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LLM","unified-llm","Large Language Model","AI Agent","Agent","OpenAI","Gemini","Claude","DeepSeek","Azure OpenAI"],"repository":{"type":"git","url":"git+https://github.com/rhyizm/unified-llm.git"},"description":"Unified LLM interface (in-memory).","maintainers":[{"name":"rhyizm","email":"rhyizm@gmail.com"}],"readme":"# @unified-llm/core\n\n[![MIT License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![npm version](https://badge.fury.io/js/%40unified-llm%2Fcore.svg)](https://badge.fury.io/js/%40unified-llm%2Fcore)\n\nA simple way to manipulate multiple LLMs (OpenAI, Anthropic, Google Gemini, DeepSeek, Azure OpenAI, Ollama) with unified interface. \n\nWhy this matters:\n- One interface for many LLMs: swap providers without changing app code.\n- Event-based streaming API: start β†’ text_delta* β†’ stop β†’ error.\n- Same field for display: use `response.text` for both chat and stream.\n- Clean streaming with tools: providers execute tool calls mid-stream; you only receive text.\n- Power when you need it: access provider-native payloads via `rawResponse` on the final chunk.\n\n## Features\n\n- πŸ€– **Multi-Provider Support** - OpenAI, Anthropic Claude, Google Gemini, DeepSeek, Azure OpenAI, Ollama\n- ⚑ **Event‑Based Streaming API** - Unified `start/text_delta/stop/error` events across providers\n- πŸ”§ **Function Calling** - Execute local functions and integrate external tools\n- πŸ“Š **Structured Output** - Guaranteed JSON schema compliance across all providers\n- πŸ’¬ **Conversation Persistence** - SQLite-based chat history and thread management\n- 🏠 **Local LLM Support** - Run models locally with Ollama's OpenAI-compatible API\n\n## Installation\n\n```bash\nnpm install @unified-llm/core\n```\n\n## Simplest way to chat with multiple LLMs\n\nOne tiny interface, many providers. Change only the `provider` and `model`.\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\nconst providers = [\n { provider: 'openai', model: 'gpt-4o-mini', apiKey: process.env.OPENAI_API_KEY },\n { provider: 'anthropic',model: 'claude-3-haiku-20240307', apiKey: process.env.ANTHROPIC_API_KEY },\n { provider: 'google', model: 'gemini-2.5-flash', apiKey: process.env.GOOGLE_API_KEY },\n { provider: 'deepseek', model: 'deepseek-chat', apiKey: process.env.DEEPSEEK_API_KEY },\n // Azure and Ollama have dedicated sections below\n];\n\nfor (const cfg of providers.filter(p => p.apiKey)) {\n const client = new LLMClient(cfg as any);\n const res = await client.chat({\n messages: [{ role: 'user', content: 'Give me one fun fact.' }]\n });\n console.log(cfg.provider, 'β†’', res.text);\n}\n```\n\n## Streaming Responses\n\nStreaming now uses an event-based streaming API across providers. Instead of provider-specific chunk shapes, you receive structured events with an `eventType` and optional `delta`. This replaces the legacy streaming example and is not backward compatible. See docs/streaming-unification.md for the full spec.\n\n### Basic Streaming Example\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\nconst client = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-mini',\n apiKey: process.env.OPENAI_API_KEY,\n systemPrompt: 'You are a helpful assistant that answers questions in Japanese.',\n});\n\nconst stream = await client.stream({\n messages: [\n {\n id: '1',\n role: 'user',\n content: 'What are some recommended tourist spots in Osaka?',\n createdAt: new Date()\n },\n ],\n});\n\nlet acc = '';\nfor await (const ev of stream) {\n switch (ev.eventType) {\n case 'start':\n // initialize UI state if needed\n break;\n case 'text_delta':\n // ev.delta?.text is the incremental piece; ev.text is the accumulator\n