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runtime for Cogitator AI agents","maintainers":[{"name":"el1fe","email":"piuro.pavel@gmail.com"}],"readme":"# @cogitator-ai/core\n\nCore runtime for Cogitator AI agents. Build and run LLM-powered agents with tool calling, streaming, reflection, Tree-of-Thought reasoning, learning optimization, and time-travel debugging.\n\n## Installation\n\n```bash\npnpm add @cogitator-ai/core zod\n```\n\n## Quick Start\n\n```typescript\nimport { Cogitator, Agent, tool } from '@cogitator-ai/core';\nimport { z } from 'zod';\n\nconst calculator = tool({\n  name: 'calculator',\n  description: 'Evaluate a math expression',\n  parameters: z.object({\n    expression: z.string(),\n  }),\n  execute: async ({ expression }) => {\n    return { result: eval(expression) };\n  },\n});\n\nconst agent = new Agent({\n  name: 'math-assistant',\n  instructions: 'You are a helpful math assistant',\n  model: 'openai/gpt-4o',\n  tools: [calculator],\n});\n\nconst cog = new Cogitator({\n  llm: {\n    providers: {\n      openai: { apiKey: process.env.OPENAI_API_KEY! },\n    },\n  },\n});\nconst result = await cog.run(agent, {\n  input: 'What is 25 * 4?',\n});\n\nconsole.log(result.output);\n```\n\n## Features\n\n- **Multi-Provider LLM Support** - Ollama, OpenAI, Anthropic, Google, vLLM\n- **Type-Safe Tools** - Zod-validated tool definitions\n- **Streaming Responses** - Real-time token streaming\n- **Memory Integration** - Redis, PostgreSQL, in-memory adapters\n- **26 Built-in Tools** - Web search, SQL, email, GitHub, filesystem, and more\n- **Reflection Engine** - Self-improvement through tool call analysis\n- **Tree-of-Thought** - Advanced reasoning with branch exploration\n- **Agent Optimizer** - DSPy-style learning from traces\n- **Time Travel** - Checkpoint, replay, fork, and compare executions\n- **Causal Reasoning** - Pearl's do-calculus, counterfactuals, d-separation\n- **Resilience** - Retry, circuit breaker, and fallback patterns\n- **Observability** - Full tracing with spans and callbacks\n\n---\n\n## LLM Backends\n\n### Supported Providers\n\n```typescript\nconst agent = new Agent({\n  name: 'assistant',\n  instructions: 'You are helpful.',\n\n  // Ollama (local, default backend when no provider prefix is present)\n  model: 'ollama/llama3.1:8b',\n\n  // Cloud providers use the same provider/model format:\n  // model: 'openai/gpt-4o'\n  // model: 'anthropic/claude-sonnet-4-5-20250929'\n  // model: 'google/gemini-2.5-flash'\n  // model: 'vllm/mistral-7b'\n});\n```\n\n### Backend Configuration\n\n```typescript\nimport { Cogitator } from '@cogitator-ai/core';\n\nconst cog = new Cogitator({\n  llm: {\n    defaultProvider: 'openai',\n    providers: {\n      ollama: {\n        baseUrl: 'http://localhost:11434',\n      },\n      openai: {\n        apiKey: process.env.OPENAI_API_KEY!,\n      },\n      anthropic: {\n        apiKey: process.env.ANTHROPIC_API_KEY!,\n      },\n      google: {\n        apiKey: process.env.GOOGLE_API_KEY!,\n      },\n      vllm: {\n        baseUrl: 'http://localhost:8000/v1',\n      },\n    },\n  },\n});\n```\n\n### Direct Backend Usage\n\n```typescript\nimport { createLLMBackend, parseModel } from '@cogitator-ai/core';\n\nconst backend = createLLMBackend('openai', {\n  providers: { openai: { apiKey: process.env.OPENAI_API_KEY } },\n});\n\nconst response = await backend.chat({\n  model: 'gpt-4o',\n  messages: [\n    { role: 'system', content: 'You are helpful.' },\n    { role: 'user', content: 'Hello!' },\n  ],\n});\n```\n\n### LLM