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capabilities","maintainers":[{"email":"sam@sarocu.com","name":"sarocu"},{"email":"me@david.gs","name":"dboskovic"},{"email":"me@alexjrock.com","name":"bangarang"},{"email":"carlbrugger@proton.me","name":"carlbrugger"},{"email":"infra@flatfile.io","name":"flatfileinfra"},{"email":"derek@flatfile.io","name":"flatderek"},{"email":"eric@flatfile.io","name":"bigcountrycrane"},{"email":"matthew.e.mccoy@gmail.com","name":"mmccooyyy"},{"email":"matthew.gerstman@obvious.ai","name":"matthew-obvious"},{"email":"nikolazaris@gmail.com","name":"nlazaris"},{"email":"lion.hummer@gmail.com","name":"lionhummer"},{"email":"alexpoddubny@googlemail.com","name":"apoddubn"}],"readme":"# @flatfile/improv\n\nA powerful TypeScript library for building AI-powered applications with three complementary APIs: Solo for simple structured outputs, Agent for complex tool-enabled workflows, and Gig for orchestrating multi-step AI operations. Features type-safe outputs, built-in retry/fallback mechanisms, and support for multiple LLM providers.\n\n## Core Features\n\n- 🎯 **Solo**: Simple, type-safe API for one-off LLM calls with structured output\n- 🤖 **Agent**: Advanced AI agents with tool usage, knowledge bases, and multi-step reasoning\n- 🎭 **Gig**: Orchestrate complex workflows with sequential/parallel execution and dependencies\n- 🧩 **Pieces**: Create reusable workflow components with built-in evaluation support\n- 🛠️ **Tool Integration**: Define and execute custom tools with type-safe parameters using Zod schemas\n- 📎 **Attachment Support**: Handle various types of attachments (documents, images, videos)\n- 🔄 **Event Streaming**: Built-in event system for real-time monitoring and response handling\n- 💾 **Memory Management**: Track and persist conversation history and agent state\n- 🔧 **Multi-Provider Support**: AWS Bedrock, OpenAI, Cohere, Gemini, Cerebras, and more\n\n## Table of Contents\n\n- [Quick Start](#quick-start)\n- [📚 Documentation](#-documentation)\n- [New: Solo, Agent, and Gig](#new-solo-agent-and-gig)\n  - [Solo - Structured LLM Calls](#solo---structured-llm-calls)\n  - [Agent - Tool-Enabled Workflows](#agent---tool-enabled-workflows)\n  - [Gig - Workflow Orchestration](#gig---workflow-orchestration)\n  - [Pieces - Reusable Components](#pieces---reusable-components)\n- [Core Components](#core-components)\n  - [Agent](#agent)\n  - [Thread](#thread)\n  - [Tool](#tool)\n  - [Message](#message)\n- [Event System](#event-system)\n- [Best Practices](#best-practices)\n- [License](#license)\n- [Contributing](#contributing)\n- [Follow-up Messages](#follow-up-messages)\n- [State Management](#state-management)\n  - [Agent State](#agent-state)\n  - [Thread State](#thread-state)\n  - [Tool State](#tool-state)\n  - [Evaluator System](#evaluator-system)\n- [State Management Patterns](#state-management-patterns)\n  - [Three-Keyed Lock Pattern](#three-keyed-lock-pattern)\n- [AWS Bedrock Integration](#aws-bedrock-integration)\n- [Model Drivers](#model-drivers)\n- [Tool Decorators](#tool-decorators)\n- [Streaming Support](#streaming-support)\n- [Reasoning Capabilities](#reasoning-capabilities)\n\n## Quick Start\n\n```typescript\nimport { Agent, Tool, BedrockThreadDriver } from '@flatfile/improv';\nimport { z } from 'zod';\n\n// Create a custom tool\nconst calculatorTool = new Tool({\n  name: 'calculator',\n  description: 'Performs basic arithmetic operations',\n  parameters: z.object({\n    operation: z.enum(['add', 'subtract', 'multiply', 'divide']),\n    a: z.number(),\n    b: z.number(),\n  }),\n  executeFn: async (args) => {\n    const { operation, a, b } = args;\n    switch (operation) {\n      case 'add': return a + b;\n      case 'subtract': return a - b;\n      case 'multiply': return a * b;\n      case 'divide': return a / b;\n    }\n  }\n});\n\n// Initialize the Bedrock driver\nconst driver = new BedrockThreadDriver({\n  model: 'anthropic.claude-3-haiku-20240307-v1:0',\n  temperature: 0.7,\n});\n\n// Create an agent\nconst agent = new Agent({\n  