process.stdout.write(ev.delta?.text ?? '');\n acc = ev.text;\n break;\n case 'stop':\n console.log('\\nComplete response:', ev.text);\n // ev.rawResponse contains provider-native final response (or stream data)\n break;\n case 'error':\n console.error('Stream error:', ev.delta);\n break;\n }\n}\n```\n\n## Function Calling\n\nThe `defineTool` helper provides type safety for tool definitions, automatically inferring argument and return types from the handler function:\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\nimport { defineTool } from '@unified-llm/core/tools';\nimport fs from 'fs/promises';\n\n// Let AI read and analyze any file\nconst readFile = defineTool({\n type: 'function',\n function: {\n name: 'readFile',\n description: 'Read any text file',\n parameters: {\n type: 'object',\n properties: {\n filename: { type: 'string', description: 'Name of file to read' }\n },\n required: ['filename']\n }\n },\n handler: async (args: { filename: string }) => {\n const content = await fs.readFile(args.filename, 'utf8');\n return content;\n }\n});\n\nconst client = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-mini',\n apiKey: process.env.OPENAI_API_KEY,\n tools: [readFile]\n});\n\n// Create a sample log file for demo\nawait fs.writeFile('app.log', `\n[ERROR] 2024-01-15 Database connection timeout\n[WARN] 2024-01-15 Memory usage at 89%\n[ERROR] 2024-01-15 Failed to authenticate user rhyizm\n[ERROR] 2024-01-15 Database connection timeout\n[INFO] 2024-01-15 Server restarted\n`);\n\n// Ask AI to analyze the log file\nconst response = await client.chat({\n messages: [{\n role: 'user',\n content: \"Read app.log and tell me what's wrong with my application\",\n createdAt: new Date()\n }]\n});\n\nconsole.log(response.message.content);\n// AI will read the actual file and give you insights about the errors!\n```\n\n### Using tools from an MCP server\n\nYou can pull tools from an MCP server and pass them directly to `LLMClient` as function-calling tools. The MCP tool `inputSchema` maps cleanly to OpenAI/Responses function parameters.\n\n```typescript\nimport { Client } from '@modelcontextprotocol/sdk/client/index.js';\nimport { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamable-http.js';\nimport { LLMClient } from '@unified-llm/core';\n\nconst MCP_SERVER_URL = process.env.MCP_SERVER_URL!; // e.g. http://localhost:3000\n\nasync function main() {\n // Connect to MCP server (Streamable HTTP example)\n const transport = new StreamableHTTPClientTransport(new URL('/mcp', MCP_SERVER_URL));\n const mcpClient = new Client(\n { name: 'local-mcp-responses-client', version: '1.0.0' },\n { capabilities: {} }\n );\n await mcpClient.connect(transport);\n\n // Fetch tools from MCP and adapt to function-calling tools\n const toolsList = await mcpClient.listTools();\n const tools = toolsList.tools.map((tool) => ({\n type: 'function' as const,\n function: {\n name: tool.name,\n description: tool.description,\n parameters: tool.inputSchema,\n },\n }));\n\n const client = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-mini',\n apiKey: process.env.OPENAI_API_KEY,\n tools,\n });\n\n const res = await client.chat({\n messages: [{\n role: 'user',\n content: 'Use available tools to help me',\n createdAt: new Date(),\n }],\n });\n\n console.log(res.message.content);\n}\n\nmain().catch(console.error);\n```\n\n### Streaming with Function Calls\n\nDuring streaming, tool calls are handled provider-side for you. When a model requests tool input mid-stream, the provider accumulates the tool call, executes your registered tool handlers, and continues streaming the final assistant text. You only observe text events: `start β†’ text_delta* β†’ stop`.