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MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nA Vercel AI SDK provider for Ollama, built on the official `ollama` package. Type-safe, cross-provider compatible, with access to native Ollama features.\n\n> **Version compatibility**\n>\n> Each `ai-sdk-ollama` major targets one AI SDK major. Every line stays published on npm, so pick the one that matches your `ai` version:\n>\n> | `ai-sdk-ollama`   | `ai` (peer) | Status                            |\n> | ----------------- | ----------- | --------------------------------- |\n> | `4.x` (`@latest`) | `ai@^7`     | Active. New features land here.   |\n> | `3.x`             | `ai@^6`     | Maintenance. Critical fixes only. |\n> | `2.x`             | `ai@^5`     | Unmaintained.                     |\n>\n> AI SDK v7 is stable, so v4 installs from `latest`. The v3 line still works for AI SDK v6.\n\n## Quick Start\n\n```bash\n# AI SDK v7 (stable)\nnpm install ai-sdk-ollama ai\n\n# AI SDK v6\nnpm install ai-sdk-ollama@^3 ai@^6\n```\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\n// Works in both Node.js and browsers\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Write a haiku about coding',\n  temperature: 0.8,\n});\n\nconsole.log(text);\n```\n\n## Why Choose AI SDK Ollama?\n\n- **Reliable tool calling** - Response synthesis adds text after tool execution, so you don't get empty replies\n- **Wrapper functions** - `generateText` and `streamText` return complete responses\n- **Built-in reliability** - Reliability features run by default\n- **Automatic JSON repair** - Cascade repair runs [jsonrepair](https://github.com/josdejong/jsonrepair) first, then an Ollama-specific fallback for trailing commas, comments, URLs, and Python constants\n- **Web search and fetch** - Built-in tools powered by [Ollama's web search API](https://ollama.com/blog/web-search) for current information\n- **Type-safe** - Full TypeScript support with strict typing\n- **Cross-environment** - Runs in Node.js and browsers automatically\n- **Native Ollama options** - Use `mirostat`, `repeat_penalty`, `num_ctx`, and more\n- **Production-ready** - Handles the tool-call and JSON edge cases that otherwise need manual workarounds\n\n## Enhanced Tool Calling\n\n> **The problem this solves**: Standard Ollama providers can execute a tool and then return empty text. These wrapper functions synthesize a response so you get complete output.\n\n```typescript\nimport { ollama, generateText, streamText } from 'ai-sdk-ollama';\n\n// Enhanced generateText - returns complete responses after tool calls\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  tools: {\n    /* your tools */\n  },\n  prompt: 'Use the tools and explain the results',\n});\n\n// Enhanced streaming - tool-aware streaming\nconst { textStream } = await streamText({\n  model: ollama('llama3.2'),\n  tools: {\n    /* your tools */\n  },\n  prompt: 'Stream with tools',\n});\n```\n\n## Web Search Tools\n\n> **Web search and fetch**: Built-in tools powered by [Ollama's web search API](https://ollama.com/blog/web-search) for current information.\n\n```typescript\nimport { generateText } from 'ai';\nimport { ollama } from 'ai-sdk-ollama';\n\n// 🔍 Web search for current information\nconst { text } = await generateText({\n  model: ollama('qwen3-coder:480b-cloud'), // Cloud models recommended for web search\n  prompt: 'What are the latest developments in AI this week?',\n  tools: {\n    webSearch: ollama.tools.webSearch({ maxResults: 5 }),\n  },\n});\n\n// 📄 Fetch specific web content\nconst { text: summary } = await generateText({\n  model: ollama('gpt-oss:120b-cloud'),\n  prompt: 'Summarize this article: https://example.com/article',\n  tools: {\n    webFetch: ollama.tools.webFetch({ maxContentLength: 5000 }),\n  },\n});\n\n// 🔄 Combine search and fetch for comprehensive research\nconst { text: research } = await generateText({\n  model: ollama('gpt-oss:120b-cloud'),\n  prompt: 'Research recent TypeScript updates and provide a detailed analysis',\n  tools: {\n    webSearch: ollama.tools.webSearch({ maxResults: 3 }),\n    webFetch: ollama.tools.webFetch(),\n  },\n});\n```\n\n### Web Search Prerequisites\n\n1. **Ollama API Key**: Set `OLLAMA_API_KEY` environment variable\n2. **Cloud Models**: Use cloud models for optimal web search performance:\n   - `qwen3-coder:480b-cloud` - Best for general web search\n   - `gpt-oss:120b-cloud` - Best for complex reasoning with web