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perfect for demos and testing","maintainers":[{"name":"joelso","email":"joel@frostdigital.se"},{"name":"jenkins-frost","email":"devops@frostdigital.se"},{"name":"johanfrost","email":"johan@frostdigital.se"}],"readme":"# @flink-app/fake-llm-adapter\n\nA fake LLM adapter for the Flink AI framework - perfect for demos, development, and testing without requiring real LLM API connections.\n\n## Overview\n\nThe `FakeLLMAdapter` implements the `LLMAdapter` interface and generates contextually relevant responses with intelligent tool calling decisions. Unlike simple mocks, it:\n\n- **Intelligently decides when to call tools** based on user messages and available tools\n- **Generates realistic tool inputs** by analyzing tool schemas and extracting values from user messages\n- **Provides contextually relevant text responses** using customizable templates\n- **Simulates realistic token usage** for testing cost/performance scenarios\n- **Supports reproducible randomness** via seeding for deterministic testing\n- **Configurable behavior modes** from text-only to tool-heavy\n\n## Installation\n\n```bash\npnpm add @flink-app/fake-llm-adapter\n```\n\n## Quick Start\n\n```typescript\nimport { FlinkApp } from \"@flink-app/flink\";\nimport { FakeLLMAdapter } from \"@flink-app/fake-llm-adapter\";\n\nconst app = new FlinkApp({\n  ai: {\n    llms: {\n      fake: new FakeLLMAdapter()\n    }\n  }\n});\n```\n\n### Creating an Agent with Fake Adapter\n\n```typescript\nimport { createAgent } from \"@flink-app/flink/ai\";\nimport { FakeLLMAdapter } from \"@flink-app/fake-llm-adapter\";\n\nconst weatherAgent = createAgent({\n  llm: new FakeLLMAdapter({\n    mode: \"tool-heavy\", // Prefer calling tools\n    preferredTools: [\"get_weather\"], // Prioritize weather tool\n  }),\n  instructions: \"You are a helpful weather assistant.\",\n  tools: [\n    {\n      name: \"get_weather\",\n      description: \"Get weather for a location\",\n      input_schema: {\n        type: \"object\",\n        properties: {\n          city: { type: \"string\" },\n          unit: { type: \"string\", enum: [\"celsius\", \"fahrenheit\"] }\n        },\n        required: [\"city\"]\n      }\n    }\n  ]\n});\n\nconst result = await weatherAgent.execute(\"What's the weather in Stockholm?\");\n// Likely calls get_weather tool with { city: \"Stockholm\", unit: \"celsius\" }\n```\n\n## Configuration\n\n```typescript\nexport interface FakeLLMAdapterConfig {\n  // Response behavior\n  mode?: AdapterMode; // \"balanced\" | \"tool-heavy\" | \"text-only\" | \"random\"\n\n  // Tool calling configuration\n  toolCallProbability?: number; // 0.0 to 1.0 (default: 0.3)\n  maxToolCallsPerTurn?: number; // default: 3\n\n  // Randomness control\n  seed?: number; // For reproducible responses\n\n  // Timing simulation\n  responseDelay?: number; // milliseconds (default: 0)\n\n  // Response customization\n  templates?: ResponseTemplate[];\n  personality?: PersonalityPreset; // \"professional\" | \"friendly\" | \"technical\" | \"concise\" | \"verbose\"\n\n  // Token simulation\n  tokenMultiplier?: number; // Adjust token counts (default: 1.0)\n\n  // Advanced options\n  debugMode?: boolean; // Log decision-making process\n  preferredTools?: string[]; // Prioritize these tool names\n}\n```\n\n## Modes Explained\n\n### `balanced` (Default)\nMix of text responses and tool calls. Base 30% probability of calling tools, adjusted based on context.\n\n**Use when:** General development and testing\n\n```typescript\nnew FakeLLMAdapter({ mode: \"balanced\" })\n```\n\n### `tool-heavy`\nStrongly prefers tool calls. Base 70% probability of calling tools.\n\n**Use when:** Testing tool calling flows, demos showcasing tools\n\n```typescript\nnew FakeLLMAdapter({ mode: \"tool-heavy\" })\n```\n\n### `text-only`\nNever calls tools. Always returns text responses.\n\n**Use when:** Testing pure conversation flows, UI without tool interactions\n\n```typescript\nnew FakeLLMAdapter({ mode: \"text-only\" })\n```\n\n### `random`\nCompletely unpredictable behavior.