\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\nimport { defineTool } from '@unified-llm/core/tools';\n\nconst getWeather = defineTool({\n type: 'function',\n function: {\n name: 'getWeather',\n description: 'Get current weather for a location',\n parameters: {\n type: 'object',\n properties: {\n location: { type: 'string', description: 'City name' }\n },\n required: ['location']\n }\n },\n handler: async (args: { location: string }) => {\n return `Weather in ${args.location}: Sunny, 27Β°C`;\n }\n});\n\nconst client = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-mini',\n apiKey: process.env.OPENAI_API_KEY,\n tools: [getWeather]\n});\n\nconst stream = await client.stream({\n messages: [{\n id: '1',\n role: 'user',\n content: \"What's the weather like in Tokyo?\",\n createdAt: new Date()\n }]\n});\n\nfor await (const ev of stream) {\n if (ev.eventType === 'text_delta') {\n process.stdout.write(ev.delta?.text ?? '');\n }\n if (ev.eventType === 'stop') {\n console.log('\\nFinal text:', ev.text);\n }\n}\n```\n\n## Structured Output\n\nStructured Output ensures that AI responses follow a specific JSON schema format across all supported providers. This is particularly useful for applications that need to parse and process AI responses programmatically.\n\n### Basic Structured Output\n\n```typescript\nimport { LLMClient, ResponseFormat } from '@unified-llm/core';\n\n// Define the expected response structure\nconst weatherFormat = new ResponseFormat({\n name: 'weather_info',\n description: 'Weather information for a location',\n schema: {\n type: 'object',\n properties: {\n location: { type: 'string' },\n temperature: { type: 'number' },\n condition: { type: 'string' },\n humidity: { type: 'number' }\n },\n required: ['location', 'temperature', 'condition']\n }\n});\n\nconst client = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-2024-08-06', // Structured output requires specific models\n apiKey: process.env.OPENAI_API_KEY\n});\n\nconst response = await client.chat({\n messages: [{\n role: 'user',\n content: 'What is the weather like in Tokyo today?'\n }],\n generationConfig: {\n responseFormat: weatherFormat\n }\n});\n\n// Response will be guaranteed to follow the schema\nconsole.log(JSON.parse(response.message.content[0].text));\n// Output: { \"location\": \"Tokyo\", \"temperature\": 25, \"condition\": \"Sunny\", \"humidity\": 60 }\n```\n\n### Multi-Provider Structured Output\n\nThe same `ResponseFormat` works across all providers with automatic conversion:\n\n```typescript\n// Works with OpenAI (uses json_schema format internally)\nconst openaiClient = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-2024-08-06',\n apiKey: process.env.OPENAI_API_KEY\n});\n\n// Works with Google Gemini (uses responseSchema format internally)\nconst geminiClient = new LLMClient({\n provider: 'google',\n model: 'gemini-1.5-pro',\n apiKey: process.env.GOOGLE_API_KEY\n});\n\n// Works with Anthropic (uses prompt engineering internally)\nconst claudeClient = new LLMClient({\n provider: 'anthropic',\n model: 'claude-3-5-sonnet-latest',\n apiKey: process.env.ANTHROPIC_API_KEY\n});\n\nconst userInfoFormat = new ResponseFormat({\n name: 'user_profile',\n schema: {\n type: 'object',\n properties: {\n name: { type: 'string' },\n age: { type: 'number' },\n email: { type: 'string' },\n interests: {\n type: 'array',\n items: { type: 'string' }\n }\n },\n required: ['name', 'age', 'email']\n }\n});\n\nconst request = {\n messages: [{ role: 'user', content: 'Create a sample user profile' }],\n generationConfig: { responseFormat: userInfoFormat }\n};\n\n// All three will return structured JSON in the same format\nconst openaiResponse = await