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TypeScript types for Cogitator","maintainers":[{"name":"el1fe","email":"piuro.pavel@gmail.com"}],"readme":"# @cogitator-ai/types\n\n[![npm version](https://img.shields.io/npm/v/@cogitator-ai/types.svg)](https://www.npmjs.com/package/@cogitator-ai/types)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nShared TypeScript types for the Cogitator AI agent runtime.\n\n## Installation\n\n```bash\npnpm add @cogitator-ai/types\n```\n\n## Quick Start\n\n```typescript\nimport type {\n  Agent,\n  AgentConfig,\n  Tool,\n  ToolConfig,\n  Message,\n  RunResult,\n  CogitatorConfig,\n} from '@cogitator-ai/types';\n```\n\n## Type Categories\n\n| Category                                          | Description                             |\n| ------------------------------------------------- | --------------------------------------- |\n| [Message](#message-types)                         | Chat messages, tool calls, tool results |\n| [Tool](#tool-types)                               | Tool definitions with Zod schemas       |\n| [Agent](#agent-types)                             | Agent configuration and interface       |\n| [LLM](#llm-types)                                 | LLM backend and provider types          |\n| [Runtime](#runtime-types)                         | Cogitator config, run options, results  |\n| [Errors](#error-types)                            | Structured error handling               |\n| [Reflection](#reflection-types)                   | Self-analyzing agent types              |\n| [Reasoning](#reasoning-types)                     | Tree-of-Thought reasoning               |\n| [Learning](#learning-types)                       | DSPy-style optimization                 |\n| [Time Travel](#time-travel-types)                 | Execution debugging                     |\n| [Knowledge Graph](#knowledge-graph-types)         | Entity-relationship memory              |\n| [Prompt Optimization](#prompt-optimization-types) | A/B testing, monitoring, rollback       |\n\n---\n\n## Message Types\n\nTypes for LLM conversations and tool interactions.\n\n```typescript\nimport type {\n  Message,\n  MessageRole,\n  ToolCall,\n  ToolResult,\n  ToolCallMessage,\n  ToolResultMessage,\n} from '@cogitator-ai/types';\n\n// Basic message\nconst userMessage: Message = {\n  role: 'user',\n  content: 'What is 2 + 2?',\n};\n\n// Assistant message with tool calls\nconst assistantMessage: ToolCallMessage = {\n  role: 'assistant',\n  content: '',\n  toolCalls: [\n    {\n      id: 'call_123',\n      name: 'calculator',\n      arguments: { expression: '2 + 2' },\n    },\n  ],\n};\n\n// Tool result message\nconst toolResult: ToolResultMessage = {\n  role: 'tool',\n  content: '4',\n  toolCallId: 'call_123',\n  name: 'calculator',\n};\n```\n\n### Message Interfaces\n\n| Type                | Description                                               |\n| ------------------- | --------------------------------------------------------- |\n| `MessageRole`       | `'system' \\| 'user' \\| 'assistant' \\| 'tool'`             |\n| `Message`           | Base message with role, content, optional name/toolCallId |\n| `ToolCallMessage`   | Assistant message containing tool calls                   |\n| `ToolResultMessage` | Tool execution result                                     |\n| `ToolCall`          | Tool invocation with id, name, arguments                  |\n| `ToolResult`        | Tool execution result with callId, name, result, error    |\n\n---\n\n## Tool Types\n\nTypes for defining agent tools with Zod schemas.