\n\n**Use when:** Stress testing agent error handling\n\n```typescript\nnew FakeLLMAdapter({ mode: \"random\" })\n```\n\n## Context-Aware Tool Calling\n\nThe adapter intelligently decides when to call tools based on:\n\n1. **Action verbs** in user message (\"get\", \"find\", \"search\", \"calculate\", etc.) → +20% probability\n2. **Tool name matches** message content → +25% probability\n3. **Temperature setting** < 0.5 → +10% probability (more deterministic)\n4. **Preferred tools** configuration → Prioritized in selection\n\n### Example: Intelligent Tool Selection\n\n```typescript\nconst adapter = new FakeLLMAdapter({\n  mode: \"balanced\",\n  preferredTools: [\"search_database\"]\n});\n\n// \"Find user John\" → Likely calls tool (has \"find\" action verb)\n// \"What's the weather?\" → May or may not call tool (depends on available tools)\n// \"Hello!\" → Unlikely to call tool (no action verbs)\n```\n\n## Custom Response Templates\n\nDefine domain-specific responses:\n\n```typescript\nconst adapter = new FakeLLMAdapter({\n  templates: [\n    {\n      pattern: /order|purchase|buy/i,\n      responses: [\n        \"I'll help you with that order.\",\n        \"Let me process your purchase.\",\n        \"I can assist with buying that.\"\n      ]\n    },\n    {\n      pattern: /cancel|refund/i,\n      responses: [\n        \"I'll help you with the cancellation.\",\n        \"Let me process that refund request.\"\n      ],\n      toolTrigger: \"process_refund\" // Prefer this tool when pattern matches\n    }\n  ]\n});\n```\n\n## Reproducible Testing with Seeds\n\nUse seeds for deterministic output in tests:\n\n```typescript\nimport { FakeLLMAdapter } from \"@flink-app/fake-llm-adapter\";\n\ndescribe(\"Agent tests\", () => {\n  it(\"should handle weather query consistently\", async () => {\n    const adapter1 = new FakeLLMAdapter({ seed: 42 });\n    const adapter2 = new FakeLLMAdapter({ seed: 42 });\n\n    const agent1 = createAgent({ llm: adapter1, tools: weatherTools });\n    const agent2 = createAgent({ llm: adapter2, tools: weatherTools });\n\n    const result1 = await agent1.execute(\"Weather in Paris?\");\n    const result2 = await agent2.execute(\"Weather in Paris?\");\n\n    // Same seed = same behavior\n    expect(result1).toEqual(result2);\n  });\n});\n```\n\n## Simulating Network Delays\n\n```typescript\nconst adapter = new FakeLLMAdapter({\n  responseDelay: 500 // 500ms delay per response\n});\n\n// Useful for:\n// - Testing loading states in UI\n// - Simulating production latency\n// - Demo scenarios with realistic pacing\n```\n\n## Token Usage Simulation\n\n```typescript\nconst adapter = new FakeLLMAdapter({\n  tokenMultiplier: 2.0 // Double all token counts\n});\n\n// Useful for:\n// - Testing token limit handling\n// - Cost estimation in development\n// - Performance testing with large contexts\n```\n\n## Personality Presets\n\n```typescript\n// Professional (default)\nnew FakeLLMAdapter({ personality: \"professional\" })\n// → \"I can assist with that request.\"\n\n// Friendly\nnew FakeLLMAdapter({ personality: \"friendly\" })\n// → \"Sure thing! Let me help you out!\"\n\n// Technical\nnew FakeLLMAdapter({ personality: \"technical\" })\n// → \"Processing query. Executing tool call.\"\n\n// Concise\nnew FakeLLMAdapter({ personality: \"concise\" })\n// → \"Processing.\"\n\n// Verbose\nnew FakeLLMAdapter({ personality: \"verbose\" })\n// → \"I understand your request and I'll be happy to assist you with that. Let me process...\"\n```\n\n## Debug Mode\n\nEnable logging to understand decision-making:\n\n```typescript\nconst adapter = new FakeLLMAdapter({\n  debugMode: true\n});\n\n// Logs:\n// [FakeLLMAdapter] Processing message: What's the weather?