openaiClient.chat(request);\nconst geminiResponse = await geminiClient.chat(request);\nconst claudeResponse = await claudeClient.chat(request);\n```\n\n### Pre-built Response Format Templates\n\nThe library provides convenient templates for common structured output patterns:\n\n```typescript\nimport { ResponseFormats } from '@unified-llm/core';\n\n// Key-value extraction\nconst contactFormat = ResponseFormats.keyValue(['name', 'email', 'phone']);\n\nconst contactResponse = await client.chat({\n messages: [{\n role: 'user',\n content: 'Extract contact info: John Doe, john@example.com, 555-1234'\n }],\n generationConfig: { responseFormat: contactFormat }\n});\n\n// Classification with confidence scores\nconst sentimentFormat = ResponseFormats.classification(['positive', 'negative', 'neutral']);\n\nconst sentimentResponse = await client.chat({\n messages: [{\n role: 'user',\n content: 'Analyze sentiment: \"I absolutely love this new feature!\"'\n }],\n generationConfig: { responseFormat: sentimentFormat }\n});\n// Returns: { \"category\": \"positive\", \"confidence\": 0.95 }\n\n// List responses\nconst taskFormat = ResponseFormats.list({\n type: 'object',\n properties: {\n task: { type: 'string' },\n priority: { type: 'string', enum: ['high', 'medium', 'low'] },\n deadline: { type: 'string' }\n }\n});\n\nconst taskResponse = await client.chat({\n messages: [{\n role: 'user',\n content: 'Create a task list for launching a mobile app'\n }],\n generationConfig: { responseFormat: taskFormat }\n});\n// Returns: { \"items\": [{ \"task\": \"Design UI\", \"priority\": \"high\", \"deadline\": \"2024-02-01\" }, ...] }\n```\n\n### Complex Nested Schemas\n\n```typescript\nconst productReviewFormat = new ResponseFormat({\n name: 'product_review',\n schema: {\n type: 'object',\n properties: {\n rating: { type: 'number', minimum: 1, maximum: 5 },\n summary: { type: 'string' },\n pros: {\n type: 'array',\n items: { type: 'string' }\n },\n cons: {\n type: 'array',\n items: { type: 'string' }\n },\n recommendation: {\n type: 'object',\n properties: {\n wouldRecommend: { type: 'boolean' },\n targetAudience: { type: 'string' },\n alternatives: {\n type: 'array',\n items: { type: 'string' }\n }\n }\n }\n },\n required: ['rating', 'summary', 'pros', 'cons', 'recommendation']\n }\n});\n\nconst reviewResponse = await client.chat({\n messages: [{\n role: 'user',\n content: 'Review this smartphone: iPhone 15 Pro - great camera, expensive, good battery life'\n }],\n generationConfig: { responseFormat: productReviewFormat }\n});\n```\n\n### Provider-Specific Notes\n\n- **OpenAI**: Supports native structured outputs with `gpt-4o-2024-08-06` and newer models\n- **Google Gemini**: Uses `responseMimeType: 'application/json'` with `responseSchema`\n- **Anthropic**: Uses prompt engineering to request JSON format responses\n- **DeepSeek**: Similar to OpenAI, supports JSON mode\n\nThe `ResponseFormat` class automatically handles the conversion to each provider's specific format, ensuring consistent behavior across all supported LLMs.\n\n## Multi-Provider Example\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\n// Create LLM clients for different providers\nconst gpt = new LLMClient({\n provider: 'openai',\n model: 'gpt-4o-mini',\n apiKey: process.env.OPENAI_API_KEY,\n systemPrompt: 'You are a helpful assistant that answers concisely.'\n});\n\nconst claude = new LLMClient({\n provider: 'anthropic', \n model: 'claude-3-haiku-20240307',\n apiKey: process.env.ANTHROPIC_API_KEY,\n systemPrompt: 'You are a thoughtful assistant that provides detailed explanations.'\n});\n\nconst gemini = new LLMClient({\n provider: 'google',\n model: 'gemini-2.0-flash',\n apiKey: process.env.GOOGLE_API_KEY,\n systemPrompt: 'You are a creative assistant that thinks outside the box.'