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Maximalist"},"license":"MIT","homepage":"https://llmjs.themaximalist.com","keywords":["llm","large","language","model","gpt"],"repository":{"type":"git","url":"git+https://github.com/themaximal1st/llm.js.git"},"description":"One interface to hundreds of LLM models, zero dependencies, tons of features, for Browser and Node.js","maintainers":[{"name":"themaximalist","email":"hello@themaximalist.com"}],"readme":"## LLM.js\n\n<img src=\"public/logo.png\" alt=\"LLM.js — Simple LLM library for JavaScript\" class=\"logo\" style=\"max-width: 400px\" />\n\n<div class=\"badges\" style=\"text-align: center; margin-top: -10px;\">\n<a href=\"https://github.com/themaximal1st/llm.js\"><img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/themaximal1st/llm.js\"></a>\n<a href=\"https://www.npmjs.com/package/@themaximalist/llm.js\"><img alt=\"NPM Downloads\" src=\"https://img.shields.io/npm/dt/%40themaximalist%2Fllm.js\"></a>\n<a href=\"https://github.com/themaximal1st/llm.js\"><img alt=\"GitHub code size in bytes\" src=\"https://img.shields.io/github/languages/code-size/themaximal1st/llm.js\"></a>\n<a href=\"https://github.com/themaximal1st/llm.js\"><img alt=\"GitHub License\" src=\"https://img.shields.io/github/license/themaximal1st/llm.js\"></a>\n</div>\n<br />\n\n**LLM.js** is a zero-dependency library to hundreds of Large Language Models.\n\nIt works in Node.js and the browser and supports all the important features for production-ready LLM apps.\n\n\n```javascript\nawait LLM(\"the color of the sky is\"); // blue\n```\n\n* Same interface for hundreds of LLMs (`OpenAI`, `Google`, `Anthropic`, `Groq`, `Ollama`, `xAI`, `DeepSeek`)\n* [Chat](#chat) using message history\n* [Stream](#streaming) responses instantly (including with thinking, tools, parsers)\n* [Thinking](#thinking) with reasoning models\n* [Tools](#tools) to call custom functions\n* [Attachments](#attachments) to send images, documents, and other files\n* [Parsers](#parsers) including `JSON`, `XML`, `codeBlock`\n* [Token Usage](#token-usage) input and output tokens on every request\n* [Model List](#models) for dynamic up-to-date list of latest models\n* [Cost Usage](#cost-usage) on every request\n* [Options](#options) for controlling `temperature`, `max_tokens`, ...\n* Abort requests mid-response\n* TypeScript with clean code\n* [Tests](https://github.com/themaximalist/llm.js/tree/main/test) with good coverage\n* Node.js and Browser supported\n* Zero-dependencies\n* MIT license\n\n## Why use LLM.js?\n\nWhy not just use the OpenAI compability API and switch out baseUrl?\n\n1. The compability API is not compatible — there are many differences between services\n2. The best features aren't on the compability API\n3. There's no support for cost tracking or model features\n\nLLM.js solves all of these and more, letting you focus on building great AI apps.\n\n## Install\n\nInstall `LLM.js` from NPM:\n\n```bash\nnpm install @themaximalist/llm.js\n```\n\nSetting up LLM.js is easy.\n\nIn Node.js api keys can be detected automatically from the environment.\n\n```bash\nexport OPENAI_API_KEY=...\nexport ANTHROPIC_API_KEY=...\nexport GOOGLE_API_KEY=...\nexport GROQ_API_KEY=...\nexport DEEPSEEK_API_KEY=...\nexport XAI_API_KEY=...\n```\n\nThey can also be included as an <a href=\"#options\">option</a> `{apiKey: \"sk-123\"}`.\n\nFor the browser, keys should be included as an option.\n\nFor local models like [Ollama](https://ollama.com/), no API key is needed, just ensure an instance is running.\n\n## Getting Started\n\nThe simplest way to call `LLM.js` is as an [async function](/docs/interfaces/LLMInterface.html) that returns a `string`.