@tsuuanmi/pi-ai

Provider and model API with automatic model discovery, provider configuration, token and cost tracking, and simple context persistence and hand-off to other models mid-session.

Note: This library only includes models that support tool calling (function calling), as this is essential for agentic applications.

Table of Contents

• Supported Providers
• Installation
• Quick Start
• Tools
  • Defining Tools
  • Handling Tool Calls
  • Streaming Tool Calls with Partial JSON
  • Validating Tool Arguments
  • Complete Event Reference
  • Notes and Limitations
• Thinking/Reasoning
  • Unified Interface
  • Provider-Specific Options
  • Streaming Thinking Content
• Model Selection
• Stop Reasons
• Error Handling
  • Aborting Requests
  • Continuing After Abort
• APIs, Models, and Providers
  • Providers and Models
  • Querying Providers and Models
  • Custom Models
  • OpenAI Compatibility Settings
  • Type Safety
• Cross-Provider Handoffs
• Context Serialization
• Browser Usage
  • Browser Compatibility Notes
  • Environment Variables
  • Provider-Scoped Environment Overrides
  • Checking Environment Variables
• OAuth Providers
  • CLI Login
  • Programmatic OAuth
  • Login Flow Example
  • Using OAuth Tokens
  • Provider Notes
• License

Supported Providers

• Anthropic
• OpenAI
• OpenAI Codex (ChatGPT Plus/Pro subscription, requires OAuth, see below)
• Any OpenAI-compatible API: Ollama, vLLM, LM Studio, LiteLLM, etc. (via custom models; see "Custom Models")

Installation

npm install @tsuuanmi/pi-ai

TypeBox exports are re-exported from @tsuuanmi/pi-ai: Type, Static, and TSchema.

Attribution

The orchestration layer was added alongside architecture adapted from open-multi-agent under the MIT license.

Quick Start

import { Type, getModel, stream, complete, Context, Tool, StringEnum } from '@tsuuanmi/pi-ai';

// Fully typed with auto-complete support for both providers and models
const model = getModel('openai', 'gpt-4o-mini');

// Define tools with TypeBox schemas for type safety and validation
const tools: Tool[] = [{
  name: 'get_time',
  description: 'Get the current time',
  parameters: Type.Object({
    timezone: Type.Optional(Type.String({ description: 'Optional timezone (e.g., America/New_York)' }))
  })
}];

// Build a conversation context (easily serializable and transferable between models)
const context: Context = {
  systemPrompt: 'You are a helpful assistant.',
  messages: [{ role: 'user', content: 'What time is it?' }],
  tools
};

// Option 1: Streaming with all event types
const s = stream(model, context);

for await (const event of s) {
  switch (event.type) {
    case 'start':
      console.log(`Starting with ${event.partial.model}`);
      break;
    case 'text_start':
      console.log('\n[Text started]');
      break;
    case 'text_delta':
      process.stdout.write(event.delta);
      break;
    case 'text_end':
      console.log('\n[Text ended]');
      break;
    case 'thinking_start':
      console.log('[Model is thinking...]');
      break;
    case 'thinking_delta':
      process.stdout.write(event.delta);
      break;
    case 'thinking_end':
      console.log('[Thinking complete]');
      break;
    case 'toolcall_start':
      console.log(`\n[Tool call started: index ${event.contentIndex}]`);
      break;
    case 'toolcall_delta':
      // Partial tool arguments are being streamed
      const partialCall = event.partial.content[event.contentIndex];
      if (partialCall.type === 'toolCall') {
        console.log(`[Streaming args for ${partialCall.name}]`);
      }
      break;
    case 'toolcall_end':
      console.log(`\nTool called: ${event.toolCall.name}`);
      console.log(`Arguments: ${JSON.stringify(event.toolCall.arguments)}`);
      break;
    case 'done':
      console.log(`\nFinished: ${event.reason}`);
      break;
    case 'error':
      console.error(`Error: ${event.error}`);
      break;
  }
}

// Get the final message after streaming, add it to the context
const finalMessage = await s.result();
context.messages.push(finalMessage);

// Handle tool calls if any
const toolCalls = finalMessage.content.filter(b => b.type === 'toolCall');
for (const call of toolCalls) {
  // Execute the tool
  const result = call.name === 'get_time'
    ? new Date().toLocaleString('en-US', {
        timeZone: call.arguments.timezone || 'UTC',
        dateStyle: 'full',
        timeStyle: 'long'
      })
    : 'Unknown tool';