\n});\n\nconst deepseek = new LLMClient({\n provider: 'deepseek',\n model: 'deepseek-chat',\n apiKey: process.env.DEEPSEEK_API_KEY,\n systemPrompt: 'You are a technical assistant specialized in coding.'\n});\n\n// Use the unified chat interface\nconst request = {\n messages: [{\n id: '1',\n role: 'user',\n content: 'What are your thoughts on AI?',\n createdAt: new Date()\n }]\n};\n\n// Each provider will respond according to their system prompt\nconst gptResponse = await gpt.chat(request);\nconst claudeResponse = await claude.chat(request);\nconst geminiResponse = await gemini.chat(request);\nconst deepseekResponse = await deepseek.chat(request);\n```\n\n### Multi-Provider Streaming\n\nStreaming works consistently across all supported providers using the unified event model:\n\n```typescript\nconst providers = [\n { name: 'OpenAI', provider: 'openai', model: 'gpt-4o-mini' },\n { name: 'Claude', provider: 'anthropic', model: 'claude-3-haiku-20240307' },\n { name: 'Gemini', provider: 'google', model: 'gemini-2.0-flash' },\n { name: 'DeepSeek', provider: 'deepseek', model: 'deepseek-chat' }\n];\n\nfor (const config of providers) {\n const client = new LLMClient({\n provider: config.provider as any,\n model: config.model,\n apiKey: process.env[`${config.provider.toUpperCase()}_API_KEY`]\n });\n\n console.log(`\\n--- ${config.name} Response ---`);\n const stream = await client.stream({\n messages: [{\n id: '1',\n role: 'user',\n content: 'Tell me a short story about AI.',\n createdAt: new Date()\n }]\n });\n\n for await (const ev of stream) {\n if (ev.eventType === 'text_delta') {\n process.stdout.write(ev.delta?.text ?? '');\n }\n }\n}\n```\n\n## Unified Response Format\n\nAll providers return responses in a consistent format, making it easy to switch between different LLMs:\n\n### Chat Response Format\n\n```typescript\n{\n id: \"chatcmpl-Blub8EgOvVaP7c3lxzmVF4TJpVCun\",\n model: \"gpt-4o-mini\",\n provider: \"openai\",\n message: {\n id: \"msg_1750758679093_r9hqdhfzh\",\n role: \"assistant\",\n content: [\n {\n type: \"text\",\n text: \"The author of this project is rhyizm.\"\n }\n ],\n createdAt: \"2025-06-24T09:51:19.093Z\"\n },\n usage: {\n inputTokens: 72,\n outputTokens: 10,\n totalTokens: 82\n },\n finish_reason: \"stop\",\n createdAt: \"2025-06-24T09:51:18.000Z\",\n rawResponse: {\n /* Original response from the provider (as returned by OpenAI, Anthropic, Google, DeepSeek, etc.) */\n }\n}\n```\n\n### Stream Response Format\n\nEach streaming event mirrors `UnifiedChatResponse` with a few additions:\n\n```typescript\n{\n id: \"chatcmpl-example\",\n model: \"gpt-4o-mini\", \n provider: \"openai\",\n message: {\n id: \"msg_example\",\n role: \"assistant\",\n content: [\n {\n type: \"text\",\n text: \"Chunk of text...\"\n }\n ],\n createdAt: \"2025-01-01T00:00:00.000Z\"\n },\n // createdAt is optional in streaming events\n eventType: \"text_delta\", // one of: start | text_delta | stop | error\n outputIndex: 0,\n delta: { type: \"text\", text: \"Chunk of text...\" }\n}\n\nOn the final `stop` event:\n- `finish_reason` may be present (e.g., \"stop\", \"length\").\n- `usage` may be present when available.\n- `rawResponse` contains the provider-native final result. For streaming providers that don’t return a single object, it contains native stream data (e.g., an array of SSE chunks). For Gemini, it includes both `{ stream, response }`.