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Boluk"},"license":"MIT","homepage":"https://github.com/f5-sales-demo/xcsh","keywords":["ai","llm","openai","anthropic","gemini","unified","api"],"repository":{"url":"git+https://github.com/f5-sales-demo/xcsh.git","type":"git","directory":"packages/ai"},"description":"Unified LLM API with automatic model discovery and provider configuration","contributors":[{"name":"Mario Zechner"}],"maintainers":[{"name":"robinmordasiewicz","email":"robin@mordasiewicz.com"}],"readme":"# @f5-sales-demo/pi-ai\n\nUnified LLM API with automatic model discovery, provider configuration, token and cost tracking, and simple context persistence and hand-off to other models mid-session.\n\n**Note**: This library only includes models that support tool calling (function calling), as this is essential for agentic workflows.\n\n## Table of Contents\n\n- [Supported Providers](#supported-providers)\n- [Installation](#installation)\n- [Quick Start](#quick-start)\n- [Tools](#tools)\n  - [Defining Tools](#defining-tools)\n  - [Handling Tool Calls](#handling-tool-calls)\n  - [Streaming Tool Calls with Partial JSON](#streaming-tool-calls-with-partial-json)\n  - [Validating Tool Arguments](#validating-tool-arguments)\n  - [Complete Event Reference](#complete-event-reference)\n- [Image Input](#image-input)\n- [Thinking/Reasoning](#thinkingreasoning)\n  - [Unified Interface](#unified-interface-streamsimplecompletesimple)\n  - [Provider-Specific Options](#provider-specific-options-streamcomplete)\n  - [Streaming Thinking Content](#streaming-thinking-content)\n- [Stop Reasons](#stop-reasons)\n- [Error Handling](#error-handling)\n  - [Aborting Requests](#aborting-requests)\n  - [Continuing After Abort](#continuing-after-abort)\n- [APIs, Models, and Providers](#apis-models-and-providers)\n  - [Providers and Models](#providers-and-models)\n  - [Querying Providers and Models](#querying-providers-and-models)\n  - [Custom Models](#custom-models)\n  - [OpenAI Compatibility Settings](#openai-compatibility-settings)\n  - [Type Safety](#type-safety)\n- [Cross-Provider Handoffs](#cross-provider-handoffs)\n- [Context Serialization](#context-serialization)\n- [Browser Usage](#browser-usage)\n  - [Environment Variables](#environment-variables-nodejs-only)\n  - [Checking Environment Variables](#checking-environment-variables)\n- [OAuth Providers](#oauth-providers)\n  - [Vertex AI (ADC)](#vertex-ai-adc)\n  - [CLI Login](#cli-login)\n  - [Programmatic OAuth](#programmatic-oauth)\n  - [Login Flow Example](#login-flow-example)\n  - [Using OAuth Tokens](#using-oauth-tokens)\n  - [Provider Notes](#provider-notes)\n- [License](#license)\n\n## Supported Providers\n\n- **OpenAI** (usage-based Platform API access through `OPENAI_API_KEY`)\n- **OpenAI Codex** (ChatGPT Plus/Pro subscription through device-code or browser OAuth)\n- **Anthropic**\n- **Google**\n- **Vertex AI** (Gemini via Vertex AI)\n- **Mistral**\n- **Groq**\n- **Cerebras**\n- **Together**\n- **Moonshot** (requires `MOONSHOT_API_KEY`)\n- **Qianfan** (requires `QIANFAN_API_KEY`)\n- **NVIDIA** (requires `NVIDIA_API_KEY`)\n- **NanoGPT** (requires `NANO_GPT_API_KEY`)\n- **Hugging Face Inference**\n- **xAI**\n- **Venice** (requires `VENICE_API_KEY`)\n- **OpenRouter**\n- **Kilo Gateway** (supports OAuth `/login kilo` or `KILO_API_KEY`)\n- **LiteLLM** (requires `LITELLM_API_KEY`)\n- **zAI** (requires `ZAI_API_KEY`)\n- **MiniMax Coding Plan** (requires `MINIMAX_CODE_API_KEY` or `MINIMAX_CODE_CN_API_KEY`)\n- **Xiaomi MiMo** (requires `XIAOMI_API_KEY`)\n- **ZenMux** (requires `ZENMUX_API_KEY`)\n- **Qwen Portal** (supports `QWEN_OAUTH_TOKEN` or `QWEN_PORTAL_API_KEY`)\n- **Cloudflare AI Gateway** (requires `CLOUDFLARE_AI_GATEWAY_API_KEY` and provider-specific gateway base URL)\n- **Ollama** (local OpenAI-compatible runtime; optional `OLLAMA_API_KEY`)\n- **llama.cpp** (local OpenAI and Anthropic compatible inference server)\n- **vLLM** (OpenAI-compatible server; `VLLM_API_KEY` for secured deployments)\n- **GitHub Copilot** (requires OAuth, see below)\n- **Google Gemini CLI** (requires OAuth, see below)\n- **Antigravity** (requires OAuth, see below)\n- **Any OpenAI-compatible API**: LM Studio, custom proxies, etc.\n\n## Installation\n\n```bash\nnpm install @f5-sales-demo/pi-ai\n```\n\n## Quick Start\n\n```typescript\nimport { Type, getModel, stream, complete, Context, Tool, StringEnum } from \"@f5-sales-demo/pi-ai\";\n\n// Fully typed with auto-complete support for both providers and models\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\n\n// Define tools with TypeBox schemas for type safety and validation\nconst tools: Tool[] = [\n {\n  name: \"get_time\",\n  description: \"Get the current time\",\n  parameters: Type.Object({\n   timezone: Type.Optional(Type.String({ description: \"Optional timezone (e.g., America/New_York)\" })),\n  }),\n },\n];\n\n// Build a conversation context (easily serializable and transferable between models)\nconst context: Context = {\n systemPrompt: \"You are a helpful assistant.\",\n messages: [{ role: \"user\", content: \"What time is it?\" }],\n tools,\n};\n\n// Option 1: Streaming with all event types\nconst s = stream(model, context);\n\nfor await (const event of s) {\n switch (event.type) {\n  case \"start\":\n   console.log(`Starting with ${event.partial.model}`);\n   break;\n  case \"text_start\":\n   console.log(\"\\n[Text started]\");\n   break;\n  case \"text_delta\":\n   process.stdout.write(event.delta);\n   break;\n  case \"text_end\":\n   console.log(\"\\n[Text ended]\");\n   break;\n  case \"thinking_start\":\n   console.log(\"[Model is thinking...]