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3% improvement on SWE-bench, 5% on TAUBench vs Chat Completions\n- **📦 First-class tool steps**: Tool calls and results are structured items, not message hacks\n- **🎯 Future-proof**: All new OpenAI features will land in Responses API first\n\n## Important: Understanding the Agent Loop\n\n**The Responses API does NOT run your agent loop.**\n\nWhat it provides:\n- Multiple tool calls in **one response** (you still execute them)\n- Structured step types (message, function_call, function_call_output)\n\nWhat **Flink handles** (via AgentRunner):\n- Tool execution\n- Multi-turn loops (API → execute tools → API → execute tools → done)\n- Deciding when to stop\n- Managing conversation state\n\nThis separation is intentional - it gives you full control over agent behavior while leveraging better API primitives.\n\n## Installation\n\n```bash\nnpm install @flink-app/openai-adapter\n# or\npnpm add @flink-app/openai-adapter\n```\n\nThe `openai` package is included as a dependency, so you don't need to install it separately.\n\n## Usage\n\n### Basic Setup\n\n```typescript\nimport { OpenAIAdapter } from \"@flink-app/openai-adapter\";\nimport { FlinkApp } from \"@flink-app/flink\";\n\nconst app = new FlinkApp({\n  ai: {\n    llms: {\n      default: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"gpt-5\"\n      }),\n    },\n  },\n});\n\nawait app.start();\n```\n\n**Legacy API (still supported):**\n```typescript\n// Backward-compatible constructor\nnew OpenAIAdapter(process.env.OPENAI_API_KEY!, \"gpt-5\")\n```\n\n### Agent Instructions\n\nDefine your agent's behavior using the `instructions` property:\n\n```typescript\n// src/agents/support_agent.ts\nexport const Agent: FlinkAgentProps = {\n  name: \"support_agent\",\n  instructions: \"You are a helpful customer support agent.\",\n  tools: [\"get_order_status\"],\n  model: { adapterId: \"default\" },\n};\n```\n\n**How it works:**\n- Instructions are prepended as a system message to every conversation\n- Follows Vercel AI SDK pattern for consistency\n- Provides stable agent behavior across all interactions\n\n#### Dynamic Context with System Messages\n\nFor per-request context, add system messages to the conversation:\n\n```typescript\nconst result = await ctx.agents.myAgent.execute({\n  message: [\n    { role: \"system\", content: \"Current user tier: Premium\" },\n    { role: \"user\", content: \"What can I do?\" }\n  ]\n});\n```\n\n**Order of messages sent to OpenAI:**\n1. Agent `instructions` (as system message)\n2. User-provided system messages (if any)\n3. Conversation messages\n\nThis gives you both static agent behavior and dynamic per-request context.\n\n### Structured Outputs\n\nEnable structured outputs with JSON schema for 100% reliability in output format:\n\n```typescript\nimport { OpenAIAdapter } from \"@flink-app/openai-adapter\";\n\nconst adapter = new OpenAIAdapter({\n  apiKey: process.env.OPENAI_API_KEY!,\n  model: \"gpt-5\",\n  structuredOutput: {\n    type: \"json_schema\",\n    name: \"car_analysis\",\n    description: \"Analysis of car specifications\",\n    schema: {\n      type: \"object\",\n      properties: {\n        brand: { type: \"string\" },\n        model: { type: \"string\" },\n        year: { type: \"number\" },\n        features: {\n          type: \"array\",\n          items: { type: \"string\" }\n        }\n      },\n      required: [\"brand\", \"model\", \"year\"],\n      additionalProperties: false\n    },\n    strict: true // Enforces 100% schema adherence\n  }\n});\n```\n\n**Benefits of Structured Outputs:**\n- 100% reliability (vs ~95% with JSON mode)\n- No need for retry logic or manual validation\n- Automatic schema validation during generation\n- Supported on all modern models: `gpt-4.1`, `gpt-5`, `o4-mini`, `o3`\n\n### Conversation Persistence (Recommended)\n\nFor multi-turn conversations, use `ConversationAgent` to automatically save conversation history:\n\n```typescript\n// src/agents/ChatAgent.ts\nimport { ConversationAgent, ConversationData } from \"@flink-app/flink\";\n\nexport default class ChatAgent extends ConversationAgent<AppContext> {\n    id = \"chat-agent\";\n    description = \"Chat assistant with automatic conversation persistence\";\n    instructions = \"You are a helpful assistant...\";\n    tools = [\"search-knowledge\"];\n\n    // Load conversation from your storage backend\n    protected async loadConversation(conversationId: string) {\n        const conv = await this.ctx.repos.conversationRepo.getById(conversationId);\n        return conv ? {\n            messages: conv.messages,\n            providerMetadata: conv.providerMetadata // ← Preserved for tracking\n        } : null;\n    }\n\n    // Save conversation with updated metadata\n    protected async saveConversation(conversationId: string, data: ConversationData) {\n        await this.ctx.repos.conversationRepo.upsert({\n            _id: conversationId,\n            messages: data.messages,\n            providerMetadata: data.providerMetadata // ← Saved automatically\n        });\n    }\n}\n```\n\n**What this enables:**\n\n1. **Automatic History Management**: Framework handles full conversation history\n2. **Metadata Tracking**: Provider metadata preserved for debugging and future use\n3. **Storage Flexibility**: Works with MongoDB, Redis, in-memory, or any backend\n4. **Type Safety**: Abstract methods enforce consistent implementation\n\n**Example Storage Backends:**\n- [MongoDB](../../packages/demo-app/src/agents/CarPersistentAgent.ts) - Production-ready persistent storage\n- [In-Memory](../../packages/demo-app/src/agents/examples/InMemoryPersistentAgent.ts) - Development/testing\n\n**How Provider Metadata Works:**\n\n1. First turn: Agent executes, adapter may return provider-specific metadata\n2. `saveConversation()` stores messages + metadata\n3. Second turn: `loadConversation()` returns messages + metadata\n4. Framework populates `input.providerMetadata` for tracking/debugging\n5. Full conversation history is always sent for reliability\n\n**Note:** Provider metadata is namespaced by adapter (e.g., `{ openai: {...}, anthropic: {...} }`) for multi-provider support.\n\n## Debug Logging\n\nThe adapter includes component-specific logging to help debug API requests and responses:\n\n```typescript\nconst adapter = new OpenAIAdapter({\n  apiKey: process.env.OPENAI_API_KEY!,\n  model: \"gpt-5\",\n  debug: true // Enable debug logging for this adapter\n});\n```\n\n**What gets logged:**\n\nWhen `debug: true`:\n- ✅ Initialization confirmation\n- ✅ Request metadata (model, message count, tool count, token limits)\n- ✅ Full request parameters sent to OpenAI\n- ✅ Tool call decisions made by the LLM\n- ✅ Token usage and performance metrics\n- ✅ Response IDs for conversation continuation\n- ✅ Error details with context\n\nWhen `debug: false` (default):\n- ✅ Request completion with token usage (info level)\n- ✅ Errors with context\n\n**Log Output Example:**\n\n```bash\n2026-02-15 [OpenAI] [INFO] OpenAI adapter initialized with debug logging enabled\n2026-02-15 [OpenAI] [DEBUG] Starting OpenAI stream request {\"model\":\"gpt-5\",\"messageCount\":2,\"toolCount\":3}\n2026-02-15 [OpenAI] [DEBUG] Stream connection established\n2026-02-15 [OpenAI] [DEBUG] LLM decided to call tool {\"name\":\"get_weather\",\"call_id\":\"call_abc123\"}\n2026-02-15 [OpenAI] [INFO] OpenAI request completed {\"inputTokens\":150,\"outputTokens\":80,\"totalTokens\":230}\n```\n\n**Environment-Based Configuration:**\n\nYou can also enable debug logging via environment variables without code changes:\n\n```bash\n# Enable debug for OpenAI adapter specifically\nDEBUG=OpenAI npm start\n\n# Enable debug for multiple components\nDEBUG=OpenAI,Database npm start\n\n# Set global log