\n\n```javascript\nimport LLM from \"@themaximalist/llm.js\"\nawait LLM(\"hello\"); // Response: hi\n```\n\nThis fires a one-off request, and doesn't store any history.\n\n## Chat\n\nInitialize an LLM instance to build up message history for [chat](/docs/classes/LLM.html#chat).\n\n```javascript\nconst llm = new LLM();\nawait llm.chat(\"what's the color of the sky in hex value?\"); // #87CEEB\nawait llm.chat(\"what about at night time?\"); // #222d5a\n```\n\nAssistant responses are added automatically.\n\nYou can also enable the `extended` option to return more information about the request.\n\n```javascript\nconst response = await LLM(\"what are the primary colors?\", { extended: true });\nconsole.log(response.content);   // \"The primary colors are red, blue, and yellow\"\nconsole.log(response.usage);     // { input_tokens: 6, output_tokens: 12, total_cost: 0.0001 }\nconsole.log(response.service);   // \"ollama\" \nconsole.log(response.messages);  // Full conversation history\n```\n\n## Streaming\n\n[Streaming](/docs/interfaces/Options.html#stream) provides a better user experience by returning results immediately, and it's as simple as passing `{stream: true}` as an option.\n\n```javascript\nconst stream = await LLM(\"the color of the sky is\", { stream: true });\nfor await (const message of stream) {\n    process.stdout.write(message);\n}\n```\n\nYou can also stream with message history.\n\n```javascript\nconst llm = new LLM({ stream: false });\nllm.system(\"You are a friendly AI assistant\");\nconst stream = await llm.chat(\"hello, how are you?\", { stream: true });\nfor await (const chunk of stream) {\n  process.stdout.write(chunk);\n}\n```\n\nJust like with chat, assistant responses are automatically added with streaming. The `extended` option works as expected too.\n\n```javascript\nconst response = await LLM(\"tell me a story\", { \n  stream: true, \n  extended: true \n});\n\n// Stream the response in real-time\nfor await (const chunk of response.stream) {\n  if (chunk.type === \"content\") {\n    process.stdout.write(chunk.content);\n  }\n}\n```\n\nAfter a stream is complete, you can call `complete()` to get the complete response with metadata, including the final result, token usage, cost, etc...\n\n\n```javascript\n// Get the complete response with metadata\nconst complete = await response.complete();\nconsole.log(complete.usage.total_cost); // 0.0023\n```\n\n## Thinking\n\nEnable [thinking mode](/docs/interfaces/Options.html#think) for models that can reason through problems step-by-step:\n\n```javascript\nconst response = await LLM(\"solve this math problem: 2x + 5 = 13\", { \n  think: true,\n});\n\n// thinking automatically enables extended mode\n\n// {\n//   thinking: \"I need to solve for x. First, I'll subtract 5 from both sides...\",\n//   content: \"x = 4\",\n//   ...\n// }\n```\n\nThinking also works with streaming for real-time reasoning:\n\n```javascript\nconst response = await LLM(\"explain quantum physics\", { \n  think: true,\n  stream: true \n});\n\nlet thinking = \"\", content = \"\";\nfor await (const chunk of response.stream) {\n  if (chunk.type === \"thinking\") {\n    thinking += chunk.content;\n    updateThinkingUI(thinking)\n  } else if (chunk.type === \"content\") {\n    content += chunk.content;\n    updateContentUI(content)\n  }\n}\n\n// thinking and content are done, can ask for completed response\nconst complete = await response.complete();\n// {\n//   thinking: \"I need to solve for x. First, I'll subtract 5 from both sides...\",\n//   content: \"x = 4\",\n//   ...