data\n\n```bash\n# Set your API key\nexport OLLAMA_API_KEY=\"your_api_key_here\"\n\n# Get your API key from: https://ollama.com/account\n```\n\n## Contents\n\n- [AI SDK Ollama Provider](#ai-sdk-ollama-provider)\n  - [Quick Start](#quick-start)\n  - [Why Choose AI SDK Ollama?](#why-choose-ai-sdk-ollama)\n  - [Enhanced Tool Calling](#enhanced-tool-calling)\n  - [Web Search Tools](#web-search-tools)\n    - [Web Search Prerequisites](#web-search-prerequisites)\n  - [Contents](#contents)\n  - [Prerequisites](#prerequisites)\n  - [Browser Support](#browser-support)\n    - [Browser Usage](#browser-usage)\n    - [Explicit Browser Import](#explicit-browser-import)\n    - [CORS Configuration](#cors-configuration)\n  - [More Examples](#more-examples)\n    - [Cross Provider Compatibility](#cross-provider-compatibility)\n    - [Native Ollama Power](#native-ollama-power)\n    - [Model Keep-Alive Control](#model-keep-alive-control)\n    - [Enhanced Tool Calling Wrappers](#enhanced-tool-calling-wrappers)\n    - [Combining Tools with Structured Output](#combining-tools-with-structured-output)\n    - [Simple and Predictable](#simple-and-predictable)\n  - [Reranking](#reranking)\n  - [Streaming Utilities](#streaming-utilities)\n    - [Smooth Stream](#smooth-stream)\n    - [Partial JSON Parsing](#partial-json-parsing)\n  - [Middleware System](#middleware-system)\n    - [Default Settings Middleware](#default-settings-middleware)\n    - [Extract Reasoning Middleware](#extract-reasoning-middleware)\n  - [ToolLoopAgent](#toolloopagent)\n  - [Advanced Features](#advanced-features)\n    - [Custom Ollama Instance](#custom-ollama-instance)\n    - [API Key Configuration](#api-key-configuration)\n    - [Using Existing Ollama Client](#using-existing-ollama-client)\n    - [Structured Output](#structured-output)\n    - [Auto-Detection of Structured Outputs](#auto-detection-of-structured-outputs)\n    - [Automatic JSON Repair](#automatic-json-repair)\n    - [Reasoning Support](#reasoning-support)\n  - [Common Issues](#common-issues)\n  - [Supported Models](#supported-models)\n  - [Testing](#testing)\n  - [Learn More](#learn-more)\n  - [License](#license)\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\n// Standard AI SDK parameters work everywhere\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Write a haiku about coding',\n  temperature: 0.8,\n  maxOutputTokens: 100,\n});\n\n// Plus access to Ollama's advanced features\nconst { text: advancedText } = await generateText({\n  model: ollama('llama3.2', {\n    options: {\n      mirostat: 2, // Advanced sampling algorithm\n      repeat_penalty: 1.1, // Fine-tune repetition\n      num_ctx: 8192, // Larger context window\n    },\n  }),\n  prompt: 'Write a haiku about coding',\n  temperature: 0.8, // Standard parameters still work\n});\n```\n\n## Prerequisites\n\n- Node.js 22+\n- [Ollama](https://ollama.com) installed locally or running on a remote server\n- AI SDK v7 (`ai` package)\n- TypeScript 5.9+ (for TypeScript users)\n\n```bash\n# Install Ollama from ollama.com\nollama serve\n\n# Pull a model\nollama pull llama3.2\n```\n\n## Browser Support\n\nSee the [browser example](../../examples/browser/).\n\nThis provider works in both Node.js and browser environments. The library automatically selects the correct Ollama client based on the environment.\n\n### Browser Usage\n\nThe same API works in browsers with automatic environment detection:\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama'; // Automatically uses browser version\nimport { generateText } from 'ai';\n\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Write a haiku about coding',\n});\n```\n\n### Explicit Browser Import\n\nYou can also explicitly import the browser version:\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama/browser';\n```\n\n### CORS Configuration\n\nFor browser usage, you have several options to handle CORS:\n\n```bash\n# Option 1: Use a proxy (recommended for development)\n# Configure your bundler (Vite, Webpack, etc.) to proxy /api/* to Ollama\n# See browser example for Vite proxy configuration\n\n# Option 2: Allow all origins (development only)\nOLLAMA_ORIGINS=* ollama serve\n\n# Option 3: Allow specific origins\nOLLAMA_ORIGINS=\"http://localhost:3000,https://myapp.com\" ollama serve\n```\n\n**Recommended**: Use a development proxy (like Vite proxy) to avoid CORS issues entirely. See the browser example for a complete working setup.