\");\n   break;\n  case \"thinking_delta\":\n   process.stdout.write(event.delta);\n   break;\n  case \"thinking_end\":\n   console.log(\"[Thinking complete]\");\n   break;\n  case \"toolcall_start\":\n   console.log(`\\n[Tool call started: index ${event.contentIndex}]`);\n   break;\n  case \"toolcall_delta\":\n   // Partial tool arguments are being streamed\n   const partialCall = event.partial.content[event.contentIndex];\n   if (partialCall.type === \"toolCall\") {\n    console.log(`[Streaming args for ${partialCall.name}]`);\n   }\n   break;\n  case \"toolcall_end\":\n   console.log(`\\nTool called: ${event.toolCall.name}`);\n   console.log(`Arguments: ${JSON.stringify(event.toolCall.arguments)}`);\n   break;\n  case \"done\":\n   console.log(`\\nFinished: ${event.reason}`);\n   break;\n  case \"error\":\n   console.error(`Error: ${event.error}`);\n   break;\n }\n}\n\n// Get the final message after streaming, add it to the context\nconst finalMessage = await s.result();\ncontext.messages.push(finalMessage);\n\n// Handle tool calls if any\nconst toolCalls = finalMessage.content.filter((b) => b.type === \"toolCall\");\nfor (const call of toolCalls) {\n // Execute the tool\n const result =\n  call.name === \"get_time\"\n   ? new Date().toLocaleString(\"en-US\", {\n     timeZone: call.arguments.timezone || \"UTC\",\n     dateStyle: \"full\",\n     timeStyle: \"long\",\n    })\n   : \"Unknown tool\";\n\n // Add tool result to context (supports text and images)\n context.messages.push({\n  role: \"toolResult\",\n  toolCallId: call.id,\n  toolName: call.name,\n  content: [{ type: \"text\", text: result }],\n  isError: false,\n  timestamp: Date.now(),\n });\n}\n\n// Continue if there were tool calls\nif (toolCalls.length > 0) {\n const continuation = await complete(model, context);\n context.messages.push(continuation);\n console.log(\"After tool execution:\", continuation.content);\n}\n\nconsole.log(`Total tokens: ${finalMessage.usage.input} in, ${finalMessage.usage.output} out`);\nconsole.log(`Cost: $${finalMessage.usage.cost.total.toFixed(4)}`);\n\n// Option 2: Get complete response without streaming\nconst response = await complete(model, context);\n\nfor (const block of response.content) {\n if (block.type === \"text\") {\n  console.log(block.text);\n } else if (block.type === \"toolCall\") {\n  console.log(`Tool: ${block.name}(${JSON.stringify(block.arguments)})`);\n }\n}\n```\n\n## Tools\n\nTools enable LLMs to interact with external systems. This library uses TypeBox schemas for type-safe tool definitions with automatic validation using AJV. TypeBox schemas can be serialized and deserialized as plain JSON, making them ideal for distributed systems.\n\n### Defining Tools\n\n```typescript\nimport { Type, Tool, StringEnum } from \"@f5-sales-demo/pi-ai\";\n\n// Define tool parameters with TypeBox\nconst weatherTool: Tool = {\n name: \"get_weather\",\n description: \"Get current weather for a location\",\n parameters: Type.Object({\n  location: Type.String({ description: \"City name or coordinates\" }),\n  units: StringEnum([\"celsius\", \"fahrenheit\"], { default: \"celsius\" }),\n }),\n};\n\n// Note: For Google API compatibility, use StringEnum helper instead of Type.Enum\n// Type.Enum generates anyOf/const patterns that Google doesn't support\n\nconst bookMeetingTool: Tool = {\n name: \"book_meeting\",\n description: \"Schedule a meeting\",\n parameters: Type.Object({\n  title: Type.String({ minLength: 1 }),\n  startTime: Type.String({ format: \"date-time\" }),\n  endTime: Type.String({ format: \"date-time\" }),\n  attendees: Type.Array(Type.String({ format: \"email\" }), { minItems: 1 }),\n }),\n};\n```\n\n### Handling Tool Calls\n\nTool results use content blocks and can include both text and images:\n\n```typescript\nimport * as fs from \"node:fs\";\n\nconst context: Context = {\n messages: [{ role: \"user\", content: \"What is the weather in London?\" }],\n tools: [weatherTool],\n};\n\nconst response = await complete(model, context);\n\n// Check for tool calls in the response\nfor (const block of response.content) {\n if (block.type === \"toolCall\") {\n  // Execute your tool with the arguments\n  // See \"Validating Tool Arguments\" section for validation\n  const result = await executeWeatherApi(block.arguments);\n\n  // Add tool result with text content\n  context.messages.push({\n   role: \"toolResult\",\n   toolCallId: block.id,\n   toolName: block.name,\n   content: [{ type: \"text\", text: JSON.stringify(result) }],\n   isError: false,\n   timestamp: Date.now(),\n  });\n }\n}\n\n// Tool results can also include images (for vision-capable models)\nconst imageBuffer = fs.readFileSync(\"chart.png\");\ncontext.messages.push({\n role: \"toolResult\",\n toolCallId: \"tool_xyz\",\n toolName: \"generate_chart\",\n content: [\n  { type: \"text\", text: \"Generated chart showing temperature trends\" },\n  { type: \"image\", data: imageBuffer.toBase64(), mimeType: \"image/png\" },\n ],\n isError: false,\n timestamp: Date.now(),\n});\n```\n\n### Streaming Tool Calls with Partial JSON\n\nDuring streaming, tool call arguments are progressively parsed as they arrive. This enables real-time UI updates before the complete arguments are available:\n\n```typescript\nconst s = stream(model, context);\n\nfor await (const event of s) {\n if (event.type === \"toolcall_delta\") {\n  const toolCall = event.partial.content[event.contentIndex];\n\n  // toolCall.arguments contains partially parsed JSON during streaming\n  // This allows for progressive UI updates\n  if (toolCall.type === \"toolCall\" && toolCall.arguments) {\n   // BE DEFENSIVE: arguments may be incomplete\n   // Example: Show file path being written even before content is complete\n   if (toolCall.name === \"write_file\" && toolCall.arguments.path) {\n    console.log(`Writing to: ${toolCall.arguments.path}`);\n\n    // Content might be partial or missing\n    if (toolCall.arguments.content) {\n     console.log(`Content preview: ${toolCall.arguments.content.substring(0, 100)}...