\n\n```typescript\nimport type { Tool, ToolConfig, ToolContext, ToolSchema } from '@cogitator-ai/types';\nimport { z } from 'zod';\n\n// Tool configuration\nconst calculatorConfig: ToolConfig<{ expression: string }, number> = {\n  name: 'calculator',\n  description: 'Evaluate mathematical expressions',\n  category: 'math',\n  parameters: z.object({\n    expression: z.string().describe('Math expression to evaluate'),\n  }),\n  execute: async (params, context) => {\n    console.log(`Run ${context.runId} executing calculator`);\n    return eval(params.expression);\n  },\n  timeout: 5000,\n  sideEffects: [],\n};\n\n// Tool context available during execution\ninterface ToolContext {\n  agentId: string;\n  runId: string;\n  signal: AbortSignal;\n}\n```\n\n### Tool Interfaces\n\n| Type                           | Description                                                                                                                   |\n| ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------- |\n| `ToolConfig<TParams, TResult>` | Tool definition with execute function                                                                                         |\n| `Tool<TParams, TResult>`       | Full tool with toJSON() method                                                                                                |\n| `ToolContext`                  | Execution context with agentId, runId, signal                                                                                 |\n| `ToolSchema`                   | JSON Schema representation for LLM                                                                                            |\n| `ToolCategory`                 | `'math' \\| 'text' \\| 'file' \\| 'network' \\| 'system' \\| 'utility' \\| 'web' \\| 'database' \\| 'communication' \\| 'development'` |\n\n### Tool Options\n\n```typescript\nconst advancedTool: ToolConfig = {\n  name: 'file_write',\n  description: 'Write content to a file',\n  parameters: z.object({\n    path: z.string(),\n    content: z.string(),\n  }),\n  execute: async (params) => {\n    /* ... */\n  },\n\n  // Optional configuration\n  category: 'file',\n  tags: ['io', 'filesystem'],\n  sideEffects: ['filesystem'],\n  requiresApproval: true, // or (params) => params.path.includes('/etc')\n  timeout: 10000,\n  sandbox: { type: 'docker', image: 'node:20' },\n};\n```\n\n---\n\n## Agent Types\n\nTypes for agent configuration.\n\n```typescript\nimport type { Agent, AgentConfig, ResponseFormat } from '@cogitator-ai/types';\n\nconst config: AgentConfig = {\n  name: 'research-agent',\n  model: 'openai/gpt-4o',\n  instructions: 'You are a research assistant...',\n\n  // Optional settings\n  description: 'Helps with research tasks',\n  tools: [calculatorTool],\n  temperature: 0.7,\n  topP: 0.9,\n  maxTokens: 4096,\n  stopSequences: ['END'],\n  maxIterations: 10,\n  timeout: 60000,\n\n  // Response format\n  responseFormat: { type: 'json' },\n};\n\n// Response format options\nconst textFormat: ResponseFormat = { type: 'text' };\nconst jsonFormat: ResponseFormat = { type: 'json' };\nconst schemaFormat: ResponseFormat = {\n  type: 'json_schema',\n  schema: z.object({ answer: z.string() }),\n};\n```\n\n---\n\n## LLM Types\n\nTypes for LLM backends and providers.