  // Add tool result to context
  context.messages.push({
    role: 'toolResult',
    toolCallId: call.id,
    toolName: call.name,
    content: [{ type: 'text', text: result }],
    isError: false,
    timestamp: Date.now()
  });
}

// Continue if there were tool calls
if (toolCalls.length > 0) {
  const continuation = await complete(model, context);
  context.messages.push(continuation);
  console.log('After tool execution:', continuation.content);
}

console.log(`Total tokens: ${finalMessage.usage.input} in, ${finalMessage.usage.output} out`);
console.log(`Cost: $${finalMessage.usage.cost.total.toFixed(4)}`);

// Option 2: Get complete response without streaming
const response = await complete(model, context);

for (const block of response.content) {
  if (block.type === 'text') {
    console.log(block.text);
  } else if (block.type === 'toolCall') {
    console.log(`Tool: ${block.name}(${JSON.stringify(block.arguments)})`);
  }
}

Tools

Tools enable LLMs to interact with external systems. This library uses TypeBox schemas for type-safe tool definitions with automatic validation using TypeBox's built-in validator and value conversion utilities. TypeBox schemas can be serialized and deserialized as plain JSON, making them ideal for distributed systems.

Defining Tools

import { Type, Tool, StringEnum } from '@tsuuanmi/pi-ai';

// Define tool parameters with TypeBox
const weatherTool: Tool = {
  name: 'get_weather',
  description: 'Get current weather for a location',
  parameters: Type.Object({
    location: Type.String({ description: 'City name or coordinates' }),
    units: StringEnum(['celsius', 'fahrenheit'], { default: 'celsius' })
  })
};

// Note: Use the StringEnum helper instead of Type.Enum
// Type.Enum generates anyOf/const patterns that some providers don't support

const bookMeetingTool: Tool = {
  name: 'book_meeting',
  description: 'Schedule a meeting',
  parameters: Type.Object({
    title: Type.String({ minLength: 1 }),
    startTime: Type.String({ format: 'date-time' }),
    endTime: Type.String({ format: 'date-time' }),
    attendees: Type.Array(Type.String({ format: 'email' }), { minItems: 1 })
  })
};

Handling Tool Calls

Tool results use text content blocks:

const context: Context = {
  messages: [{ role: 'user', content: 'What is the weather in London?' }],
  tools: [weatherTool]
};

const response = await complete(model, context);

// Check for tool calls in the response
for (const block of response.content) {
  if (block.type === 'toolCall') {
    // Execute your tool with the arguments
    // See "Validating Tool Arguments" section for validation
    const result = await executeWeatherApi(block.arguments);

    // Add tool result with text content
    context.messages.push({
      role: 'toolResult',
      toolCallId: block.id,
      toolName: block.name,
      content: [{ type: 'text', text: JSON.stringify(result) }],
      isError: false,
      timestamp: Date.now()
    });
  }
}

Streaming Tool Calls with Partial JSON

During streaming, tool call arguments are progressively parsed as they arrive. This enables real-time UI updates before the complete arguments are available:

const s = stream(model, context);

for await (const event of s) {
  if (event.type === 'toolcall_delta') {
    const toolCall = event.partial.content[event.contentIndex];

    // toolCall.arguments contains partially parsed JSON during streaming
    // This allows for progressive UI updates
    if (toolCall.type === 'toolCall' && toolCall.arguments) {
      // BE DEFENSIVE: arguments may be incomplete
      // Example: Show file path being written even before content is complete
      if (toolCall.name === 'write_file' && toolCall.arguments.path) {
        console.log(`Writing to: ${toolCall.arguments.path}`);

        // Content might be partial or missing
        if (toolCall.arguments.content) {
          console.log(`Content preview: ${toolCall.arguments.content.substring(0, 100)}...`);
        }
      }
    }
  }

  if (event.type === 'toolcall_end') {
    // Here toolCall.arguments is complete (but not yet validated)
    const toolCall = event.toolCall;
    console.log(`Tool completed: ${toolCall.name}`, toolCall.arguments);
  }
}

Important notes about partial tool arguments:

• During toolcall_delta events, arguments contains the best-effort parse of partial JSON
• Fields may be missing or incomplete - always check for existence before use
• String values may be truncated mid-word
• Arrays may be incomplete
• Nested objects may be partially populated
• At minimum, arguments will be an empty object {}, never undefined

Validating Tool Arguments

When 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.