\n```\n\nKey benefits:\n- **Consistent structure** across all providers (OpenAI, Anthropic, Google, DeepSeek, Azure)\n- **Event-based streaming** with `eventType` and `delta` for incremental text\n- **Unified usage tracking** when the provider reports it\n- **Provider identification** to know which service generated the response\n- **Raw response access** on the final chunk for provider-specific features\n\n## Persistent LLM Client Configuration\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\n// Save LLM client configuration\nconst savedClientId = await LLMClient.save({\n name: 'My AI Assistant',\n provider: 'openai',\n model: 'gpt-4o-mini',\n systemPrompt: 'You are a helpful coding assistant.',\n tags: ['development', 'coding'],\n isActive: true\n});\n\n// Load saved LLM client\nconst client = await LLMClient.fromSaved(\n savedClientId,\n process.env.OPENAI_API_KEY\n);\n\n// List all saved LLM clients\nconst clients = await LLMClient.list({\n provider: 'openai',\n includeInactive: false\n});\n```\n\n## Azure OpenAI Example\n\nAzure OpenAI requires a different initialization pattern compared to other providers:\n\n```typescript\nimport { AzureOpenAIProvider } from '@unified-llm/core/providers/azure';\n\n// Azure OpenAI uses a different constructor pattern\n// First parameter: Azure-specific configuration\n// Second parameter: Base options (apiKey, tools, etc.)\nconst azureOpenAI = new AzureOpenAIProvider(\n {\n endpoint: process.env.AZURE_OPENAI_ENDPOINT!, // Azure resource endpoint\n deployment: process.env.AZURE_OPENAI_DEPLOYMENT!, // Model deployment name\n apiVersion: '2024-10-21', // Optional, defaults to 'preview'\n useV1: true // Use /openai/v1 endpoint format\n },\n {\n apiKey: process.env.AZURE_OPENAI_KEY!,\n tools: [] // Optional tools\n }\n);\n\n// Compare with standard LLMClient initialization:\n// const client = new LLMClient({\n// provider: 'openai',\n// model: 'gpt-4o-mini',\n// apiKey: process.env.OPENAI_API_KEY\n// });\n\n// Use it like any other provider\nconst response = await azureOpenAI.chat({\n messages: [{\n id: '1',\n role: 'user',\n content: 'Hello from Azure!',\n createdAt: new Date()\n }]\n});\n```\n\n## Ollama & OpenAI-Compatible APIs\n\n### Ollama (Local LLM) Example\n\nOllama provides an OpenAI-compatible API, allowing you to run large language models locally. You can use either `provider: 'ollama'`.\n\n### Prerequisites\n\n1. Install Ollama from [ollama.ai](https://ollama.ai)\n2. Pull a model: `ollama pull llama3` (or any other model)\n3. Start Ollama server (usually runs automatically at `http://localhost:11434`)\n\n### Basic Usage\n\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\n// Ollama configuration - no API key required\nconst ollama = new LLMClient({\n provider: 'ollama',\n model: 'llama3', // or 'mistral', 'codellama', etc.\n baseURL: 'http://localhost:11434/v1', // Optional - this is the default\n systemPrompt: 'You are a helpful assistant running locally.'\n});\n\n// Use it just like any other provider\nconst response = await ollama.chat({\n messages: [{\n id: '1',\n role: 'user',\n content: 'Explain quantum computing in simple terms.',\n createdAt: new Date()\n }]\n});\n\nconsole.log(response.message.content);\n```\n\n### Remote Ollama Server\n\nIf you're running Ollama on a different machine or port:\n\n```typescript\nconst remoteOllama = new LLMClient({\n provider: 'ollama',\n model: 'llama3',\n baseURL: 'http://your-server:11434/v1' // Replace with your server address\n});\n```\n\n### Available Models\n\nPopular models you can use with Ollama:\n- `llama3` - Meta's Llama 3\n- `mistral` - Mistral AI's models\n- `codellama` - Code-focused Llama variant\n- `phi` - Microsoft's Phi models\n- `gemma` - Google's Gemma models\n- `mixtral` - Mixture of experts model\n\nCheck available models with: `ollama list`\n\n### Why Use Ollama Provider?