`);\n    }\n   }\n  }\n }\n\n if (event.type === \"toolcall_end\") {\n  // Here toolCall.arguments is complete (but not yet validated)\n  const toolCall = event.toolCall;\n  console.log(`Tool completed: ${toolCall.name}`, toolCall.arguments);\n }\n}\n```\n\n**Important notes about partial tool arguments:**\n\n- During `toolcall_delta` events, `arguments` contains the best-effort parse of partial JSON\n- Fields may be missing or incomplete - always check for existence before use\n- String values may be truncated mid-word\n- Arrays may be incomplete\n- Nested objects may be partially populated\n- At minimum, `arguments` will be an empty object `{}`, never `undefined`\n- The Google provider does not support function call streaming. Instead, you will receive a single `toolcall_delta` event with the full arguments.\n\n### Validating Tool Arguments\n\nWhen using `agentLoop`, tool arguments are automatically validated against your TypeBox schemas before execution. If validation fails, the error is returned to the model as a tool result, allowing it to retry.\n\nWhen implementing your own tool execution loop with `stream()` or `complete()`, use `validateToolCall` to validate arguments before passing them to your tools:\n\n```typescript\nimport { stream, validateToolCall, Tool } from \"@f5-sales-demo/pi-ai\";\n\nconst tools: Tool[] = [weatherTool, calculatorTool];\nconst s = stream(model, { messages, tools });\n\nfor await (const event of s) {\n if (event.type === \"toolcall_end\") {\n  const toolCall = event.toolCall;\n\n  try {\n   // Validate arguments against the tool's schema (throws on invalid args)\n   const validatedArgs = validateToolCall(tools, toolCall);\n   const result = await executeMyTool(toolCall.name, validatedArgs);\n   // ... add tool result to context\n  } catch (error) {\n   // Validation failed - return error as tool result so model can retry\n   context.messages.push({\n    role: \"toolResult\",\n    toolCallId: toolCall.id,\n    toolName: toolCall.name,\n    content: [{ type: \"text\", text: error.message }],\n    isError: true,\n    timestamp: Date.now(),\n   });\n  }\n }\n}\n```\n\n### Complete Event Reference\n\nAll streaming events emitted during assistant message generation:\n\n| Event Type       | Description              | Key Properties                                                                              |\n| ---------------- | ------------------------ | ------------------------------------------------------------------------------------------- |\n| `start`          | Stream begins            | `partial`: Initial assistant message structure                                              |\n| `text_start`     | Text block starts        | `contentIndex`: Position in content array                                                   |\n| `text_delta`     | Text chunk received      | `delta`: New text, `contentIndex`: Position                                                 |\n| `text_end`       | Text block complete      | `content`: Full text, `contentIndex`: Position                                              |\n| `thinking_start` | Thinking block starts    | `contentIndex`: Position in content array                                                   |\n| `thinking_delta` | Thinking chunk received  | `delta`: New text, `contentIndex`: Position                                                 |\n| `thinking_end`   | Thinking block complete  | `content`: Full thinking, `contentIndex`: Position                                          |\n| `toolcall_start` | Tool call begins         | `contentIndex`: Position in content array                                                   |\n| `toolcall_delta` | Tool arguments streaming | `delta`: JSON chunk, `partial.content[contentIndex].arguments`: Partial parsed args         |\n| `toolcall_end`   | Tool call complete       | `toolCall`: Complete validated tool call with `id`, `name`, `arguments`                     |\n| `done`           | Stream complete          | `reason`: Stop reason (\"stop\", \"length\", \"toolUse\"), `message`: Final assistant message     |\n| `error`          | Error occurred           | `reason`: Error type (\"error\" or \"aborted\"), `error`: AssistantMessage with partial content |\n\n## Image Input\n\nModels with vision capabilities can process images. You can check if a model supports images via the `input` property. If you pass images to a non-vision model, they are silently ignored.\n\n```typescript\nimport * as fs from \"node:fs\";\nimport { getModel, complete } from \"@f5-sales-demo/pi-ai\";\n\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\n\n// Check if model supports images\nif (model.input.includes(\"image\")) {\n console.log(\"Model supports vision\");\n}\n\nconst imageBuffer = fs.readFileSync(\"image.png\");\nconst base64Image = imageBuffer.toBase64();\n\nconst response = await complete(model, {\n messages: [\n  {\n   role: \"user\",\n   content: [\n    { type: \"text\", text: \"What is in this image?\" },\n    { type: \"image\", data: base64Image, mimeType: \"image/png\" },\n   ],\n  },\n ],\n});\n\n// Access the response\nfor (const block of response.content) {\n if (block.type === \"text\") {\n  console.log(block.text);\n }\n}\n```\n\n## Thinking/Reasoning\n\nMany models support thinking/reasoning capabilities where they can show their internal thought process. You can check if a model supports reasoning via the `reasoning` property. If you pass reasoning options to a non-reasoning model, they are silently ignored.