level\nLOG_LEVEL=debug npm start\n```\n\n**Programmatic Control:**\n\nFor dynamic control at runtime:\n\n```typescript\nimport { FlinkLogFactory } from \"@flink-app/flink\";\n\n// Enable debug for OpenAI adapter\nFlinkLogFactory.setComponentLevel(\"OpenAI\", \"debug\");\n\n// Later, disable it\nFlinkLogFactory.setComponentLevel(\"OpenAI\", \"info\");\n```\n\n**Benefits:**\n- 🔍 Debug API requests without flooding other logs\n- 📊 Track token usage per request\n- 🐛 Investigate tool calling issues\n- ⚡ Monitor performance and latency\n- 🎯 Component-specific logging (OpenAI logs don't affect Database, Cache, etc.)\n\nSee [Flink Logging Guide](../../docs/LOGGING.md) for more details on component-specific logging.\n\n## Cache Metrics & Cost Optimization\n\nThe OpenAI adapter automatically captures detailed cache metrics to help you optimize costs and understand performance:\n\n```typescript\nconst result = await ctx.agents.myAgent.execute({\n  message: \"What's the weather?\"\n});\n\nconsole.log(result.usage);\n// {\n//   inputTokens: 2006,\n//   outputTokens: 300,\n//   cachedInputTokens: 1920  // 95% cache hit!\n// }\n\n// Calculate actual billed input tokens\nconst billedInputTokens = result.usage.inputTokens - (result.usage.cachedInputTokens || 0);\nconsole.log(`Only ${billedInputTokens} tokens charged at full rate`);\n// \"Only 86 tokens charged at full rate\"\n\n// Estimate cost savings (cached tokens are ~10% of regular cost)\nconst regularCost = billedInputTokens * 1.0;\nconst cachedCost = (result.usage.cachedInputTokens || 0) * 0.1;\nconst totalCost = regularCost + cachedCost;\nconsole.log(`Estimated cost factor: ${totalCost} (vs ${result.usage.inputTokens} without caching)`);\n// \"Estimated cost factor: 278 (vs 2006 without caching)\" - 86% savings!\n```\n\n**Cache Metrics:**\n- `inputTokens`: Total input tokens (including cached)\n- `outputTokens`: Total output tokens\n- `cachedInputTokens`: Input tokens served from cache (10% cost)\n\n**OpenAI Caching:**\n- Cached tokens cost ~10% of regular tokens\n- Automatic caching for repeated context\n- No configuration needed - OpenAI handles it automatically\n\n**When Caching Helps Most:**\n- Long system prompts used across requests\n- Repeated context in multi-turn conversations\n- Few-shot examples in prompts\n- Large knowledge bases in context\n\n**Logging:**\nWhen debug mode is enabled, cache metrics are automatically logged:\n\n```bash\n2026-02-23 [flink.ai.openai] [INFO] OpenAI request completed {\n  \"inputTokens\": 2006,\n  \"outputTokens\": 300,\n  \"cachedInputTokens\": 1920,\n  \"totalTokens\": 2306,\n  \"status\": \"completed\",\n  \"model\": \"gpt-5\"\n}\n```\n\n## Multiple Adapters\n\nYou can register multiple OpenAI adapters with different configurations:\n\n```typescript\nimport { OpenAIAdapter } from \"@flink-app/openai-adapter\";\nimport { FlinkApp } from \"@flink-app/flink\";\n\nconst app = new FlinkApp({\n  ai: {\n    llms: {\n      // Default GPT-5 - best for general tasks\n      default: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"gpt-5\"\n      }),\n\n      // Fast reasoning model - cost-efficient\n      fast: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"o4-mini\"\n      }),\n\n      // Maximum intelligence for complex reasoning\n      smart: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"o3\"\n      }),\n\n      // With structured output\n      structured: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"gpt-5\",\n        structuredOutput: {\n          type: \"json_schema\",\n          name: \"response\",\n          schema: { /* your schema */ },\n          strict: true\n        }\n      }),\n    },\n  },\n});\n```\n\n## Using in Agents\n\nReference the adapter by its registered ID in your agent configuration:\n\n```typescript\n// src/agents/support_agent.ts\nimport { FlinkAgentProps } from \"@flink-app/flink\";\n\nexport const Agent: FlinkAgentProps = {\n  name: \"support_agent\",\n  description: \"Customer support assistant\",\n  instructions: \"You are a helpful customer support agent.