When implementing your own tool execution loop with stream() or complete(), use validateToolCall to validate arguments before passing them to your tools:

import { stream, validateToolCall, Tool } from '@tsuuanmi/pi-ai';

const tools: Tool[] = [weatherTool, calculatorTool];
const s = stream(model, { messages, tools });

for await (const event of s) {
  if (event.type === 'toolcall_end') {
    const toolCall = event.toolCall;

    try {
      // Validate arguments against the tool's schema (throws on invalid args)
      const validatedArgs = validateToolCall(tools, toolCall);
      const result = await executeMyTool(toolCall.name, validatedArgs);
      // ... add tool result to context
    } catch (error) {
      // Validation failed - return error as tool result so model can retry
      context.messages.push({
        role: 'toolResult',
        toolCallId: toolCall.id,
        toolName: toolCall.name,
        content: [{ type: 'text', text: error.message }],
        isError: true,
        timestamp: Date.now()
      });
    }
  }
}

Complete Event Reference

All streaming events emitted during assistant message generation:

| Event Type | Description | Key Properties | |------------|-------------|----------------| | start | Stream begins | partial: Initial assistant message structure | | text_start | Text block starts | contentIndex: Position in content array | | text_delta | Text chunk received | delta: New text, contentIndex: Position | | text_end | Text block complete | content: Full text, contentIndex: Position | | thinking_start | Thinking block starts | contentIndex: Position in content array | | thinking_delta | Thinking chunk received | delta: New text, contentIndex: Position | | thinking_end | Thinking block complete | content: Full thinking, contentIndex: Position | | toolcall_start | Tool call begins | contentIndex: Position in content array | | toolcall_delta | Tool arguments streaming | delta: JSON chunk, partial.content[contentIndex].arguments: Partial parsed args | | toolcall_end | Tool call complete | toolCall: Complete validated tool call with id, name, arguments | | done | Stream complete | reason: Stop reason ("stop", "length", "toolUse"), message: Final assistant message | | error | Error occurred | reason: Error type ("error" or "aborted"), error: AssistantMessage with partial content |

Streaming events for different content blocks are not guaranteed to be contiguous. Providers may emit deltas for text, thinking, and tool calls in the same upstream chunk, and pi may surface corresponding events interleaved, for example text_start, text_delta, toolcall_start, text_delta, toolcall_delta. Consumers must use contentIndex to associate each delta/end event with its block and must not assume that a block's *_start/*_delta/*_end sequence is uninterrupted by events for other blocks.

Thinking/Reasoning

Many 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.

Unified Interface (stream/complete)

import { getModel, stream, complete } from '@tsuuanmi/pi-ai';

// Many models across providers support thinking/reasoning
const model = getModel('anthropic', 'claude-sonnet-4-20250514');
// or getModel('openai', 'gpt-5-mini');
// or getModel('openai-codex', 'gpt-5-codex');

// Check if model supports reasoning
if (model.reasoning) {
  console.log('Model supports reasoning/thinking');
}

// Use the simplified reasoning option
const response = await complete(model, {
  messages: [{ role: 'user', content: 'Solve: 2x + 5 = 13' }]
}, {
  reasoning: 'medium'  // 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' | 'max' | 'ultra'
});

// Access thinking and text blocks
for (const block of response.content) {
  if (block.type === 'thinking') {
    console.log('Thinking:', block.thinking);
  } else if (block.type === 'text') {
    console.log('Response:', block.text);
  }
}

Provider-Specific Options (stream/complete)

For fine-grained control, use the provider-specific options:

import { getModel, complete } from '@tsuuanmi/pi-ai';

// OpenAI Reasoning (o1, o3, gpt-5)
const openaiModel = getModel('openai', 'gpt-5-mini');
await complete(openaiModel, context, {
  reasoningEffort: 'medium',
  reasoningSummary: 'detailed'  // OpenAI Responses API only
});

// Anthropic Thinking (Claude Sonnet 4)
const anthropicModel = getModel('anthropic', 'claude-sonnet-4-20250514');
await complete(anthropicModel, context, {
  thinkingEnabled: true,
  effort: 'high'  // adaptive thinking effort
});