\n\n### Unified Interface (streamSimple/completeSimple)\n\n```typescript\nimport { getModel, streamSimple, completeSimple } from \"@f5-sales-demo/pi-ai\";\n\n// Many models across providers support thinking/reasoning\nconst model = getModel(\"anthropic\", \"claude-sonnet-4-20250514\");\n// or getModel('openai', 'gpt-5-mini');\n// or getModel('google', 'gemini-2.5-flash');\n// or getModel('xai', 'grok-code-fast-1');\n// or getModel('groq', 'openai/gpt-oss-20b');\n// or getModel('cerebras', 'gpt-oss-120b');\n// or getModel('openrouter', 'z-ai/glm-4.5v');\n\n// Check if model supports reasoning\nif (model.reasoning) {\n console.log(\"Model supports reasoning/thinking\");\n}\n\n// Use the simplified reasoning option\nconst response = await completeSimple(\n model,\n {\n  messages: [{ role: \"user\", content: \"Solve: 2x + 5 = 13\" }],\n },\n {\n  reasoning: \"medium\", // 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' (xhigh maps to high on non-OpenAI providers)\n }\n);\n\n// Access thinking and text blocks\nfor (const block of response.content) {\n if (block.type === \"thinking\") {\n  console.log(\"Thinking:\", block.thinking);\n } else if (block.type === \"text\") {\n  console.log(\"Response:\", block.text);\n }\n}\n```\n\n### Provider-Specific Options (stream/complete)\n\nFor fine-grained control, use the provider-specific options:\n\n```typescript\nimport { getModel, complete } from \"@f5-sales-demo/pi-ai\";\n\n// OpenAI Reasoning (o1, o3, gpt-5)\nconst openaiModel = getModel(\"openai\", \"gpt-5-mini\");\nawait complete(openaiModel, context, {\n reasoningEffort: \"medium\",\n reasoningSummary: \"detailed\", // OpenAI Responses API only\n});\n\n// Anthropic Thinking (Claude Sonnet 4)\nconst anthropicModel = getModel(\"anthropic\", \"claude-sonnet-4-20250514\");\nawait complete(anthropicModel, context, {\n thinkingEnabled: true,\n thinkingBudgetTokens: 8192, // Optional token limit\n});\n\n// Google Gemini Thinking\nconst googleModel = getModel(\"google\", \"gemini-2.5-flash\");\nawait complete(googleModel, context, {\n thinking: {\n  enabled: true,\n  budgetTokens: 8192, // -1 for dynamic, 0 to disable\n },\n});\n```\n\n### Streaming Thinking Content\n\nWhen streaming, thinking content is delivered through specific events:\n\n```typescript\nconst s = streamSimple(model, context, { reasoning: \"high\" });\n\nfor await (const event of s) {\n switch (event.type) {\n  case \"thinking_start\":\n   console.log(\"[Model started thinking]\");\n   break;\n  case \"thinking_delta\":\n   process.stdout.write(event.delta); // Stream thinking content\n   break;\n  case \"thinking_end\":\n   console.log(\"\\n[Thinking complete]\");\n   break;\n }\n}\n```\n\n## Stop Reasons\n\nEvery `AssistantMessage` includes a `stopReason` field that indicates how the generation ended:\n\n- `\"stop\"` - Normal completion, the model finished its response\n- `\"length\"` - Output hit the maximum token limit\n- `\"toolUse\"` - Model is calling tools and expects tool results\n- `\"error\"` - An error occurred during generation\n- `\"aborted\"` - Request was cancelled via abort signal\n\n## Error Handling\n\nWhen a request ends with an error (including aborts and tool call validation errors), the streaming API emits an error event:\n\n```typescript\n// In streaming\nfor await (const event of stream) {\n if (event.type === \"error\") {\n  // event.reason is either \"error\" or \"aborted\"\n  // event.error is the AssistantMessage with partial content\n  console.error(`Error (${event.reason}):`, event.error.errorMessage);\n  console.log(\"Partial content:\", event.error.content);\n }\n}\n\n// The final message will have the error details\nconst message = await stream.result();\nif (message.stopReason === \"error\" || message.stopReason === \"aborted\") {\n console.error(\"Request failed:\", message.errorMessage);\n // message.content contains any partial content received before the error\n // message.usage contains partial token counts and costs\n}\n```\n\n### Aborting Requests\n\nThe abort signal allows you to cancel in-progress requests. Aborted requests have `stopReason === 'aborted'`:\n\n```typescript\nimport { getModel, stream } from \"@f5-sales-demo/pi-ai\";\n\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\n\n// Abort after 2 seconds\nconst signal = AbortSignal.timeout(2000);\n\nconst s = stream(\n model,\n {\n  messages: [{ role: \"user\", content: \"Write a long story\" }],\n },\n {\n  signal,\n }\n);\n\nfor await (const event of s) {\n if (event.type === \"text_delta\") {\n  process.stdout.write(event.delta);\n } else if (event.type === \"error\") {\n  // event.reason tells you if it was \"error\" or \"aborted\"\n  console.log(`${event.reason === \"aborted\" ? \"Aborted\" : \"Error\"}:`, event.error.errorMessage);\n }\n}\n\n// Get results (may be partial if aborted)\nconst response = await s.result();\nif (response.stopReason === \"aborted\") {\n console.log(\"Request was aborted:\", response.errorMessage);\n console.log(\"Partial content received:\", response.content);\n console.log(\"Tokens used:\", response.usage);\n}\n```\n\n### Continuing After Abort\n\nAborted messages can be added to the conversation context and continued in subsequent requests:\n\n```typescript\nconst context = {\n messages: [{ role: \"user\", content: \"Explain quantum computing in detail\" }],\n};\n\n// First request gets aborted after 2 seconds\nconst controller1 = new AbortController();\nsetTimeout(() => controller1.abort(), 2000);\n\nconst partial = await complete(model, context, { signal: controller1.signal });\n\n// Add the partial response to context\ncontext.messages.push(partial);\ncontext.messages.push({ role: \"user\", content: \"Please continue\" });\n\n// Continue the conversation\nconst continuation = await complete(model, context);\n```\n\n### Common Stream Options\n\nAll providers accept the base `StreamOptions` (in