\n\n```typescript\nimport type {\n  LLMProvider,\n  LLMConfig,\n  LLMBackend,\n  ChatRequest,\n  ChatResponse,\n  ChatStreamChunk,\n} from '@cogitator-ai/types';\n\n// Supported providers\ntype LLMProvider =\n  | 'ollama'\n  | 'openai'\n  | 'anthropic'\n  | 'google'\n  | 'azure'\n  | 'bedrock'\n  | 'vllm'\n  | 'mistral'\n  | 'groq'\n  | 'together'\n  | 'deepseek';\n\n// LLM configuration\nconst llmConfig: LLMConfig = {\n  provider: 'openai',\n  model: 'gpt-4o',\n  temperature: 0.7,\n  maxTokens: 4096,\n};\n\n// Chat request\nconst request: ChatRequest = {\n  model: 'gpt-4o',\n  messages: [{ role: 'user', content: 'Hello' }],\n  tools: [{ name: 'calc', description: '...', parameters: { type: 'object', properties: {} } }],\n  stream: true,\n};\n\n// Chat response\nconst response: ChatResponse = {\n  id: 'chatcmpl-123',\n  content: 'Hello! How can I help?',\n  finishReason: 'stop',\n  usage: { inputTokens: 10, outputTokens: 20, totalTokens: 30 },\n};\n```\n\n---\n\n## Runtime Types\n\nTypes for Cogitator runtime configuration and execution.\n\n```typescript\nimport type { CogitatorConfig, RunOptions, RunResult, Span } from '@cogitator-ai/types';\n\n// Cogitator configuration\nconst config: CogitatorConfig = {\n  llm: {\n    defaultProvider: 'openai',\n    defaultModel: 'gpt-4o',\n    providers: {\n      openai: { apiKey: process.env.OPENAI_API_KEY! },\n      ollama: { baseUrl: 'http://localhost:11434' },\n      anthropic: { apiKey: process.env.ANTHROPIC_API_KEY! },\n    },\n  },\n  limits: {\n    maxConcurrentRuns: 10,\n    defaultTimeout: 30000,\n    maxTokensPerRun: 100000,\n  },\n  memory: { adapter: 'redis', redis: { host: 'localhost' } },\n  sandbox: { defaults: { type: 'docker', image: 'node:20-alpine' } },\n  reflection: { enabled: true, reflectAfterError: true },\n};\n\n// Run options with callbacks\nconst runOptions: RunOptions = {\n  input: 'Calculate 2 + 2',\n  context: { userId: '123' },\n  threadId: 'thread_abc',\n  timeout: 30000,\n  stream: true,\n\n  // Callbacks\n  onToken: (token) => process.stdout.write(token),\n  onToolCall: (call) => console.log('Tool called:', call.name),\n  onToolResult: (result) => console.log('Tool result:', result.result),\n  onRunStart: ({ runId }) => console.log('Started:', runId),\n  onRunComplete: (result) => console.log('Done:', result.output),\n  onRunError: (error) => console.error('Error:', error),\n  onSpan: (span) => console.log('Span:', span.name),\n\n  // Memory options\n  useMemory: true,\n  loadHistory: true,\n  saveHistory: true,\n};\n```\n\n### RunResult\n\n```typescript\nconst result: RunResult = {\n  output: 'The answer is 4',\n  structured: { answer: 4 }, // if responseFormat was json_schema\n  runId: 'run_123',\n  agentId: 'agent_456',\n  threadId: 'thread_789',\n  usage: {\n    inputTokens: 100,\n    outputTokens: 50,\n    totalTokens: 150,\n    cost: 0.0023,\n    duration: 1500,\n  },\n  toolCalls: [{ id: 'call_1', name: 'calculator', arguments: { expression: '2+2' } }],\n  messages: [\n    /* conversation history */\n  ],\n  trace: {\n    traceId: 'trace_abc',\n    spans: [\n      /* execution spans */\n    ],\n  },\n  reflections: [\n    /* if reflection enabled */\n  ],\n  reflectionSummary: {\n    /* summary stats */\n  },\n};\n```\n\n---\n\n## Error Types\n\nStructured error handling with typed error codes.