@yeshwanthyk/ai

Unified LLM 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 workflows.

Supported Providers

• OpenAI
• Anthropic
• Google
• Mistral
• Groq
• Cerebras
• xAI
• OpenRouter
• GitHub Copilot (requires OAuth, see below)
• Any OpenAI-compatible API: Ollama, vLLM, LM Studio, etc.

Installation

npm install @yeshwanthyk/ai

Quick Start

import { Type, getModel, stream, complete, Context, Tool, StringEnum } from '@yeshwanthyk/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 (supports text and images)
  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 AJV. TypeBox schemas can be serialized and deserialized as plain JSON, making them ideal for distributed systems.

Defining Tools

import { Type, Tool, StringEnum } from '@yeshwanthyk/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: For Google API compatibility, use StringEnum helper instead of Type.Enum
// Type.Enum generates anyOf/const patterns that Google doesn'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 content blocks and can include both text and images:

import { readFileSync } from 'fs';

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()
    });
  }
}

// Tool results can also include images (for vision-capable models)
const imageBuffer = readFileSync('chart.png');
context.messages.push({
  role: 'toolResult',
  toolCallId: 'tool_xyz',
  toolName: 'generate_chart',
  content: [
    { type: 'text', text: 'Generated chart showing temperature trends' },
    { type: 'image', data: imageBuffer.toString('base64'), mimeType: 'image/png' }
  ],
  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
• The Google provider does not support function call streaming. Instead, you will receive a single toolcall_delta event with the full arguments.

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 '@yeshwanthyk/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 |

Image Input

Models 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.

import { readFileSync } from 'fs';
import { getModel, complete } from '@yeshwanthyk/ai';

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

// Check if model supports images
if (model.input.includes('image')) {
  console.log('Model supports vision');
}

const imageBuffer = readFileSync('image.png');
const base64Image = imageBuffer.toString('base64');

const response = await complete(model, {
  messages: [{
    role: 'user',
    content: [
      { type: 'text', text: 'What is in this image?' },
      { type: 'image', data: base64Image, mimeType: 'image/png' }
    ]
  }]
});

// Access the response
for (const block of response.content) {
  if (block.type === 'text') {
    console.log(block.text);
  }
}

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 (streamSimple/completeSimple)

import { getModel, streamSimple, completeSimple } from '@yeshwanthyk/ai';

// Many models across providers support thinking/reasoning
const model = getModel('anthropic', 'claude-sonnet-4-20250514');
// or getModel('openai', 'gpt-5-mini');
// or getModel('google', 'gemini-2.5-flash');
// or getModel('xai', 'grok-code-fast-1');
// or getModel('groq', 'openai/gpt-oss-20b');
// or getModel('cerebras', 'gpt-oss-120b');
// or getModel('openrouter', 'z-ai/glm-4.5v');

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

// Use the simplified reasoning option
const response = await completeSimple(model, {
  messages: [{ role: 'user', content: 'Solve: 2x + 5 = 13' }]
}, {
  reasoning: 'medium'  // 'minimal' | 'low' | 'medium' | 'high' | 'xhigh' (xhigh maps to high on non-OpenAI providers)
});

// 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 '@yeshwanthyk/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,
  thinkingBudgetTokens: 8192  // Optional token limit
});

// Google Gemini Thinking
const googleModel = getModel('google', 'gemini-2.5-flash');
await complete(googleModel, context, {
  thinking: {
    enabled: true,
    budgetTokens: 8192  // -1 for dynamic, 0 to disable
  }
});

Streaming Thinking Content

When streaming, thinking content is delivered through specific events:

const s = streamSimple(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;
  }
}

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

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 '@yeshwanthyk/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);

APIs, Models, and Providers

The library implements 4 API interfaces, each with its own streaming function and options:

• anthropic-messages: Anthropic's Messages API (streamAnthropic, AnthropicOptions)
• google-generative-ai: Google's Generative AI API (streamGoogle, GoogleOptions)
• openai-completions: OpenAI's Chat Completions API (streamOpenAICompletions, OpenAICompletionsOptions)
• openai-responses: OpenAI's Responses API (streamOpenAIResponses, OpenAIResponsesOptions)

