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CLM - Command Line LLM Tool

A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.

Features

  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables

Installation

Prerequisites

  • Rust 1.70+ (uses 2024 edition)

Build from source

git clone <repository-url>cd clm
cargo build --release

The binary will be available at target/release/clm.

Install globally

make install # or `cargo install --path .`

Uninstall

make uninstall # or `cargo uninstall clm`

Usage

Basic usage

clm What is the capital of France
clm Explain quantum computing in simple terms

Multi-word prompts using punctuation with quotes

clm "How do I implement a binary search tree in Rust?"

Response Format

Each response includes metadata at the bottom showing:

[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:

  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used

Configuration

CLM uses environment variables for configuration:

Provider Selection

Set the CLM_PROVIDER environment variable to choose your provider:

export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama

API Keys

Each provider requires its respective API key:

# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)

Model Configuration

Set the CLM_MODEL environment variable to specify which model to use:

# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default

Provider-Specific Defaults

When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:

  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"

OpenAI reasoning options (optional)

CLM can pass OpenAI's reasoning parameters via environment variables:

  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)

Project Structure

src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions

This project was partially enhanced with ClaudeCode.

Custom Provider

The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:

export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"

You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.

The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.

About

Commandline LLM tool

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
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Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
CLM - Command Line LLM Tool
A unified command-line interface for interacting with multiple Large Language Model providers including OpenAI, Anthropic, Google, and Ollama.
Features
  • Multi-provider support: Switch between OpenAI, Anthropic, Google, Ollama, and Custom providers
  • Simple CLI interface: Just pass your prompt as arguments
  • Usage tracking: See token usage, response time, model, and provider for each query
  • Environment-based configuration: Easy provider switching via environment variables
Installation
Prerequisites
  • Rust 1.70+ (uses 2024 edition)
Build from source
git clone <repository-url>cd clm
cargo build --release
The binary will be available at target/release/clm.
Install globally
make install # or `cargo install --path .`
Uninstall
make uninstall # or `cargo uninstall clm`
Usage
Basic usage
clm What is the capital of France
clm Explain quantum computing in simple terms
Multi-word prompts using punctuation with quotes
clm "How do I implement a binary search tree in Rust?"
Response Format
Each response includes metadata at the bottom showing:
[Tokens: 150 | Time: 2.34s | Model: gemini-2.5-flash | Provider: google]

This provides visibility into:
  • Tokens: Number of tokens used (when available from the provider)
  • Time: Response time in seconds
  • Model: The specific model that processed the request
  • Provider: Which AI provider was used
Configuration
CLM uses environment variables for configuration:
Provider Selection
Set the CLM_PROVIDER environment variable to choose your provider:
export CLM_PROVIDER=google # Defaultexport CLM_PROVIDER=openai
export CLM_PROVIDER=anthropic
export CLM_PROVIDER=openrouter
export CLM_PROVIDER=ollama
API Keys
Each provider requires its respective API key:
# OpenAIexport OPENAI_API_KEY="your-openai-api-key"# Anthropicexport ANTHROPIC_API_KEY="your-anthropic-api-key"# Googleexport GOOGLE_AI_API_KEY="your-google-api-key"# OpenRouterexport OPENROUTER_API_KEY="your-openrouter-api-key"# Ollama (runs locally, no API key needed)
Model Configuration
Set the CLM_MODEL environment variable to specify which model to use:
# Global model setting (applies to all providers)export CLM_MODEL="gemini-2.5-flash"# Defaultexport CLM_MODEL="gpt-4.1-mini"# For OpenAIexport CLM_MODEL="claude-4-sonnet"# For Anthropicexport CLM_MODEL="google/gemini-2.5-flash"# For OpenRouterexport CLM_MODEL="llama3.2"# For Ollama# Ollama base URL (optional)export OLLAMA_BASE_URL="http://localhost:11434"# Default
Provider-Specific Defaults
When using the global default model (gemini-2.5-flash), each provider maps to its appropriate default:
  • Google: Uses the specified model directly
  • OpenAI: Defaults to "gpt-4.1-mini"
  • Anthropic: Defaults to "claude-4-sonnet"
  • OpenRouter: Defaults to "google/gemini-2.5-flash"
  • Ollama: Defaults to "llama3.2"
OpenAI reasoning options (optional)
CLM can pass OpenAI's reasoning parameters via environment variables:
  • OPENAI_REASONING_EFFORT: one of minimal, low, medium, high
  • OPENAI_MAX_COMPLETION_TOKENS: integer (e.g., 1024)
Project Structure
src/
├── main.rs # CLI entry point and argument parsing
└── providers/
├── mod.rs # Provider trait and factory
├── openai.rs # OpenAI GPT integration
├── anthropic.rs # Anthropic Claude integration
├── google.rs # Google AI integration
├── ollama.rs # Ollama local model integration
└── custom.rs # Custom provider integration

Contributions
This project was partially enhanced with ClaudeCode.
Custom Provider
The custom provider allows you to integrate any LLM service that follows a standard request/response format. To use it, set the following environment variables:
export CLM_PROVIDER=custom
export CUSTOM_PROVIDER_API_KEY="your-custom-api-key"export CUSTOM_PROVIDER_API_URL="https://api.yourservice.com/v1/chat/completions"export CUSTOM_PROVIDER_NAME="Your Custom Provider"
You can also specify a model with CLM_MODEL. If omitted, the provider defaults to google/gemini-2.5-flash.
The custom provider implements the same AiResponse structure as the built‑in providers, so it works seamlessly with the existing CLI commands.
About
Commandline LLM tool
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages