Metadata-Version: 2.4
Name: llms-py
Version: 2.0.29
Summary: A lightweight CLI tool and OpenAI-compatible server for querying multiple Large Language Model (LLM) providers
Home-page: https://github.com/ServiceStack/llms
Author: ServiceStack
Author-email: ServiceStack <team@servicestack.net>
Maintainer-email: ServiceStack <team@servicestack.net>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/ServiceStack/llms
Project-URL: Documentation, https://github.com/ServiceStack/llms#readme
Project-URL: Repository, https://github.com/ServiceStack/llms
Project-URL: Bug Reports, https://github.com/ServiceStack/llms/issues
Keywords: llm,ai,openai,anthropic,google,gemini,groq,mistral,ollama,cli,server,chat,completion
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Internet :: WWW/HTTP :: HTTP Servers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: System :: Systems Administration
Classifier: Topic :: Utilities
Classifier: Environment :: Console
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: aiohttp
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# llms.py

Lightweight CLI, API and ChatGPT-like alternative to Open WebUI for accessing multiple LLMs, entirely offline, with all data kept private in browser storage.

Configure additional providers and models in [llms.json](llms/llms.json)
 - Mix and match local models with models from different API providers
 - Requests automatically routed to available providers that supports the requested model (in defined order)
 - Define free/cheapest/local providers first to save on costs
 - Any failures are automatically retried on the next available provider

## Features

- **Lightweight**: Single [llms.py](https://github.com/ServiceStack/llms/blob/main/llms/main.py) Python file with single `aiohttp` dependency (Pillow optional)
- **Multi-Provider Support**: OpenRouter, Ollama, Anthropic, Google, OpenAI, Grok, Groq, Qwen, Z.ai, Mistral
- **OpenAI-Compatible API**: Works with any client that supports OpenAI's chat completion API
- **Built-in Analytics**: Built-in analytics UI to visualize costs, requests, and token usage
- **Configuration Management**: Easy provider enable/disable and configuration management
- **CLI Interface**: Simple command-line interface for quick interactions
- **Server Mode**: Run an OpenAI-compatible HTTP server at `http://localhost:{PORT}/v1/chat/completions`
- **Image Support**: Process images through vision-capable models
  - Auto resizes and converts to webp if exceeds configured limits
- **Audio Support**: Process audio through audio-capable models
- **Custom Chat Templates**: Configurable chat completion request templates for different modalities
- **Auto-Discovery**: Automatically discover available Ollama models
- **Unified Models**: Define custom model names that map to different provider-specific names
- **Multi-Model Support**: Support for over 160+ different LLMs

## llms.py UI

Access all your local all remote LLMs with a single ChatGPT-like UI:

[![](https://servicestack.net/img/posts/llms-py-ui/bg.webp)](https://servicestack.net/posts/llms-py-ui)

#### Dark Mode Support

[![](https://servicestack.net/img/posts/llms-py-ui/dark-attach-image.webp?)](https://servicestack.net/posts/llms-py-ui)

#### Monthly Costs Analysis

[![](https://servicestack.net/img/posts/llms-py-ui/analytics-costs.webp)](https://servicestack.net/posts/llms-py-ui)

#### Monthly Token Usage (Dark Mode)

[![](https://servicestack.net/img/posts/llms-py-ui/dark-analytics-tokens.webp?)](https://servicestack.net/posts/llms-py-ui)

#### Monthly Activity Log

[![](https://servicestack.net/img/posts/llms-py-ui/analytics-activity.webp)](https://servicestack.net/posts/llms-py-ui)

[More Features and Screenshots](https://servicestack.net/posts/llms-py-ui).

#### Check Provider Reliability and Response Times

Check the status of configured providers to test if they're configured correctly, reachable and what their response times is for the simplest `1+1=` request:

```bash
# Check all models for a provider:
llms --check groq

# Check specific models for a provider:
llms --check groq kimi-k2 llama4:400b gpt-oss:120b
```

[![llms-check.webp](https://servicestack.net/img/posts/llms-py-ui/llms-check.webp)](https://servicestack.net/img/posts/llms-py-ui/llms-check.webp)

As they're a good indicator for the reliability and speed you can expect from different providers we've created a 
[test-providers.yml](https://github.com/ServiceStack/llms/actions/workflows/test-providers.yml) GitHub Action to
test the response times for all configured providers and models, the results of which will be frequently published to
[/checks/latest.txt](https://github.com/ServiceStack/llms/blob/main/docs/checks/latest.txt)

## Installation

### Using pip

```bash
pip install llms-py
```

- [Using Docker](#using-docker)

## Quick Start

### 1. Set API Keys

Set environment variables for the providers you want to use:

```bash
export OPENROUTER_API_KEY="..."
```

| Provider        | Variable                  | Description         | Example |
|-----------------|---------------------------|---------------------|---------|
| openrouter_free | `OPENROUTER_API_KEY` | OpenRouter FREE models API key | `sk-or-...` |
| groq            | `GROQ_API_KEY`            | Groq API key        | `gsk_...` |
| google_free     | `GOOGLE_FREE_API_KEY`     | Google FREE API key | `AIza...` |
| codestral       | `CODESTRAL_API_KEY`       | Codestral API key   | `...` |
| ollama          | N/A                       | No API key required | |
| openrouter      | `OPENROUTER_API_KEY`      | OpenRouter API key  | `sk-or-...` |
| google          | `GOOGLE_API_KEY`          | Google API key      | `AIza...` |
| anthropic       | `ANTHROPIC_API_KEY`       | Anthropic API key   | `sk-ant-...` |
| openai          | `OPENAI_API_KEY`          | OpenAI API key      | `sk-...` |
| grok            | `GROK_API_KEY`            | Grok (X.AI) API key | `xai-...` |
| qwen            | `DASHSCOPE_API_KEY`       | Qwen (Alibaba) API key | `sk-...` |
| z.ai            | `ZAI_API_KEY`             | Z.ai API key        | `sk-...` |
| mistral         | `MISTRAL_API_KEY`         | Mistral API key     | `...` |

### 2. Run Server

Start the UI and an OpenAI compatible API on port **8000**:

```bash
llms --serve 8000
```

Launches UI at `http://localhost:8000` and OpenAI Endpoint at `http://localhost:8000/v1/chat/completions`.

To see detailed request/response logging, add `--verbose`:

```bash
llms --serve 8000 --verbose
```

### Use llms.py CLI

```bash
llms "What is the capital of France?"
```

### Enable Providers

Any providers that have their API Keys set and enabled in `llms.json` are automatically made available.

Providers can be enabled or disabled in the UI at runtime next to the model selector, or on the command line:

```bash
# Disable free providers with free models and free tiers
llms --disable openrouter_free codestral google_free groq

# Enable paid providers
llms --enable openrouter anthropic google openai grok z.ai qwen mistral
```

## Using Docker

#### a) Simple - Run in a Docker container:

Run the server on port `8000`:

```bash
docker run -p 8000:8000 -e GROQ_API_KEY=$GROQ_API_KEY ghcr.io/servicestack/llms:latest
```

Get the latest version:

```bash
docker pull ghcr.io/servicestack/llms:latest
```

Use custom `llms.json` and `ui.json` config files outside of the container (auto created if they don't exist):

```bash
docker run -p 8000:8000 -e GROQ_API_KEY=$GROQ_API_KEY \
  -v ~/.llms:/home/llms/.llms \
  ghcr.io/servicestack/llms:latest
```

#### b) Recommended - Use Docker Compose:

Download and use [docker-compose.yml](https://raw.githubusercontent.com/ServiceStack/llms/refs/heads/main/docker-compose.yml):

```bash
curl -O https://raw.githubusercontent.com/ServiceStack/llms/refs/heads/main/docker-compose.yml
```

Update API Keys in `docker-compose.yml` then start the server:

```bash
docker-compose up -d
```

#### c) Build and run local Docker image from source:

```bash
git clone https://github.com/ServiceStack/llms

docker-compose -f docker-compose.local.yml up -d --build
```

After the container starts, you can access the UI and API at `http://localhost:8000`.


See [DOCKER.md](DOCKER.md) for detailed instructions on customizing configuration files.

## GitHub OAuth Authentication

llms.py supports optional GitHub OAuth authentication to secure your web UI and API endpoints. When enabled, users must sign in with their GitHub account before accessing the application.

```json
{
    "auth": {
        "enabled": true,
        "github": {
            "client_id": "$GITHUB_CLIENT_ID",
            "client_secret": "$GITHUB_CLIENT_SECRET",
            "redirect_uri": "http://localhost:8000/auth/github/callback",
            "restrict_to": "$GITHUB_USERS"
        }
    }
}
```

`GITHUB_USERS` is optional but if set will only allow access to the specified users.

See [GITHUB_OAUTH_SETUP.md](GITHUB_OAUTH_SETUP.md) for detailed setup instructions.

## Configuration

The configuration file [llms.json](llms/llms.json) is saved to `~/.llms/llms.json` and defines available providers, models, and default settings. Key sections:

### Defaults
- `headers`: Common HTTP headers for all requests
- `text`: Default chat completion request template for text prompts
- `image`: Default chat completion request template for image prompts
- `audio`: Default chat completion request template for audio prompts
- `file`: Default chat completion request template for file prompts
- `check`: Check request template for testing provider connectivity
- `limits`: Override Request size limits
- `convert`: Max image size and length limits and auto conversion settings

### Providers

Each provider configuration includes:
- `enabled`: Whether the provider is active
- `type`: Provider class (OpenAiProvider, GoogleProvider, etc.)
- `api_key`: API key (supports environment variables with `$VAR_NAME`)
- `base_url`: API endpoint URL
- `models`: Model name mappings (local name → provider name)
- `pricing`: Pricing per token (input/output) for each model
- `default_pricing`: Default pricing if not specified in `pricing`
- `check`: Check request template for testing provider connectivity

## Command Line Usage

### Basic Chat

```bash
# Simple question
llms "Explain quantum computing"

# With specific model
llms -m gemini-2.5-pro "Write a Python function to sort a list"
llms -m grok-4 "Explain this code with humor"
llms -m qwen3-max "Translate this to Chinese"

# With system prompt
llms -s "You are a helpful coding assistant" "How do I reverse a string in Python?"

# With image (vision models)
llms --image image.jpg "What's in this image?"
llms --image https://example.com/photo.png "Describe this photo"

# Display full JSON Response
llms "Explain quantum computing" --raw
```

### Using a Chat Template

By default llms uses the `defaults/text` chat completion request defined in [llms.json](llms/llms.json).

You can instead use a custom chat completion request with `--chat`, e.g:

```bash
# Load chat completion request from JSON file
llms --chat request.json

# Override user message
llms --chat request.json "New user message"

# Override model
llms -m kimi-k2 --chat request.json
```

Example `request.json`:

```json
{
  "model": "kimi-k2",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user",   "content": ""}
  ],
  "temperature": 0.7,
  "max_tokens": 150
}
```

### Image Requests

Send images to vision-capable models using the `--image` option:

```bash
# Use defaults/image Chat Template (Describe the key features of the input image)
llms --image ./screenshot.png

# Local image file
llms --image ./screenshot.png "What's in this image?"

# Remote image URL
llms --image https://example.org/photo.jpg "Describe this photo"

# Data URI
llms --image "data:image/png;base64,$(base64 -w 0 image.png)" "Describe this image"

# With a specific vision model
llms -m gemini-2.5-flash --image chart.png "Analyze this chart"
llms -m qwen2.5vl --image document.jpg "Extract text from this document"

# Combined with system prompt
llms -s "You are a data analyst" --image graph.png "What trends do you see?"

# With custom chat template
llms --chat image-request.json --image photo.jpg
```

Example of `image-request.json`:

```json
{
    "model": "qwen2.5vl",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": ""
                    }
                },
                {
                    "type": "text",
                    "text": "Caption this image"
                }
            ]
        }
    ]
}
```

**Supported image formats**: PNG, WEBP, JPG, JPEG, GIF, BMP, TIFF, ICO

**Image sources**:
- **Local files**: Absolute paths (`/path/to/image.jpg`) or relative paths (`./image.png`, `../image.jpg`)
- **Remote URLs**: HTTP/HTTPS URLs are automatically downloaded
- **Data URIs**: Base64-encoded images (`data:image/png;base64,...`)

Images are automatically processed and converted to base64 data URIs before being sent to the model.

### Vision-Capable Models

Popular models that support image analysis:
- **OpenAI**: GPT-4o, GPT-4o-mini, GPT-4.1
- **Anthropic**: Claude Sonnet 4.0, Claude Opus 4.1
- **Google**: Gemini 2.5 Pro, Gemini Flash
- **Qwen**: Qwen2.5-VL, Qwen3-VL, QVQ-max
- **Ollama**: qwen2.5vl, llava

Images are automatically downloaded and converted to base64 data URIs.