\n\n## More Examples\n\n### Cross Provider Compatibility\n\nWrite code that works with any AI SDK provider:\n\n```typescript\n// This exact code works with OpenAI, Anthropic, or Ollama\nconst { text } = await generateText({\n  model: ollama('llama3.2'), // or openai('gpt-4') or anthropic('claude-3')\n  prompt: 'Write a haiku',\n  temperature: 0.8,\n  maxOutputTokens: 100,\n  topP: 0.9,\n});\n```\n\n### Native Ollama Power\n\nAccess Ollama's advanced features without losing portability:\n\n```typescript\nconst { text } = await generateText({\n  model: ollama('llama3.2', {\n    options: {\n      mirostat: 2, // Advanced sampling algorithm\n      repeat_penalty: 1.1, // Repetition control\n      num_ctx: 8192, // Context window size\n    },\n  }),\n  prompt: 'Write a haiku',\n  temperature: 0.8, // Standard parameters still work\n});\n```\n\n> **Parameter Precedence**: When both AI SDK parameters and Ollama options are specified, **Ollama options take precedence**. For example, if you set `temperature: 0.5` in Ollama options and `temperature: 0.8` in the `generateText` call, the final value will be `0.5`. This allows you to use standard AI SDK parameters for portability while having fine-grained control with Ollama-specific options when needed.\n\n### Model Keep-Alive Control\n\nControl how long models stay loaded in memory after requests using the `keep_alive` parameter:\n\n```typescript\n// Keep model loaded for 10 minutes\nconst model = ollama('llama3.2', { keep_alive: '10m' });\n\n// Keep model loaded for 1 hour (3600 seconds)\nconst model2 = ollama('llama3.2', { keep_alive: 3600 });\n\n// Keep model loaded indefinitely\nconst model3 = ollama('llama3.2', { keep_alive: -1 });\n\n// Unload model immediately after each request\nconst model4 = ollama('llama3.2', { keep_alive: 0 });\n\nconst { text } = await generateText({\n  model,\n  prompt: 'Write a haiku',\n});\n```\n\n**Accepted values:**\n\n- Duration strings: `\"10m\"`, `\"24h\"`, `\"30s\"` (minutes, hours, seconds)\n- Numbers: seconds as a number (e.g., `3600` for 1 hour)\n- Negative numbers: keep loaded indefinitely (e.g., `-1`)\n- `0`: unload immediately after the request\n\n**Default behavior**: If not specified, Ollama keeps models loaded for 5 minutes to facilitate quicker response times for subsequent requests.\n\n### Enhanced Tool Calling Wrappers\n\nFor reliable tool calling, use the enhanced wrapper functions that return complete responses:\n\n```typescript\nimport { ollama, generateText, streamText } from 'ai-sdk-ollama';\nimport { tool } from 'ai';\nimport { z } from 'zod';\n\n// Enhanced generateText with automatic response synthesis\nconst result = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'What is 15 + 27? Use the math tool to calculate it.',\n  tools: {\n    math: tool({\n      description: 'Perform math calculations',\n      inputSchema: z.object({\n        operation: z.string().describe('Math operation like \"15 + 27\"'),\n      }),\n      execute: async ({ operation }) => {\n        return { result: eval(operation), operation };\n      },\n    }),\n  },\n  // Optional: Configure reliability behavior\n  enhancedOptions: {\n    enableSynthesis: true, // Default: true\n    maxSynthesisAttempts: 2, // Default: 2\n    minResponseLength: 10, // Default: 10\n  },\n});\n\nconsole.log(result.text); // \"15 + 27 equals 42. Using the math tool, I calculated...\"\n```\n\n### Combining Tools with Structured Output\n\nThe `enableToolsWithStructuredOutput` option allows you to use both tool calling and structured output together:\n\n```typescript\nimport { ollama, generateText } from 'ai-sdk-ollama';\nimport { Output, tool } from 'ai';\nimport { z } from 'zod';\n\nconst weatherTool = tool({\n  description: 'Get current weather for a location',\n  inputSchema: z.object({\n    location: z.string().describe('City name'),\n  }),\n  execute: async ({ location }) => ({\n    location,\n    temperature: 22,\n    condition: 'sunny',\n    humidity: 60,\n  }),\n});\n\n// AI SDK v7: tools and structured output work together by default\nconst result = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Get weather for San Francisco and provide a structured summary',\n  tools: { getWeather: weatherTool },\n  output: Output.object({\n    schema: z.object({\n      location: z.string(),\n      temperature: z.number(),\n      summary: z.string(),\n    }),\n  }),\n  toolChoice: 'required',\n});\n// Result: Tool is called AND structured output is generated\n```\n\n**When to Use Enhanced Wrappers:**\n\n- **Critical