\",\n  tools: [\"get_order_status\", \"search_knowledge_base\"],\n  model: {\n    adapterId: \"default\", // Uses the \"default\" adapter\n    maxTokens: 2000,\n    temperature: 0.7,\n  },\n};\n```\n\n## Supported Models\n\nThis adapter works with all OpenAI models available via the Responses API. The latest models (as of 2026) offer significant improvements:\n\n### GPT-5 Series (Recommended)\n\n- **GPT-5**: `gpt-5`\n  - Latest and most capable model\n  - Best for general-purpose applications\n  - Excellent at coding, reasoning, and agentic tasks\n\n### GPT-4.1 Series\n\n- **GPT-4.1**: `gpt-4.1`\n  - Smartest non-reasoning model\n  - Excellent at coding tasks\n  - Strong at precise instruction following\n  - Best for web development and technical tasks\n\n- **GPT-4.1 mini**: `gpt-4.1-mini`\n  - Smaller, faster, more cost-efficient\n  - Good balance of capability and cost\n\n- **GPT-4.1 nano**: `gpt-4.1-nano`\n  - Ultra-fast and cost-efficient\n  - Best for simple, high-volume tasks\n\n### O-Series Reasoning Models\n\n- **o4-mini**: `o4-mini` (recommended for reasoning tasks)\n  - Fast, cost-efficient reasoning model\n  - Best-performing on AIME 2024 and 2025 benchmarks\n  - Optimized for mathematical and logical reasoning\n\n- **o3**: `o3`\n  - Advanced reasoning model for complex tasks\n  - State-of-the-art performance on coding, math, and science\n  - Excellent at Codeforces, SWE-bench, and MMMU\n\n- **o3-pro**: `o3-pro`\n  - Premium reasoning model (Pro users only)\n  - Designed to think longer and provide most reliable responses\n\n### Legacy Models\n\nFor backwards compatibility:\n\n- **GPT-4 Turbo**: `gpt-4-turbo`\n- **GPT-4**: `gpt-4`\n- **GPT-3.5 Turbo**: `gpt-3.5-turbo`\n\n**Note**: Some older models (GPT-4o, early GPT-4.1 variants) are being retired in 2026. Migrate to the latest models for continued support.\n\n## Model Selection Guide\n\n| Use Case | Recommended Model | Why |\n|----------|------------------|-----|\n| General development | `gpt-5` | Latest and most capable |\n| Coding & technical | `gpt-4.1` | Best instruction following |\n| High-volume tasks | `gpt-4.1-mini` | Cost-efficient with good performance |\n| Mathematical reasoning | `o4-mini` | Optimized for math, fast and cost-efficient |\n| Complex problem-solving | `o3` | State-of-the-art reasoning |\n| Mission-critical | `o3-pro` | Maximum reliability (Pro users) |\n\n## Features\n\n- ✅ **Step-based tool calling** - Multiple tool calls in one response as typed items\n- ✅ **Event-based streaming** - Proper event taxonomy (not just token streaming)\n- ✅ **Structured outputs** with JSON schema (100% reliability)\n- ✅ **First-class tool steps** - function_call and function_call_output as explicit types\n- ✅ Support for all OpenAI models\n- ✅ 3-5% performance improvement over Chat Completions\n\n### What Makes Responses API Different\n\nThe key difference is **how tool calls are represented**, not who executes them:\n\n**Chat Completions (old):**\n```\nResponse: {\n  message: {\n    content: \"...\",\n    tool_calls: [...]  // Tool calls embedded in message\n  }\n}\n\nYou: Extract tool calls, execute them, create new messages, call API again\n```\n\n**Responses API (new):**\n```\nResponse: {\n  output: [\n    { type: \"message\", content: \"...\" },\n    { type: \"function_call\", name: \"...\", call_id: \"...\" },\n    { type: \"function_call\", name: \"...\", call_id: \"...\" }  // Multiple calls!