### Audio Requests

Send audio files to audio-capable models using the `--audio` option:

```bash
# Use defaults/audio Chat Template (Transcribe the audio)
llms --audio ./recording.mp3

# Local audio file
llms --audio ./meeting.wav "Summarize this meeting recording"

# Remote audio URL
llms --audio https://example.org/podcast.mp3 "What are the key points discussed?"

# With a specific audio model
llms -m gpt-4o-audio-preview --audio interview.mp3 "Extract the main topics"
llms -m gemini-2.5-flash --audio interview.mp3 "Extract the main topics"

# Combined with system prompt
llms -s "You're a transcription specialist" --audio talk.mp3 "Provide a detailed transcript"

# With custom chat template
llms --chat audio-request.json --audio speech.wav
```

Example of `audio-request.json`:

```json
{
    "model": "gpt-4o-audio-preview",
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": "",
                        "format": "mp3"
                    }
                },
                {
                    "type": "text",
                    "text": "Please transcribe this audio"
                }
            ]
        }
    ]
}
```

**Supported audio formats**: MP3, WAV

**Audio sources**:
- **Local files**: Absolute paths (`/path/to/audio.mp3`) or relative paths (`./audio.wav`, `../recording.m4a`)
- **Remote URLs**: HTTP/HTTPS URLs are automatically downloaded
- **Base64 Data**: Base64-encoded audio

Audio files are automatically processed and converted to base64 data before being sent to the model.

### Audio-Capable Models

Popular models that support audio processing:
- **OpenAI**: gpt-4o-audio-preview
- **Google**: gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite

Audio files are automatically downloaded and converted to base64 data URIs with appropriate format detection.

### File Requests

Send documents (e.g. PDFs) to file-capable models using the `--file` option:

```bash
# Use defaults/file Chat Template (Summarize the document)
llms --file ./docs/handbook.pdf

# Local PDF file
llms --file ./docs/policy.pdf "Summarize the key changes"

# Remote PDF URL
llms --file https://example.org/whitepaper.pdf "What are the main findings?"

# With specific file-capable models
llms -m gpt-5               --file ./policy.pdf   "Summarize the key changes"
llms -m gemini-flash-latest --file ./report.pdf   "Extract action items"
llms -m qwen2.5vl           --file ./manual.pdf   "List key sections and their purpose"

# Combined with system prompt
llms -s "You're a compliance analyst" --file ./policy.pdf "Identify compliance risks"

# With custom chat template
llms --chat file-request.json --file ./docs/handbook.pdf
```

Example of `file-request.json`:

```json
{
  "model": "gpt-5",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "file",
          "file": {
            "filename": "",
            "file_data": ""
          }
        },
        {
          "type": "text",
          "text": "Please summarize this document"
        }
      ]
    }
  ]
}
```

**Supported file formats**: PDF

Other document types may work depending on the model/provider.

**File sources**:
- **Local files**: Absolute paths (`/path/to/file.pdf`) or relative paths (`./file.pdf`, `../file.pdf`)
- **Remote URLs**: HTTP/HTTPS URLs are automatically downloaded
- **Base64/Data URIs**: Inline `data:application/pdf;base64,...` is supported

Files are automatically downloaded (for URLs) and converted to base64 data URIs before being sent to the model.

### File-Capable Models

Popular multi-modal models that support file (PDF) inputs:
- OpenAI: gpt-5, gpt-5-mini, gpt-4o, gpt-4o-mini
- Google: gemini-flash-latest, gemini-2.5-flash-lite
- Grok: grok-4-fast (OpenRouter)
- Qwen: qwen2.5vl, qwen3-max, qwen3-vl:235b, qwen3-coder, qwen3-coder-flash (OpenRouter)
- Others: kimi-k2, glm-4.5-air, deepseek-v3.1:671b, llama4:400b, llama3.3:70b, mai-ds-r1, nemotron-nano:9b

## Server Mode

Run as an OpenAI-compatible HTTP server:

```bash
# Start server on port 8000
llms --serve 8000
```

The server exposes a single endpoint:
- `POST /v1/chat/completions` - OpenAI-compatible chat completions

Example client usage:

```bash
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k2",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'
```

### Configuration Management

```bash
# List enabled providers and models
llms --list
llms ls

# List specific providers
llms ls ollama
llms ls google anthropic

# Enable providers
llms --enable openrouter
llms --enable anthropic google_free groq

# Disable providers
llms --disable ollama
llms --disable openai anthropic

# Set default model
llms --default grok-4
```

### Update

```bash
pip install llms-py --upgrade
```

### Advanced Options

```bash
# Use custom config file
llms --config /path/to/config.json "Hello"

# Get raw JSON response
llms --raw "What is 2+2?"

# Enable verbose logging
llms --verbose "Tell me a joke"

# Custom log prefix
llms --verbose --logprefix "[DEBUG] " "Hello world"

# Set default model (updates config file)
llms --default grok-4

# Pass custom parameters to chat request (URL-encoded)
llms --args "temperature=0.7&seed=111" "What is 2+2?"

# Multiple parameters with different types
llms --args "temperature=0.5&max_completion_tokens=50" "Tell me a joke"

# URL-encoded special characters (stop sequences)
llms --args "stop=Two,Words" "Count to 5"

# Combine with other options
llms --system "You are helpful" --args "temperature=0.3" --raw "Hello"
```

