-workflows
✓ Unloaded knowledge base: docker-workflows

# Unload all knowledge bases
TmuxAI » /kb unload --all
✓ Unloaded all knowledge bases (2 KB(s))

You can also load knowledge bases directly from the command line when starting TmuxAI:

# Load single knowledge base
tmuxai --kb docker-workflows

# Load multiple knowledge bases (comma-separated)
tmuxai --kb docker-workflows,git-conventions

Auto-Loading Knowledge Bases

You can configure knowledge bases to load automatically on startup by adding them to your ~/.config/tmuxai/config.yaml:

knowledge_base:
  auto_load:
    - docker-workflows
    - git-conventions
  # path: /custom/path  # Optional: use custom KB directory

Important Notes:

• Loaded knowledge bases consume tokens from your context budget
• Use /info to see how many tokens your loaded KBs are using
• Knowledge bases are injected after the system prompt but before conversation history
• Unloading a KB removes it from future messages immediately

Skills

The Skills feature extends the Knowledge Base system with structured, metadata-rich instructions that teach TmuxAI new capabilities. Unlike KBs (which provide passive reference material), skills can be auto-discovered, lazily loaded, and optionally auto-matched to incoming messages.

Each skill lives in a directory with a SKILL.md file containing frontmatter metadata and body content. Ancillary files (scripts, templates, reference docs) can coexist in the same directory.

Enabling Skills

Skills are disabled by default. Enable them in ~/.config/tmuxai/config.yaml:

knowledge_base:
  skills:
    enabled: true

Creating Skills

Skills are stored in ~/.config/tmuxai/skills/<skill-name>/:

mkdir -p ~/.config/tmuxai/skills/git-hooks

Create SKILL.md with frontmatter:

cat > ~/.config/tmuxai/skills/git-hooks/SKILL.md << 'EOF'
---
name: git-hooks
description: Git pre-commit and linting setup. Auto-stage hooks, conventional commits, branch protection.
disable-model-invocation: false
---

# Git Hooks Guide

## Pre-commit Setup

```bash
git config core.hooksPath .husky
npm install husky --save-dev

...rest of the skill body... EOF

**Frontmatter fields:**

| Field | Required | Description |
|-------|----------|-------------|
| `name` | Yes | Unique skill name (must match directory name, alphanumeric + hyphens only) |
| `description` | Yes | Brief description shown in L1 discovery block and `/skill list` |
| `disable-model-invocation` | No | If `true`, disables auto-match — skill must be loaded manually |

Optional ancillary files (`.sh`, `.txt`, `.py`, `.json`) can be placed alongside `SKILL.md`. When a skill is loaded, TmuxAI includes a manifest listing those helper file paths so the model can request them if needed.

### Using Skills

```bash
# List available skills
TmuxAI » /skill
Available skills:
  [ ] docker-workflows
  [ ] git-hooks                [manual]
  [ ] terraform-best-practices

# Load a skill (lazy-load body + ancillary file manifest)
TmuxAI » /skill load git-hooks
✓ Loaded skill: git-hooks (1,240 chars)

# List again to see loaded status
TmuxAI » /skill
Available skills:
  [✓] docker-workflows               (850 chars)
  [✓] git-hooks                      (1,240 chars)
  [ ] terraform-best-practices

Loaded: 2/3 skill(s), 2,090/32,000 chars

# View skill details without loading body
TmuxAI » /skill info git-hooks
Name:        git-hooks
Description: Git pre-commit and linting setup.
Disabled:    false
Loaded:      true
Body Size:   1,240 chars
Directory:   ~/.config/tmuxai/skills/git-hooks
File:        ~/.config/tmuxai/skills/git-hooks/SKILL.md

# Validate all skills
TmuxAI » /skill validate
Validated 3 skill(s):
  ✓ OK  docker-workflows
  ✓ OK  git-hooks
  ✓ OK  terraform-best-practices

# Unload a skill
TmuxAI » /skill unload git-hooks
✓ Unloaded skill: git-hooks

# Unload all skills
TmuxAI » /skill unload --all
✓ Unloaded all skills (2 skill(s))

Auto-Match

You can enable automatic skill matching against incoming messages:

knowledge_base:
  skills:
    enabled: true
    auto_match: true
    auto_match_threshold: 0.1  # Match sensitivity (0.0–1.0, lower = more aggressive)

With auto-match enabled, TmuxAI analyzes incoming messages and loads relevant skills based on term frequency and description relevance. Skills marked [manual] (via disable-model-invocation: true) require explicit loading.