\n// }\n```\n\n## Tools\n\nEnable LLMs to call custom functions with [tool support](/docs/interfaces/Options.html#tools):\n\n```javascript\nconst getCurrentWeather = {\n  name: \"get_current_weather\",\n  description: \"Get the current weather for a city\",\n  input_schema: {\n    type: \"object\",\n    properties: {\n      city: { type: \"string\", description: \"The name of the city\" }\n    },\n    required: [\"city\"]\n  }\n};\n\nconst response = await LLM(\"What's the weather in Tokyo?\", {\n  tools: [getCurrentWeather],\n});\n\n// tool use automatically enables extended mode\n\nconsole.log(response.tool_calls); \n// [{ id: \"call_123\", name: \"get_current_weather\", input: { city: \"Tokyo\" } }]\n```\n\nTools work with streaming for real-time function calling:\n\n```javascript\nconst response = await LLM(\"What's the weather in Tokyo?\", {\n  tools: [getCurrentWeather],\n  stream: true\n});\n\nfor await (const chunk of response.stream) {\n  if (chunk.type === \"tool_calls\") {\n    console.log(\"🔧 Tool called:\", chunk.content);\n  } else if (chunk.type === \"content\") {\n    process.stdout.write(chunk.content); // sometimes LLMs will return content with tool calls\n  } else if (chunk.type === \"thinking\") {\n    process.stdout.write(chunk.content); // with `think: true` they'll also return thinking\n  }\n}\n\nconst completed = await response.complete();\nconsole.log(\"Final result:\", completed.tool_calls);\n```\n\nTool calls are automatically added to message history, making multi-turn tool conversations seamless.\n\n## Parsers\n\n`LLM.js` ships with helpful [parsers](/docs/modules/parsers.html) that work with every LLM:\n\n```javascript\n// JSON Parsing\nconst colors = await LLM(\"Please return the primary colors in a JSON array\", {\n  parser: LLM.parsers.json\n});\n// [\"red\", \"green\", \"blue\"]\n\n// JSON Mode (automatic JSON formatting + parsing)\nconst data = await LLM(\"Return the primary colors as a JSON object\", {\n  json: true\n});\n// { colors: [\"red\", \"green\", \"blue\"] }\n\n// Markdown Code Block Parsing  \nconst story = await LLM(\"Please return a story wrapped in a Markdown story code block\", {\n  parser: LLM.parsers.codeBlock(\"story\")\n});\n// A long time ago...\n\n// XML Parsing\nconst code = await LLM(\"Please write HTML and put it inside <WEBSITE></WEBSITE> tags\", {\n  parser: LLM.parsers.xml(\"WEBSITE\")                       \n});\n// <html>...\n```\n\nParsers work seamlessly with streaming, thinking and extended responses.\n\n```javascript\nconst response = await LLM(\"return a JSON object in the form of {color: '...'} containing the color of the sky in english. no other text\", {\n  stream: true,\n  think: true,\n  json: true,\n  extended: true, // implied automatically from `think: true`\n});\n\nfor await (const chunk of response.stream) {\n  if (chunk.type === \"content\") {\n    process.stdout.write(chunk.content);\n  } else if (chunk.type === \"thinking\") {\n    process.stdout.write(chunk.content);\n  }\n}\n\nconst completed = await response.complete();\n// { content: { color: \"blue\" } }\n```\n\n## Attachments\n\nSend images, documents, and other files alongside your prompts using [attachments](/docs/classes/Attachment.html):\n\n```javascript\n// Image from base64 data\nconst data = fs.readFileSync(\"file.jpg\", \"base64\");\nconst image = LLM.Attachment.fromJPEG(data);\n\nconst response = await LLM(\"What's in this image?\", { attachments: [image] });\n```\n\nCreate attachments from different sources:\n\n```javascript\n// From base64 data\nconst jpeg = LLM.Attachment.fromJPEG(base64Data);\nconst pdf = LLM.Attachment.fromPDF(base64Data);\n\n// From image URL\nconst image = LLM.Attachment.fromImageURL(\"https://example.com/image.jpg\");\n\n// Use with chat\nconst llm = new LLM();\nawait llm.chat(\"Describe this image\", { attachments: [jpeg] });\nawait llm.chat(\"What color is the main object?