#### Custom Parameters with `--args`

The `--args` option allows you to pass URL-encoded parameters to customize the chat request sent to LLM providers:

**Parameter Types:**
- **Floats**: `temperature=0.7`, `frequency_penalty=0.2`
- **Integers**: `max_completion_tokens=100`
- **Booleans**: `store=true`, `verbose=false`, `logprobs=true`
- **Strings**: `stop=one`
- **Lists**: `stop=two,words`

**Common Parameters:**
- `temperature`: Controls randomness (0.0 to 2.0)
- `max_completion_tokens`: Maximum tokens in response
- `seed`: For reproducible outputs
- `top_p`: Nucleus sampling parameter
- `stop`: Stop sequences (URL-encode special chars)
- `store`: Whether or not to store the output
- `frequency_penalty`: Penalize new tokens based on frequency
- `presence_penalty`: Penalize new tokens based on presence
- `logprobs`: Include log probabilities in response
- `parallel_tool_calls`: Enable parallel tool calls
- `prompt_cache_key`: Cache key for prompt
- `reasoning_effort`: Reasoning effort (low, medium, high, *minimal, *none, *default)
- `safety_identifier`: A string that uniquely identifies each user
- `seed`: For reproducible outputs
- `service_tier`: Service tier (free, standard, premium, *default)
- `top_logprobs`: Number of top logprobs to return
- `top_p`: Nucleus sampling parameter
- `verbosity`: Verbosity level (0, 1, 2, 3, *default)
- `enable_thinking`: Enable thinking mode (Qwen)
- `stream`: Enable streaming responses

### Default Model Configuration

The `--default MODEL` option allows you to set the default model used for all chat completions. This updates the `defaults.text.model` field in your configuration file:

```bash
# Set default model to gpt-oss
llms --default gpt-oss:120b

# Set default model to Claude Sonnet
llms --default claude-sonnet-4-0

# The model must be available in your enabled providers
llms --default gemini-2.5-pro
```

When you set a default model:
- The configuration file (`~/.llms/llms.json`) is automatically updated
- The specified model becomes the default for all future chat requests
- The model must exist in your currently enabled providers
- You can still override the default using `-m MODEL` for individual requests

### Updating llms.py

```bash
pip install llms-py --upgrade
```

### Beautiful rendered Markdown

Pipe Markdown output to [glow](https://github.com/charmbracelet/glow) to beautifully render it in the terminal:

```bash
llms "Explain quantum computing" | glow
```

## Supported Providers

Any OpenAI-compatible providers and their models can be added by configuring them in [llms.json](./llms.json). By default only AI Providers with free tiers are enabled which will only be "available" if their API Key is set. 

You can list the available providers, their models and which are enabled or disabled with:

```bash
llms ls
```

They can be enabled/disabled in your `llms.json` file or with:

```bash
llms --enable <provider>
llms --disable <provider>
```

For a provider to be available, they also require their API Key configured in either your Environment Variables
or directly in your `llms.json`.

### Environment Variables

| Provider        | Variable                  | Description         | Example |
|-----------------|---------------------------|---------------------|---------|
| openrouter_free | `OPENROUTER_API_KEY` | OpenRouter FREE models API key | `sk-or-...` |
| groq            | `GROQ_API_KEY`            | Groq API key        | `gsk_...` |
| google_free     | `GOOGLE_FREE_API_KEY`     | Google FREE API key | `AIza...` |
| codestral       | `CODESTRAL_API_KEY`       | Codestral API key   | `...` |
| ollama          | N/A                       | No API key required | |
| openrouter      | `OPENROUTER_API_KEY`      | OpenRouter API key  | `sk-or-...` |
| google          | `GOOGLE_API_KEY`          | Google API key      | `AIza...` |
| anthropic       | `ANTHROPIC_API_KEY`       | Anthropic API key   | `sk-ant-...` |
| openai          | `OPENAI_API_KEY`          | OpenAI API key      | `sk-...` |
| grok            | `GROK_API_KEY`            | Grok (X.AI) API key | `xai-...` |
| qwen            | `DASHSCOPE_API_KEY`       | Qwen (Alibaba) API key | `sk-...` |
| z.ai            | `ZAI_API_KEY`             | Z.ai API key        | `sk-...` |
| mistral         | `MISTRAL_API_KEY`         | Mistral API key     | `...` |