addition to provider-specific options):\n\n- `apiKey`: Override the provider API key\n- `headers`: Extra request headers merged on top of model-defined headers\n- `sessionId`: Provider-specific session identifier (prompt caching/routing)\n- `signal`: Abort in-flight requests\n- `onPayload`: Callback invoked with the provider request payload just before sending\n\nExample:\n\n```typescript\nconst response = await complete(model, context, {\n apiKey: \"sk-live\",\n headers: { \"X-Debug-Trace\": \"true\" },\n onPayload: (payload) => {\n  console.log(\"request payload\", payload);\n },\n});\n```\n\n## APIs, Models, and Providers\n\nThe library implements 4 API interfaces, each with its own streaming function and options:\n\n- **`anthropic-messages`**: Anthropic's Messages API (`streamAnthropic`, `AnthropicOptions`)\n- **`google-generative-ai`**: Google's Generative AI API (`streamGoogle`, `GoogleOptions`)\n- **`openai-completions`**: OpenAI's Chat Completions API (`streamOpenAICompletions`, `OpenAICompletionsOptions`)\n- **`openai-responses`**: OpenAI's Responses API (`streamOpenAIResponses`, `OpenAIResponsesOptions`)\n\n### Providers and Models\n\nA **provider** offers models through a specific API. For example:\n\n- **Anthropic** models use the `anthropic-messages` API\n- **Google** models use the `google-generative-ai` API\n- **OpenAI** models use the `openai-responses` API\n- **Mistral, xAI, Cerebras, Groq, etc.** models use the `openai-completions` API (OpenAI-compatible)\n\n### Querying Providers and Models\n\n```typescript\nimport { getProviders, getModels, getModel } from \"@f5-sales-demo/pi-ai\";\n\n// Get all available providers\nconst providers = getProviders();\nconsole.log(providers); // ['openai', 'anthropic', 'google', 'xai', 'groq', ...]\n\n// Get all models from a provider (fully typed)\nconst anthropicModels = getModels(\"anthropic\");\nfor (const model of anthropicModels) {\n console.log(`${model.id}: ${model.name}`);\n console.log(`  API: ${model.api}`); // 'anthropic-messages'\n console.log(`  Context: ${model.contextWindow} tokens`);\n console.log(`  Vision: ${model.input.includes(\"image\")}`);\n console.log(`  Reasoning: ${model.reasoning}`);\n}\n\n// Get a specific model (both provider and model ID are auto-completed in IDEs)\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\nconsole.log(`Using ${model.name} via ${model.api} API`);\n```\n\n### Custom Models\n\nYou can create custom models for local inference servers or custom endpoints:\nFor Ollama, `OLLAMA_API_KEY` is optional and mainly needed for authenticated/self-hosted gateways.\n\n```typescript\nimport { Model, stream } from \"@f5-sales-demo/pi-ai\";\n\n// Example: Ollama using OpenAI-compatible API\nconst ollamaModel: Model<\"openai-completions\"> = {\n id: \"llama-3.1-8b\",\n name: \"Llama 3.1 8B (Ollama)\",\n api: \"openai-completions\",\n provider: \"ollama\",\n baseUrl: \"http://localhost:11434/v1\",\n reasoning: false,\n input: [\"text\"],\n cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },\n contextWindow: 128000,\n maxTokens: 32000,\n};\n\n// Example: LiteLLM proxy with explicit compat settings\nconst litellmModel: Model<\"openai-completions\"> = {\n id: \"gpt-4o\",\n name: \"GPT-4o (via LiteLLM)\",\n api: \"openai-completions\",\n provider: \"litellm\",\n baseUrl: \"http://localhost:4000/v1\",\n reasoning: false,\n input: [\"text\", \"image\"],\n cost: { input: 2.5, output: 10, cacheRead: 0, cacheWrite: 0 },\n contextWindow: 128000,\n maxTokens: 16384,\n compat: {\n  supportsStore: false, // LiteLLM doesn't support the store field\n },\n};\n\n// Example: Custom endpoint with headers (bypassing Cloudflare bot detection)\nconst proxyModel: Model<\"anthropic-messages\"> = {\n id: \"claude-sonnet-4\",\n name: \"Claude Sonnet 4 (Proxied)\",\n api: \"anthropic-messages\",\n provider: \"custom-proxy\",\n baseUrl: \"https://proxy.example.com/v1\",\n reasoning: true,\n input: [\"text\", \"image\"],\n cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },\n contextWindow: 200000,\n maxTokens: 8192,\n headers: {\n  \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36\",\n  \"X-Custom-Auth\": \"bearer-token-here\",\n },\n};\n\n// Use the custom model\nconst response = await stream(ollamaModel, context, {\n apiKey: process.env.OLLAMA_API_KEY, // Optional; local Ollama usually runs without auth\n});\n```\n\n### OpenAI Compatibility Settings\n\nThe `openai-completions` API is implemented by many providers with minor differences. By default, the library auto-detects compatibility settings based on `baseUrl` for known providers (Cerebras, xAI, Mistral, Chutes, etc.). For custom proxies or unknown endpoints, you can override these settings via the `compat` field:\n\n```typescript\ninterface OpenAICompat {\n supportsStore?: boolean; // Whether provider supports the `store` field (default: true)\n supportsDeveloperRole?: boolean; // Whether provider supports `developer` role vs `system` (default: true)\n supportsReasoningEffort?: boolean; // Whether provider supports `reasoning_effort` (default: true)\n maxTokensField?: \"max_completion_tokens\" | \"max_tokens\"; // Which field name to use (default: max_completion_tokens)\n extraBody?: Record<string, unknown>; // Extra request-body fields for custom proxy routing or provider-specific options\n}\n```\n\nIf `compat` is not set, the library falls back to URL-based detection. If `compat` is partially set, unspecified fields use the detected defaults. This is useful for:\n\n- **LiteLLM proxies**: May not support `store` field\n- **Custom inference servers**: May use non-standard field names\n- **Self-hosted endpoints**: May have different feature support\n\n### Type Safety\n\nModels are typed by their API, ensuring type-safe options:\n\n```typescript\n// TypeScript knows this is an Anthropic model\nconst claude = getModel(\"anthropic\", \"claude-sonnet-4-20250514\");\n\n// So these options are type-checked for AnthropicOptions\nawait stream(claude, context, {\n thinkingEnabled: true, // ✓ Valid for anthropic-messages\n thinkingBudgetTokens: 2048, // ✓ Valid for anthropic-messages\n // reasoningEffort: 'high'  // ✗ TypeScript error: not valid for anthropic-messages\n});\n```\n\n## Cross-Provider Handoffs\n\nThe library supports seamless handoffs between different LLM providers within the same conversation. This allows you to switch models mid-conversation while preserving context, including thinking blocks, tool calls, and tool results.