Streaming Thinking Content

When streaming, thinking content is delivered through specific events:

const s = stream(model, context, { reasoning: 'high' });

for await (const event of s) {
  switch (event.type) {
    case 'thinking_start':
      console.log('[Model started thinking]');
      break;
    case 'thinking_delta':
      process.stdout.write(event.delta);  // Stream thinking content
      break;
    case 'thinking_end':
      console.log('\n[Thinking complete]');
      break;
  }
}

Model Selection

ThinkingLevel is the canonical model-level union: off, minimal, low, medium, high, xhigh, max, and ultra. Provider request options use ReasoningLevel, which excludes off.

Use parseModelPattern for provider-independent model matching:

import { parseModelPattern, type Model } from "@tsuuanmi/pi-ai";

const result = parseModelPattern("openai/gpt-5:high", models);
if (result.model) {
  console.log(result.model.id, result.thinkingLevel);
}

The parser accepts model IDs containing colons and returns an optional warning for an invalid thinking-level suffix. Registry loading, authentication, and CLI policy remain outside this package.

Stop Reasons

Every AssistantMessage includes a stopReason field that indicates how the generation ended:

• "stop" - Normal completion, the model finished its response
• "length" - Output hit the maximum token limit
• "toolUse" - Model is calling tools and expects tool results
• "error" - An error occurred during generation
• "aborted" - Request was cancelled via abort signal

AssistantMessage may also include responseId, a provider-specific upstream response or message identifier when the underlying API exposes one. Do not assume it is always present across providers.

Error Handling

When a request ends with an error (including aborts and tool call validation errors), the streaming API emits an error event:

// In streaming
for await (const event of stream) {
  if (event.type === 'error') {
    // event.reason is either "error" or "aborted"
    // event.error is the AssistantMessage with partial content
    console.error(`Error (${event.reason}):`, event.error.errorMessage);
    console.log('Partial content:', event.error.content);
  }
}

// The final message will have the error details
const message = await stream.result();
if (message.stopReason === 'error' || message.stopReason === 'aborted') {
  console.error('Request failed:', message.errorMessage);
  // message.content contains any partial content received before the error
  // message.usage contains partial token counts and costs
}

Aborting Requests

The abort signal allows you to cancel in-progress requests. Aborted requests have stopReason === 'aborted':

import { getModel, stream } from '@tsuuanmi/pi-ai';

const model = getModel('openai', 'gpt-4o-mini');
const controller = new AbortController();

// Abort after 2 seconds
setTimeout(() => controller.abort(), 2000);

const s = stream(model, {
  messages: [{ role: 'user', content: 'Write a long story' }]
}, {
  signal: controller.signal
});

for await (const event of s) {
  if (event.type === 'text_delta') {
    process.stdout.write(event.delta);
  } else if (event.type === 'error') {
    // event.reason tells you if it was "error" or "aborted"
    console.log(`${event.reason === 'aborted' ? 'Aborted' : 'Error'}:`, event.error.errorMessage);
  }
}

// Get results (may be partial if aborted)
const response = await s.result();
if (response.stopReason === 'aborted') {
  console.log('Request was aborted:', response.errorMessage);
  console.log('Partial content received:', response.content);
  console.log('Tokens used:', response.usage);
}

Continuing After Abort

Aborted messages can be added to the conversation context and continued in subsequent requests:

const context = {
  messages: [
    { role: 'user', content: 'Explain quantum computing in detail' }
  ]
};

// First request gets aborted after 2 seconds
const controller1 = new AbortController();
setTimeout(() => controller1.abort(), 2000);

const partial = await complete(model, context, { signal: controller1.signal });

// Add the partial response to context
context.messages.push(partial);
context.messages.push({ role: 'user', content: 'Please continue' });

// Continue the conversation
const continuation = await complete(model, context);

Debugging Provider Payloads

Use the onPayload callback to inspect the request payload sent to the provider. This is useful for debugging request formatting issues or provider validation errors.

const response = await complete(model, context, {
  onPayload: (payload) => {
    console.log('Provider payload:', JSON.stringify(payload, null, 2));
  }
});

The callback is supported by stream, complete, stream, and complete.