### OpenAI
- **Type**: `OpenAiProvider`
- **Models**: GPT-5, GPT-5 Codex, GPT-4o, GPT-4o-mini, o3, etc.
- **Features**: Text, images, function calling

```bash
export OPENAI_API_KEY="your-key"
llms --enable openai
```

### Anthropic (Claude)
- **Type**: `OpenAiProvider`
- **Models**: Claude Opus 4.1, Sonnet 4.0, Haiku 3.5, etc.
- **Features**: Text, images, large context windows

```bash
export ANTHROPIC_API_KEY="your-key"
llms --enable anthropic
```

### Google Gemini
- **Type**: `GoogleProvider`
- **Models**: Gemini 2.5 Pro, Flash, Flash-Lite
- **Features**: Text, images, safety settings

```bash
export GOOGLE_API_KEY="your-key"
llms --enable google_free
```

### OpenRouter
- **Type**: `OpenAiProvider`
- **Models**: 100+ models from various providers
- **Features**: Access to latest models, free tier available

```bash
export OPENROUTER_API_KEY="your-key"
llms --enable openrouter
```

### Grok (X.AI)
- **Type**: `OpenAiProvider`
- **Models**: Grok-4, Grok-3, Grok-3-mini, Grok-code-fast-1, etc.
- **Features**: Real-time information, humor, uncensored responses

```bash
export GROK_API_KEY="your-key"
llms --enable grok
```

### Groq
- **Type**: `OpenAiProvider`
- **Models**: Llama 3.3, Gemma 2, Kimi K2, etc.
- **Features**: Fast inference, competitive pricing

```bash
export GROQ_API_KEY="your-key"
llms --enable groq
```

### Ollama (Local)
- **Type**: `OllamaProvider`
- **Models**: Auto-discovered from local Ollama installation
- **Features**: Local inference, privacy, no API costs

```bash
# Ollama must be running locally
llms --enable ollama
```

### Qwen (Alibaba Cloud)
- **Type**: `OpenAiProvider`
- **Models**: Qwen3-max, Qwen-max, Qwen-plus, Qwen2.5-VL, QwQ-plus, etc.
- **Features**: Multilingual, vision models, coding, reasoning, audio processing

```bash
export DASHSCOPE_API_KEY="your-key"
llms --enable qwen
```

### Z.ai
- **Type**: `OpenAiProvider`
- **Models**: GLM-4.6, GLM-4.5, GLM-4.5-air, GLM-4.5-x, GLM-4.5-airx, GLM-4.5-flash, GLM-4:32b
- **Features**: Advanced language models with strong reasoning capabilities

```bash
export ZAI_API_KEY="your-key"
llms --enable z.ai
```

### Mistral
- **Type**: `OpenAiProvider`
- **Models**: Mistral Large, Codestral, Pixtral, etc.
- **Features**: Code generation, multilingual

```bash
export MISTRAL_API_KEY="your-key"
llms --enable mistral
```

### Codestral
- **Type**: `OpenAiProvider`
- **Models**: Codestral
- **Features**: Code generation

```bash
export CODESTRAL_API_KEY="your-key"
llms --enable codestral
```

## Model Routing

The tool automatically routes requests to the first available provider that supports the requested model. If a provider fails, it tries the next available provider with that model.

Example: If both OpenAI and OpenRouter support `kimi-k2`, the request will first try OpenRouter (free), then fall back to Groq than OpenRouter (Paid) if requests fails.

## Configuration Examples

### Minimal Configuration

```json
{
  "defaults": {
    "headers": {"Content-Type": "application/json"},
    "text": {
      "model": "kimi-k2",
      "messages": [{"role": "user", "content": ""}]
    }
  },
  "providers": {
    "groq": {
      "enabled": true,
      "type": "OpenAiProvider",
      "base_url": "https://api.groq.com/openai",
      "api_key": "$GROQ_API_KEY",
      "models": {
        "llama3.3:70b": "llama-3.3-70b-versatile",
        "llama4:109b": "meta-llama/llama-4-scout-17b-16e-instruct",
        "llama4:400b": "meta-llama/llama-4-maverick-17b-128e-instruct",
        "kimi-k2": "moonshotai/kimi-k2-instruct-0905",
        "gpt-oss:120b": "openai/gpt-oss-120b",
        "gpt-oss:20b": "openai/gpt-oss-20b",
        "qwen3:32b": "qwen/qwen3-32b"
      }
    }
  }
}
```