\n\n### How It Works\n\nWhen messages from one provider are sent to a different provider, the library automatically transforms them for compatibility:\n\n- **User and tool result messages** are passed through unchanged\n- **Assistant messages from the same provider/API** are preserved as-is\n- **Assistant messages from different providers** have their thinking blocks converted to text with `<thinking>` tags\n- **Tool calls and regular text** are preserved unchanged\n\n### Example: Multi-Provider Conversation\n\n```typescript\nimport { getModel, complete, Context } from \"@f5-sales-demo/pi-ai\";\n\n// Start with Claude\nconst claude = getModel(\"anthropic\", \"claude-sonnet-4-20250514\");\nconst context: Context = {\n messages: [],\n};\n\ncontext.messages.push({ role: \"user\", content: \"What is 25 * 18?\" });\nconst claudeResponse = await complete(claude, context, {\n thinkingEnabled: true,\n});\ncontext.messages.push(claudeResponse);\n\n// Switch to GPT-5 - it will see Claude's thinking as <thinking> tagged text\nconst gpt5 = getModel(\"openai\", \"gpt-5-mini\");\ncontext.messages.push({ role: \"user\", content: \"Is that calculation correct?\" });\nconst gptResponse = await complete(gpt5, context);\ncontext.messages.push(gptResponse);\n\n// Switch to Gemini\nconst gemini = getModel(\"google\", \"gemini-2.5-flash\");\ncontext.messages.push({ role: \"user\", content: \"What was the original question?\" });\nconst geminiResponse = await complete(gemini, context);\n```\n\n### Provider Compatibility\n\nAll providers can handle messages from other providers, including:\n\n- Text content\n- Tool calls and tool results (including images in tool results)\n- Thinking/reasoning blocks (transformed to tagged text for cross-provider compatibility)\n- Aborted messages with partial content\n\nThis enables flexible workflows where you can:\n\n- Start with a fast model for initial responses\n- Switch to a more capable model for complex reasoning\n- Use specialized models for specific tasks\n- Maintain conversation continuity across provider outages\n\n## Context Serialization\n\nThe `Context` object can be easily serialized and deserialized using standard JSON methods, making it simple to persist conversations, implement chat history, or transfer contexts between services:\n\n```typescript\nimport { Context, getModel, complete } from \"@f5-sales-demo/pi-ai\";\n\n// Create and use a context\nconst context: Context = {\n systemPrompt: \"You are a helpful assistant.\",\n messages: [{ role: \"user\", content: \"What is TypeScript?\" }],\n};\n\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\nconst response = await complete(model, context);\ncontext.messages.push(response);\n\n// Serialize the entire context\nconst serialized = JSON.stringify(context);\nconsole.log(\"Serialized context size:\", serialized.length, \"bytes\");\n\n// Save to database, localStorage, file, etc.\nlocalStorage.setItem(\"conversation\", serialized);\n\n// Later: deserialize and continue the conversation\nconst restored: Context = JSON.parse(localStorage.getItem(\"conversation\")!);\nrestored.messages.push({ role: \"user\", content: \"Tell me more about its type system\" });\n\n// Continue with any model\nconst newModel = getModel(\"anthropic\", \"claude-haiku-4-5-20251001\");\nconst continuation = await complete(newModel, restored);\n```\n\n> **Note**: If the context contains images (encoded as base64 as shown in the Image Input section), those will also be serialized.\n\n## Browser Usage\n\nThe library supports browser environments. You must pass the API key explicitly since environment variables are not available in browsers:\n\n```typescript\nimport { getModel, complete } from \"@f5-sales-demo/pi-ai\";\n\n// API key must be passed explicitly in browser\nconst model = getModel(\"anthropic\", \"claude-haiku-4-5-20251001\");\n\nconst response = await complete(\n model,\n {\n  messages: [{ role: \"user\", content: \"Hello!\" }],\n },\n {\n  apiKey: \"your-api-key\",\n }\n);\n```\n\n> **Security Warning**: Exposing API keys in frontend code is dangerous. Anyone can extract and abuse your keys. Only use this approach for internal tools or demos. For production applications, use a backend proxy that keeps your API keys secure.