\n\nWhile Ollama is OpenAI-compatible and could work with `provider: 'openai'`, using `provider: 'ollama'` offers:\n- **Clearer intent** - Makes it obvious you're using a local model\n- **No API key required** - Ollama doesn't need authentication\n- **Future compatibility** - If we add Ollama-specific features, your code won't need changes\n\n### Running Examples\n\nYou can run TypeScript examples directly with tsx:\n\n```bash\n# Add tsx to your project\nnpm install --save-dev tsx\n\n# Run the example\nnpx tsx example.ts\n```\n\nExample file (`example.ts`):\n```typescript\nimport { LLMClient } from '@unified-llm/core';\n\nasync function runOllamaExample() {\n const ollamaClient = new LLMClient({\n provider: 'ollama',\n model: 'llama3',\n baseURL: 'http://localhost:11434/v1'\n });\n\n const response = await ollamaClient.chat({\n messages: [{\n id: '1',\n role: 'user',\n content: 'Hello, introduce yourself in Japanese.',\n createdAt: new Date()\n }]\n });\n\n console.log('Ollama Response:', JSON.stringify(response, null, 2));\n}\n\nrunOllamaExample().catch(console.error);\n```\n\n## Environment Variables\n\n```env\nOPENAI_API_KEY=your-openai-key\nANTHROPIC_API_KEY=your-anthropic-key\nGOOGLE_API_KEY=your-google-key\nDEEPSEEK_API_KEY=your-deepseek-key\nAZURE_OPENAI_KEY=your-azure-key # For Azure OpenAI\nAZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com # For Azure OpenAI\nAZURE_OPENAI_DEPLOYMENT=your-deployment-name # For Azure OpenAI\nUNIFIED_LLM_DB_PATH=./chat-history.db # Optional custom DB path\n```\n\n## Supported Providers\n\n| Provider | Models | Features |\n|----------|---------|----------|\n| **OpenAI** | GPT-4o, GPT-4o-mini, GPT-4, GPT-3.5 | Function calling, streaming, vision, structured output |\n| **Anthropic** | Claude 3.5 (Sonnet), Claude 3 (Opus, Sonnet, Haiku) | Tool use, streaming, long context, structured output |\n| **Google** | Gemini 2.0 Flash, Gemini 1.5 Pro/Flash | Function calling, multimodal, structured output |\n| **DeepSeek** | DeepSeek-Chat, DeepSeek-Coder | Function calling, streaming, code generation, structured output |\n| **Azure OpenAI** | GPT-4o, GPT-4, GPT-3.5 (via Azure deployments) | Function calling, streaming, structured output |\n| **Ollama** | Llama 3, Mistral, CodeLlama, Phi, Gemma, Mixtral, etc. | Local execution, OpenAI-compatible API, no API key required |\n\n## API Methods\n\n### Core Methods\n\n```typescript\n// Main chat method - returns complete response\nawait client.chat(request: UnifiedChatRequest)\n\n// Streaming responses (returns async generator)\nfor await (const chunk of client.stream(request)) {\n console.log(chunk);\n}\n```\n\n**Note:** Persistence methods are experimental; there is a possibility that they may be removed or moved to a separate package in the future.\n\n### Persistence Methods\n\n```typescript\n// Save LLM client configuration\nawait LLMClient.save(config: LLMClientConfig)\n\n// Load saved LLM client\nawait LLMClient.fromSaved(id: string, apiKey?: string)\n\n// Get saved configuration\nawait LLMClient.getConfig(id: string)\n\n// List saved LLM clients\nawait LLMClient.list(options?: { provider?: string, includeInactive?: boolean })\n\n// Update configuration\nawait LLMClient.update(id: string, updates: Partial)\n\n// Soft delete LLM client\nawait LLMClient.delete(id: string)\n```\n\n## Requirements\n\n- Node.js 20 or higher\n- TypeScript 5.4.5 or higher (for development)\n\n## License\n\nMIT - see [LICENSE](https://github.com/rhyizm/unified-llm/blob/main/LICENSE) for details.\n\n## Links\n\n- 🏠 [Homepage](https://github.com/rhyizm/unified-llm)\n- πŸ› [Report Issues](https://github.com/rhyizm/unified-llm/issues)\n- πŸ’¬ [Discussions](https://github.com/rhyizm/unified-llm/discussions)\n","readmeFilename":"README.md"}