### Multi-Provider Setup

```json
{
  "providers": {
    "openrouter": {
      "enabled": false,
      "type": "OpenAiProvider",
      "base_url": "https://openrouter.ai/api",
      "api_key": "$OPENROUTER_API_KEY",
      "models": {
        "grok-4": "x-ai/grok-4",
        "glm-4.5-air": "z-ai/glm-4.5-air",
        "kimi-k2": "moonshotai/kimi-k2",
        "deepseek-v3.1:671b": "deepseek/deepseek-chat",
        "llama4:400b": "meta-llama/llama-4-maverick"
      }
    },
    "anthropic": {
      "enabled": false,
      "type": "OpenAiProvider",
      "base_url": "https://api.anthropic.com",
      "api_key": "$ANTHROPIC_API_KEY",
      "models": {
        "claude-sonnet-4-0": "claude-sonnet-4-0"
      }
    },
    "ollama": {
      "enabled": false,
      "type": "OllamaProvider",
      "base_url": "http://localhost:11434",
      "models": {},
      "all_models": true
    }
  }
}
```

## Usage

    usage: llms [-h] [--config FILE] [-m MODEL] [--chat REQUEST] [-s PROMPT] [--image IMAGE] [--audio AUDIO] [--file FILE]
                [--args PARAMS] [--raw] [--list] [--check PROVIDER] [--serve PORT] [--enable PROVIDER] [--disable PROVIDER]
                [--default MODEL] [--init] [--root PATH] [--logprefix PREFIX] [--verbose]

    llms v2.0.24

    options:
      -h, --help            show this help message and exit
      --config FILE         Path to config file
      -m, --model MODEL     Model to use
      --chat REQUEST        OpenAI Chat Completion Request to send
      -s, --system PROMPT   System prompt to use for chat completion
      --image IMAGE         Image input to use in chat completion
      --audio AUDIO         Audio input to use in chat completion
      --file FILE           File input to use in chat completion
      --args PARAMS         URL-encoded parameters to add to chat request (e.g. "temperature=0.7&seed=111")
      --raw                 Return raw AI JSON response
      --list                Show list of enabled providers and their models (alias ls provider?)
      --check PROVIDER      Check validity of models for a provider
      --serve PORT          Port to start an OpenAI Chat compatible server on
      --enable PROVIDER     Enable a provider
      --disable PROVIDER    Disable a provider
      --default MODEL       Configure the default model to use
      --init                Create a default llms.json
      --root PATH           Change root directory for UI files
      --logprefix PREFIX    Prefix used in log messages
      --verbose             Verbose output

## Docker Deployment

### Quick Start with Docker

The easiest way to run llms-py is using Docker:

```bash
# Using docker-compose (recommended)
docker-compose up -d

# Or pull and run directly
docker run -p 8000:8000 \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest
```

### Docker Images

Pre-built Docker images are automatically published to GitHub Container Registry:

- **Latest stable**: `ghcr.io/servicestack/llms:latest`
- **Specific version**: `ghcr.io/servicestack/llms:v2.0.24`
- **Main branch**: `ghcr.io/servicestack/llms:main`

### Environment Variables

Pass API keys as environment variables:

```bash
docker run -p 8000:8000 \
  -e OPENROUTER_API_KEY="sk-or-..." \
  -e GROQ_API_KEY="gsk_..." \
  -e GOOGLE_FREE_API_KEY="AIza..." \
  -e ANTHROPIC_API_KEY="sk-ant-..." \
  -e OPENAI_API_KEY="sk-..." \
  ghcr.io/servicestack/llms:latest
```

### Using docker-compose

Create a `docker-compose.yml` file (or use the one in the repository):

```yaml
version: '3.8'

services:
  llms:
    image: ghcr.io/servicestack/llms:latest
    ports:
      - "8000:8000"
    environment:
      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY}
      - GROQ_API_KEY=${GROQ_API_KEY}
      - GOOGLE_FREE_API_KEY=${GOOGLE_FREE_API_KEY}
    volumes:
      - llms-data:/home/llms/.llms
    restart: unless-stopped

volumes:
  llms-data:
```

Create a `.env` file with your API keys:

```bash
OPENROUTER_API_KEY=sk-or-...
GROQ_API_KEY=gsk_...
GOOGLE_FREE_API_KEY=AIza...
```