\n\n### Environment Variables (Node.js only)\n\nIn Node.js environments, you can set environment variables to avoid passing API keys:\n\n| Provider              | Environment Variable(s)                                                                                             |\n| --------------------- | ------------------------------------------------------------------------------------------------------------------- |\n| OpenAI                | `OPENAI_API_KEY`                                                                                                    |\n| Anthropic             | `ANTHROPIC_API_KEY` or `ANTHROPIC_OAUTH_TOKEN` (or `ANTHROPIC_FOUNDRY_API_KEY` when `CLAUDE_CODE_USE_FOUNDRY=true`) |\n| Google                | `GEMINI_API_KEY`                                                                                                    |\n| Vertex AI             | `GOOGLE_CLOUD_PROJECT` (or `GCLOUD_PROJECT`) + `GOOGLE_CLOUD_LOCATION` + ADC                                        |\n| Mistral               | `MISTRAL_API_KEY`                                                                                                   |\n| Groq                  | `GROQ_API_KEY`                                                                                                      |\n| Cerebras              | `CEREBRAS_API_KEY`                                                                                                  |\n| Together              | `TOGETHER_API_KEY`                                                                                                  |\n| Qianfan               | `QIANFAN_API_KEY`                                                                                                   |\n| Hugging Face          | `HUGGINGFACE_HUB_TOKEN` or `HF_TOKEN`                                                                               |\n| Synthetic             | `SYNTHETIC_API_KEY`                                                                                                 |\n| NVIDIA                | `NVIDIA_API_KEY`                                                                                                    |\n| NanoGPT               | `NANO_GPT_API_KEY`                                                                                                  |\n| Venice                | `VENICE_API_KEY`                                                                                                    |\n| Moonshot              | `MOONSHOT_API_KEY`                                                                                                  |\n| xAI                   | `XAI_API_KEY`                                                                                                       |\n| OpenRouter            | `OPENROUTER_API_KEY`                                                                                                |\n| LiteLLM               | `LITELLM_API_KEY`                                                                                                   |\n| Ollama                | `OLLAMA_API_KEY` (optional for local deployments)                                                                   |\n| Qwen Portal           | `QWEN_OAUTH_TOKEN` or `QWEN_PORTAL_API_KEY`                                                                         |\n| zAI                   | `ZAI_API_KEY`                                                                                                       |\n| MiniMax Code          | `MINIMAX_CODE_API_KEY` (international) or `MINIMAX_CODE_CN_API_KEY` (China)                                         |\n| Xiaomi MiMo           | `XIAOMI_API_KEY`                                                                                                    |\n| ZenMux                | `ZENMUX_API_KEY`                                                                                                    |\n| vLLM                  | `VLLM_API_KEY`                                                                                                      |\n| Cloudflare AI Gateway | `CLOUDFLARE_AI_GATEWAY_API_KEY`                                                                                     |\n| GitHub Copilot        | `COPILOT_GITHUB_TOKEN` or `GH_TOKEN` or `GITHUB_TOKEN`                                                              |\n\nFor Cloudflare AI Gateway models, use provider base URL format\n`https://gateway.ai.cloudflare.com/v1/<account>/<gateway>/anthropic`.\n\nFor Anthropic Foundry routing, set `CLAUDE_CODE_USE_FOUNDRY=true` plus:\n`FOUNDRY_BASE_URL`, `ANTHROPIC_FOUNDRY_API_KEY`, optional `ANTHROPIC_CUSTOM_HEADERS`,\nand optional mTLS material (`CLAUDE_CODE_CLIENT_CERT`, `CLAUDE_CODE_CLIENT_KEY`, `NODE_EXTRA_CA_CERTS`).\n\nProvider endpoint defaults for the current OpenAI-compatible integrations:\n\n- Together: `https://api.together.xyz/v1`\n- Moonshot: `https://api.moonshot.ai/v1`\n- Qianfan: `https://qianfan.baidubce.com/v2`\n- NVIDIA: `https://integrate.api.nvidia.com/v1`\n- NanoGPT: `https://nano-gpt.com/api/v1`\n- Hugging Face Inference: `https://router.huggingface.co/v1`\n- Venice: `https://api.venice.ai/api/v1`\n- Xiaomi MiMo: `https://api.xiaomimimo.com/anthropic`\n- ZenMux (OpenAI): `https://zenmux.ai/api/v1`\n- ZenMux (Anthropic models): `https://zenmux.ai/api/anthropic`\n- vLLM: `http://127.0.0.1:8000/v1`\n- Ollama: local OpenAI-compatible runtime\n- LiteLLM: `http://localhost:4000/v1`\n- Cloudflare AI Gateway: `https://gateway.ai.cloudflare.com/v1/<account>/<gateway>/anthropic`\n- Qwen Portal: `https://portal.qwen.ai/v1`\nWhen set, the library automatically uses these keys:\n\n```typescript\n// Uses OPENAI_API_KEY from environment\nconst model = getModel(\"openai\", \"gpt-4o-mini\");\nconst response = await complete(model, context);\n\n// Or override with explicit key\nconst response = await complete(model, context, {\n apiKey: \"sk-different-key\",\n});\n```\n\n### Checking Environment Variables\n\n```typescript\nimport { getEnvApiKey } from \"@f5-sales-demo/pi-ai\";\n\n// Check if an API key is set in environment variables\nconst key = getEnvApiKey(\"openai\"); // checks OPENAI_API_KEY\n```\n\n## OAuth Providers\n\nSeveral providers support OAuth authentication (some also support static API keys):\n\n- **Anthropic** (Claude Pro/Max subscription)\n- **GitHub Copilot** (Copilot subscription)\n- **Google Gemini CLI** (Gemini 2.0/2.5 via Google Cloud Code Assist; free tier or paid subscription)\n- **Antigravity** (Free Gemini 3, Claude, GPT-OSS via Google Cloud)\n- **Qwen Portal** (Qwen OAuth token or API key)\n\nFor paid Cloud Code Assist subscriptions, set `GOOGLE_CLOUD_PROJECT` or `GOOGLE_CLOUD_PROJECT_ID` to your project ID.