knowledge: [\n    { fact: 'The agent can perform basic arithmetic operations.' }\n  ],\n  instructions: [\n    { instruction: 'Use the calculator tool for arithmetic operations.', priority: 1 }\n  ],\n  tools: [calculatorTool],\n  driver,\n});\n\n// Create and use a thread\nconst thread = agent.createThread({\n  prompt: 'What is 25 multiplied by 4?',\n  onResponse: async (message) => {\n    console.log('Agent response:', message.content);\n  }\n});\n\n// Send the thread\nawait thread.send();\n\n// Stream the response\nconst stream = await thread.stream();\nfor await (const text of stream) {\n  process.stdout.write(text); // Print each chunk as it arrives\n}\n```\n\n## 📚 Documentation\n\n**Comprehensive guides and API references:**\n\n- **[📖 Complete Documentation](./docs/)** - All guides and references\n- **[🚀 API Overview](./docs/api-overview.md)** - Complete overview of all APIs\n- **[🎯 Event System Guide](./docs/events-guide.md)** - Using events and monitoring\n- **[📋 Events Reference](./docs/events.md)** - Complete event registry\n- **[🔄 Structured Output](./docs/structured-output.md)** - Type-safe LLM responses\n- **[🤖 Agent & Solo Patterns](./docs/agent-solo-patterns.md)** - When to use each API\n- **[🧩 Creating Pieces](./docs/creating-pieces.md)** - Building reusable components\n\n## New: Solo, Agent, and Gig\n\nImprov now provides simplified APIs for different AI use cases:\n\n### Solo - Structured LLM Calls\n\nFor simple, one-off LLM calls with structured output:\n\n```typescript\nimport { Solo } from '@flatfile/improv';\nimport { z } from 'zod';\n\nconst solo = new Solo({\n  driver,\n  outputSchema: z.object({\n    sentiment: z.enum([\"positive\", \"negative\", \"neutral\"]),\n    confidence: z.number().min(0).max(1)\n  })\n});\n\nconst result = await solo.ask(\"Analyze the sentiment: 'This product is amazing!'\");\n// result.output is fully typed: { sentiment: \"positive\", confidence: 0.95 }\n```\n\n**Key Features:**\n\n- Type-safe structured output with Zod schemas\n- Built-in retry logic with exponential backoff\n- Fallback driver support\n- Streaming support\n- Simple API for classification and extraction tasks\n\n### Agent - Tool-Enabled Workflows\n\nFor complex, multi-step workflows that require tool usage:\n\n```typescript\nimport { Agent, Tool } from '@flatfile/improv';\n\nconst searchTool = new Tool({\n  name: \"search\",\n  description: \"Search the knowledge base\",\n  parameters: z.object({ query: z.string() }),\n  executeFn: async ({ query }) => {\n    // Your search implementation\n    return { results: [\"Result 1\", \"Result 2\"] };\n  }\n});\n\nconst agent = new Agent({\n  driver,\n  tools: [searchTool],\n  instructions: [\n    { instruction: \"Always search before answering\", priority: 1 }\n  ]\n});\n\nconst thread = agent.createThread({ \n  prompt: \"What are the best practices for error handling?\" \n});\nawait thread.send();\n```\n\n### Gig - Workflow Orchestration\n\nOrchestrate multiple AI operations with dependencies and control flow:\n\n```typescript\nimport { Gig, PieceDefinition } from '@flatfile/improv';\n\nconst gig = new Gig({ \n  label: \"Customer Support Workflow\", \n  driver \n});\n\n// Add pieces sequentially (default behavior)\ngig\n  .add(\"classify\", groove => \n    `Classify this support request: \"${groove.feelVibe(\"request\")}\"`, {\n    outputSchema: z.enum([\"technical\", \"billing\", \"general\"])\n  })\n  \n  .add(\"sentiment\", groove => \n    `Analyze sentiment: \"${groove.feelVibe(\"request\")}\"`, {\n    outputSchema: z.enum([\"positive\", \"negative\", \"neutral\"])\n  })\n  \n  // Pieces can access previous results\n  .add(\"research\", groove => {\n    const category = groove.recall(\"classify\");\n    return `Research solutions for ${category} issue`;\n  }, {\n    tools: [searchTool],\n    driver: cheaperModel // Override driver for cost optimization\n  })\n  \n  // Explicit parallel execution when needed\n  .parallel([\n    [\"check_status\", \"Check system status\"],\n    [\"find_similar\", \"Find similar resolved issues\"]\n  ])\n  \n  .add(\"respond\", groove => {\n    const sentiment = groove.recall(\"sentiment\");\n    const research = groove.recall(\"research\");\n    return `Generate ${sentiment} response using: ${research}`;\n  });\n\n// Execute the workflow\nconst results = await gig.perform({ \n  request: \"My invoice is wrong and I'm very frustrated!