Start the service:

```bash
docker-compose up -d
```

### Building Locally

Build the Docker image from source:

```bash
# Using the build script
./docker-build.sh

# Or manually
docker build -t llms-py:latest .

# Run your local build
docker run -p 8000:8000 \
  -e OPENROUTER_API_KEY="your-key" \
  llms-py:latest
```

### Volume Mounting

To persist configuration and analytics data between container restarts:

```bash
# Using a named volume (recommended)
docker run -p 8000:8000 \
  -v llms-data:/home/llms/.llms \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest

# Or mount a local directory
docker run -p 8000:8000 \
  -v $(pwd)/llms-config:/home/llms/.llms \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest
```

### Custom Configuration Files

Customize llms-py behavior by providing your own `llms.json` and `ui.json` files:

**Option 1: Mount a directory with custom configs**

```bash
# Create config directory with your custom files
mkdir -p config
# Add your custom llms.json and ui.json to config/

# Mount the directory
docker run -p 8000:8000 \
  -v $(pwd)/config:/home/llms/.llms \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest
```

**Option 2: Mount individual config files**

```bash
docker run -p 8000:8000 \
  -v $(pwd)/my-llms.json:/home/llms/.llms/llms.json:ro \
  -v $(pwd)/my-ui.json:/home/llms/.llms/ui.json:ro \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest
```

**With docker-compose:**

```yaml
volumes:
  # Use local directory
  - ./config:/home/llms/.llms

  # Or mount individual files
  # - ./my-llms.json:/home/llms/.llms/llms.json:ro
  # - ./my-ui.json:/home/llms/.llms/ui.json:ro
```

The container will auto-create default config files on first run if they don't exist. You can customize these to:
- Enable/disable specific providers
- Add or remove models
- Configure API endpoints
- Set custom pricing
- Customize chat templates
- Configure UI settings

See [DOCKER.md](DOCKER.md) for detailed configuration examples.

### Custom Port

Change the port mapping to run on a different port:

```bash
# Run on port 3000 instead of 8000
docker run -p 3000:8000 \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest
```

### Docker CLI Usage

You can also use the Docker container for CLI commands:

```bash
# Run a single query
docker run --rm \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest \
  llms "What is the capital of France?"

# List available models
docker run --rm \
  -e OPENROUTER_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest \
  llms --list

# Check provider status
docker run --rm \
  -e GROQ_API_KEY="your-key" \
  ghcr.io/servicestack/llms:latest \
  llms --check groq
```

### Health Checks

The Docker image includes a health check that verifies the server is responding:

```bash
# Check container health
docker ps

# View health check logs
docker inspect --format='{{json .State.Health}}' llms-server
```

### Multi-Architecture Support

The Docker images support multiple architectures:
- `linux/amd64` (x86_64)
- `linux/arm64` (ARM64/Apple Silicon)

Docker will automatically pull the correct image for your platform.

## Troubleshooting

### Common Issues

**Config file not found**
```bash
# Initialize default config
llms --init

# Or specify custom path
llms --config ./my-config.json
```

**No providers enabled**

```bash
# Check status
llms --list

# Enable providers
llms --enable google anthropic
```

**API key issues**
```bash
# Check environment variables
echo $ANTHROPIC_API_KEY

# Enable verbose logging
llms --verbose "test"
```

**Model not found**

```bash
# List available models
llms --list

# Check provider configuration
llms ls openrouter
```

### Debug Mode

Enable verbose logging to see detailed request/response information:

```bash
llms --verbose --logprefix "[DEBUG] " "Hello"
```

This shows:
- Enabled providers
- Model routing decisions
- HTTP request details
- Error messages with stack traces

## Development

### Project Structure

- `llms/main.py` - Main script with CLI and server functionality
- `llms/llms.json` - Default configuration file
- `llms/ui.json` - UI configuration file
- `requirements.txt` - Python dependencies, required: `aiohttp`, optional: `Pillow`

### Provider Classes

- `OpenAiProvider` - Generic OpenAI-compatible provider
- `OllamaProvider` - Ollama-specific provider with model auto-discovery
- `GoogleProvider` - Google Gemini with native API format
- `GoogleOpenAiProvider` - Google Gemini via OpenAI-compatible endpoint

### Adding New Providers

1. Create a provider class inheriting from `OpenAiProvider`
2. Implement provider-specific authentication and formatting
3. Add provider configuration to `llms.json`
4. Update initialization logic in `init_llms()`

## Contributing

Contributions are welcome! Please submit a PR to add support for any missing OpenAI-compatible providers.