Plugin System\n\nRegister custom LLM backends:\n\n```typescript\nimport { registerLLMBackend, defineBackend, createLLMBackendFromPlugin } from '@cogitator-ai/core';\n\nconst myPlugin = defineBackend({\n  provider: 'my-provider',\n  metadata: {\n    name: 'My Custom Provider',\n    version: '1.0.0',\n    description: 'Custom LLM backend',\n  },\n  create: (config) => new MyBackend(config),\n});\n\nregisterLLMBackend(myPlugin);\nconst backend = createLLMBackendFromPlugin('my-provider', { apiKey: '...' });\n```\n\n### LLM Debug Wrapper\n\nWrap any backend for request/response logging:\n\n```typescript\nimport { withDebug } from '@cogitator-ai/core';\n\nconst debugBackend = withDebug(backend, {\n  logStream: true,\n  maxContentLength: 500,\n});\n```\n\n### LLM Error Handling\n\n```typescript\nimport { LLMError, llmUnavailable, llmTimeout } from '@cogitator-ai/core';\n\ntry {\n  await backend.chat(request);\n} catch (error) {\n  if (error instanceof LLMError) {\n    console.log('Provider:', error.provider);\n    console.log('Status:', error.statusCode);\n    console.log('Retryable:', error.retryable);\n  }\n}\n```\n\n---\n\n## Agent Configuration\n\n```typescript\nimport { Agent } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  id: 'custom-id',\n  name: 'research-assistant',\n  instructions: 'You research topics thoroughly',\n  model: 'openai/gpt-4o',\n  tools: [webSearch, calculator],\n\n  temperature: 0.7,\n  topP: 0.9,\n  maxTokens: 4096,\n  maxIterations: 15,\n  timeout: 120_000,\n  stopSequences: ['DONE'],\n});\n\n// Clone with modifications\nconst variant = agent.clone({\n  name: 'fast-assistant',\n  temperature: 0.3,\n  maxTokens: 1024,\n});\n```\n\n---\n\n## Tools\n\n### Creating Tools\n\n```typescript\nimport { tool } from '@cogitator-ai/core';\nimport { z } from 'zod';\n\nconst weatherTool = tool({\n  name: 'get_weather',\n  description: 'Get current weather for a location',\n  parameters: z.object({\n    city: z.string().describe('City name'),\n    units: z.enum(['celsius', 'fahrenheit']).optional(),\n  }),\n  execute: async ({ city, units = 'celsius' }, context) => {\n    console.log(`Run ID: ${context.runId}`);\n    return { temperature: 22, units, city };\n  },\n});\n```\n\n### Tool Context\n\nEvery tool receives a context object:\n\n```typescript\ninterface ToolContext {\n  agentId: string;\n  runId: string;\n  signal: AbortSignal;\n}\n```\n\n### Sandboxed Tools\n\nExecute tools in isolated Docker or WASM environments:\n\n```typescript\nconst shellTool = tool({\n  name: 'run_shell',\n  description: 'Execute shell commands safely',\n  parameters: z.object({\n    command: z.string(),\n  }),\n  sandbox: {\n    type: 'docker',\n    image: 'ubuntu:22.04',\n  },\n  timeout: 30000,\n  execute: async ({ command }) => command,\n});\n```\n\n### Tool Registry\n\n```typescript\nimport { ToolRegistry } from '@cogitator-ai/core';\n\nconst registry = new ToolRegistry();\n\nregistry.register(calculator);\nregistry.registerMany([datetime, webSearch, fileRead]);\n\nconst tool = registry.get('calculator');\nconst names = registry.getNames();\nconst schemas = registry.getSchemas();\n```\n\n### Built-in Tools\n\n#### Utility Tools\n\n| Tool            | Description                      |\n| --------------- | -------------------------------- |\n| `calculator`    | Evaluate math expressions        |\n| `datetime`      | Get current date/time            |\n| `uuid`          | Generate UUIDs                   |\n| `randomNumber`  | Random number generation         |\n| `randomString`  | Random string generation         |\n| `hash`          | Hash strings (md5, sha256, etc.) |\n| `base64Encode`  | Encode to base64                 |\n| `base64Decode`  | Decode from base64               |\n| `sleep`         | Delay execution                  |\n| `jsonParse`     | Parse JSON strings               |\n| `jsonStringify` | Stringify to JSON                |\n| `regexMatch`    | Match regex patterns             |\n| `regexReplace`  | Replace with regex               |\n| `fileRead`      | Read file contents               |\n| `fileWrite`     | Write to files                   |\n| `fileList`      | List directory contents          |\n| `fileExists`    | Check if file exists             |\n| `fileDelete`    | Delete files                     |\n| `httpRequest`   | Make HTTP requests               |\n| `exec`          | Execute shell commands           |\n\n#### Web & Search Tools\n\n| Tool        | Description                            |\n| ----------- | -------------------------------------- |\n| `webSearch` | Search the web (Tavily, Brave, Serper) |\n| `webScrape` | Extract content from web pages         |\n\n#### Database Tools\n\n| Tool           | Description                                |\n| -------------- | ------------------------------------------ |\n| `sqlQuery`     | Execute SQL queries (PostgreSQL, SQLite)   |\n| `vectorSearch` | Semantic search with embeddings (pgvector) |\n\n#### Communication Tools\n\n| Tool        | Description                    |\n| ----------- | ------------------------------ |\n| `sendEmail` | Send emails (Resend API, SMTP) |\n\n#### Development Tools\n\n| Tool        | Description                                      |\n| ----------- | ------------------------------------------------ |\n| `githubApi` | GitHub API (issues, PRs, files, commits, search) |\n\n```typescript\nimport { builtinTools, calculator, datetime } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  name: 'utility-agent',\n  instructions: 'Use your tools to help users',\n  model: 'openai/gpt-4o',\n  tools: builtinTools,\n});\n```\n\n### Web Search Tool\n\nSearch the web using Tavily, Brave, or Serper APIs:\n\n```typescript\nimport { webSearch } from '@cogitator-ai/core';\n\n// Auto-detects from TAVILY_API_KEY, BRAVE_API_KEY, or SERPER_API_KEY\nconst agent = new Agent({\n  tools: [webSearch],\n});\n\n// Or specify provider explicitly in tool call\n// provider: 'tavily' | 'brave' | 'serper'\n```\n\n### Web Scrape Tool\n\nExtract content from web pages:\n\n```typescript\nimport { webScrape } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  tools: [webScrape],\n});\n\n// Supports CSS selectors, text/markdown/html output, link/image extraction\n```\n\n### SQL Query Tool\n\nExecute SQL queries against PostgreSQL or SQLite:\n\n```typescript\nimport { sqlQuery } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  tools: [sqlQuery],\n});\n\n// Uses DATABASE_URL env var by default\n// Supports parameterized queries for safety\n// Read-only by default (SELECT, WITH, SHOW, DESCRIBE, EXPLAIN)\n```\n\n**Dependencies:** `pg` for PostgreSQL, `better-sqlite3` for SQLite\n\n### Vector Search Tool\n\nSemantic search using embeddings with pgvector:\n\n```typescript\nimport { vectorSearch } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  tools: [vectorSearch],\n});\n\n// Embedding providers: OpenAI, Ollama, Google\n// Auto-detects from OPENAI_API_KEY, OLLAMA_BASE_URL, or GOOGLE_API_KEY\n```\n\n**Dependencies:** `pg` with pgvector extension\n\n### Email