Budget Controls

Skills share context budget with your conversation. Defaults:

Setting	Default	Description
max_l1_chars	8,000	Maximum chars for the L1 discovery block
max_loaded_chars	32,000	Maximum chars across all loaded skill bodies
max_skill_chars	20,000	Maximum chars per individual skill body; set to 0 to disable the per-skill cap

Use /info to monitor context usage with skills loaded.

Important Notes:

• Skills are injected after the system prompt and KBs, before conversation history
• The L1 discovery block tells the model which skills exist and their load status
• Body content is only loaded on demand (lazy loading)
• A 1MB cap per SKILL.md prevents runaway memory usage
• SKILL.md frontmatter fences (---) are matched line-by-line; standalone --- lines in multi-line YAML values will be misinterpreted as the closing fence

Model Configuration

TmuxAI supports configuring multiple AI model configurations and easily switching between them. This allows you to define different AI providers, models, and settings for various use cases.

Setting Up Multiple Models

Configure multiple AI models in your ~/.config/tmuxai/config.yaml:

# Optional: specify which model to use by default
# If not set, the first model alphabetically will be used automatically
default_model: "fast"

models:
  fast:
    provider: "openrouter"
    model: "anthropic/claude-haiku-4.5"
    api_key: "sk-or-your-openrouter-key"

  smart:
    provider: "openrouter"
    model: "google/gemini-2.5-prod"
    api_key: "sk-or-...-key"

  # Requesty (OpenAI-compatible router, defaults to https://router.requesty.ai/v1)
  # Get a key at https://app.requesty.ai/api-keys ; browse models at https://app.requesty.ai/router/list
  requesty:
    provider: "requesty"
    model: "openai/gpt-4o-mini"
    api_key: "${REQUESTY_API_KEY}"

  # You can use any chat completion compatible endpoint as base_url
  anthropic:
    provider: "openrouter"
    model: "claude-3-5-sonnet-20241022"
    api_key: "your-anthropic-api-key"
    base_url: "https://api.anthropic.com"

  # GitHub Copilot — requires the `copilot` CLI in PATH and `gh auth login`
  # No api_key needed; the CLI uses your existing gh auth credentials
  github-copilot:
    provider: "github-copilot"
    model: "claude-sonnet-4.5"

  local-llama:
    provider: "openrouter"
    model: "gemma3:1b"
    api_key: "sk-or-your-openrouter-key"
    base_url: http://localhost:11434/v1

  # Responses API
  codex:
    provider: "openai"
    model: "gpt-5-codex"
    api_key: "sk-or-your-openrouter-key"

  azure-gpt4:
    provider: "azure"
    model: "gpt-4o"
    api_key: "your-azure-openai-api-key"
    api_base: "https://your-resource.openai.azure.com/"
    api_version: "2025-04-01-preview"
    deployment_name: "gpt-4o"

  # Gemini API (direct access)
  gemini-flash:
    provider: "gemini"
    model: "gemini-2.5-flash"
    api_key: "${GOOGLE_API_KEY}"

Supported Providers:

• openai - OpenAI Responses API (GPT-4, GPT-5, etc.)
• openrouter - Universal Chat Completion API, defaults to openrouter base url
• requesty - Requesty router (OpenAI-compatible Chat Completion API), defaults to https://router.requesty.ai/v1. Browse models at app.requesty.ai/router/list
• azure - Azure Chat Completions API
• gemini - Google Gemini API (direct access via go-genai SDK)
• github-copilot - GitHub Copilot (via official copilot-sdk/go — see setup below)
• bedrock - AWS Bedrock (via the Converse API — supports Anthropic, Meta, Mistral, Amazon Nova/Titan, Cohere, AI21, etc.)