\"); // References previous image\n```\n\nAttachments work seamlessly with streaming:\n\n```javascript\nconst response = await LLM(\"Analyze this document\", { \n  attachments: [pdf],\n  stream: true \n});\n\nfor await (const chunk of response) {\n  process.stdout.write(chunk);\n}\n```\n\n**Note:** Attachment support varies by service. Images are widely supported, Documents (PDF) and Images from URLs are supported by some.\n\n## Token Usage\n\nEvery `extended` request automatically tracks [input and output tokens](/docs/interfaces/Usage.html):\n\n```javascript\nconst response = await LLM(\"explain quantum physics\", { extended: true });\nconsole.log(response.usage.input_tokens);  // 3\nconsole.log(response.usage.output_tokens); // 127\nconsole.log(response.usage.total_tokens);  // 130\n```\n\nToken counting works with all features including streaming, thinking, and tools.\n\n```javascript\nconst response = await LLM(\"explain quantum physics\", { \n  stream: true,\n  extended: true,\n});\n\nfor await (const chunk of response.stream) {\n  // ...\n}\n\nconst complete = await response.complete();\n// {\n//   usage: {\n//     input_tokens: 3,\n//     output_tokens: 127,\n//     total_tokens: 130,\n//     ...\n//   }\n// }\n```\n\n## Cost Usage\n\nEvery `extended` request automatically tracks [cost](#model-features-and-cost) based on current model pricing:\n\n```javascript\nconst response = await LLM(\"write a haiku\", { \n  service: \"openai\",\n  model: \"gpt-4o-mini\",\n  extended: true \n});\n// {\n//   usage: {\n//     input_cost: 0.000045,\n//     output_cost: 0.000234,\n//     total_cost: 0.000279,\n//     ...\n//   }\n// }\n```\n\nCost usage works with all features including streaming, thinking, and tools.\n\n```javascript\nconst response = await LLM(\"explain quantum physics\", { \n  stream: true,\n  extended: true,\n});\n\nfor await (const chunk of response.stream) {\n  // ...\n}\n\nconst complete = await response.complete();\n// {\n//   usage: {\n//     input_cost: 0.000045,\n//     output_cost: 0.000234,\n//     total_cost: 0.000279,\n//     ...\n//   }\n// }\n```\n\n\nLocal models (like Ollama) show `$0` cost and are marked as `local: true`.\n\n## System Prompts\n\nTell models to specialize at specific tasks using [llm.system(input)](/docs/classes/LLM.html#system).\n\n```javascript\nconst llm = new LLM();\nllm.system(\"You are a friendly chat bot.\");\nawait llm.chat(\"what's the color of the sky in hex value?\"); // Response: sky blue\nawait llm.chat(\"what about at night time?\"); // Response: darker value (uses previous context)\n```\n\n## Message History\n\n`LLM.js` supports simple string prompts, but also full [message history](/docs/interfaces/Message.html):\n\n```javascript\nawait LLM(\"hello\"); // hi\n\nawait LLM([\n    { role: \"user\", content: \"remember the secret codeword is blue\" },\n    { role: \"assistant\", content: \"OK I will remember\" },\n    { role: \"user\", content: \"what is the secret codeword I just told you?\" },\n]); // blue\n```\n\n## Options\n\n`LLM.js` provides comprehensive [configuration options](/docs/interfaces/Options.html) for all scenarios:\n\n```javascript\nconst llm = new LLM(input, {\n  service: \"openai\",        // LLM service provider\n  apiKey: \"sk-123\"          // apiKey\n  model: \"gpt-4o\",          // Specific model\n  max_tokens: 1000,         // Maximum response length\n  temperature: 0.7,         // \"Creativity\" (0-2)\n  stream: true,             // Enable streaming\n  extended: true,           // Extended responses with metadata\n  messages: [],             // message history\n  think: true,              // Enable thinking mode\n  parser: LLM.parsers.json, // Content parser\n  tools: [...],             // Available tools\n  max_thinking_tokens: 500, // Max tokens for