\" \n});\n\nconsole.log(results.recordings.get(\"classify\")); // \"billing\"\nconsole.log(results.recordings.get(\"sentiment\")); // \"negative\"\n```\n\n**Key Features:**\n\n- Sequential execution by default (predictable flow)\n- Explicit `parallel()` for concurrent operations\n- Auto-detection: uses Solo for structured output, Agent for tools\n- Driver overrides per piece for optimization\n- Access previous results with `groove.recall()`\n- Type-safe with full TypeScript support\n\n### Pieces - Reusable Components\n\nCreate reusable workflow components that can be shared across projects:\n\n```typescript\nimport { PieceDefinition } from '@flatfile/improv';\nimport { z } from 'zod';\n\n// Define a reusable piece\nexport const sentimentAnalysis: PieceDefinition<\"positive\" | \"negative\" | \"neutral\"> = {\n  name: \"sentiment\",\n  play: (groove) => {\n    const text = groove.feelVibe(\"text\");\n    return `Analyze sentiment of: \"${text}\"`;\n  },\n  config: {\n    outputSchema: z.enum([\"positive\", \"negative\", \"neutral\"]),\n    temperature: 0.1\n  },\n  meta: {\n    version: \"1.0.0\",\n    description: \"Analyzes emotional tone of text\"\n  }\n};\n\n// Use in any Gig\ngig.add(sentimentAnalysis);\n```\n\n**Organizing Pieces with Evaluations:**\n\n```\nsrc/\n  pieces/\n    sentiment/\n      index.ts      # Piece definition (production)\n      eval.ts       # Evaluation data (dev only)\n```\n\nThis separation ensures evaluation datasets don't get bundled in production builds.\n\n## Reasoning Capabilities\n\nImprov supports advanced reasoning capabilities through the `reasoning_config` option in the thread driver. This allows the AI to perform step-by-step reasoning before providing a final answer.\n\n```typescript\nimport { Agent, BedrockThreadDriver } from '@flatfile/improv';\n\nconst driver = new BedrockThreadDriver({\n  model: 'anthropic.claude-3-7-sonnet-20250219-v1:0',\n  temperature: 1,\n  reasoning_config: {\n    budget_tokens: 1024,\n    type: 'enabled',\n  },\n});\n\nconst agent = new Agent({\n  driver,\n});\n\nconst thread = agent.createThread({\n  systemPrompt: 'You are a helpful assistant that can answer questions about the world.',\n  prompt: 'How many people will live in the world in 2040?',\n});\n\nconst result = await thread.send();\n\nconsole.log(result.last());\n```\n\nThis example enables the AI to work through its reasoning process with a token budget of 1024 tokens before providing a final answer about population projections.\n\n## Core Components\n\n### Agent\n\nThe main agent class that manages knowledge, instructions, tools, and conversation threads.\n\n```typescript\nconst agent = new Agent({\n  knowledge?: AgentKnowledge[],  // Array of facts with optional source and timestamp\n  instructions?: AgentInstruction[],  // Array of prioritized instructions\n  memory?: AgentMemory[],  // Array of stored thread histories\n  systemPrompt?: string,  // Base system prompt\n  tools?: Tool[],  // Array of available tools\n  driver: ThreadDriver,  // Thread driver implementation\n  evaluators?: Evaluator[]  // Array of evaluators for response processing\n});\n```\n\n### Thread\n\nManages a single conversation thread with message history and tool execution.\n\n```typescript\nconst thread = new Thread({\n  messages?: Message[],  // Array of conversation messages\n  tools?: Tool[],  // Array of available tools\n  driver: ThreadDriver,  // Thread driver implementation\n  toolChoice?: 'auto' | 'any',  // Tool selection mode\n  maxSteps?: number  // Maximum number of tool execution steps\n});\n```\n\n### Tool\n\nDefine custom tools that the agent can use during conversations.