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Tech Inc.","email":"contact@getmirai.co"},"license":"MIT","homepage":"https://github.com/trymirai/uzu#readme","repository":{"url":"git+https://github.com/trymirai/uzu.git","type":"git"},"description":"A high-performance inference engine for AI models","maintainers":[{"name":"alexey.trymirai","email":"dev@trymirai.com"},{"name":"matterai","email":"matterai.net@gmail.com"}],"readme":"

\n \n \"Mirai\"\n \n

\n\n\"Discord\" \"Contact \"Read [![License](https://img.shields.io/badge/License-MIT-brightgreen)](LICENSE) [![Build](https://github.com/trymirai/uzu/actions/workflows/tests.yml/badge.svg)](https://github.com/trymirai/uzu/actions) [![TypeScript](https://img.shields.io/badge/TypeScript-yellow)](crates/legacy/uzu/bindings/typescript) [![Package](https://img.shields.io/npm/v/@trymirai/uzu?color=yellow&label=Package&v=0.6.0)](https://www.npmjs.com/package/@trymirai/uzu) [![Downloads](https://img.shields.io/npm/dm/@trymirai/uzu?color=yellow&label=Downloads&v=0.6.0)](https://www.npmjs.com/package/@trymirai/uzu) \n\n# uzu\n\nA high-performance inference engine for AI models. It allows you to deploy AI directly in your app with **zero latency**, **full data privacy**, and **no inference costs**. Key features:\n\n- Simple, high-level API\n- Unified model configurations, making it easy to add support for new models\n- Traceable computations to ensure correctness against the source-of-truth implementation\n- Utilizes unified memory on Apple devices\n- [Broad model support](https://trymirai.com/local-models)\n\n## Quick Start\n\n\n\nAdd the dependency:\n\n```bash\npnpm add @trymirai/uzu@0.6.0\n```\n\nRun the code below:\n\n```ts\nimport { ChatConfig, ChatMessage, ChatReplyConfig, Engine, EngineConfig } from '@trymirai/uzu';\n\nasync function main() {\n let engineConfig = EngineConfig.create();\n let engine = await Engine.create(engineConfig);\n\n let model = await engine.model('alibaba:qwen3.5:0.8b:mirai:mirai-m:4');\n if (!model) {\n throw new Error('Model not found');\n }\n\n for await (const update of await engine.download(model)) {\n process.stdout.write(`\\rDownload progress: ${(update.progress * 100).toFixed(2)}%`);\n }\n console.log();\n\n let session = await engine.chat(model, ChatConfig.create());\n\n let messages = [\n ChatMessage.system().withText('You are a helpful assistant'),\n ChatMessage.user().withText('Tell me a short, funny story about a robot')\n ];\n\n let reply = await session.reply(messages, ChatReplyConfig.create());\n let message = reply[0]?.message;\n\n if (message) {\n console.log('Reasoning: ', message.reasoning);\n console.log('Text: ', message.text);\n }\n}\n\nmain().catch((error) => {\n console.error(error);\n});\n```\n\n\n
\n\nEverything from model downloading to inference configuration is handled automatically. Refer to the [documentation](https://docs.trymirai.com) for details on how to customize each step of the process.\n\n## Examples\n\nYou can run any example via `cargo tools example` \\<**typescript**\\> \\<**chat** | **chat-cloud** | **chat-shared-instance** | **chat-structured-output** | **quick-start** | **tool-calls**\\>:\n\n### Chat\n\nIn this example, we will download a model and get a reply to a specific list of messages:\n\n```ts\nimport {\n ChatConfig,\n ChatMessage,\n ChatReplyConfig,\n ChatSessionStreamChunkError,\n ChatSessionStreamChunkReplies,\n Engine,\n EngineConfig\n} from '@trymirai/uzu';\n\nasync function main() {\n let engineConfig = EngineConfig.create();\n let engine = await Engine.create(engineConfig);\n\n let model = await engine.model('alibaba:qwen3.5:0.8b:mirai:mirai-m:4');\n if (!model) {\n throw new Error('Model not found');\n }\n for await (const update of await engine.download(model)) {\n process.stdout.write(`\\rDownload progress: ${(update.progress * 100).toFixed(2)}%`);\n }\n console.log();\n\n let messages = [\n ChatMessage.system().withText('You are a helpful assistant'),\n ChatMessage.user().withText('Tell me a short, funny story about a robot')\n ];\n let session = await engine.chat(model, ChatConfig.create());\n let stream = await session.replyWithStream(messages, ChatReplyConfig.create());\n let message: ChatMessage | undefined;\n for await (const chunk of stream) {\n if (chunk instanceof ChatSessionStreamChunkReplies) {\n message = chunk.replies[0]?.message;\n console.log('Generated tokens: ', chunk.replies[0]?.stats.tokensCountOutput);\n } else if (chunk instanceof ChatSessionStreamChunkError) {\n console.error('Error: ', chunk.error);\n }\n }\n console.log('Reasoning: ', message?.reasoning);\n console.log('Text: ', message?.text);\n}\n\nmain().catch((error) => {\n console.error(error);\n});\n```\n\n