thinking\n});\n```\n\n**Key Options:**\n\n* **`service`**: Provider (`openai`, `anthropic`, `google`, `xai`, `groq`, `deepseek`, `ollama`)\n* **`apiKey`**: API key for service, if not specified attempts to read from environment\n* **`model`**: Specific model name (auto-detected from service if not provided)\n* **`stream`**: Enable real-time streaming responses\n* **`extended`**: Return detailed response with usage, costs, and metadata\n* **`think`**: Enable reasoning mode for supported models\n* **`temperature`**: Controls randomness (0 = deterministic, 2 = very creative)\n* **`max_tokens`**: Maximum response length\n* **`parser`**: Transform response content (JSON, XML, codeBlock, etc.)\n* **`tools`**: Functions the model can call\n\n## Models\n\n`LLM.js` handles everything needed to quickly switch between and manage models from difference LLM services.\n\n* A single interface to every model\n* Fetching the latest models\n* Fetching the latest features and cost data\n* Quality filtering to return best models\n\n### Switch Models\n\n`LLM.js` supports most popular Large Language Models across both local and remote providers:\n\n```javascript\n// Defaults to Ollama (local)\nawait LLM(\"the color of the sky is\");\n\n// OpenAI\nawait LLM(\"the color of the sky is\", { model: \"gpt-4o-mini\", service: \"openai\" });\n\n// Anthropic\nawait LLM(\"the color of the sky is\", { model: \"claude-3-5-sonnet-latest\", service: \"anthropic\" });\n\n// Google\nawait LLM(\"the color of the sky is\", { model: \"gemini-1.5-pro\", service: \"google\" });\n\n// xAI\nawait LLM(\"the color of the sky is\", { service: \"xai\", model: \"grok-beta\" });\n\n// DeepSeek with thinking\nawait LLM(\"solve this puzzle\", { service: \"deepseek\", model: \"deepseek-reasoner\", think: true });\n\n// Ollama (local)\nawait LLM(\"the color of the sky is\", { model: \"llama3.2:3b\", service: \"ollama\" });\n```\n\nAll features work the same whether local or remote, with automatic token and cost tracking. Local models track token usage, but cost is always $0.\n\n\n### Fetch Latest Models\n\nGet the [latest available models](/docs/classes/LLM.html#fetchmodels) directly from providers:\n\n```javascript\nconst llm = new LLM({ service: \"openai\" });\nconst models = await llm.fetchModels();\n\nconsole.log(models.length); // 50+ models\nconsole.log(models[0]);     // { name: \"gpt-4o\", created: Date, service: \"openai\", ... }\n```\n\nHere's an example of the models available with the [Quality Filter](#quality-models).\n\n<div style=\"font-size: 14px; color: #888; max-height: 400px; overflow-y: auto; line-height: 1.2; font-family: monospace; background-color: #f5f5f5; padding: 10px; border-radius: 5px;\">\n<div>anthropic/claude-opus-4-20250514</div>\n<div>anthropic/claude-sonnet-4-20250514</div>\n<div>anthropic/claude-3-7-sonnet-20250219</div>\n<div>anthropic/claude-3-5-sonnet-20241022</div>\n<div>anthropic/claude-3-5-haiku-20241022</div>\n<div>anthropic/claude-3-5-sonnet-20240620</div>\n<div>anthropic/claude-3-haiku-20240307</div>\n<div>anthropic/claude-3-opus-20240229</div>\n<div>anthropic/claude-3-sonnet-20240229</div>\n<div>ollama/deepseek-r1:8b</div>\n<div>ollama/mistral-small:latest</div>\n<div>ollama/phi4:latest</div>\n<div>ollama/llama3.2-vision:latest</div>\n<div>ollama/llava-llama3:latest</div>\n<div>ollama/bakllava:latest</div>\n<div>ollama/minicpm-v:latest</div>\n<div>ollama/llava:latest</div>\n<div>ollama/llava-phi3:latest</div>\n<div>ollama/moondream:latest</div>\n<div>ollama/granite3.2-vision:latest</div>\n<div>ollama/phi4-mini:latest</div>\n<div>ollama/gemma3:12b</div>\n<div>ollama/gemma3:4b</div>\n<div>ollama/llama3.3:latest</div>\n<div>ollama/llama3.2:latest</div>\n<div>ollama/llama3.2:1b</div>\n<div>ollama/llama3-