Tool\n\nSend emails via Resend API or SMTP:\n\n```typescript\nimport { sendEmail } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  tools: [sendEmail],\n});\n\n// Resend: Set RESEND_API_KEY\n// SMTP: Set SMTP_HOST, SMTP_USER, SMTP_PASS\n// Supports HTML, CC/BCC, reply-to\n```\n\n**Dependencies:** `nodemailer` for SMTP\n\n### GitHub API Tool\n\nInteract with GitHub repositories:\n\n```typescript\nimport { githubApi } from '@cogitator-ai/core';\n\nconst agent = new Agent({\n  tools: [githubApi],\n});\n\n// Set GITHUB_TOKEN env var\n// Actions: get_repo, list_issues, get_issue, create_issue, update_issue,\n//          list_prs, get_pr, create_pr, get_file, list_commits,\n//          search_code, search_issues\n```\n\n---\n\n## Run Options\n\n```typescript\nimport { Cogitator, Agent } from '@cogitator-ai/core';\n\nconst cog = new Cogitator();\nconst agent = new Agent({\n  /* ... */\n});\n\nconst result = await cog.run(agent, {\n  input: 'Analyze this data...',\n\n  threadId: 'thread_123',\n  context: { userId: 'user_456', task: 'analysis' },\n\n  timeout: 60000,\n  stream: true,\n  onToken: (token) => process.stdout.write(token),\n\n  useMemory: true,\n  loadHistory: true,\n  saveHistory: true,\n\n  onToolCall: (call) => console.log('Tool:', call.name),\n  onToolResult: (result) => console.log('Result:', result.result),\n  onSpan: (span) => console.log('Span:', span.name),\n  onRunStart: ({ runId }) => console.log('Started:', runId),\n  onRunComplete: (result) => console.log('Completed'),\n  onRunError: (error) => console.error('Error:', error),\n  onMemoryError: (error, op) => console.warn(`Memory ${op} failed`),\n});\n```\n\n### Run Result\n\n```typescript\ninterface RunResult {\n  output: string;\n  runId: string;\n  agentId: string;\n  threadId: string;\n  usage: {\n    inputTokens: number;\n    outputTokens: number;\n    totalTokens: number;\n    cost: number;\n    duration: number;\n  };\n  toolCalls: ToolCall[];\n  messages: Message[];\n  trace: { traceId: string; spans: Span[] };\n  reflections?: Reflection[];\n  reflectionSummary?: ReflectionSummary;\n}\n```\n\n---\n\n## Memory Integration\n\nConfigure memory for conversation persistence:\n\n```typescript\nconst cog = new Cogitator({\n  memory: {\n    adapter: 'redis',\n    redis: {\n      url: 'redis://localhost:6379',\n      keyPrefix: 'cogitator:',\n    },\n    contextBuilder: {\n      maxTokens: 4000,\n      strategy: 'recent',\n    },\n  },\n});\n\n// Or PostgreSQL\nconst cog = new Cogitator({\n  memory: {\n    adapter: 'postgres',\n    postgres: {\n      connectionString: 'postgresql://...',\n    },\n  },\n});\n\n// Or in-memory\nconst cog = new Cogitator({\n  memory: {\n    adapter: 'memory',\n    inMemory: {\n      maxEntries: 1000,\n    },\n  },\n});\n```\n\n---\n\n## Reflection Engine\n\nEnable self-improvement through reflection on tool calls and runs:\n\n```typescript\nimport { Cogitator, ReflectionEngine, InMemoryInsightStore } from '@cogitator-ai/core';\n\nconst cog = new Cogitator({\n  reflection: {\n    enabled: true,\n    reflectionModel: 'openai/gpt-4o-mini',\n    reflectAfterToolCall: true,\n    reflectAtEnd: true,\n    minConfidenceToStore: 0.7,\n    maxInsightsPerAgent: 50,\n  },\n});\n\nconst result = await cog.run(agent, { input: 'Complete this task...' });\n\nconsole.log('Reflections:', result.reflections);\nconsole.log('Summary:', result.reflectionSummary);\n\nconst insights = await cog.getInsights(agent.id);\nconsole.log('Learned insights:', insights);\n```\n\n### Standalone Reflection