APIs, Models, and Providers

The library uses a registry of API implementations. Built-in APIs include:

• anthropic-messages: Anthropic Messages API (streamAnthropic, AnthropicOptions)
• openai-completions: OpenAI Chat Completions API (streamOpenAICompletions, OpenAICompletionsOptions)
• openai-responses: OpenAI Responses API (streamOpenAIResponses, OpenAIResponsesOptions)
• openai-codex-responses: OpenAI Codex Responses API (streamOpenAICodexResponses, OpenAICodexResponsesOptions)

Providers and Models

A provider offers models through a specific API. For example:

• Anthropic models use the anthropic-messages API
• OpenAI models use the openai-responses API
• OpenAI-compatible servers (Ollama, vLLM, LiteLLM, etc.) use the openai-completions API

Querying Providers and Models

import { getModelProviders, getModels, getModel } from '@tsuuanmi/pi-ai';

// Get all available providers
const providers = getModelProviders();
console.log(providers); // ['anthropic', 'ollama-cloud', 'openai']

// Get all models from a provider (fully typed)
const anthropicModels = getModels('anthropic');
for (const model of anthropicModels) {
  console.log(`${model.id}: ${model.name}`);
  console.log(`  API: ${model.api}`); // 'anthropic-messages'
  console.log(`  Context: ${model.contextWindow} tokens`);
  console.log(`  Reasoning: ${model.reasoning}`);
}

// Get a specific model (both provider and model ID are auto-completed in IDEs)
const model = getModel('openai', 'gpt-4o-mini');
console.log(`Using ${model.name} via ${model.api} API`);

Custom Models

You can create custom models for local inference servers or custom endpoints:

import { Model, stream } from '@tsuuanmi/pi-ai';

// Example: Ollama using OpenAI-compatible API
const ollamaModel: Model<'openai-completions'> = {
  id: 'llama-3.1-8b',
  name: 'Llama 3.1 8B (Ollama)',
  api: 'openai-completions',
  provider: 'ollama',
  baseUrl: 'http://localhost:11434/v1',
  reasoning: false,
  input: ['text'],
  cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
  contextWindow: 128000,
  maxTokens: 32000
};

// Example: LiteLLM proxy with explicit compat settings
const litellmModel: Model<'openai-completions'> = {
  id: 'gpt-4o',
  name: 'GPT-4o (via LiteLLM)',
  api: 'openai-completions',
  provider: 'litellm',
  baseUrl: 'http://localhost:4000/v1',
  reasoning: false,
  input: ['text'],
  cost: { input: 2.5, output: 10, cacheRead: 0, cacheWrite: 0 },
  contextWindow: 128000,
  maxTokens: 16384,
  compat: {
    supportsStore: false,  // LiteLLM doesn't support the store field
  }
};

// Example: Custom endpoint with headers (bypassing Cloudflare bot detection)
const proxyModel: Model<'anthropic-messages'> = {
  id: 'claude-sonnet-4',
  name: 'Claude Sonnet 4 (Proxied)',
  api: 'anthropic-messages',
  provider: 'custom-proxy',
  baseUrl: 'https://proxy.example.com/v1',
  reasoning: true,
  input: ['text'],
  cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },
  contextWindow: 200000,
  maxTokens: 8192,
  headers: {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36',
    'X-Custom-Auth': 'bearer-token-here'
  }
};

// Use the custom model
const response = await stream(ollamaModel, context, {
  apiKey: 'dummy' // Ollama doesn't need a real key
});

Some OpenAI-compatible servers do not understand the developer role used for reasoning-capable models. For those providers, set compat.supportsDeveloperRole to false so the system prompt is sent as a system message instead. If the server also does not support reasoning_effort, set compat.supportsReasoningEffort to false too.

Use model-level thinkingLevelMap to describe model-specific thinking controls. Keys are pi thinking levels (off, minimal, low, medium, high, xhigh). Missing keys use provider defaults, string values are sent to the provider, and null marks a level unsupported.