gradient:latest</div>\n<div>ollama/llama3:latest</div>\n<div>ollama/llama2:7b</div>\n<div>ollama/llama2:latest</div>\n<div>openai/gpt-4-0613</div>\n<div>openai/gpt-4</div>\n<div>openai/gpt-3.5-turbo</div>\n<div>openai/gpt-4.1-nano</div>\n<div>openai/gpt-4-1106-preview</div>\n<div>openai/gpt-3.5-turbo-1106</div>\n<div>openai/gpt-4-0125-preview</div>\n<div>openai/gpt-4-turbo-preview</div>\n<div>openai/gpt-3.5-turbo-0125</div>\n<div>openai/gpt-4-turbo</div>\n<div>openai/gpt-4-turbo-2024-04-09</div>\n<div>openai/gpt-4o</div>\n<div>openai/gpt-4o-2024-05-13</div>\n<div>openai/gpt-4o-mini-2024-07-18</div>\n<div>openai/gpt-4o-mini</div>\n<div>openai/gpt-4o-2024-08-06</div>\n<div>openai/chatgpt-4o-latest</div>\n<div>openai/o1-preview-2024-09-12</div>\n<div>openai/o1-preview</div>\n<div>openai/o1-mini-2024-09-12</div>\n<div>openai/o1-mini</div>\n<div>openai/o1-2024-12-17</div>\n<div>openai/o1</div>\n<div>openai/o3-mini</div>\n<div>openai/o3-mini-2025-01-31</div>\n<div>openai/gpt-4o-2024-11-20</div>\n<div>openai/gpt-4.5-preview</div>\n<div>openai/gpt-4.5-preview-2025-02-27</div>\n<div>openai/gpt-4o-search-preview-2025-03-11</div>\n<div>openai/gpt-4o-search-preview</div>\n<div>openai/gpt-4o-mini-search-preview-2025-03-11</div>\n<div>openai/gpt-4o-mini-search-preview</div>\n<div>openai/o1-pro-2025-03-19</div>\n<div>openai/o1-pro</div>\n<div>openai/o3-2025-04-16</div>\n<div>openai/o4-mini-2025-04-16</div>\n<div>openai/o3</div>\n<div>openai/o4-mini</div>\n<div>openai/gpt-4.1-2025-04-14</div>\n<div>openai/gpt-4.1</div>\n<div>openai/gpt-4.1-mini-2025-04-14</div>\n<div>openai/gpt-4.1-mini</div>\n<div>openai/gpt-4.1-nano-2025-04-14</div>\n<div>openai/gpt-3.5-turbo-16k</div>\n<div>google/gemini-2.5-pro-exp-03-25</div>\n<div>google/gemini-2.5-pro-preview-03-25</div>\n<div>google/gemini-2.5-flash-preview-04-17</div>\n<div>google/gemini-2.5-flash-preview-05-20</div>\n<div>google/gemini-2.5-flash-preview-04-17-thinking</div>\n<div>google/gemini-2.5-pro-preview-05-06</div>\n<div>google/gemini-2.5-pro-preview-06-05</div>\n<div>google/gemini-2.0-flash-exp</div>\n<div>google/gemini-2.0-flash</div>\n<div>google/gemini-2.0-flash-001</div>\n<div>google/gemini-2.0-flash-lite-001</div>\n<div>google/gemini-2.0-flash-lite</div>\n<div>google/gemini-2.0-flash-lite-preview-02-05</div>\n<div>google/gemini-2.0-flash-lite-preview</div>\n<div>google/gemini-2.0-pro-exp</div>\n<div>google/gemini-2.0-pro-exp-02-05</div>\n<div>google/gemini-exp-1206</div>\n<div>google/gemini-2.0-flash-thinking-exp-01-21</div>\n<div>google/gemini-2.0-flash-thinking-exp</div>\n<div>google/gemini-2.0-flash-thinking-exp-1219</div>\n<div>google/gemini-2.5-flash-preview-tts</div>\n<div>google/gemini-2.5-pro-preview-tts</div>\n<div>xai/grok-2-1212</div>\n<div>xai/grok-3</div>\n<div>xai/grok-3-fast</div>\n<div>xai/grok-3-mini</div>\n<div>xai/grok-3-mini-fast</div>\n<div>groq/llama3-8b-8192</div>\n<div>groq/compound-beta</div>\n<div>groq/llama-3.1-8b-instant</div>\n<div>groq/qwen-qwq-32b</div>\n<div>groq/gemma2-9b-it</div>\n<div>groq/deepseek-r1-distill-llama-70b</div>\n<div>groq/allam-2-7b</div>\n<div>groq/meta-llama/llama-4-maverick-17b-128e-instruct</div>\n<div>groq/meta-llama/llama-prompt-guard-2-86m</div>\n<div>groq/llama3-70b-8192</div>\n<div>groq/llama-guard-3-8b</div>\n<div>groq/meta-llama/llama-prompt-guard-2-22m</div>\n<div>groq/compound-beta-mini</div>\n<div>groq/meta-llama/llama-guard-4-12b</div>\n<div>groq/meta-llama/llama-4-scout-17b-16e-instruct</div>\n<div>groq/mistral-saba-24b</div>\n<div>groq/llama-3.3-70b-versatile</div>\n<div>deepseek/deepseek-chat</div>\n<div>deepseek/deepseek-reasoner</div>\n</div>\n\n### Model Features and Cost\n\n`LLM.js` combines the fetched models from each provider, with the [feature and cost list](/docs/classes/ModelUsage.html) from [LiteLLM](https://litellm.ai/).