\n\n### Vertex AI (ADC)\n\nVertex AI models use Application Default Credentials (ADC):\n\n- **Local development**: Run `gcloud auth application-default login`\n- **CI/Production**: Set `GOOGLE_APPLICATION_CREDENTIALS` to point to a service account JSON key file\n\nAlso set `GOOGLE_CLOUD_PROJECT` (or `GCLOUD_PROJECT`) and `GOOGLE_CLOUD_LOCATION`. You can also pass `project`/`location` in the call options.\n\nExample:\n\n```bash\n# Local (uses your user credentials)\ngcloud auth application-default login\nexport GOOGLE_CLOUD_PROJECT=\"my-project\"\nexport GOOGLE_CLOUD_LOCATION=\"us-central1\"\n\n# CI/Production (service account key file)\nexport GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/service-account.json\"\n```\n\n```typescript\nimport { getModel, complete } from \"@f5-sales-demo/pi-ai\";\n\n(async () => {\n const model = getModel(\"google-vertex\", \"gemini-2.5-flash\");\n const response = await complete(model, {\n  messages: [{ role: \"user\", content: \"Hello from Vertex AI\" }],\n });\n\n for (const block of response.content) {\n  if (block.type === \"text\") console.log(block.text);\n }\n})().catch(console.error);\n```\n\nOfficial docs: [Application Default Credentials](https://cloud.google.com/docs/authentication/application-default-credentials)\n\n### CLI Login\n\nThe quickest way to authenticate:\n\n```bash\nbunx @f5-sales-demo/pi-ai login              # interactive provider selection\nbunx @f5-sales-demo/pi-ai login anthropic    # login to specific provider\nbunx @f5-sales-demo/pi-ai login vllm         # store vLLM API key (or placeholder for local no-auth)\nbunx @f5-sales-demo/pi-ai list               # list available providers\n```\n\nCredentials are saved to `agent.db` in the agent directory. `/login qianfan` opens the Qianfan console and stores the pasted API key.\n\n`login` supports OAuth providers (Anthropic, GitHub Copilot, Gemini CLI, Antigravity) and API-key onboarding flows.\nOpenAI has two credential paths. Use `OPENAI_API_KEY` for usage-based Platform API access. Use `login openai-codex` for ChatGPT Plus/Pro subscription OAuth; SSH/headless terminals automatically receive a short device code, while local desktops use browser PKCE. `login openai` explains the API-key path.\n\nFor the current OpenAI-compatible integrations, API-key onboarding covers Together, Moonshot, Qianfan, NVIDIA, NanoGPT, Hugging Face, Venice, Xiaomi, vLLM, LiteLLM, Cloudflare AI Gateway, and Qwen Portal. Ollama is typically local and unauthenticated; set `OLLAMA_API_KEY` only when your Ollama deployment enforces bearer auth.\n\n### Programmatic OAuth\n\nThe library provides login and token refresh functions. Credential storage is the caller's responsibility.\n\n```typescript\nimport {\n // Login functions (return credentials, do not store)\n loginAnthropic,\n loginGitHubCopilot,\n loginGeminiCli,\n loginAntigravity,\n loginCloudflareAiGateway,\n loginHuggingface,\n loginLiteLLM,\n loginMoonshot,\n loginNvidia,\n loginNanoGPT,\n loginQianfan,\n loginQwenPortal,\n loginTogether,\n loginVenice,\n loginVllm,\n loginXiaomi,\n\n // Token management\n refreshOAuthToken, // (provider, credentials) => new credentials\n getOAuthApiKey, // (provider, credentialsMap) => { newCredentials, apiKey } | null\n\n // Types\n type OAuthProvider,\n type OAuthCredentials,\n} from \"@f5-sales-demo/pi-ai\";\n```\n\n### Login Flow Example\n\n```typescript\nimport { loginGitHubCopilot } from \"@f5-sales-demo/pi-ai\";\nimport * as fs from \"node:fs\";\n\nconst credentials = await loginGitHubCopilot({\n onAuth: (url, instructions) => {\n  console.log(`Open: ${url}`);\n  if (instructions) console.log(instructions);\n },\n onPrompt: async (prompt) => {\n  return await getUserInput(prompt.message);\n },\n onProgress: (message) => console.log(message),\n});\n\n// Store credentials yourself\nconst auth = { \"github-copilot\": { type: \"oauth\", ...credentials } };\nfs.writeFileSync(\"credentials.json\", JSON.stringify(auth, null, 2));\n```\n\n### Using OAuth Tokens\n\nUse `getOAuthApiKey()` to get an API key, automatically refreshing if expired:\n\n```typescript\nimport { getModel, complete, getOAuthApiKey } from \"@f5-sales-demo/pi-ai\";\nimport * as fs from \"node:fs\";\n\n// Load your stored credentials\nconst auth = JSON.parse(fs.readFileSync(\"credentials.json\", \"utf-8\"));\n\n// Get API key (refreshes if expired)\nconst result = await getOAuthApiKey(\"github-copilot\", auth);\nif (!result) throw new Error(\"Not logged in\");\n\n// Save refreshed credentials\nauth[\"github-copilot\"] = { type: \"oauth\", ...result.newCredentials };\nfs.writeFileSync(\"credentials.json\", JSON.stringify(auth, null, 2));\n\n// Use the API key\nconst model = getModel(\"github-copilot\", \"gpt-4o\");\nconst response = await complete(\n model,\n {\n  messages: [{ role: \"user\", content: \"Hello!\" }],\n },\n { apiKey: result.apiKey }\n);\n```\n\n### Provider Notes\n\n**OpenAI**: Set `OPENAI_API_KEY` for usage-based Platform API access, or use the `openai-codex` OAuth provider for ChatGPT Plus/Pro subscription access. Remote terminals use device-code authentication without a tunnel or localhost callback.\n\n**GitHub Copilot**: If you get \"The requested model is not supported\" error, enable the model manually in VS Code: open Copilot Chat, click the model selector, select the model (warning icon), and click \"Enable\".\n\n**Google Gemini CLI / Antigravity**: These use Google Cloud OAuth. The `apiKey` returned by `getOAuthApiKey()` is a JSON string containing both the token and project ID, which the library handles automatically.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}