tool calling scenarios** where you need a text response after the tool runs\n- **Production applications** that can't handle empty responses after tool execution\n- **Complex multi-step tool interactions** requiring reliable synthesis\n\n**Standard vs Enhanced Comparison:**\n\n| Function                   | Standard `generateText`   | Enhanced `generateText`           |\n| -------------------------- | ------------------------- | --------------------------------- |\n| **Simple prompts**         | ✅ Perfect                | ✅ Works (slight overhead)        |\n| **Tool calling**           | ⚠️ May return empty text  | ✅ **Returns complete responses** |\n| **Complete responses**     | ❌ Manual handling needed | ✅ **Automatic completion**       |\n| **Production reliability** | ⚠️ Unpredictable          | ✅ **Reliable**                   |\n\n### Simple and Predictable\n\nThe provider works the same way with any model - just try the features you need:\n\n```typescript\n// No capability checking required - just use any model\nconst { text } = await generateText({\n  model: ollama('any-model'),\n  prompt: 'What is the weather?',\n  tools: {\n    /* ... */\n  }, // If the model doesn't support tools, you'll get a clear error\n});\n\n// The provider is simple and predictable\n// - Try any feature with any model\n// - Get clear error messages if something doesn't work\n// - No hidden complexity or capability detection\n```\n\n## Reranking\n\n> **Reranking**: Rank documents by semantic relevance for search results and RAG pipelines.\n\nSince Ollama doesn't have native reranking yet, we provide embedding-based reranking using cosine similarity:\n\n```typescript\nimport { rerank } from 'ai';\nimport { ollama } from 'ai-sdk-ollama';\n\n// Rerank documents by relevance to a query\nconst { ranking, rerankedDocuments } = await rerank({\n  model: ollama.embeddingReranking('nomic-embed-text'),\n  query: 'How do I get a refund?',\n  documents: [\n    'To reset your password, click Forgot Password on the login page.',\n    'Refunds are available within 14 days of purchase. Go to Settings > Cancel Plan.',\n    'Enable 2FA for extra security in Settings > Security.',\n  ],\n  topN: 2, // Return top 2 most relevant\n});\n\nconsole.log('Most relevant:', rerankedDocuments[0]);\n// Output: \"Refunds are available within 14 days...\"\n\n// Each ranking item includes score and original index\nranking.forEach((item, i) => {\n  console.log(\n    `${i + 1}. Score: ${item.score.toFixed(3)}, Index: ${item.originalIndex}`,\n  );\n});\n```\n\n**Use Cases:**\n\n- **RAG Pipelines**: Rerank retrieved documents before passing to LLM\n- **Search Results**: Improve relevance of search results\n- **Customer Support**: Find most relevant help articles\n\n**Recommended Models**: `embeddinggemma` (best score separation), `nomic-embed-text`, `bge-m3`\n\n## Streaming Utilities\n\n### Smooth Stream\n\nCreate smoother streaming output by chunking text into words, lines, or custom patterns:\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { streamText, smoothStream } from 'ai';\n\n// Word-by-word streaming with delay\nconst result = streamText({\n  model: ollama('llama3.2'),\n  prompt: 'Write a poem about the ocean.',\n  experimental_transform: smoothStream({\n    delayInMs: 50, // 50ms between chunks\n    chunking: 'word', // 'word' | 'line' | RegExp\n  }),\n});\n\nfor await (const chunk of result.textStream) {\n  process.stdout.write(chunk); // Smooth, word-by-word output\n}\n```\n\n**Chunking Options:**\n\n- `'word'` - Emit word by word (default)\n- `'line'` - Emit line by line\n- `RegExp` - Custom pattern (e.g., `/[.!?]\\s+/` for sentences)\n\n### Partial JSON Parsing\n\nParse incomplete JSON from streaming responses - useful for progressive UI updates:\n\n```typescript\nimport { parsePartialJson } from 'ai';\n\n// As JSON streams in, parse what's available\nconst partial = '{\"name\": \"Alice\", \"age\": 25, \"city\": \"New';\nconst result = await parsePartialJson(partial);\n\nif (result.state === 'repaired-parse' || result.state === 'successful-parse') {\n  console.log(result.value); // { name: \"Alice\", age: 25, city: \"New\" }\n}\n```\n\n**Note**: `createStitchableStream` and other advanced stream utilities are internal to the AI SDK. Use standard `ReadableStream` APIs for stream manipulation, or import utilities directly from `'ai'` when available.