streamlm 0.1.12

A command-line interface for interacting with various Large Language Models with streaming markdown output

StreamLM

A command-line interface for interacting with various Large Language Models with beautiful markdown-formatted responses.

Design Principle: Frictionless interaction. Just type lm hello - no need for subcommands. The CLI defaults to chat mode for the fastest possible workflow.

Installation

uv (recommended)

uv tool install streamlm

PyPI

pip install streamlm

Homebrew (macOS/Linux)

brew install jeffmylife/streamlm/streamlm

Usage

Basic Usage

After installation, you can use the lm command. The CLI defaults to chat mode - just type your prompt:

lm explain quantum computing
lm -m gpt-4o "write a Python function"
lm -m claude-3-5-sonnet "analyze this data"

# Explicit 'chat' command also works
lm chat "hello world"

Gateway Routing

StreamLM supports routing requests through different gateways for cost optimization and flexibility:

# Route through Vercel AI Gateway (no markup, low latency)
lm --gateway vercel "explain quantum computing"

# Route through OpenRouter (model discovery, transparent pricing)
lm --gateway openrouter -m gpt-4o "write a function"

# Direct provider access (default, supports reasoning models)
lm --gateway direct "analyze this data"

Gateway Benefits:

• Vercel: $5/month free credits, no token markup, <20ms latency
• OpenRouter: Model discovery, pricing transparency, bring-your-own-key
• Direct: Full provider feature support, reasoning models, lowest latency

Configuration

StreamLM can be configured via config file (~/.streamlm/config.yaml), environment variables, or CLI flags:

# Interactive setup wizard
lm config setup

# Set default gateway
lm config set gateway.default vercel

# Configure gateway API keys
lm config set gateway.vercel.api_key sk-your-ai-gateway-key

# Set default model
lm config set models.default gpt-4o

# View current configuration
lm config get

# Validate configuration and API keys
lm config validate

# List available gateways
lm config list-gateways

Configuration Priority (highest to lowest):

1. CLI flags (--gateway, --model)
2. Environment variables (STREAMLM_GATEWAY, provider API keys)
3. Config file (~/.streamlm/config.yaml)
4. Defaults (direct gateway, gemini-2.5-flash model)

Model Aliases

Define shortcuts for your favorite models in the config:

# ~/.streamlm/config.yaml
models:
  aliases:
    gpt: "gpt-4o"
    claude: "claude-3-5-sonnet"
    fast: "gemini/gemini-2.5-flash"
    smart: "gpt-4o"

Then use them:

lm -m fast "quick question"    # Uses gemini-2.5-flash
lm -m smart "complex analysis"  # Uses gpt-4o

Raw Markdown Output

StreamLM includes beautiful built-in markdown formatting, but you can also output raw markdown for piping to other tools:

# Output raw markdown without Rich formatting
lm --md "explain machine learning" > output.md

# Pipe to your favorite markdown formatter (like glow)
lm --md "write a Python tutorial" | glow

# Use with other markdown tools
lm --raw "create documentation" | pandoc -f markdown -t html

Supported Models

StreamLM provides access to various Large Language Models including:

• OpenAI: GPT-4o, o1, o3-mini, GPT-4o-mini
• Anthropic: Claude-3-7-sonnet, Claude-3-5-sonnet, Claude-3-5-haiku
• Google: Gemini-2.5-flash, Gemini-2.5-pro, Gemini-2.0-flash-thinking
• DeepSeek: DeepSeek-R1, DeepSeek-V3
• xAI: Grok-4, Grok-3-beta, Grok-3-mini-beta
• Local models: Via Ollama (Llama3.3, Qwen2.5, DeepSeek-Coder, etc.)

Chat Command Options

• --model / -m: Choose the LLM model (or use alias from config)
• --gateway / -g: Route through gateway (direct, vercel, openrouter)
• --image / -i: Include image files for vision models
• --context / -c: Add context from a file
• --max-tokens / -t: Set maximum response length
• --temperature / -temp: Control response creativity (0.0-1.0)
• --think: Show reasoning process (reasoning models, direct gateway only)
• --debug / -d: Enable debug mode
• --raw / --md: Output raw markdown without Rich formatting

Config Command Actions

• lm config setup: Interactive configuration wizard
• lm config get [key]: Get configuration value
• lm config set <key> <value>: Set configuration value
• lm config validate: Validate configuration and API keys
• lm config list-gateways: Show available gateways and their status

Features

• 🎨 Beautiful markdown-formatted responses
• 🌐 Gateway routing (Vercel AI Gateway, OpenRouter, or direct)
• ⚙️ Flexible configuration (config file, env vars, CLI flags)
• 🔑 Model aliases for quick access to favorite models
• 🖼️ Image input support for compatible models
• 📁 Context file support
• 🧠 Reasoning model support (DeepSeek, OpenAI o1, etc.)
• 🔧 Extensive model support across providers
• ⚡ Fast and lightweight
• 🛠️ Easy configuration management

Links

• PyPI Package
• Homebrew Tap
• Issues

License

MIT License - see LICENSE file for details.

Development

Setup

# Clone the repository
git clone https://github.com/jeffmylife/streamlm.git
cd streamlm

# Install with dev dependencies
uv pip install -e ".[dev]"

Running Tests

All tests use uv run for consistency:

# Run all tests
uv run pytest tests/ -v

# Run with coverage
uv run pytest tests/ -v --cov=src --cov-report=term-missing

# Run specific test types
uv run pytest tests/test_cli.py -v                    # Unit tests only
uv run pytest tests/test_integration.py -v            # Integration tests only

Release Process

# Make your changes
uv version --bump patch
git add .
git commit -m "feat: your changes"
git push

# Create GitHub release (this triggers everything automatically)
gh release create v0.1.11 --generate-notes