\n\n```typescript\nconst tool = new Tool({\n  name: string,  // Tool name\n  description: string,  // Tool description\n  parameters: z.ZodTypeAny,  // Zod schema for parameter validation\n  followUpMessage?: string,  // Optional message to guide response evaluation\n  executeFn: (args: Record<string, any>, toolCall: ToolCall) => Promise<any>  // Tool execution function\n});\n```\n\n### Message\n\nRepresents a single message in a conversation thread.\n\n```typescript\nconst message = new Message({\n  content?: string,  // Message content\n  role: 'system' | 'user' | 'assistant' | 'tool',  // Message role\n  toolCalls?: ToolCall[],  // Array of tool calls\n  toolResults?: ToolResult[],  // Array of tool results\n  attachments?: Attachment[],  // Array of attachments\n  cache?: boolean  // Whether to cache the message\n});\n```\n\n## Event System\n\nThe library uses an event-driven architecture. All major components extend `EventSource`, allowing you to listen for various events:\n\n```typescript\n// Agent events\nagent.on('agent.thread-added', ({ agent, thread }) => {});\nagent.on('agent.thread-removed', ({ agent, thread }) => {});\nagent.on('agent.knowledge-added', ({ agent, knowledge }) => {});\nagent.on('agent.instruction-added', ({ agent, instruction }) => {});\n\n// Thread events\nthread.on('thread.response', ({ thread, message }) => {});\nthread.on('thread.max_steps_reached', ({ thread, steps }) => {});\n\n// Tool events\ntool.on('tool.execution.started', ({ tool, name, args }) => {});\ntool.on('tool.execution.completed', ({ tool, name, args, result }) => {});\ntool.on('tool.execution.failed', ({ tool, name, args, error }) => {});\n```\n\n## Best Practices\n\n1. **Choosing the Right API**\n   - Use **Solo** for: Classification, extraction, structured output, simple Q&A\n   - Use **Agent** for: Tool usage, multi-step reasoning, complex decision making\n   - Use **Gig** for: Orchestrating multiple operations, workflows with dependencies\n\n2. **Piece Design**\n   - Keep pieces focused on a single task\n   - Use descriptive names that indicate the piece's purpose\n   - Leverage `groove.recall()` to access previous results\n   - Override drivers for cost optimization (e.g., cheaper models for simple tasks)\n\n3. **Tool Design**\n   - Keep tools atomic and focused on a single responsibility\n   - Use Zod schemas for robust parameter validation\n   - Implement proper error handling in tool execution\n   - Use follow-up messages to guide response evaluation\n\n4. **Workflow Organization**\n   - Default to sequential execution for predictable flow\n   - Use `parallel()` only when operations are truly independent\n   - Separate evaluation data into `eval.ts` files\n   - Create reusable pieces for common operations\n\n5. **Error Handling & Resilience**\n   - Configure retry logic with appropriate backoff\n   - Set up fallback drivers for critical operations\n   - Use `onError` handlers in Gig pieces\n   - Monitor events for debugging and observability\n\n6. **Type Safety**\n   - Define output schemas with Zod for Solo operations\n   - Use `PieceDefinition<T>` for type-safe pieces\n   - Leverage TypeScript's type inference with `groove.recall()`\n\n## License\n\nMIT\n\n## Contributing\n\nContributions are welcome! Please read our contributing guidelines for details.\n\n## Follow-up Messages\n\nTools can include follow-up messages that guide the AI's evaluation of tool responses. This is particularly useful for:\n\n- Providing context for tool results\n- Guiding the AI's interpretation of data\n- Maintaining consistent response patterns\n- Suggesting next steps or actions\n\n```typescript\nconst tool = new Tool({\n  name: 'dataAnalyzer',\n  description: 'Analyzes data and returns insights',\n  parameters: z.object({\n    data: z.array(z.any()),\n    metrics: z.array(z.string())\n  }),\n  followUpMessage: `Review the analysis results:\n1. What are the key insights from the data?\n2. Are there any concerning patterns?\n3. What actions should be taken based on these results?