\n\n## Middleware System\n\nWrap language models with middleware for parameter transformation, logging, or custom behavior:\n\n```typescript\nimport {\n  ollama,\n  wrapLanguageModel,\n  defaultSettingsMiddleware,\n} from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\n// Apply default settings to all calls\nconst model = wrapLanguageModel({\n  model: ollama('llama3.2'),\n  middleware: defaultSettingsMiddleware({\n    settings: {\n      temperature: 0.7,\n      maxOutputTokens: 1000,\n    },\n  }),\n});\n\n// Temperature and maxOutputTokens are now applied by default\nconst { text } = await generateText({\n  model,\n  prompt: 'Write a story.',\n});\n```\n\n### Default Settings Middleware\n\nApply default parameters that can be overridden per-call:\n\n```typescript\nimport { defaultSettingsMiddleware } from 'ai-sdk-ollama';\n\nconst middleware = defaultSettingsMiddleware({\n  settings: {\n    temperature: 0.7,\n    maxOutputTokens: 500,\n  },\n});\n```\n\n### Extract Reasoning Middleware\n\nExtract reasoning/thinking from model outputs that use XML tags:\n\n```typescript\nimport {\n  ollama,\n  wrapLanguageModel,\n  extractReasoningMiddleware,\n} from 'ai-sdk-ollama';\n\nconst model = wrapLanguageModel({\n  model: ollama('deepseek-r1:7b'),\n  middleware: extractReasoningMiddleware({\n    tagName: 'think', // Extract content from <think> tags\n    separator: '\\n', // Separator for multiple reasoning blocks\n    startWithReasoning: true, // Model starts with reasoning\n  }),\n});\n```\n\n**Combining Multiple Middlewares:**\n\n```typescript\nconst model = wrapLanguageModel({\n  model: ollama('llama3.2'),\n  middleware: [\n    defaultSettingsMiddleware({ settings: { temperature: 0.5 } }),\n    extractReasoningMiddleware({ tagName: 'thinking' }),\n  ],\n});\n```\n\n## ToolLoopAgent\n\nAn agent that runs tools in a loop until a stop condition is met:\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { ToolLoopAgent, stepCountIs, hasToolCall, tool } from 'ai';\nimport { z } from 'zod';\n\nconst agent = new ToolLoopAgent({\n  model: ollama('llama3.2'),\n  instructions: 'You are a helpful assistant.',\n  tools: {\n    weather: tool({\n      description: 'Get weather for a location',\n      inputSchema: z.object({ location: z.string() }),\n      execute: async ({ location }: { location: string }) => ({\n        temp: 72,\n        condition: 'sunny',\n      }),\n    }),\n    done: tool({\n      description: 'Call when task is complete',\n      inputSchema: z.object({ summary: z.string() }),\n      execute: async ({ summary }: { summary: string }) => ({\n        completed: true,\n        summary,\n      }),\n    }),\n  },\n  maxOutputTokens: 1000,\n  stopWhen: [\n    stepCountIs(10), // Stop after 10 steps max\n    hasToolCall('done'), // Stop when 'done' tool is called\n  ],\n  onStepFinish: (stepResult) => {\n    console.log(`Step:`, stepResult.toolCalls.length, 'tool calls');\n  },\n});\n\nconst result = await agent.generate({\n  prompt: 'What is the weather in San Francisco?',\n});\n\nconsole.log('Final:', result.text);\nconsole.log('Steps:', result.steps.length);\nconsole.log('Tokens:', result.totalUsage.totalTokens ?? 'undefined');\n```\n\n**Stop Conditions:**\n\n- `stepCountIs(n)` - Stop after n steps\n- `hasToolCall(name)` - Stop when specific tool is called\n- Custom: `(options: { steps: StepResult[] }) => boolean | Promise<boolean>`\n\n## Advanced Features\n\n### Custom Ollama Instance\n\nYou can create a custom Ollama provider instance with specific configuration:\n\n```typescript\nimport { createOllama } from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\nconst ollama = createOllama({\n  baseURL: 'http://my-ollama-server:11434',\n  headers: {\n    'Custom-Header': 'value',\n  },\n});\n\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Hello!',\n});\n```\n\n### API Key Configuration\n\nFor cloud Ollama services, pass your API key explicitly using `createOllama`:\n\n```typescript\nimport { createOllama } from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\nconst ollama = createOllama({\n  apiKey: process.env.OLLAMA_API_KEY,\n  baseURL: 'https://ollama.com',\n});\n\nconst { text } = await generateText({\n  model: ollama('llama3.2'),\n  prompt: 'Hello!',\n});\n```\n\n**Why explicit over auto-detection?**\n\nDifferent runtimes handle environment variables differently:\n\n| Runtime         | `.env` Auto-Loading           |\n| --------------- | ----------------------------- |\n| Node.js         | ❌ No (requires `dotenv`)     |\n| Bun             | ✅ Yes (usually)              |\n| Deno            | ❌ No                         |\n| Edge/Serverless | ❌ No (platform injects vars) |\n\nPassing `apiKey` explicitly works reliably everywhere and avoids surprises.