Engine\n\n```typescript\nimport { ReflectionEngine, InMemoryInsightStore, createLLMBackend } from '@cogitator-ai/core';\n\nconst backend = createLLMBackend('openai', { apiKey: '...' });\nconst insightStore = new InMemoryInsightStore();\n\nconst engine = new ReflectionEngine({\n  llm: backend,\n  insightStore,\n  config: {\n    reflectAfterToolCall: true,\n    minConfidenceToStore: 0.7,\n  },\n});\n\nconst result = await engine.reflectOnToolCall(action, agentContext);\nif (result.shouldAdjustStrategy) {\n  console.log('Suggested action:', result.suggestedAction);\n}\n```\n\n---\n\n## Tree-of-Thought Reasoning\n\nExplore multiple reasoning paths before deciding:\n\n```typescript\nimport { ThoughtTreeExecutor, BranchGenerator, BranchEvaluator } from '@cogitator-ai/core';\n\nconst executor = new ThoughtTreeExecutor(cogitator, {\n  maxBranches: 5,\n  maxDepth: 3,\n  explorationStrategy: 'best-first',\n  pruneThreshold: 0.3,\n});\n\nconst result = await executor.run(agent, {\n  input: 'Solve this complex problem...',\n  explorationBudget: 10,\n});\n\nconsole.log('Best path:', result.bestPath);\nconsole.log('All branches explored:', result.tree.branches.length);\nconsole.log('Stats:', result.stats);\n```\n\n### ToT Configuration\n\n```typescript\nconst executor = new ThoughtTreeExecutor(cogitator, {\n  maxBranches: 5,\n  maxDepth: 3,\n  explorationStrategy: 'breadth-first',\n  pruneThreshold: 0.3,\n  branchTemperature: 0.8,\n  evaluationModel: 'openai/gpt-4o-mini',\n});\n```\n\n---\n\n## Agent Optimizer (Learning)\n\nDSPy-style optimization through execution traces:\n\n```typescript\nimport {\n  AgentOptimizer,\n  InMemoryTraceStore,\n  createSuccessMetric,\n  createContainsMetric,\n} from '@cogitator-ai/core';\n\nconst traceStore = new InMemoryTraceStore();\nconst optimizer = new AgentOptimizer(cogitator, {\n  traceStore,\n  optimizationModel: 'openai/gpt-4o',\n  maxCandidates: 5,\n  evaluationRuns: 3,\n});\n\nconst result = await optimizer.compile(agent, {\n  demos: [\n    { input: 'Calculate 2+2', expectedOutput: '4' },\n    { input: 'Calculate 10*5', expectedOutput: '50' },\n  ],\n  metric: createSuccessMetric(),\n  maxIterations: 10,\n});\n\nconsole.log('Optimized instructions:', result.optimizedAgent.instructions);\nconsole.log('Improvement:', result.improvement);\n```\n\n### Built-in Metrics\n\n```typescript\nimport {\n  createSuccessMetric,\n  createExactMatchMetric,\n  createContainsMetric,\n  MetricEvaluator,\n} from '@cogitator-ai/core';\n\nconst successMetric = createSuccessMetric();\n\nconst exactMatch = createExactMatchMetric();\n\nconst containsMetric = createContainsMetric(['error', 'failed'], { negate: true });\n```\n\n### Demo Selection\n\n```typescript\nimport { DemoSelector } from '@cogitator-ai/core';\n\nconst selector = new DemoSelector({\n  strategy: 'diverse',\n  maxDemos: 5,\n});\n\nconst selectedDemos = selector.select(allDemos, currentInput);\n```\n\n---\n\n## Prompt Auto-Optimization\n\nCapture prompts, run A/B tests, monitor performance, and automatically optimize agent instructions.