Providers and Models

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

• Anthropic models use the anthropic-messages API
• Google models use the google-generative-ai API
• OpenAI models use the openai-responses API
• Mistral, xAI, Cerebras, Groq, etc. models use the openai-completions API (OpenAI-compatible)

Querying Providers and Models

import { getProviders, getModels, getModel } from '@yeshwanthyk/ai';

// Get all available providers
const providers = getProviders();
console.log(providers); // ['openai', 'anthropic', 'google', 'xai', 'groq', ...]

// 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(`  Vision: ${model.input.includes('image')}`);
  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 '@yeshwanthyk/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', 'image'],
  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', 'image'],
  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
});

OpenAI Compatibility Settings

The 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:

interface OpenAICompat {
  supportsStore?: boolean;           // Whether provider supports the `store` field (default: true)
  supportsDeveloperRole?: boolean;   // Whether provider supports `developer` role vs `system` (default: true)
  supportsReasoningEffort?: boolean; // Whether provider supports `reasoning_effort` (default: true)
  maxTokensField?: 'max_completion_tokens' | 'max_tokens';  // Which field name to use (default: max_completion_tokens)
}

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, ensuring type-safe options:

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

// So these options are type-checked for AnthropicOptions
await stream(claude, context, {
  thinkingEnabled: true,      // ✓ Valid for anthropic-messages
  thinkingBudgetTokens: 2048, // ✓ Valid for anthropic-messages
  // reasoningEffort: 'high'  // ✗ TypeScript error: not valid for anthropic-messages
});

Cross-Provider Handoffs

The 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.

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 '@yeshwanthyk/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);

// Switch to Gemini
const gemini = getModel('google', 'gemini-2.5-flash');
context.messages.push({ role: 'user', content: 'What was the original question?' });
const geminiResponse = await complete(gemini, context);

Provider Compatibility

All providers can handle messages from other providers, including:

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

This enables flexible workflows 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 '@yeshwanthyk/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);

Note: If the context contains images (encoded as base64 as shown in the Image Input section), those will also be serialized.

Agent API

The Agent API provides a higher-level interface for building agents with tools. It handles tool execution, validation, and provides detailed event streaming for interactive applications.

Event System

The Agent API streams events during execution, allowing you to build reactive UIs and track agent progress. The agent processes prompts in turns, where each turn consists of:

1. An assistant message (the LLM's response)
2. Optional tool executions if the assistant calls tools
3. Tool result messages that are fed back to the LLM

This continues until the assistant produces a response without tool calls.

Event Flow Example

Given a prompt asking to calculate two expressions and sum them:

import { agentLoop, AgentContext, calculateTool } from '@yeshwanthyk/ai';

const context: AgentContext = {
  systemPrompt: 'You are a helpful math assistant.',
  messages: [],
  tools: [calculateTool]
};

const stream = agentLoop(
  { role: 'user', content: 'Calculate 15 * 20 and 30 * 40, then sum the results', timestamp: Date.now() },
  context,
  { model: getModel('openai', 'gpt-4o-mini') }
);