\n\n**Runtime-specific examples:**\n\n```typescript\n// Node.js (with dotenv)\nimport 'dotenv/config';\nconst ollama = createOllama({ apiKey: process.env.OLLAMA_API_KEY });\n\n// Bun\nconst ollama = createOllama({ apiKey: Bun.env.OLLAMA_API_KEY });\n\n// Deno\nconst ollama = createOllama({ apiKey: Deno.env.get('OLLAMA_API_KEY') });\n\n// Production (Vercel, Railway, Fly.io, etc.)\n// Env vars are injected by the platform - no .env files needed\nconst ollama = createOllama({ apiKey: process.env.OLLAMA_API_KEY });\n```\n\n**Note**: The API key is set as `Authorization: Bearer {apiKey}` header. If you provide both an `apiKey` and a pre-existing `Authorization` header, the existing header takes precedence.\n\n### Using Existing Ollama Client\n\nYou can also pass an existing Ollama client instance to reuse your configuration.\nThe provider accepts the exported structural `OllamaClient` interface, so the\nofficial client, the maintained compatibility fork, or a custom adapter can be\ninjected in Node.js and browser builds.\n\n```typescript\nimport { Ollama } from 'ollama';\nimport { createOllama } from 'ai-sdk-ollama';\n\n// Create your existing Ollama client\nconst existingClient = new Ollama({\n  host: 'http://my-ollama-server:11434',\n  // Add any custom configuration\n});\n\n// Use it with the AI SDK provider\nconst ollamaSdk = createOllama({ client: existingClient });\n\n// Use both clients as needed\nawait existingClient.list(); // Direct Ollama operations\nconst { text } = await generateText({\n  model: ollamaSdk('llama3.2'),\n  prompt: 'Hello!',\n});\n```\n\n#### Custom client adapters\n\n`OllamaClient` is structural, so any object with the right shape works — no\n`Ollama` instance required. Streaming methods return an `AbortableStream`, an\nasync iterable with an `abort()`:\n\n```typescript\nimport type { AbortableStream, OllamaClient } from 'ai-sdk-ollama';\nimport { createOllama } from 'ai-sdk-ollama';\n\nconst client: OllamaClient = {\n  chat: myChat, // returns a ChatResponse, or an AbortableStream of them\n  embed: myEmbed,\n  webSearch: myWebSearch,\n  webFetch: myWebFetch,\n};\n\nconst ollama = createOllama({ client });\n```\n\nPer-request cancellation is forwarded to the client as an `AbortSignal`, and an\naborted request rejects rather than resolving with a retried or fallback result.\n\n### Structured Output\n\n```typescript\nimport { generateText, Output } from 'ai';\nimport { z } from 'zod';\n\n// Auto-detection: structuredOutputs is automatically enabled for object generation\nconst { output } = await generateText({\n  model: ollama('llama3.2'), // No need to set structuredOutputs: true\n  output: Output.object({\n    schema: z.object({\n      name: z.string(),\n      age: z.number(),\n      interests: z.array(z.string()),\n    }),\n  }),\n  prompt: 'Generate a random person profile',\n});\n\nconsole.log(output);\n// { name: \"Alice\", age: 28, interests: [\"reading\", \"hiking\"] }\n\n// Explicit setting still works\nconst { output: explicitOutput } = await generateText({\n  model: ollama('llama3.2', { structuredOutputs: true }), // Explicit\n  output: Output.object({\n    schema: z.object({\n      name: z.string(),\n      age: z.number(),\n    }),\n  }),\n  prompt: 'Generate a person',\n});\n```\n\n### Auto-Detection of Structured Outputs\n\nThe provider automatically detects when structured outputs are needed:\n\n- **Object Generation**: `generateText` with `Output.object()` and `streamText` with `Output.object()` automatically enable `structuredOutputs: true`\n- **Text Generation**: `generateText` and `streamText` require explicit `structuredOutputs: true` for JSON output\n- **Backward Compatibility**: Explicit settings are respected, with warnings when overridden\n- **No Breaking Changes**: Existing code continues to work as expected\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { generateText, Output } from 'ai';\nimport { z } from 'zod';\n\n// This works without explicit structuredOutputs: true\nconst { output } = await generateText({\n  model: ollama('llama3.2'),\n  output: Output.object({\n    schema: z.object({ name: z.string() }),\n  }),\n  prompt: 'Generate a name',\n});\n\n// This still requires explicit setting for JSON output\nconst { text } = await generateText({\n  model: ollama('llama3.2', { structuredOutputs: true }),\n  prompt: 'Generate JSON with a message field',\n});\n```\n\n### Automatic JSON Repair\n\n> **Cascade JSON repair**: Built-in repair fixes malformed model output during object generation.