AWS Bedrock Setup

TmuxAI talks to AWS Bedrock via the Converse API, which provides a unified interface across all Bedrock-hosted model families. No api_key is required — credentials flow through the standard AWS credential chain (environment variables, ~/.aws/credentials, IAM role, SSO, etc.).

Before first use:

1. Request access to the models you want in the AWS Bedrock console (Model access → Enable models).
2. Ensure your AWS credentials are configured (aws configure, aws sso login, an IAM role, or AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY env vars).
3. Add a model config:

models:
  claude-bedrock:
    provider: "bedrock"
    model: "anthropic.claude-3-5-sonnet-20241022-v2:0"
    region: "us-east-1"      # optional if AWS_REGION is set
    aws_profile: "default"   # optional — named profile from ~/.aws/credentials

  nova-pro:
    provider: "bedrock"
    model: "amazon.nova-pro-v1:0"
    region: "us-east-1"

The model field must be a Bedrock model ID (or inference-profile ARN). See the Bedrock model IDs documentation for a full list.

GitHub Copilot Setup

TmuxAI integrates with GitHub Copilot via the official Go SDK, which communicates with the copilot CLI. No api_key is required — authentication uses your existing gh credentials.

Follow the GitHub Copilot CLI installation guide to install and authenticate the CLI, then configure TmuxAI:

models:
  fast:
    provider: "github-copilot"
    model: "claude-haiku-4.5"
  smart:
    provider: "github-copilot"
    model: "claude-sonnet-4.5"

Interactive Commands:

# List available models and see current selection
TmuxAI » /model

Available Models
  [ ] claude-sonnet (openrouter: anthropic/claude-3.5-sonnet)
  [ ] fast (openrouter: anthropic/claude-haiku-4.5)
  [✓] smart (openrouter: google/gemini-2.5-prod)
  [ ] local-llama (openrouter: meta-llama/llama-3.1-8b-instruct:free)

Current Model:
  Configuration: smart
  Provider: openrouter
  Model: google/gemini-2.5-prod

# Switch to a different model
TmuxAI » /model claude-sonnet
✓ Switched to claude-sonnet (openrouter: anthropic/claude-3.5-sonnet)

# Status bar shows current model when using non-default
TmuxAI [claude-sonnet] »

Web Search & Fetch

TmuxAI can search the web and fetch webpage content without leaving your terminal. Search and fetch are manual, non-agentic. You initiate when to search and fetch to add to TmuxAI context.

TmuxAI » /websearch how to set up WireGuard
TmuxAI » /websearch -f 3 latest tmux best practices
TmuxAI » /webfetch https://example.com/docs

• Providers: Brave Search API (primary) or self-hosted SearXNG
• Fallback chain: direct fetch → Wayback Machine → Google Cache
• Safety: Fetched content is sanitized and injected as assistant context, not user input
• Configure providers and limits under web_search and web_fetch in config.yaml (see Configuration)

Note: Using /websearch -f N auto-fetches the top N search results and injects them into TmuxAI's context. Each fetch is capped by web_fetch.max_chars (default 25000 for direct /webfetch, or web_search.fetch_max_chars default 15000 for auto-fetched search results). The cumulative total is not capped. With -f 5 or higher on large pages, this can consume a significant portion of the context window. Prefer -f 1 to -f 3 unless you need broader coverage.

MCP Server Tools

TmuxAI supports the Model Context Protocol (MCP), allowing you to connect external MCP servers and make their tools available to the AI alongside built-in commands. This is opt-in — if no config file exists, there is zero MCP overhead.

Configuration

Create ~/.config/tmuxai/mcp.json to define your MCP servers:

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@upstash/context7-mcp@latest"]
    },
    "remote-mcp": {
      "type": "streamable-http",
      "url": "http://localhost:3050/mcp",
      "headers": { "X-API-Key": "${MCP_API_KEY}" }
    }
  }
}

TmuxAI supports three transport types:

Transport	When to use	Required fields
stdio	Local MCP servers (spawned as child process)	command (and optionally args)
SSE	Remote servers using Server-Sent Events	url
streamable-http	Remote servers using MCP Streamable HTTP	type: "streamable-http" + url

For stdio and SSE servers, TmuxAI auto-detects the transport from the presence of command or url — no type field needed. For streamable HTTP, set "type": "streamable-http" explicitly.