\n\n```typescript\nimport {\n  CogitatorError,\n  ErrorCode,\n  ERROR_STATUS_CODES,\n  isRetryableError,\n  getRetryDelay,\n} from '@cogitator-ai/types';\n\n// Create a structured error\nconst error = new CogitatorError({\n  message: 'LLM backend unavailable',\n  code: ErrorCode.LLM_UNAVAILABLE,\n  details: { provider: 'openai', endpoint: 'https://api.openai.com' },\n  retryable: true,\n  retryAfter: 5000,\n});\n\n// Check error type\nif (CogitatorError.isCogitatorError(error)) {\n  console.log(error.code); // 'LLM_UNAVAILABLE'\n  console.log(error.statusCode); // 503\n  console.log(error.retryable); // true\n}\n\n// Wrap any error\nconst wrapped = CogitatorError.wrap(new Error('timeout'), ErrorCode.LLM_TIMEOUT);\n\n// Check if retryable\nif (isRetryableError(error)) {\n  const delay = getRetryDelay(error, 1000);\n  await sleep(delay);\n}\n```\n\n### Error Codes\n\n| Domain   | Codes                                                                                                                               |\n| -------- | ----------------------------------------------------------------------------------------------------------------------------------- |\n| LLM      | `LLM_UNAVAILABLE`, `LLM_RATE_LIMITED`, `LLM_TIMEOUT`, `LLM_INVALID_RESPONSE`, `LLM_CONTEXT_LENGTH_EXCEEDED`, `LLM_CONTENT_FILTERED` |\n| Sandbox  | `SANDBOX_UNAVAILABLE`, `SANDBOX_TIMEOUT`, `SANDBOX_OOM`, `SANDBOX_EXECUTION_FAILED`, `SANDBOX_INVALID_MODULE`                       |\n| Tool     | `TOOL_NOT_FOUND`, `TOOL_INVALID_ARGS`, `TOOL_EXECUTION_FAILED`, `TOOL_TIMEOUT`                                                      |\n| Memory   | `MEMORY_UNAVAILABLE`, `MEMORY_WRITE_FAILED`, `MEMORY_READ_FAILED`                                                                   |\n| Agent    | `AGENT_NOT_FOUND`, `AGENT_ALREADY_RUNNING`, `AGENT_MAX_ITERATIONS`                                                                  |\n| Workflow | `WORKFLOW_NOT_FOUND`, `WORKFLOW_STEP_FAILED`, `WORKFLOW_CYCLE_DETECTED`                                                             |\n| Swarm    | `SWARM_NO_WORKERS`, `SWARM_CONSENSUS_FAILED`                                                                                        |\n| General  | `VALIDATION_ERROR`, `CONFIGURATION_ERROR`, `INTERNAL_ERROR`, `NOT_IMPLEMENTED`, `CIRCUIT_OPEN`                                      |\n\n---\n\n## Reflection Types\n\nTypes for self-analyzing agents that learn from their actions.\n\n```typescript\nimport type {\n  Reflection,\n  ReflectionConfig,\n  ReflectionAction,\n  Insight,\n  InsightStore,\n  ReflectionSummary,\n} from '@cogitator-ai/types';\n\n// Reflection configuration\nconst reflectionConfig: ReflectionConfig = {\n  enabled: true,\n  reflectAfterToolCall: true,\n  reflectAfterError: true,\n  reflectAtEnd: true,\n  storeInsights: true,\n  maxInsightsPerAgent: 100,\n  minConfidenceToStore: 0.7,\n  useSmallModelForReflection: true,\n  reflectionModel: 'gpt-4o-mini',\n};\n\n// Insight types\ntype InsightType = 'pattern' | 'mistake' | 'success' | 'tip' | 'warning';\n\n// Reflection result\nconst reflection: Reflection = {\n  id: 'ref_123',\n  runId: 'run_456',\n  agentId: 'agent_789',\n  timestamp: new Date(),\n  action: {\n    type: 'tool_call',\n    toolName: 'search',\n    input: { query: 'AI news' },\n    output: { results: [] },\n    duration: 500,\n  },\n  analysis: {\n    wasSuccessful: false,\n    confidence: 0.8,\n    reasoning: 'Search returned no results, should try broader query',\n    whatCouldImprove: 'Use more general search terms',\n  },\n  insights: [\n    {\n      id: 'ins_1',\n      type: 'tip',\n      content: 'Broaden search queries when results are empty',\n      context: 'search operations',\n      confidence: 0.85,\n      usageCount: 0,\n      createdAt: new Date(),\n      lastUsedAt: new Date(),\n      agentId: 'agent_789',\n      source: { runId: 'run_456', reflectionId: 'ref_123' },\n    },\n  ],\n  goal: 'Find recent AI news',\n  iterationIndex: 2,\n};\n```\n\n---\n\n## Reasoning Types\n\nTree-of-Thought (ToT) reasoning types for branching exploration.\n\n```typescript\nimport type {\n  ToTConfig,\n  ThoughtTree,\n  ThoughtNode,\n  ThoughtBranch,\n  ToTResult,\n  ExplorationStrategy,\n} from '@cogitator-ai/types';\n\n// ToT configuration\nconst totConfig: ToTConfig = {\n  branchFactor: 3, // Generate 3 candidate thoughts per step\n  beamWidth: 2, // Keep top 2 branches\n  maxDepth: 5, // Max reasoning depth\n  explorationStrategy: 'beam', // 'beam' | 'best-first' | 'dfs'\n\n  confidenceThreshold: 0.3,\n  terminationConfidence: 0.8,\n  maxTotalNodes: 50,\n  maxIterationsPerBranch: 3,\n\n  // Callbacks\n  onBranchGenerated: (node, branches) => console.log('Generated:', branches.length),\n  onBranchEvaluated: (branch, score) => console.log('Score:', score.composite),\n  onNodeExplored: (node) => console.log('Explored:', node.id),\n  onBacktrack: (from, to) => console.log('Backtracking...'),\n};\n\n// Thought branch with proposed action\nconst branch: ThoughtBranch = {\n  id: 'branch_1',\n  parentId: 'node_0',\n  thought: 'I should search for the latest information first',\n  proposedAction: { type: 'tool_call', toolName: 'search', arguments: { query: 'AI news 2024' } },\n  score: { confidence: 0.8, progress: 0.3, novelty: 0.6, composite: 0.57, reasoning: '...' },\n  messagesSnapshot: [\n    /* ... */\n  ],\n};\n```\n\n---\n\n## Learning Types\n\nDSPy-inspired agent optimization types.\n\n```typescript\nimport type {\n  ExecutionTrace,\n  TraceStore,\n  Demo,\n  OptimizerConfig,\n  OptimizationResult,\n  LearningConfig,\n} from '@cogitator-ai/types';\n\n// Learning configuration\nconst learningConfig: LearningConfig = {\n  enabled: true,\n  captureTraces: true,\n  autoOptimize: true,\n  optimizeAfterRuns: 10,\n  maxDemosPerAgent: 5,\n  minScoreForDemo: 0.8,\n  defaultMetrics: ['success', 'tool_accuracy', 'efficiency'],\n};\n\n// Optimizer configuration\nconst optimizerConfig: OptimizerConfig = {\n  type: 'full', // 'bootstrap-few-shot' | 'instruction' | 'full'\n  maxBootstrappedDemos: 5,\n  maxRounds: 3,\n  instructionCandidates: 3,\n  metricThreshold: 0.7,\n  teacherModel: 'gpt-4o',\n};\n\n// Execution trace\nconst trace: ExecutionTrace = {\n  id: 'trace_123',\n  runId: 'run_456',\n  agentId: 'agent_789',\n  threadId: 'thread_abc',\n  input: 'Calculate compound interest',\n  output: 'The compound interest is $1,628.89',\n  steps: [],\n  toolCalls: [],\n  reflections: [],\n  metrics: {\n    success: true,\n    toolAccuracy: 0.95,\n    efficiency: 0.8,\n    completeness: 1.0,\n    coherence: 0.9,\n  },\n  score: 0.92,\n  model: 'gpt-4o',\n  createdAt: new Date(),\n  duration: 1500,\n  usage: {\n    inputTokens: 100,\n    outputTokens: 50,\n    cost: 0.0023,\n  },\n  isDemo: true,\n};\n```\n\n---\n\n## Time Travel Types\n\nExecution debugging with checkpoints, replay, and forking.