\n\nRepair uses a **cascade**: the [jsonrepair](https://github.com/josdejong/jsonrepair) library runs first (standard JSON issues), then the built-in Ollama-specific repair runs if needed (e.g. Python `True`/`None`, URLs with `//`, smart quotes). You can use the default, disable repair with `enableTextRepair: false`, or pass a custom `repairText` function. Exported helpers: `cascadeRepairText`, `enhancedRepairText` (see [test-cascade-repair example](../../examples/node/src/test-cascade-repair.ts)).\n\nThe provider handles 14+ types of common JSON issues from LLM outputs:\n\n````typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { generateText, Output } from 'ai';\nimport { z } from 'zod';\n\n// JSON repair is enabled by default for all object generation\nconst { output } = await generateText({\n  model: ollama('llama3.2'),\n  output: Output.object({\n    schema: z.object({\n      name: z.string(),\n      email: z.string().email(),\n      age: z.number(),\n    }),\n  }),\n  prompt: 'Generate a person profile',\n  // reliableObjectGeneration: true is the default\n});\n\n// Automatically handles:\n// ✅ Trailing commas: {\"name\": \"John\",}\n// ✅ Single quotes: {'name': 'John'}\n// ✅ Unquoted keys: {name: \"John\"}\n// ✅ Python constants: {active: True, value: None}\n// ✅ Comments: {\"name\": \"John\" // comment}\n// ✅ URLs in strings: {\"url\": \"https://example.com\" // comment}\n// ✅ Escaped quotes: {\"text\": \"It's // fine\"}\n// ✅ JSONP wrappers: callback({\"name\": \"John\"})\n// ✅ Markdown code blocks: ```json\\n{...}\\n```\n// ✅ Incomplete objects/arrays\n// ✅ Smart quotes and special characters\n// ✅ And more...\n````\n\n**Control Options:**\n\n```typescript\n// Disable all reliability features (not recommended)\nconst { output } = await generateText({\n  model: ollama('llama3.2', {\n    reliableObjectGeneration: false, // Everything off\n  }),\n  output: Output.object({\n    schema: z.object({ message: z.string() }),\n  }),\n  prompt: 'Generate a message',\n});\n\n// Fine-grained control: disable only repair, keep retries\nconst { output: withRetries } = await generateText({\n  model: ollama('llama3.2', {\n    reliableObjectGeneration: true,\n    objectGenerationOptions: {\n      enableTextRepair: false, // Disable repair only\n      maxRetries: 3, // But keep retries\n    },\n  }),\n  output: Output.object({\n    schema: z.object({ message: z.string() }),\n  }),\n  prompt: 'Generate a message',\n});\n\n// Custom repair function (advanced)\nconst { output: custom } = await generateText({\n  model: ollama('llama3.2', {\n    objectGenerationOptions: {\n      repairText: async ({ text, error }) => {\n        // Your custom repair logic\n        return text.replace(/,(\\s*[}\\]])/g, '$1');\n      },\n    },\n  }),\n  output: Output.object({\n    schema: z.object({ message: z.string() }),\n  }),\n  prompt: 'Generate a message',\n});\n```\n\n### Reasoning Support\n\nUse `think` to request reasoning generation from models that support it. Returned `message.thinking` is exposed as AI SDK reasoning, separately from final text, in both generation results and streams (including terminal chunks).\n\n```typescript\nimport { ollama } from 'ai-sdk-ollama';\nimport { generateText } from 'ai';\n\n// Enable reasoning for models that support it (e.g., deepseek-r1:7b)\nconst model = ollama('deepseek-r1:7b', { think: true });\n\n// Generate text with reasoning\nconst { text, reasoningText } = await generateText({\n  model,\n  prompt:\n    'Solve: If I have 3 boxes, each with 4 smaller boxes, and each smaller box has 5 items, how many items total?',\n});\n\nconsole.log('Answer:', text);\nconsole.log('Reasoning:', reasoningText);\n\n// Compare with reasoning disabled\nconst modelNoReasoning = ollama('deepseek-r1:7b', { think: false });\nconst { text: noReasoningText } = await generateText({\n  model: modelNoReasoning,\n  prompt: 'Calculate 3 × 4 × 5',\n});\nconsole.log('Answer:', noReasoningText);\n```\n\n`think` controls the request, not filtering of the response. If the server returns thinking with `think` omitted or `false`, the adapter still preserves it as reasoning; it does not turn it into final text. If no thinking is returned, no reasoning content is added.