`,\n  executeFn: async (args) => {\n    // Tool implementation\n  }\n});\n```\n\n## State Management\n\nThe library provides several mechanisms for managing state:\n\n### Agent State\n\n- Knowledge base for storing facts\n- Prioritized instructions for behavior guidance\n- Memory system for storing thread histories\n- System prompt for base context\n\n### Thread State\n\n- Message history tracking\n- Tool execution state\n- Maximum step limits\n- Response handlers\n\n### Tool State\n\n- Parameter validation\n- Execution tracking\n- Result processing\n- Event emission\n\n### Evaluator System\n\nEvaluators provide a way to process and validate agent responses:\n\n```typescript\nconst evaluator: Evaluator = async ({ thread, agent }, complete) => {\n  // Process the thread response\n  const lastMessage = thread.last();\n  \n  if (lastMessage?.content.includes('done')) {\n    complete();  // Signal completion\n  } else {\n    // Continue processing\n    thread.send(new Message({\n      content: 'Please continue with the task...'\n    }));\n  }\n};\n\nconst agent = new Agent({\n  // ... other options ...\n  evaluators: [evaluator]\n});\n```\n\nEvaluators can:\n\n- Process agent responses\n- Trigger additional actions\n- Control conversation flow\n- Validate results\n\n## State Management Patterns\n\n### Three-Keyed Lock Pattern\n\nThe three-keyed lock pattern is a state management pattern that ensures controlled flow through tool execution, evaluation, and completion phases. It's implemented as a reusable evaluator:\n\n```typescript\nimport { threeKeyedLockEvaluator } from '@flatfile/improv';\n\nconst agent = new Agent({\n  // ... other options ...\n  evaluators: [\n    threeKeyedLockEvaluator({\n      evalPrompt: \"Are there other items to process? If not, say 'done'\",\n      exitPrompt: \"Please provide a final summary of all actions taken.\"\n    })\n  ]\n});\n```\n\nThe pattern works through three distinct states:\n\n```mermaid\nstateDiagram-v2\n    [*] --> ToolExecution\n    \n    state \"Tool Execution\" as ToolExecution {\n        [*] --> Running\n        Running --> Complete\n        Complete --> [*]\n    }\n    \n    state \"Evaluation\" as Evaluation {\n        [*] --> CheckMore\n        CheckMore --> [*]\n    }\n    \n    state \"Summary\" as Summary {\n        [*] --> Summarize\n        Summarize --> [*]\n    }\n    \n    ToolExecution --> Evaluation: Non-tool response\n    Evaluation --> ToolExecution: Tool called\n    Evaluation --> Summary: No more items\n    Summary --> [*]: Complete\n\n    note right of ToolExecution\n        isEvaluatingTools = true\n        Handles tool execution\n    end note\n    \n    note right of Evaluation\n        isEvaluatingTools = false\n        nextMessageIsSummary = false\n        Checks for more work\n    end note\n    \n    note right of Summary\n        nextMessageIsSummary = true\n        Gets final summary\n    end note\n```\n\nThe evaluator manages these states through:\n\n1. **Tool Execution State**\n   - Tracks when tools are being executed\n   - Resets state when new tools are called\n   - Handles multiple tool executions\n\n2. **Evaluation State**\n   - Triggered after tool completion\n   - Prompts for more items to process\n   - Can return to tool execution if needed\n\n3. **Summary State**\n   - Final state before completion\n   - Gathers summary of actions\n   - Signals completion when done\n\nKey features:\n\n- Automatic state transitions\n- Event-based flow control\n- Clean event listener management\n- Configurable prompts\n- Support for multiple tool executions\n\nExample usage with custom prompts:\n\n```typescript\nconst workflowAgent = new Agent({\n  // ... agent configuration ...\n  evaluators: [\n    threeKeyedLockEvaluator({\n      evalPrompt: \"Review the results. Should we process more items?\",\n      exitPrompt: \"Provide a detailed summary of all processed items.