\n\n```typescript\nimport type {\n  ExecutionCheckpoint,\n  TimeTravelCheckpointStore,\n  ReplayOptions,\n  ReplayResult,\n  ForkOptions,\n  TraceDiff,\n} from '@cogitator-ai/types';\n\n// Checkpoint\nconst checkpoint: ExecutionCheckpoint = {\n  id: 'cp_123',\n  traceId: 'trace_456',\n  runId: 'run_789',\n  agentId: 'agent_abc',\n  stepIndex: 5,\n  messages: [\n    /* conversation at this point */\n  ],\n  toolResults: { call_1: 42, call_2: 'result' },\n  pendingToolCalls: [],\n  label: 'before-critical-decision',\n  createdAt: new Date(),\n};\n\n// Replay options\nconst replayOptions: ReplayOptions = {\n  fromCheckpoint: 'cp_123',\n  mode: 'live', // 'deterministic' | 'live'\n  modifiedToolResults: { call_3: 'different_result' },\n  skipTools: ['expensive_api'],\n  onStep: (step, index) => console.log(`Step ${index}:`, step.type),\n  pauseAt: 8,\n};\n\n// Fork options (branch execution with modifications)\nconst forkOptions: ForkOptions = {\n  checkpointId: 'cp_123',\n  input: 'Try a different approach',\n  additionalContext: 'Focus on efficiency',\n  mockToolResults: { api_call: { mocked: true } },\n  label: 'efficiency-experiment',\n};\n\n// Compare two execution traces\nconst diff: TraceDiff = {\n  trace1Id: 'trace_a',\n  trace2Id: 'trace_b',\n  stepDiffs: [\n    { index: 0, status: 'identical' },\n    { index: 1, status: 'similar', differences: ['different tool args'] },\n    {\n      index: 2,\n      status: 'different',\n      step1: {\n        /* ... */\n      },\n      step2: {\n        /* ... */\n      },\n    },\n  ],\n  divergencePoint: 2,\n  metricsDiff: {\n    success: { trace1: true, trace2: false },\n    score: { trace1: 0.9, trace2: 0.6, delta: -0.3 },\n    /* ... */\n  },\n};\n```\n\n---\n\n## Memory Types\n\nSee [@cogitator-ai/memory](../memory) for detailed memory adapter types.\n\n---\n\n## Knowledge Graph Types\n\nEntity-relationship memory with traversal and inference.\n\n```typescript\nimport type {\n  GraphNode,\n  GraphEdge,\n  EntityType,\n  RelationType,\n  GraphAdapter,\n  TraversalOptions,\n  TraversalResult,\n  GraphPath,\n  EntityExtractionResult,\n  InferredEdge,\n} from '@cogitator-ai/types';\n\n// Entity types\ntype EntityType = 'person' | 'organization' | 'location' | 'concept' | 'event' | 'object';\n\n// Relationship types\ntype RelationType =\n  | 'knows'\n  | 'works_at'\n  | 'located_in'\n  | 'part_of'\n  | 'related_to'\n  | 'created_by'\n  | 'owns'\n  | 'member_of'\n  | 'causes'\n  | 'depends_on';\n\n// Graph node\nconst node: GraphNode = {\n  id: 'node_123',\n  agentId: 'agent-1',\n  type: 'person',\n  name: 'Alice',\n  aliases: ['alice_dev'],\n  description: 'Software engineer',\n  properties: { role: 'developer', team: 'platform' },\n  embedding: [0.1, 0.2, ...],\n  confidence: 1.0,\n  source: 'extracted',\n  createdAt: new Date(),\n};\n\n// Graph edge\nconst edge: GraphEdge = {\n  id: 'edge_456',\n  agentId: 'agent-1',\n  sourceNodeId: 'node_123',\n  targetNodeId: 'node_789',\n  type: 'works_at',\n  label: 'Senior Developer',\n  