Field	Description
type	Transport type: "stdio", "sse", or "streamable-http" (auto-detected if omitted)
command / url	Server command (stdio) or endpoint URL (SSE / streamable-http)
args	Command-line arguments (stdio only)
env	Environment variables (supports ${VAR} expansion)
headers	HTTP headers (SSE and streamable-http, supports ${VAR} expansion)
timeout_seconds	Per-tool-call timeout (default: 30s)
disabled	Set true to skip without removing the entry

On startup, TmuxAI connects to each enabled server, lists available tools, and injects their definitions into the AI's system prompt. The AI can then call MCP tools using <MCPToolCall> tags, with results automatically fed back for continued reasoning.

Commands

TmuxAI » /mcp                      # List servers with status and tool counts
TmuxAI » /mcp tools                 # List all available MCP tools
TmuxAI » /mcp tools context7        # List tools for a specific server
TmuxAI » /mcp load                  # Reload config and reconnect all servers
TmuxAI » /mcp reload                # Hot reload — only reconnects changed servers
TmuxAI » /mcp unload                # Disconnect all MCP servers

Use /info to see active MCP servers, tool counts, and estimated token usage.

Core Commands

Command	Description
/info	Display system information, pane details, and context statistics
/clear	Clear chat history.
/reset	Clear chat history and reset all panes.
/config	View current configuration settings
/config set <key> <value>	Override configuration for current session
/model	List available models and show current active model
/model <name>	Switch to a different model configuration
/squash	Manually trigger context summarization
/prepare [shell]	Initialize Prepared Mode for the Exec Pane (e.g., bash, zsh)
/watch <description>	Enable Watch Mode with specified goal
/kb	List available knowledge bases with loaded status
/kb load <name>	Load a knowledge base into conversation context
/kb unload <name>	Unload a specific knowledge base
/kb unload --all	Unload all knowledge bases
/skill	List available skills with loaded status
/skill load <name>	Load a skill into conversation context
/skill unload <name>	Unload a specific skill
/skill unload --all	Unload all skills
/skill info <name>	View skill details without loading
/skill validate	Validate all discovered skills
/websearch [-f N] <query>	Search the web via Brave or SearXNG; use -f N to auto-fetch top N results
/webfetch <url>	Fetch readable content from a URL, with Wayback Machine fallback
/mcp	List MCP servers with status and tool counts
/mcp tools [server]	List available MCP tools, optionally filtered by server
/mcp load	Reload MCP config and reconnect all servers
/mcp reload	Hot reload — only reconnects changed servers
/mcp unload	Disconnect all MCP servers
/exit	Exit TmuxAI

Command-Line Usage

You can start tmuxai with an initial message, task file, model configuration, or knowledge bases from the command line:

• Direct Message:

  tmuxai your initial message

• Task File:

  tmuxai -f path/to/your_task.txt

• Specify Model:

  # Use a specific model configuration
  tmuxai --model gpt4 "Write a Go function"
  tmuxai --model claude-sonnet

• Load Knowledge Bases:

  # Single knowledge base
  tmuxai --kb docker-workflows
  
  # Multiple knowledge bases
  tmuxai --kb docker-workflows,git-conventions

• Choose Tmux Panes Explicitly:

  # Force a specific exec pane by tmux pane ID
  tmuxai --exec-pane %3
  
  # Restrict read context to specific panes
  tmuxai --read-panes %1,%2
  
  # Fully control both execution and read context
  tmuxai --exec-pane %3 --read-panes %1,%2

  Notes:

  • --exec-pane forces TmuxAI to use that pane for command execution and disables auto-picking or auto-creating an exec pane.
  • --read-panes limits read context to the listed pane IDs in the current tmux window.
  • The TmuxAI chat pane cannot be used as an exec pane or read pane.
  • Pane IDs must exist in the current tmux window.