This commonly applies to Ollama, vLLM, SGLang, and similar OpenAI-compatible servers. You can set compat at the provider level or per model.

const ollamaReasoningModel: Model<'openai-completions'> = {
  id: 'gpt-oss:20b',
  name: 'GPT-OSS 20B (Ollama)',
  api: 'openai-completions',
  provider: 'ollama',
  baseUrl: 'http://localhost:11434/v1',
  reasoning: true,
  input: ['text'],
  cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
  contextWindow: 131072,
  maxTokens: 32000,
  thinkingLevelMap: {
    minimal: null,
    low: null,
    medium: null,
    high: 'high',
    xhigh: null,
  },
  compat: {
    supportsDeveloperRole: false,
    supportsReasoningEffort: false,
  }
};

OpenAI Compatibility Settings

The openai-completions API is implemented by many providers with minor differences. For custom proxies or unknown endpoints, you can override compatibility settings via the compat field. For openai-responses models, the compat field supports Responses-specific flags.

interface OpenAICompletionsCompat {
  supportsStore?: boolean;           // Whether provider supports the `store` field (default: auto-detected from URL)
  supportsDeveloperRole?: boolean;   // Whether provider supports `developer` role vs `system` (default: auto-detected from URL)
  supportsReasoningEffort?: boolean; // Whether provider supports `reasoning_effort` (default: auto-detected from URL)
  supportsUsageInStreaming?: boolean; // Whether provider supports `stream_options: { include_usage: true }` (default: true)
  maxTokensField?: 'max_completion_tokens' | 'max_tokens';  // Which field name to use (default: auto-detected from URL)
  requiresToolResultName?: boolean;  // Whether tool results require the `name` field (default: auto-detected from URL)
  requiresAssistantAfterToolResult?: boolean; // Whether tool results must be followed by an assistant message (default: auto-detected from URL)
  requiresThinkingAsText?: boolean;  // Whether thinking blocks must be converted to text blocks with <thinking> delimiters (default: auto-detected from URL)
  requiresReasoningContentOnAssistantMessages?: boolean; // Whether all replayed assistant messages must include empty reasoning_content when reasoning is enabled (default: auto-detected)
  thinkingFormat?: 'openai' | 'string-thinking'; // Format for reasoning/thinking parameter. 'openai' uses reasoning_effort, 'string-thinking' uses a top-level `thinking` string (default: 'openai')
  supportsStrictMode?: boolean;      // Whether provider supports `strict` in tool definitions (default: true)
  cacheControlFormat?: 'anthropic';  // Anthropic-style cache_control on system prompt, last tool, and last user/assistant text content
  sendSessionAffinityHeaders?: boolean; // Whether to send `session_id`, `x-client-request-id`, and `x-session-affinity` from `sessionId` when caching is enabled (default: false)
  supportsLongCacheRetention?: boolean; // Whether the provider supports long prompt cache retention (`prompt_cache_retention: "24h"` or Anthropic-style `cache_control.ttl: "1h"`, depending on format). Default: true
}

interface OpenAIResponsesCompat {
  supportsDeveloperRole?: boolean;   // Whether provider supports `developer` role vs `system` (default: true)
  sendSessionIdHeader?: boolean;     // Whether to send `session_id` from `sessionId` when caching is enabled (default: true)
  supportsLongCacheRetention?: boolean; // Whether provider supports `prompt_cache_retention: "24h"` (default: true)
}

If 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:

• LiteLLM proxies: May not support store field
• Custom inference servers: May use non-standard field names
• Self-hosted endpoints: May have different feature support

Type Safety

Models are typed by their API, which keeps the model metadata accurate. Provider-specific option types are enforced when you call the provider functions directly. The generic stream and complete functions accept StreamOptions with additional provider fields.

import { streamAnthropic, type AnthropicOptions } from '@tsuuanmi/pi-ai';

// TypeScript knows this is an Anthropic model
const claude = getModel('anthropic', 'claude-sonnet-4-20250514');

const options: AnthropicOptions = {
  thinkingEnabled: true,
  effort: 'high'
};

await streamAnthropic(claude, context, options);

Cross-Provider Handoffs

The library supports seamless handoffs between different provider within the same conversation. This allows you to switch models mid-conversation while preserving context, including thinking blocks, tool calls, and tool results.