// Expected event sequence:
// 1. agent_start          - Agent begins processing
// 2. turn_start           - First turn begins
// 3. message_start        - User message starts
// 4. message_end          - User message ends
// 5. message_start        - Assistant message starts
// 6. message_update       - Assistant streams response with tool calls
// 7. message_end          - Assistant message ends
// 8. tool_execution_start  - First calculation (15 * 20)
// 9. tool_execution_update - Streaming progress (for long-running tools)
// 10. tool_execution_end   - Result: 300
// 11. tool_execution_start - Second calculation (30 * 40)
// 12. tool_execution_update - Streaming progress
// 13. tool_execution_end   - Result: 1200
// 12. message_start       - Tool result message for first calculation
// 13. message_end         - Tool result message ends
// 14. message_start       - Tool result message for second calculation
// 15. message_end         - Tool result message ends
// 16. turn_end            - First turn ends with 2 tool results
// 17. turn_start          - Second turn begins
// 18. message_start       - Assistant message starts
// 19. message_update      - Assistant streams response with sum calculation
// 20. message_end         - Assistant message ends
// 21. tool_execution_start - Sum calculation (300 + 1200)
// 22. tool_execution_end   - Result: 1500
// 23. message_start       - Tool result message for sum
// 24. message_end         - Tool result message ends
// 25. turn_end            - Second turn ends with 1 tool result
// 26. turn_start          - Third turn begins
// 27. message_start       - Final assistant message starts
// 28. message_update      - Assistant streams final answer
// 29. message_end         - Final assistant message ends
// 30. turn_end            - Third turn ends with 0 tool results
// 31. agent_end           - Agent completes with all messages

Handling Events

for await (const event of stream) {
  switch (event.type) {
    case 'agent_start':
      console.log('Agent started');
      break;

    case 'turn_start':
      console.log('New turn started');
      break;

    case 'message_start':
      console.log(`${event.message.role} message started`);
      break;

    case 'message_update':
      // Only for assistant messages during streaming
      if (event.message.content.some(c => c.type === 'text')) {
        console.log('Assistant:', event.message.content);
      }
      break;

    case 'tool_execution_start':
      console.log(`Calling ${event.toolName} with:`, event.args);
      break;

    case 'tool_execution_update':
      // Streaming progress for long-running tools (e.g., bash output)
      console.log(`Progress:`, event.partialResult.content);
      break;

    case 'tool_execution_end':
      if (event.isError) {
        console.error(`Tool failed:`, event.result);
      } else {
        console.log(`Tool result:`, event.result.content);
      }
      break;

    case 'turn_end':
      console.log(`Turn ended with ${event.toolResults.length} tool calls`);
      break;

    case 'agent_end':
      console.log(`Agent completed with ${event.messages.length} new messages`);
      break;
  }
}

// Get all messages generated during this agent execution
// These include the user message and can be directly appended to context.messages
const messages = await stream.result();
context.messages.push(...messages);

Continuing from Existing Context

Use agentLoopContinue to resume an agent loop without adding a new user message. This is useful for:

• Retrying after context overflow (after compaction reduces context size)
• Resuming from tool results that were added manually to the context

import { agentLoopContinue, AgentContext } from '@yeshwanthyk/ai';

// Context already has messages - last must be 'user' or 'toolResult'
const context: AgentContext = {
  systemPrompt: 'You are helpful.',
  messages: [userMessage, assistantMessage, toolResult],
  tools: [myTool]
};

// Continue processing from the tool result
const stream = agentLoopContinue(context, { model });

for await (const event of stream) {
  // Same events as agentLoop, but no user message events emitted
}

const newMessages = await stream.result();

Validation: Throws if context has no messages or if the last message is an assistant message.

Defining Tools with TypeBox

Tools use TypeBox schemas for runtime validation and type inference:

import { Type, Static, AgentTool, AgentToolResult, StringEnum } from '@yeshwanthyk/ai';

const weatherSchema = Type.Object({
  city: Type.String({ minLength: 1 }),
  units: StringEnum(['celsius', 'fahrenheit'], { default: 'celsius' })
});

type WeatherParams = Static<typeof weatherSchema>;

const weatherTool: AgentTool<typeof weatherSchema, { temp: number }> = {
  label: 'Get Weather',
  name: 'get_weather',
  description: 'Get current weather for a city',
  parameters: weatherSchema,
  execute: async (toolCallId, args, signal, onUpdate) => {
    // args is fully typed: { city: string, units: 'celsius' | 'fahrenheit' }
    // signal: AbortSignal for cancellation
    // onUpdate: Optional callback for streaming progress (emits tool_execution_update events)
    const temp = Math.round(Math.random() * 30);
    return {
      content: [{ type: 'text', text: `Temperature in ${args.city}: ${temp}°${args.units[0].toUpperCase()}` }],
      details: { temp }
    };
  }
};