Once loaded, the same `ChatSession` can be reused for multiple requests until you drop it. Each model may consume a significant amount of RAM, so it's important to keep only one session loaded at a time. For iOS apps, we recommend adding the [Increased Memory Capability](https://developer.apple.com/documentation/bundleresources/entitlements/com.apple.developer.kernel.increased-memory-limit) entitlement to ensure your app can allocate the required memory.\n\n### Chat with the cloud model\n\nIn this example, we will get a reply to a specific list of messages from a cloud model:\n\n```ts\nimport { ChatConfig, ChatMessage, ChatReplyConfig, Engine, EngineConfig, ReasoningEffort } from '@trymirai/uzu';\n\nasync function main() {\n let engineConfig = EngineConfig.create().withOpenaiApiKey('OPENAI_API_KEY');\n let engine = await Engine.create(engineConfig);\n\n let model = await engine.model('gpt-5');\n if (!model) {\n throw new Error('Model not found');\n }\n\n let messages = [\n ChatMessage.system().withReasoningEffort(\"Low\" as ReasoningEffort),\n ChatMessage.user().withText('How LLMs work')\n ];\n\n let session = await engine.chat(model, ChatConfig.create());\n let reply = await session.reply(messages, ChatReplyConfig.create());\n let message = reply[0]?.message;\n if (message) {\n console.log('Reasoning: ', message.reasoning);\n console.log('Text: ', message.text);\n }\n}\n\nmain().catch((error) => {\n console.error(error);\n});\n```\n\n### Chat with shared instance\n\nThis example shows how to reuse chat instance without reloading model into memory:\n\n```ts\nimport { ChatConfig, ChatMessage, ChatReplyConfig, Engine, EngineConfig } from '@trymirai/uzu';\n\nasync function main() {\n let engineConfig = EngineConfig.create();\n let engine = await Engine.create(engineConfig);\n\n let model = await engine.model('alibaba:qwen3.5:0.8b:mirai:mirai-m:4');\n if (!model) {\n throw new Error('Model not found');\n }\n for await (const update of await engine.download(model)) {\n process.stdout.write(`\\rDownload progress: ${(update.progress * 100).toFixed(2)}%`);\n }\n console.log();\n\n // The chat instance owns the loaded model and can be shared between sessions.\n let chatInstance = await engine.chatInstance(model, ChatConfig.create());\n\n let firstSession = await engine.chatWithInstance(chatInstance);\n let replies = await firstSession.reply(\n [ChatMessage.user().withText('Tell me a short, funny story about a robot')],\n ChatReplyConfig.create(),\n );\n let reply = replies[replies.length - 1];\n if (reply) {\n console.log('First session reasoning: ', reply.message.reasoning);\n console.log('First session text: ', reply.message.text);\n }\n\n // The second session reuses the already-loaded weights instead of loading the model again.\n let secondSession = await engine.chatWithInstance(chatInstance);\n replies = await secondSession.reply(\n [ChatMessage.user().withText('What is the capital of France?')],\n ChatReplyConfig.create(),\n );\n reply = replies[replies.length - 1];\n if (reply) {\n console.log('\\nSecond session reasoning: ', reply.message.reasoning);\n console.log('Second session text: ', reply.message.text);\n }\n}\n\nmain().catch((error) => {\n console.error(error);\n});\n```\n\n### Chat with structured output\n\nSometimes you want the generated output to be valid JSON with predefined fields. You can use `Grammar` to manually specify a JSON schema for the response you want to receive:\n\n```ts\nimport { ChatConfig, ChatMessage, ChatReplyConfig, Engine, EngineConfig, GrammarJsonSchema, ReasoningEffort } from '@trymirai/uzu';\nimport * as z from \"zod\";\n\nconst CountryType = z.object({\n name: z.string(),\n capital: z.string(),\n});\nconst CountryListType = z.array(CountryType);\n\nfunction structuredResponse(response: string | null | undefined, type: T): z.infer | undefined {\n if (!response) {\n return undefined;\n }\n const data = JSON.parse(response);\n const result = type.parse(data);\n return result;\n}\n\nasync function main() {\n let engineConfig = EngineConfig.create();\n let engine = await Engine.create(engineConfig);\n\n let model = await engine.model('alibaba:qwen3.5:0.8b:mirai:mirai-m:4');\n if (!model) {\n throw new Error('Model not found');\n }\n for await (const update of await engine.download(model)) {\n process.stdout.write(`\\rDownload progress: ${(update.progress * 100).toFixed(2)}%`);\n }\n console.log();\n\n let schema = z.toJSONSchema(CountryListType);\n let schemaString = JSON.stringify(schema);\n let messages = [\n ChatMessage.system().withReasoningEffort(\"Disabled\" as ReasoningEffort),\n ChatMessage.user().withText('Give me a JSON object containing a list of 3 countries, where each country has name and capital fields')\n ];\n\n let session = await engine.chat(model, ChatConfig.create());\n let reply = await session.reply(messages, ChatReplyConfig.create().withGrammar(new GrammarJsonSchema(schemaString)));\n let message = reply[0]?.message;\n let countries = structuredResponse(message?.text, CountryListType);\n console.log(countries);\n}\n\nmain().catch((error) => {\n console.error(error);\n});\n```\n\n### Tool calls\n\nThis example shows how to use external tools:\n\n```ts\nimport {\n ChatConfig,\n ChatMessage,\n ChatReplyConfig,\n Engine,\n EngineConfig,\n SamplingMethodGreedy,\n SamplingPolicyCustom,\n uzuToolFunction,\n} from '@trymirai/uzu';\nimport * as z from 'zod';\n\n\nconst Coordinate = z.object({\n latitude: z.number().describe('Latitude in decimal degrees.'),\n longitude: z.number().describe('Longitude in decimal degrees.'),\n});\n\ntype Coordinate = z.infer;\n\n\nconst getCurrentLocation = uzuToolFunction({\n name: 'get_location',\n description: 'Return the current location in coordinates',\n parameters: z.object({}),\n returns: Coordinate,\n handler: (): Coordinate => ({\n latitude: 51.5074,\n longitude: -0.1278,\n }),\n});\n\n\nasync function calculateCurrentTemperature({latitude, longitude}: Coordinate): Promise {\n if (!Number.isFinite(Math.hypot(latitude, longitude))) {\n throw new RangeError('Coordinates must be finite');\n }\n return 25;\n}\n\nconst getCurrentTemperature = uzuToolFunction({\n name: 'get_current_temperature',\n description: 'Return the temperature at the provided coordinates',\n parameters: Coordinate,\n returns: z.number(),\n handler: calculateCurrentTemperature,\n});\n\n\nasync function main() {\n const engine = await Engine.create(EngineConfig.create());\n const model = await engine.model('alibaba:qwen3.5:0.8b:mirai:mirai-m:4');\n if (!model) {\n throw new Error('Model not found');\n }\n for await (const update of await engine.download(model)) {\n process.stdout.write(`\\rDownload progress: ${(update.progress * 100).toFixed(2)}%`);\n }\n process.stdout.write('\\n');\n\n const session = await engine.chat(model, ChatConfig.create());\n await session.addTool(getCurrentLocation);\n await session.addTool(getCurrentTemperature);\n\n const messages = [\n ChatMessage.system().withText('You are a helpful assistant'),\n ChatMessage.user().withText('What temperature is it now at my location?'),\n ];\n const config = ChatReplyConfig.create().withSamplingPolicy(\n new SamplingPolicyCustom(new SamplingMethodGreedy()),\n );\n const replies = await session.reply(messages, config);\n const message = replies[replies.length - 1]?.message;\n if (message) {\n console.log('Reasoning:', message.reasoning ?? '');\n console.log('Text:', message.text ?? '');\n }\n}\n\nmain().catch((error: unknown) => {\n console.error(error);\n});\n```\n\n\n\n## Troubleshooting\n\nIf you experience any problems, please contact us via [Discord](https://discord.com/invite/trymirai) or [email](mailto:contact@getmirai.co).\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","readmeFilename":"README.md"}