How It Works

When messages from one provider are sent to a different provider, the library automatically transforms them for compatibility:

• User and tool result messages are passed through unchanged
• Assistant messages from the same provider/API are preserved as-is
• Assistant messages from different providers have their thinking blocks converted to text with <thinking> tags
• Tool calls and regular text are preserved unchanged

Example: Multi-Provider Conversation

import { getModel, complete, Context } from '@tsuuanmi/pi-ai';

// Start with Claude
const claude = getModel('anthropic', 'claude-sonnet-4-20250514');
const context: Context = {
  messages: []
};

context.messages.push({ role: 'user', content: 'What is 25 * 18?' });
const claudeResponse = await complete(claude, context, {
  thinkingEnabled: true
});
context.messages.push(claudeResponse);

// Switch to GPT-5 - it will see Claude's thinking as <thinking> tagged text
const gpt5 = getModel('openai', 'gpt-5-mini');
context.messages.push({ role: 'user', content: 'Is that calculation correct?' });
const gptResponse = await complete(gpt5, context);
context.messages.push(gptResponse);

Provider Compatibility

All providers can handle messages from other providers, including:

• Text content
• Tool calls and tool results
• Thinking/reasoning blocks (transformed to tagged text for cross-provider compatibility)
• Aborted messages with partial content

This enables flexible multi-provider applications where you can:

• Start with a fast model for initial responses
• Switch to a more capable model for complex reasoning
• Use specialized models for specific tasks
• Maintain conversation continuity across provider outages

Context Serialization

The 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:

import { Context, getModel, complete } from '@tsuuanmi/pi-ai';

// Create and use a context
const context: Context = {
  systemPrompt: 'You are a helpful assistant.',
  messages: [
    { role: 'user', content: 'What is TypeScript?' }
  ]
};

const model = getModel('openai', 'gpt-4o-mini');
const response = await complete(model, context);
context.messages.push(response);

// Serialize the entire context
const serialized = JSON.stringify(context);
console.log('Serialized context size:', serialized.length, 'bytes');

// Save to database, localStorage, file, etc.
localStorage.setItem('conversation', serialized);

// Later: deserialize and continue the conversation
const restored: Context = JSON.parse(localStorage.getItem('conversation')!);
restored.messages.push({ role: 'user', content: 'Tell me more about its type system' });

// Continue with any model
const newModel = getModel('anthropic', 'claude-3-5-haiku-20241022');
const continuation = await complete(newModel, restored);

Browser Usage

The library supports browser environments. You must pass the API key explicitly since environment variables are not available in browsers:

import { getModel, complete } from '@tsuuanmi/pi-ai';

// API key must be passed explicitly in browser
const model = getModel('anthropic', 'claude-3-5-haiku-20241022');

const response = await complete(model, {
  messages: [{ role: 'user', content: 'Hello!' }]
}, {
  apiKey: 'your-api-key'
});

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.

Browser Compatibility Notes

• OAuth login flows are not supported in browser environments. Use the @tsuuanmi/pi-ai/oauth entry point in Node.js.
• Use a server-side proxy or backend service if you need OAuth-based auth from a web app.

Environment Variables (Node.js only)

In Node.js environments, you can set environment variables to avoid passing API keys:

| Provider | Environment Variable(s) | |----------|------------------------| | Anthropic | ANTHROPIC_API_KEY or ANTHROPIC_OAUTH_TOKEN | | OpenAI | OPENAI_API_KEY |

When set, the library automatically uses these keys:

// Uses OPENAI_API_KEY from environment
const model = getModel('openai', 'gpt-4o-mini');
const response = await complete(model, context);

// Or override with explicit key
const response = await complete(model, context, {
  apiKey: 'sk-different-key'
});

Provider-Scoped Environment Overrides

Pass env in stream options to scope provider configuration such as PI_CACHE_RETENTION and HTTP_PROXY/HTTPS_PROXY to a request. API credentials are supplied by the caller, typically from auth.json in Pi.

OAuth Providers

The following providers require OAuth authentication instead of static API keys:

• Anthropic (Claude Pro/Max subscription)
• OpenAI Codex (ChatGPT Plus/Pro subscription, access to GPT-5.x Codex models)

Programmatic OAuth

The library provides login and token refresh functions via the @tsuuanmi/pi-ai/oauth entry point. Credential storage is the caller's responsibility.

import {
  // Login functions (return credentials, do not store)
  loginAnthropic,
  loginOpenAICodex,

  // Token management
  getOAuthApiKey,      // (provider, credentialsMap) => { newCredentials, apiKey } | null

  // Types
  type OAuthProviderId,
  type OAuthCredentials,
} from '@tsuuanmi/pi-ai/oauth';