\n\nOllama supplies a combined output token count. The adapter preserves that total and the raw counters, but leaves the text/reasoning token breakdown unknown instead of attributing all output tokens to text.\n\n**Recommended Reasoning Models**:\n\n- `deepseek-r1:7b` - Balanced performance and reasoning capability (5GB)\n- `deepseek-r1:1.5b` - Lightweight option (2.5GB)\n- `deepseek-r1:8b` - Llama-based distilled version (5.5GB)\n\nInstall with: `ollama pull deepseek-r1:7b`\n\n**Note**: The reasoning feature is model-dependent. Models without reasoning support will work normally without showing thinking process.\n\n## Common Issues\n\n- **Make sure Ollama is running** - Run `ollama serve` before using the provider\n- **Pull models first** - Use `ollama pull model-name` before generating text\n- **Model compatibility errors** - The provider will throw errors if you try to use unsupported features (e.g., tools with non-compatible models)\n- **Network issues** - Verify Ollama is accessible at the configured URL\n- **TypeScript support** - Full type safety with TypeScript 5.9+\n- **AI SDK v7 compatibility** - Built for the AI SDK v7 specification\n\n## Supported Models\n\nWorks with any model in your Ollama installation:\n\n- **Chat**: `llama3.2`, `mistral`, `phi4-mini`, `qwen2.5`, `codellama`, `gpt-oss:20b`\n- **Vision**: `llava`, `bakllava`, `llama3.2-vision`, `minicpm-v`\n- **Embeddings**: `nomic-embed-text`, `all-minilm`, `mxbai-embed-large`\n- **Reasoning**: `deepseek-r1:7b`, `deepseek-r1:1.5b`, `deepseek-r1:8b`\n- **Cloud Models** (for web search): `qwen3-coder:480b-cloud`, `gpt-oss:120b-cloud`\n\n## Testing\n\nThe project includes unit and integration tests:\n\n```bash\n# Run unit tests only (fast, no external dependencies)\nnpm test\n\n# Run all tests (unit + integration)\nnpm run test:all\n\n# Run integration tests only (requires Ollama running)\nnpm run test:integration\n```\n\n> **Note**: Integration tests may occasionally fail due to the non-deterministic nature of AI model outputs. This is expected behavior - the tests use loose assertions to account for LLM output variability. Some tests may also skip if required models aren't available locally.\n\nFor detailed testing information, see [Integration Tests Documentation](./src/integration-tests/README.md).\n\n## Learn More\n\n📚 **[Examples Directory](../../examples/)** - Comprehensive usage patterns with real working code\n\n🚀 **[Quick Start Guide](../../examples/node/src/basic-chat.ts)** - Get running in 2 minutes\n\n⚙️ **[Dual Parameters Demo](../../examples/node/src/dual-parameter-example.ts)** - See the key feature in action\n\n🔧 **[Tool Calling Guide](../../examples/node/src/simple-tool-test.ts)** - Function calling with Ollama\n\n🖼️ **[Image Processing Guide](../../examples/node/src/image-handling-example.ts)** - Vision models with LLaVA\n\n📡 **[Streaming Examples](../../examples/node/src/streaming-simple-test.ts)** - Real-time responses\n\n🌐 **[Web Search Tools](../../examples/node/src/web-search-ai-sdk-ollama.ts)** - Web search and fetch capabilities\n\n🔄 **[Reranking Example](../../examples/node/src/v6-reranking-example.ts)** - Document reranking with embeddings\n\n🌊 **[SmoothStream Example](../../examples/node/src/smooth-stream-example.ts)** - Smooth chunked streaming output\n\n🔌 **[Middleware Example](../../examples/node/src/middleware-example.ts)** - Model wrapping and middleware system\n\n🤖 **[ToolLoopAgent Example](../../examples/node/src/tool-loop-agent-example.ts)** - Autonomous tool-calling agents\n\n🛡️ **[Tool Approval Example](../../examples/node/src/v6-tool-approval-example.ts)** - Human-in-the-loop tool execution approval\n\n📦 **[Structured Output + Tools](../../examples/node/src/v6-structured-output-example.ts)** - Tool calling with structured output generation\n\n🔗 **[MCP Tools Example](../../examples/node/src/mcp-tools-example.ts)** - Model Context Protocol integration\n\n🧩 **[MCP Apps Example](../../examples/node/src/mcp-apps-example.ts)** - MCP Apps host flow (ui:// resources, split tools, iframe proxy)\n\n🔧 **[JSON repair example](../../examples/node/src/json-repair-example.ts)** - Object generation with repair options and custom repair\n\n🔀 **[Cascade repair example](../../examples/node/src/test-cascade-repair.ts)** - `cascadeRepairText` and `enhancedRepairText` (jsonrepair + Ollama-specific)\n\n## License\n\nMIT © [Jag Reehal](https://jagreehal.com)\n\nSee [LICENSE](./LICENSE) for details.\n","readmeFilename":"README.md"}