// Tools can also return images alongside text
const chartTool: AgentTool<typeof Type.Object({ data: Type.Array(Type.Number()) })> = {
  label: 'Generate Chart',
  name: 'generate_chart',
  description: 'Generate a chart from data',
  parameters: Type.Object({ data: Type.Array(Type.Number()) }),
  execute: async (toolCallId, args) => {
    const chartImage = await generateChartImage(args.data);
    return {
      content: [
        { type: 'text', text: `Generated chart with ${args.data.length} data points` },
        { type: 'image', data: chartImage.toString('base64'), mimeType: 'image/png' }
      ]
    };
  }
};

// Tools can stream progress via the onUpdate callback (emits tool_execution_update events)
const bashTool: AgentTool<typeof Type.Object({ command: Type.String() }), { exitCode: number }> = {
  label: 'Run Bash',
  name: 'bash',
  description: 'Execute a bash command',
  parameters: Type.Object({ command: Type.String() }),
  execute: async (toolCallId, args, signal, onUpdate) => {
    let output = '';
    const child = spawn('bash', ['-c', args.command]);

    child.stdout.on('data', (data) => {
      output += data.toString();
      // Stream partial output to UI via tool_execution_update events
      onUpdate?.({
        content: [{ type: 'text', text: output }],
        details: { exitCode: -1 }  // Not finished yet
      });
    });

    const exitCode = await new Promise<number>((resolve) => {
      child.on('close', resolve);
    });

    return {
      content: [{ type: 'text', text: output }],
      details: { exitCode }
    };
  }
};

Validation and Error Handling

Tool arguments are automatically validated using AJV with the TypeBox schema. Invalid arguments result in detailed error messages:

// If the LLM calls with invalid arguments:
// get_weather({ city: '', units: 'kelvin' })

// The tool execution will fail with:
/*
Validation failed for tool "get_weather":
  - city: must NOT have fewer than 1 characters
  - units: must be equal to one of the allowed values

Received arguments:
{
  "city": "",
  "units": "kelvin"
}
*/

Built-in Example Tools

The library includes example tools for common operations:

import { calculateTool, getCurrentTimeTool } from '@yeshwanthyk/ai';

const context: AgentContext = {
  systemPrompt: 'You are a helpful assistant.',
  messages: [],
  tools: [calculateTool, getCurrentTimeTool]
};

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 '@yeshwanthyk/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.

Environment Variables (Node.js only)

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

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
MISTRAL_API_KEY=...
GROQ_API_KEY=gsk_...
CEREBRAS_API_KEY=csk-...
XAI_API_KEY=xai-...
ZAI_API_KEY=...
OPENROUTER_API_KEY=sk-or-...

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'
});

Programmatic API Key Management

You can also set and get API keys programmatically:

import { setApiKey, getApiKey } from '@yeshwanthyk/ai';

// Set API key for a provider
setApiKey('openai', 'sk-...');
setApiKey('anthropic', 'sk-ant-...');

// Get API key for a provider (checks both programmatic and env vars)
const key = getApiKey('openai');

GitHub Copilot

GitHub Copilot is available as a provider, requiring OAuth authentication via GitHub's device flow.

Using with the coding-agent app: Use /login and select "GitHub Copilot" to authenticate. All models are automatically enabled after login. Token stored in ~/.pi/agent/oauth.json.

Using standalone: If you have a valid Copilot OAuth token (e.g., from the coding agent's oauth.json):

import { getModel, complete } from '@yeshwanthyk/ai';

const model = getModel('github-copilot', 'gpt-4o');

const response = await complete(model, {
  messages: [{ role: 'user', content: 'Hello!' }]
}, {
  apiKey: 'tid=...;exp=...;proxy-ep=...'  // OAuth token from ~/.pi/agent/oauth.json
});

Note: OAuth tokens expire and need periodic refresh. The coding agent handles this automatically.

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".

License

MIT