\n\nThis provides real-time cost per input/output token, and model features like context window, tool support, thinking support, and more!\n\n```javascript\nimport { ModelUsage } from \"@themaximalist/llm.js\";\n\n// Get all cached models\nconst allModels = ModelUsage.getAll();\nconsole.log(allModels.length); // 100+\n\n// Refresh from latest sources  \nconst refreshedModels = await ModelUsage.refresh();\nconsole.log(refreshedModels.length); // Even more models\n\n// Get specific model info\nconst gpt4 = ModelUsage.get(\"openai\", \"gpt-4o\");\nconsole.log(gpt4.input_cost_per_token);  // 0.0000025\nconsole.log(gpt4.max_input_tokens);      // 128000\n```\n\nWhen using the `extended` option — token usage and cost are automatically added to responses.\n\n\n### Quality Models\n\nThe model APIs return every model supported by the platform. If you need to present these to users — it's a mess.\n\nThe [Quality Models](/docs/classes/LLM.html#getqualitymodels) filter out things like embeddings, tts, instruct, audio, image, etc... models to only present the best LLM models.\n\n```javascript\nconst llm = new LLM({ service: \"anthropic\" });\nconst qualityModels = await llm.getQualityModels();\n\nfor (const model of qualityModels) {\n  console.log(model.model);                 // \"claude-3-5-sonnet-latest\"\n  console.log(model.input_cost_per_token);  // 0.000003\n  console.log(model.output_cost_per_token); // 0.000015\n  console.log(model.max_tokens);            // 8192\n  console.log(model.created);               // 2024-10-22T00:00:00.000Z\n}\n```\n\n### Custom Models\n\nIf the refreshed model list doesn't have a model you need, or you have a custom model — you can [add custom token and pricing information](/docs/classes/ModelUsage.html#addcustom).\n\n```javascript\nimport { ModelUsage } from \"@themaximalist/llm.js\";\n\nModelUsage.addCustom({\n  model: \"my-custom-gpt\",\n  service: \"openai\", \n  input_cost_per_token: 0.00001,\n  output_cost_per_token: 0.00003,\n  max_tokens: 4096\n});\n\n// Now use it like any other model\nconst response = await LLM(\"hello\", { \n  service: \"openai\", \n  model: \"my-custom-gpt\",\n  extended: true \n});\nconsole.log(response.usage.total_cost); // Uses your custom pricing\n```\n\n## Custom Services\n\nYou can add custom services to `LLM.js` by passing a custom object:\n\n```javascript\nconst llm = new LLM({\n    service: \"together\",\n    baseUrl: \"https://api.together.xyz/v1\",\n    model: \"meta-llama/Llama-3-70b-chat-hf\",\n    apiKey,\n});\n```\n\nYou can also create a custom service by extending the `LLM.APIv1` class:\n\n```javascript\nclass Together extends LLM.APIv1 {\n    static readonly service: ServiceName = \"together\";\n    static DEFAULT_BASE_URL: string = \"https://api.together.xyz/v1\";\n    static DEFAULT_MODEL: string = \"meta-llama/Llama-3-70b-chat-hf\";\n}\n\nconst llm = new Together();\n```\n\nYou can even register the custom services with `LLM.js` to make them available globally:\n\n```javascript\n\nLLM.register(Together);\nconst llm = new LLM({ service: \"together\" });\n```\n\nTo implement a fully custom model, subclass `LLM` and implement the `parse` methods:\n\n```javascript\nclass Custom extends LLM {\n    static readonly service: ServiceName = \"secretAGI\";\n    static DEFAULT_BASE_URL: string = \"http://localhost:9876\";\n    static DEFAULT_MODEL: string = \"gpt-999\";\n    static isLocal: boolean = false; // don't track pricing\n    static isBearerAuth: boolean = false;\n\n    get chatUrl() { return `${this.baseUrl}/chat` }\n    get modelsUrl() { return `${this.baseUrl}/models` }\n\n    parseContent(data: any): string { ... }\n    parseTools(data: any): ToolCall[] { ... }\n    parseThinking(data: any): string { ... }\n    parseModel(model: any): Model { ... }\n    parseOptions(options: Options): Options { ... }\n    parseTokenUsage(usage: any): InputOutputTokens | null { ... }\n    parseUsage(tokenUsage: InputOutputTokens): Usage { ... }\n\n    // streaming methods\n    parseToolsChunk(chunk: any): ToolCall[] { return this.parseTools(chunk) }\n    parseContentChunk(chunk: any): string { return this.parseContent(chunk) }\n    parseThinkingChunk(chunk: any): string { return this.parseThinking(chunk) }\n}\n```\n\n## Connection Verification\n\nTest your setup and API keys with built-in connection verification:\n\n```javascript\nconst llm = new LLM({ service: \"openai\" });\nconst isConnected = await llm.verifyConnection();\nconsole.log(isConnected); // true if API key and service work\n```\n\nThis is a light check that doesn't perform a LLM chat response. For non-local services it detects if it can fetch models. For local services it detects if an instance is up and running.\n\n\n## Examples\n\nThe [test suite](https://github.com/themaximalist/llm.js/tree/main/test) contains comprehensive examples of all features.\n\n\n## API Reference\n\nSee the full [API reference](/docs/modules.html).\n\n## Debug\n\n`LLM.js` uses a `debug` like logging system with the `llm.js` namespace.\n\nView debug logs by setting the `DEBUG` environment variable:\n\n```bash\n> DEBUG=llm.js* node your-script.js\n# debug logs\nblue\n```\n\n\n## Projects\n\n`LLM.js` is currently used in production by:\n\n-   [Infinity Arcade](https://infinityarcade.com) — play any text adventure game\n-   [News Score](https://newsscore.com) — score and sort the news\n-   [AI Image Explorer](https://aiimageexplorer.com) — image explorer\n-   [Think Machine](https://thinkmachine.com) — AI research assistant\n-   [Thinkable Type](https://thinkabletype.com) — Information Architecture Language\n-   [Minds App](https://mindsapp.com) — AI chat in your menubar\n\n## Changelog\n\n- 06/22/2025 — `v1.0.1` — Attachment support (images and PDF), Better model features support and tags\n- 06/13/2025 — `v1.0.0` — Added thinking mode, extended responses, token/cost usage, model management, TypeScript. Removed Together, Perplexity, Llamafile\n- 01/27/2025 — `v0.8.0` — Added DeepSeek\n- 12/19/2024 — `v0.7.1` — Fixed Anthropic streaming bug\n- 10/25/2024 — `v0.7.0` — Added Perplexity, upgraded all models to latest\n- 04/24/2024 — `v0.6.6` — Added browser support\n- 04/18/2024 — `v0.6.5` — Added Llama 3 and Together\n- 03/25/2024 — `v0.6.4` — Added Groq and abort()\n- 03/17/2024 — `v0.6.3` — Added JSON/XML/Markdown parsers and a stream handler\n- 03/15/2024 — `v0.6.2` — Fix bug with Google streaming\n- 03/15/2024 — `v0.6.1` — Fix bug to not add empty responses\n- 03/04/2024 — `v0.6.0` — Added Anthropic Claude 3\n- 03/02/2024 — `v0.5.9` — Added Ollama\n- 02/15/2024 — `v0.5.4` — Added Google Gemini\n- 02/13/2024 — `v0.5.3` — Added Mistral\n- 01/15/2024 — `v0.5.0` — Created website\n- 01/12/2024 — `v0.4.7` — OpenAI Tools, JSON stream\n- 01/07/2024 — `v0.3.5` — Added ModelDeployer\n- 01/05/2024 — `v0.3.2` — Added Llamafile\n- 04/26/2023 — `v0.2.5` — Added Anthropic, CLI\n- 04/24/2023 — `v0.2.4` — Chat options\n- 04/23/2023 — `v0.2.2` — Unified LLM() interface, streaming\n- 04/22/2023 — `v0.1.2` — Docs, system prompt\n- 04/21/2023 — `v0.0.1` — Created LLM.js with OpenAI support\n\n## License\n\nMIT\n\n## Author\n\nCreated by [Brad Jasper](https://bradjasper.com/), a product developer working on AI-powered apps and tools.\n\n**Need help with your LLM project?** I'm available for consulting on web, desktop, mobile, and AI development. [Get in touch →](https://bradjasper.com/)\n","readmeFilename":"README.md"}