Login Flow Example

import { loginOpenAICodex } from '@tsuuanmi/pi-ai/oauth';
import { writeFileSync } from 'fs';

const credentials = await loginOpenAICodex({
  onAuth: (url, instructions) => {
    console.log(`Open: ${url}`);
    if (instructions) console.log(instructions);
  },
  onPrompt: async (prompt) => {
    return await getUserInput(prompt.message);
  },
  onProgress: (message) => console.log(message)
});

// Store credentials yourself
const auth = { 'openai-codex': { type: 'oauth', ...credentials } };
writeFileSync('auth.json', JSON.stringify(auth, null, 2));

Using OAuth Tokens

Use getOAuthApiKey() to get an API key, automatically refreshing if expired:

import { getModel, complete } from '@tsuuanmi/pi-ai';
import { getOAuthApiKey } from '@tsuuanmi/pi-ai/oauth';
import { readFileSync, writeFileSync } from 'fs';

// Load your stored credentials
const auth = JSON.parse(readFileSync('auth.json', 'utf-8'));

// Get API key (refreshes if expired)
const result = await getOAuthApiKey('openai-codex', auth);
if (!result) throw new Error('Not logged in');

// Save refreshed credentials
auth['openai-codex'] = { type: 'oauth', ...result.newCredentials };
writeFileSync('auth.json', JSON.stringify(auth, null, 2));

// Use the API key
const model = getModel('openai-codex', 'gpt-5.5');
const response = await complete(model, {
  messages: [{ role: 'user', content: 'Hello!' }]
}, { apiKey: result.apiKey });

Provider Notes

OpenAI Codex: Requires a ChatGPT Plus or Pro subscription. Provides access to GPT-5.x Codex models with extended context windows and reasoning capabilities. The library automatically handles session-based prompt caching when sessionId is provided in stream options. You can set transport in stream options to "sse", "websocket", or "auto" for Codex Responses transport selection. When using WebSocket with a sessionId, connections are reused per session and expire after 5 minutes of inactivity.

Development

Adding a New Provider

Adding a new provider requires changes across multiple files. This checklist covers all necessary steps:

1. Shared Types (src/model/index.ts and src/protocol/*)

• Add the API identifier to KnownApi (for example "bedrock-converse-stream")
• Create an options interface extending StreamOptions (for example BedrockOptions)
• Add the provider name to KnownProviderId (for example "amazon-bedrock")

2. Provider Implementation (src/provider/<provider>/)

Create a provider-specific folder and entry file (for example src/provider/amazon-bedrock/index.ts) that exports:

• stream<Provider>() function returning AssistantMessageEventStream
• Provider-specific option mapping when required
• Provider-specific options interface
• Message conversion functions to transform Context to provider format
• Tool conversion if the provider supports tools
• Response parsing to emit standardized events (text, tool_call, thinking, usage, stop)

3. API Registry Integration (src/provider/built-ins.ts)

• Register the API with registerProvider()
• Add a package subpath export in package.json for the provider module (./dist/provider/<provider>/index.js)
• Add module loaders in src/provider/built-ins.ts, do not statically import provider implementation modules there
• Add any root-level export type re-exports in src/index.ts that should remain available from @tsuuanmi/pi-ai
• Ensure caller-supplied credentials in StreamOptions.apiKey are passed through correctly

4. Model Generation (src/model/generate.ts)

• Add provider-specific source parsing under src/provider/<provider>/
• Map chat/tool-capable provider model data to the standardized Model interface
• Keep cross-provider orchestration in src/model/generate.ts and shared generator utilities under src/model/generator/

5. Tests (test/)

Add tests under packages/ai/test/ covering the new provider: streaming and tool use, token usage reporting, request abort, and context replay. The existing suites are provider-specific; follow that pattern.

6. Pi Integration (../pi/)

Update src/model/model-resolver.ts:

• Add a default model ID for the provider in defaultModelPerProvider

Update README.md:

• Add the provider to the providers section with setup instructions

7. Documentation

Update packages/ai/README.md:

• Add to the Supported Providers table
• Document any provider-specific options or authentication requirements
• Add environment variable to the Environment Variables section

8. Changelog

Add an entry to packages/ai/CHANGELOG.md under ## [Unreleased]:

### Added
- Added support for [Provider Name] provider ([#PR](link) by [@author](link))

License

MIT