\"\n    })\n  ]\n});\n\n// The evaluator will automatically:\n// 1. Let tools execute freely\n// 2. After each tool completion, check if more processing is needed\n// 3. When no more items need processing, request a final summary\n// 4. Complete the evaluation after receiving the summary\n```\n\nThis pattern is particularly useful for:\n\n- Processing multiple items sequentially\n- Workflows requiring validation between steps\n- Tasks with dynamic tool usage\n- Operations requiring final summaries\n\n## AWS Bedrock Integration\n\nThe library uses AWS Bedrock (Claude) as its default LLM provider. Configure your AWS credentials:\n\n```typescript\n// Required environment variables\nprocess.env.AWS_ACCESS_KEY_ID = 'your-access-key';\nprocess.env.AWS_SECRET_ACCESS_KEY = 'your-secret-key';\nprocess.env.AWS_REGION = 'your-region';\n\n// Initialize the driver\nconst driver = new BedrockThreadDriver({\n  model: 'anthropic.claude-3-haiku-20240307-v1:0',  // Default model\n  temperature?: number,  // Default: 0.7\n  maxTokens?: number,  // Default: 4096\n  cache?: boolean  // Default: false\n});\n```\n\n## Model Drivers\n\nImprov supports multiple LLM providers through dedicated thread drivers:\n\n### Available Drivers\n\n| Driver | Provider | Documentation |\n|--------|----------|---------------|\n| `BedrockThreadDriver` | AWS Bedrock (Claude) | [Bedrock Driver Documentation](src/model.drivers/bedrock.driver.md) |\n| `OpenAIThreadDriver` | OpenAI | [OpenAI Driver Documentation](src/model.drivers/openai.driver.md) |\n| `CohereThreadDriver` | Cohere | [Cohere Driver Documentation](src/model.drivers/cohere.driver.md) |\n| `GeminiThreadDriver` | Google Gemini | [Gemini Driver Documentation](src/model.drivers/gemini.driver.md) |\n| `CerebrasThreadDriver` | Cerebras | [Cerebras Driver Documentation](src/model.drivers/cerebras.driver.md) |\n\nEach driver provides a consistent interface while supporting model-specific features:\n\n```typescript\n// OpenAI example\nimport { OpenAIThreadDriver } from '@flatfile/improv';\n\nconst driver = new OpenAIThreadDriver({\n  model: 'gpt-4o',\n  apiKey: process.env.OPENAI_API_KEY,\n  temperature: 0.7\n});\n\n// Cohere example\nimport { CohereThreadDriver } from '@flatfile/improv';\n\nconst driver = new CohereThreadDriver({\n  model: 'command-r-plus',\n  apiKey: process.env.COHERE_API_KEY\n});\n\n// Gemini example\nimport { GeminiThreadDriver } from '@flatfile/improv';\n\nconst driver = new GeminiThreadDriver({\n  model: 'gemini-1.5-pro',\n  apiKey: process.env.GOOGLE_API_KEY\n});\n\n// Cerebras example\nimport { CerebrasThreadDriver } from '@flatfile/improv';\n\nconst driver = new CerebrasThreadDriver({\n  model: 'llama-4-scout-17b-16e-instruct',\n  apiKey: process.env.CEREBRAS_API_KEY\n});\n```\n\nRefer to each driver's documentation for available models and specific configuration options.\n\n## Tool Decorators\n\nThe library provides decorators for creating tools directly on agent classes:\n\n```typescript\nclass CustomAgent extends Agent {\n  @ToolName(\"sampleData\")\n  @ToolDescription(\"Sample the original data with the mapping program\")\n  private async sampleData(\n    @ToolParam(\"count\", \"Number of records to sample\", z.number())\n    count: number,\n    @ToolParam(\"seed\", \"Random seed\", z.number().optional())\n    seed?: number\n  ): Promise<any> {\n    return { count, seed };\n  }\n}\n```\n\nThis provides:\n\n- Type-safe tool definitions\n- Automatic parameter validation\n- Clean method-based tools\n- Integrated error handling\n\n## Streaming Support\n\nThe library provides built-in support for streaming responses from the AI model. Keys features:\n\n- Real-time text chunks as they're generated\n- Automatic message management in the thread\n- Event emission for stream progress\n- Error handling and recovery\n- Compatible with all thread features including tool calls\n\n```typescript\nconst thread = agent.createThread({\n  prompt: 'What is 25 multiplied by 4?',\n});\n\nconst stream = await thread.stream();\n\nfor await (const text of stream) {\n  process.stdout.write(text);\n}\n\n// The final response is also available in the thread\nconsole.log(thread.last()?.content);\n```\n","readmeFilename":"README.md"}