\n// [FakeLLMAdapter] Generated tool calls: [{ name: \"get_weather\", ... }]\n```\n\n## Tool Input Generation\n\nThe adapter generates realistic tool inputs by:\n\n1. **Extracting from message**: Searches for cities, numbers, dates in user message\n2. **Schema-based defaults**: Generates appropriate values based on property types\n3. **Smart field matching**: Recognizes common field names (email, city, name, date)\n\n```typescript\n// Tool schema:\n{\n  name: \"create_user\",\n  input_schema: {\n    properties: {\n      email: { type: \"string\" },\n      city: { type: \"string\" },\n      age: { type: \"number\" }\n    }\n  }\n}\n\n// Message: \"Create user in Stockholm\"\n// Generated input:\n{\n  email: \"user@example.com\",  // Smart default for email field\n  city: \"Stockholm\",           // Extracted from message\n  age: 42                      // Random number\n}\n```\n\n## Comparison: fake-llm-adapter vs mockLLMAdapter\n\n| Feature | `FakeLLMAdapter` | `mockLLMAdapter` (test-utils) |\n|---------|------------------|-------------------------------|\n| **Purpose** | Development & demos | Unit testing only |\n| **Tool calling** | Intelligent, context-aware | Always returns predefined responses |\n| **Responses** | Dynamic, template-based | Static, hard-coded |\n| **Configuration** | Highly configurable modes | Minimal config |\n| **Reproducibility** | Seeded randomness | Fully deterministic |\n| **Use case** | Running app without APIs | Mocking in tests |\n\n**Rule of thumb:**\n- Use `FakeLLMAdapter` for running your app, demos, and development\n- Use `mockLLMAdapter` for unit tests where you need exact, predictable outputs\n\n## Use Cases\n\n### 1. Development Without API Keys\n\n```typescript\n// Develop AI features without spending money or setting up API keys\nconst app = new FlinkApp({\n  ai: {\n    llms: {\n      default: new FakeLLMAdapter()\n    }\n  }\n});\n```\n\n### 2. Demos and Presentations\n\n```typescript\n// Reliable demos that don't depend on external APIs\nconst demoAdapter = new FakeLLMAdapter({\n  mode: \"tool-heavy\",\n  responseDelay: 800, // Realistic pacing\n  seed: 12345 // Same demo every time\n});\n```\n\n### 3. Testing Tool Calling Logic\n\n```typescript\n// Test your agent's tool handling without real LLM\nconst testAdapter = new FakeLLMAdapter({\n  toolCallProbability: 1.0, // Always call tools\n  maxToolCallsPerTurn: 5\n});\n```\n\n### 4. UI Development\n\n```typescript\n// Test loading states and UI behavior\nconst uiTestAdapter = new FakeLLMAdapter({\n  responseDelay: 1500,\n  personality: \"verbose\" // Longer responses to test text wrapping\n});\n```\n\n### 5. Integration Tests\n\n```typescript\n// Consistent behavior across test runs\ndescribe(\"Agent integration\", () => {\n  const adapter = new FakeLLMAdapter({ seed: 999 });\n\n  it(\"should handle multi-turn conversation\", async () => {\n    // Test logic here - same results every run\n  });\n});\n```\n\n## Advanced Example: Custom E-commerce Agent\n\n```typescript\nconst ecommerceAdapter = new FakeLLMAdapter({\n  mode: \"tool-heavy\",\n  preferredTools: [\"search_products\", \"get_order_status\"],\n  templates: [\n    {\n      pattern: /track|status|where.*order/i,\n      responses: [\"Let me check your order status.\"],\n      toolTrigger: \"get_order_status\"\n    },\n    {\n      pattern: /find|search|looking for/i,\n      responses: [\"I'll search our catalog for you.\"],\n      toolTrigger: \"search_products\"\n    },\n    {\n      pattern: /return|refund/i,\n      responses: [\"I'll help you process that return.\"],\n      toolTrigger: \"process_return\"\n    }\n  ],\n  personality: \"friendly\",\n  debugMode: true\n});\n\nconst agent = createAgent({\n  llm: ecommerceAdapter,\n  instructions: \"You are a helpful e-commerce assistant.\",\n  tools: [searchProducts, getOrderStatus, processReturn]\n});\n```\n\n## Limitations (Phase 1)\n\n- **No streaming support**: `stream()` method throws an error. Use `execute()` instead.\n- **Simple tool input generation**: Uses heuristics, may not handle complex nested schemas\n- **Template matching**: Basic regex patterns, not semantic understanding\n\nFuture phases may add streaming and more sophisticated input generation.\n\n## TypeScript Types\n\nAll types are fully exported:\n\n```typescript\nimport {\n  FakeLLMAdapter,\n  FakeLLMAdapterConfig,\n  AdapterMode,\n  PersonalityPreset,\n  ResponseTemplate\n} from \"@flink-app/fake-llm-adapter\";\n```\n\n## License\n\nMIT\n\n## Contributing\n\nIssues and PRs welcome at https://github.com/FrostDigital/flink-framework\n","readmeFilename":"README.md"}