• Combine Options:

  tmuxai --model gpt4 --kb docker-workflows --exec-pane %3 --read-panes %1,%2 "Debug this Docker issue"

• Yolo Mode (Skip Confirmations):

  # Skip all confirmation prompts - commands execute immediately
  tmuxai --yolo "Install and configure nginx"

  Warning: Use --yolo with caution. This mode skips all safety confirmations and executes commands directly. Only use when you trust the AI's command suggestions completely.

Configuration

The configuration can be managed through a YAML file, environment variables, or via runtime commands.

TmuxAI looks for its configuration file at ~/.config/tmuxai/config.yaml. For a sample configuration file, see config.example.yaml.

Status Line Customization

By default, TmuxAI uses its built-in interactive prompt:

TmuxAI »

You can customize that prompt/status prefix with status_line:

status_line: "{app} ({context}) - {model} >> "

Available placeholders:

Placeholder	Description
{app}	TmuxAI
{context}	Approximate used/max context tokens, e.g. 37k/256k
{context_used}	Approximate used context tokens
{context_max}	Configured max context tokens
{model}	Current model configuration name
{state}	Current state symbol, if active
{state_badge}	Current state symbol in brackets, if active
{model_changed}	Current model badge when it differs from the configured default; requires the models: config section

Examples:

# Compact prompt with context usage
status_line: "✨ ({context}) >> "

# Include model information
status_line: "{app} ({context}) - {model} >> "

You can also try a status line for the current session only:

TmuxAI » /config set status_line ✨ ({context}) >>

tmux pane split configuration

You can customize how TmuxAI creates its exec pane by setting raw tmux split-window arguments:

tmux:
  exec_split_args: ["-d", "-h"]

These args are injected as:

tmux split-window <exec_split_args...> -t <target> -P -F "#{pane_id}"

Reserved flags -t, -P, and -F are managed internally and must not be included in exec_split_args.

If omitted, TmuxAI uses the legacy default: -d -h.

Web Search & Fetch Configuration

Enable web search (via Brave or SearXNG) and web fetching (with Wayback/Google Cache fallback):

web_search:
  enabled: true
  default_provider: brave
  max_results: 5
  max_result_chars: 6000
  timeout_seconds: 10
  providers:
    brave:
      api_key: "YOUR_BRAVE_API_KEY"
    searxng:
      base_url: "http://127.0.0.1:8888"

web_fetch:
  enabled: true
  max_chars: 25000
  timeout_seconds: 8
  allowed_redirects: false

Environment Variables

All configuration options can also be set via environment variables, which take precedence over the config file. Use the prefix TMUXAI_ followed by the uppercase configuration key:

# General settings
export TMUXAI_DEBUG=true
export TMUXAI_MAX_CAPTURE_LINES=300
export TMUXAI_MAX_CONTEXT_SIZE=150000

# Quick setup with environment variables (alternative to model configurations)
export TMUXAI_OPENAI_API_KEY="your-openai-api-key-here"
export TMUXAI_OPENAI_MODEL="gpt-4"
export TMUXAI_OPENROUTER_API_KEY="your-openrouter-api-key-here"
export TMUXAI_REQUESTY_API_KEY="your-requesty-api-key-here"

You can also use environment variables directly within your configuration file values. The application will automatically expand these variables when loading the configuration:

# Example config.yaml with environment variable expansion
openai:
  api_key: "${OPENAI_API_KEY}"
  model: "${OPENAI_MODEL:-gpt-4}"

openrouter:
  api_key: "${OPENROUTER_API_KEY}"
  base_url: "${OPENROUTER_BASE_URL:-https://openrouter.ai/api/v1}"

requesty:
  api_key: "${REQUESTY_API_KEY}"
  base_url: "${REQUESTY_BASE_URL:-https://router.requesty.ai/v1}"

Session-Specific Configuration

You can override configuration values for your current TmuxAI session using the /config command:

# View current configuration
TmuxAI » /config

# Override a configuration value for this session
TmuxAI » /config set max_capture_lines 300
TmuxAI » /config set wait_interval 3

These changes will persist only for the current session and won't modify your config file.

Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue.
Don't forget to give the project a star!

License

Distributed under the Apache License. See Apache License for more information.

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