weight: 1.0,\n  bidirectional: false,\n  confidence: 0.95,\n  source: 'extracted',\n  properties: { since: '2020' },\n  createdAt: new Date(),\n};\n\n// Traversal options\nconst traversalOptions: TraversalOptions = {\n  startNodeId: 'node_123',\n  maxDepth: 3,\n  direction: 'outgoing',\n  edgeTypes: ['works_at', 'knows'],\n  nodeTypes: ['person', 'organization'],\n  minConfidence: 0.7,\n  maxNodes: 100,\n};\n\n// Traversal result\nconst result: TraversalResult = {\n  visitedNodes: [node1, node2, ...],\n  traversedEdges: [edge1, edge2, ...],\n  paths: [[node1, edge1, node2], ...],\n  totalNodesVisited: 15,\n  maxDepthReached: 3,\n};\n```\n\n---\n\n## Prompt Optimization Types\n\nA/B testing, monitoring, and version control for agent instructions.\n\n```typescript\nimport type {\n  CapturedPrompt,\n  PromptStore,\n  ABTest,\n  ABTestResults,\n  ABTestOutcome,\n  ABTestStore,\n  InstructionVersion,\n  InstructionVersionStore,\n  PromptPerformanceMetrics,\n  DegradationAlert,\n  OptimizationRun,\n} from '@cogitator-ai/types';\n\n// Captured prompt\nconst prompt: CapturedPrompt = {\n  id: 'prompt_123',\n  runId: 'run_456',\n  agentId: 'agent-1',\n  model: 'gpt-4o',\n  systemPrompt: 'You are a helpful assistant.',\n  messages: [{ role: 'user', content: 'Hello' }],\n  tools: [{ name: 'calculator', description: '...' }],\n  promptTokens: 150,\n  response: {\n    content: 'Hi there!',\n    completionTokens: 10,\n    latencyMs: 450,\n  },\n  createdAt: new Date(),\n};\n\n// A/B test\nconst abTest: ABTest = {\n  id: 'test_123',\n  agentId: 'agent-1',\n  name: 'Instruction Experiment',\n  description: 'Testing concise vs verbose',\n  status: 'running',\n  controlInstructions: 'You are helpful.',\n  treatmentInstructions: 'Be concise and direct.',\n  treatmentAllocation: 0.5,\n  minSampleSize: 100,\n  maxDuration: 7 * 24 * 60 * 60 * 1000,\n  confidenceLevel: 0.95,\n  metricToOptimize: 'score',\n  controlResults: { sampleSize: 50, avgScore: 0.82, ... },\n  treatmentResults: { sampleSize: 48, avgScore: 0.87, ... },\n  createdAt: new Date(),\n  startedAt: new Date(),\n};\n\n// A/B test outcome\nconst outcome: ABTestOutcome = {\n  winner: 'treatment',\n  pValue: 0.023,\n  confidenceInterval: [0.02, 0.08],\n  effectSize: 0.45,\n  isSignificant: true,\n  recommendation: 'Treatment performs significantly better.',\n};\n\n// Instruction version\nconst version: InstructionVersion = {\n  id: 'ver_123',\n  agentId: 'agent-1',\n  version: 3,\n  instructions: 'Optimized instructions...',\n  source: 'optimization',\n  sourceId: 'opt-run-456',\n  deployedAt: new Date(),\n  metrics: { runCount: 100, avgScore: 0.88, successRate: 0.95 },\n};\n\n// Degradation alert\nconst alert: DegradationAlert = {\n  id: 'alert_123',\n  agentId: 'agent-1',\n  type: 'score_drop',\n  severity: 'warning',\n  currentValue: 0.72,\n  baselineValue: 0.85,\n  threshold: 0.15,\n  percentChange: 0.153,\n  detectedAt: new Date(),\n  autoAction: 'rollback',\n  actionTaken: false,\n};\n```\n\n---\n\n## Sandbox Types\n\nSee [@cogitator-ai/sandbox](../sandbox) for detailed sandbox execution types.\n\n## Workflow Types\n\nSee [@cogitator-ai/workflows](../workflows) for detailed workflow types.\n\n## Swarm Types\n\nSee [@cogitator-ai/swarms](../swarms) for detailed swarm coordination types.\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}