\n  ]\n}\n\nYou: Extract tool calls, execute them, create function_call_output items, call API again\n```\n\n**Key improvements:**\n- **Multiple tool calls per response**: Model can request several tools at once\n- **Explicit step types**: No more message role gymnastics\n- **Better structured**: function_call_output vs cramming into user messages\n- **Clearer semantics**: Steps are first-class, not message metadata\n\n**Still your responsibility:**\n- Executing the tools\n- Deciding when to stop the loop\n- Managing conversation history\n\n## API\n\n### `OpenAIAdapter`\n\n```typescript\ninterface OpenAIAdapterOptions {\n  apiKey: string;\n  model: string;\n  structuredOutput?: {\n    type: \"json_schema\";\n    name: string;\n    description?: string;\n    schema: Record<string, any>;\n    strict?: boolean;\n  };\n}\n\nclass OpenAIAdapter implements LLMAdapter {\n  constructor(options: OpenAIAdapterOptions);\n  constructor(apiKey: string, model: string); // Legacy\n}\n```\n\n#### Parameters\n\n- `apiKey`: Your OpenAI API key\n- `model`: The OpenAI model to use (e.g., \"gpt-5\", \"o4-mini\")\n- `structuredOutput`: Optional JSON schema for structured outputs\n\n## Architecture Notes\n\n### Responses API vs Chat Completions\n\nThis adapter uses OpenAI's **Responses API**, which differs from Chat Completions in several ways:\n\n**Request Format:**\n- Chat Completions: `messages` array with system/user/assistant roles\n- Responses API: `input` array with typed items (messages, function_call_outputs, etc.) + separate `instructions` field\n\n**Response Format:**\n- Chat Completions: `choices[0].message.content`\n- Responses API: `output` array of items with type `message`, `function_call`, etc.\n\n**Tool/Function Format:**\n- Chat Completions: Externally-tagged `{ type: \"function\", function: {...} }`\n- Responses API: Internally-tagged `{ type: \"function\", name: \"...\", ... }` (strict by default)\n\n**Structured Outputs:**\n- Chat Completions: `response_format: { type: \"json_schema\", json_schema: {...} }`\n- Responses API: `text: { format: { type: \"json_schema\", ... } }`\n\n### Flink Integration\n\nThe adapter seamlessly integrates with Flink's `LLMAdapter` interface:\n\n- Flink's `instructions` → Responses API `instructions`\n- Flink's `messages` → Converted to Responses API `input` items (typed steps)\n- Flink's tool schema → Converted to Responses API function format (internally-tagged, strict by default)\n- Responses `output` → Extracted to Flink's `LLMResponse` format\n\n**Each API call is one turn:**\n- Flink calls `adapter.execute()` → One Responses API request\n- Response may contain multiple tool calls (as separate items)\n- Flink's AgentRunner executes those tools\n- Flink calls `adapter.execute()` again with tool results → Another Responses API request\n- Repeat until no more tool calls\n\nThis is the standard agent loop architecture used by modern frameworks (LangGraph, Vercel AI SDK, etc.)\n\n## Migration from Chat Completions\n\nIf you're coming from the Chat Completions API, the good news is: **no code changes needed!**\n\nThe adapter handles all the differences internally:\n\n```typescript\n// This works the same way with both APIs\nconst app = new FlinkApp({\n  ai: {\n    llms: {\n      default: new OpenAIAdapter({\n        apiKey: process.env.OPENAI_API_KEY!,\n        model: \"gpt-5\" // Just update the model\n      }),\n    },\n  },\n});\n```\n\nBenefits of upgrading:\n- ✅ Step-aware reasoning (multiple tool calls per response)\n- ✅ Better performance (3-5% improvement on benchmarks)\n- ✅ First-class tool steps (cleaner than message role hacks)\n- ✅ Future-proof (new features land here first)\n\n## Requirements\n\n- Node.js >= 18\n- @flink-app/flink >= 1.0.0\n- openai >= 4.77.0 (with Responses API support)\n\n## License\n\nMIT\n\n## Resources\n\n- [OpenAI Responses API Documentation](https://platform.openai.com/docs/api-reference/responses)\n- [Migrate to the Responses API Guide](https://platform.openai.com/docs/guides/migrate-to-responses)\n- [Why we built the Responses API](https://developers.openai.com/blog/responses-api/)\n- [Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs)\n","readmeFilename":"README.md"}