\n\n### Prompt Logger\n\nWrap any LLM backend to capture all prompts:\n\n```typescript\nimport { wrapWithPromptLogger, PostgresTraceStore } from '@cogitator-ai/core';\n\nconst store = new PostgresTraceStore({\n  connectionString: process.env.DATABASE_URL!,\n});\n\nconst wrappedBackend = wrapWithPromptLogger(openaiBackend, store, {\n  captureSystemPrompt: true,\n  captureTools: true,\n  captureResponse: true,\n});\n```\n\n### A/B Testing Framework\n\nTest instruction variants with statistical analysis:\n\n```typescript\nimport { ABTestingFramework } from '@cogitator-ai/core';\n\nconst abTesting = new ABTestingFramework({\n  store: abTestStore,\n  defaultConfidenceLevel: 0.95,\n  defaultMinSampleSize: 50,\n});\n\nconst test = await abTesting.createTest({\n  agentId: 'agent-1',\n  name: 'Instruction Experiment',\n  controlInstructions: 'You are a helpful assistant.',\n  treatmentInstructions: 'You are an expert assistant. Be concise.',\n  treatmentAllocation: 0.5,\n});\n\nawait abTesting.startTest(test.id);\n\nconst variant = abTesting.selectVariant(test);\nconst instructions = abTesting.getInstructionsForVariant(test, variant);\n\nawait abTesting.recordResult(test.id, variant, score, latency, cost);\n\nconst { test: completed, outcome } = await abTesting.completeTest(test.id);\nconsole.log('Winner:', outcome.winner);\nconsole.log('p-value:', outcome.pValue);\nconsole.log('Effect size:', outcome.effectSize);\n```\n\n### Prompt Monitor\n\nReal-time performance monitoring with degradation detection:\n\n```typescript\nimport { PromptMonitor } from '@cogitator-ai/core';\n\nconst monitor = new PromptMonitor({\n  windowSize: 60 * 60 * 1000,\n  scoreDropThreshold: 0.15,\n  latencySpikeThreshold: 2.0,\n  errorRateThreshold: 0.1,\n  enableAutoRollback: true,\n  onAlert: (alert) => {\n    console.log(`Alert: ${alert.type} (${alert.severity})`);\n  },\n});\n\nconst alerts = monitor.recordExecution(trace);\n\nconst metrics = monitor.getCurrentMetrics('agent-1');\nconsole.log('Avg score:', metrics.avgScore);\nconsole.log('P95 latency:', metrics.p95Latency);\n```\n\n### Rollback Manager\n\nVersion control for agent instructions:\n\n```typescript\nimport { RollbackManager } from '@cogitator-ai/core';\n\nconst rollback = new RollbackManager({ store: versionStore });\n\nconst version = await rollback.deployVersion(\n  'agent-1',\n  'New optimized instructions',\n  'optimization',\n  'opt-run-123'\n);\n\nconst result = await rollback.rollbackToPrevious('agent-1');\nif (result.success) {\n  console.log('Rolled back to version:', result.previousVersion?.version);\n}\n\nconst history = await rollback.getVersionHistory('agent-1', 10);\n```\n\n### Auto-Optimizer\n\nAutomated optimization pipeline with A/B testing and rollback:\n\n```typescript\nimport { AutoOptimizer } from '@cogitator-ai/core';\n\nconst optimizer = new AutoOptimizer({\n  enabled: true,\n  triggerAfterRuns: 100,\n  minRunsForOptimization: 20,\n  requireABTest: true,\n  maxOptimizationsPerDay: 3,\n  agentOptimizer,\n  abTesting,\n  monitor,\n  rollbackManager,\n  onOptimizationComplete: (run) => {\n    console.log('Optimization completed:', run.status);\n  },\n  onRollback: (agentId, reason) => {\n    console.log('Rollback triggered:', reason);\n  },\n});\n\nawait optimizer.recordExecution(trace);\n```\n\n---\n\n## Time Travel Debugging\n\nCheckpoint, replay, fork, and compare agent executions:\n\n```typescript\nimport { TimeTravel, InMemoryCheckpointStore } from '@cogitator-ai/core';\n\nconst timeTravel = new TimeTravel(cogitator);\n\nconst result = await cogitator.run(agent, { input: 'Original task...' });\nconst checkpoints = await timeTravel.checkpointAll(result, 'original');\n\nconst replayResult = await timeTravel.replayLive(agent, checkpoints[2].id);\nconsole.log('Replayed from step 2:', replayResult.result.output);\n\nconst forkResult = await timeTravel.fork(agent, checkpoints[2].id, {\n  newInput: 'Modified task...',\n});\nconsole.log('Forked result:', forkResult.result.output);\n\nconst diff = await timeTravel.compareWithOriginal(forkResult);\nconsole.log(timeTravel.formatDiff(diff));\n```\n\n### Forking Variants\n\n```typescript\nconst forkWithContext = await timeTravel.forkWithContext(\n  agent,\n  checkpointId,\n  'Additional context: the user is an expert'\n);\n\nconst forkWithMock = await timeTravel.forkWithMockedTool(agent, checkpointId, 'api_call', {\n  status: 'success',\n  data: 'mocked data',\n});\n\nconst forkWithMocks = await timeTravel.forkWithMockedTools(agent, checkpointId, {\n  api_call: { status: 'success' },\n  database_query: { rows: [] },\n});\n\nconst forkWithNewInput = await timeTravel.forkWithNewInput(\n  agent,\n  checkpointId,\n  'Completely different task...'\n);\n\nconst variants = await timeTravel.forkMultiple(agent, checkpointId, [\n  { newInput: 'Variant A' },\n  { newInput: 'Variant B' },\n  { additionalContext: 'Be more concise' },\n]);\n```\n\n### Replay Modes\n\n```typescript\nconst deterministicReplay = await timeTravel.replayDeterministic(agent, checkpointId);\n\nconst liveReplay = await timeTravel.replayLive(agent, checkpointId, {\n  maxSteps: 5,\n});\n```\n\n---\n\n## Causal Reasoning\n\nFull causal reasoning framework implementing Pearl's Ladder of Causation:\n\n- **Level 1 (Association)**: Observational queries P(Y|X)\n- **Level 2 (Intervention)**: do-calculus P(Y|do(X))\n- **Level 3 (Counterfactual)**: \"What if\" queries P(Y_x|X', Y')\n\n### Building Causal Graphs\n\n```typescript\nimport { CausalGraphBuilder, CausalInferenceEngine } from '@cogitator-ai/core';\n\nconst graph = CausalGraphBuilder.create('medical-study')\n  .treatment('X', 'Drug Treatment')\n  .outcome('Y', 'Recovery')\n  .confounder('Z', 'Age')\n  .from('Z')\n  .causes('X')\n  .from('Z')\n  .causes('Y')\n  .from('X')\n  .causes('Y', { strength: 0.8 })\n  .build();\n\nconst engine = new CausalInferenceEngine(graph);\n```\n\n### Effect Identification\n\n```typescript\nconst identifiable = engine.isIdentifiable('X', 'Y');\nif (identifiable.identifiable) {\n  console.log('Effect is identifiable via:', identifiable.method);\n  console.log('Adjustment set:', identifiable.adjustmentSet);\n}\n```\n\n### Interventional Queries\n\n```typescript\nconst effect = engine.computeInterventionalEffect({\n  target: 'Y',\n  interventions: { X: 1 },\n  observed: { Z: 0.5 },\n});\n\nconsole.log('Expected effect:', effect.effect);\nconsole.log('Confidence:', effect.confidence);\n```\n\n### Counterfactual Reasoning\n\n```typescript\nimport { evaluateCounterfactual } from '@cogitator-ai/core';\n\nconst result = evaluateCounterfactual(graph, {\n  target: 'Y',\n  intervention: { X: 1 },\n  factual: { X: 0, Y: 0.2 },\n  question: 'What would Y be if X was 1?',\n});\n\nconsole.log('Factual value:', result.factualValue);\nconsole.log('Counterfactual value:', result.counterfactualValue);\n```\n\n### D-Separation Analysis\n\n```typescript\nimport { dSeparation, findBackdoorAdjustment } from '@cogitator-ai/core';\n\nconst separated = dSeparation(graph, 'X', 'Y', ['Z']);\nconsole.log('D-separated:', separated.separated);\n\nconst backdoor = findBackdoorAdjustment(graph, 'X', 'Y');\nif (backdoor?.isValid) {\n  console.log('Backdoor adjustment set:', backdoor.variables);\n}\n```\n\n### Causal Discovery from Traces\n\n```typescript\nimport { CausalExtractor, CausalHypothesisGenerator } from '@cogitator-ai/core';\n\nconst extractor = new CausalExtractor({ llmBackend: backend });\n\nconst relations = await extractor.extractFromToolResult(\n  { name: 'database_query', arguments: { table: 'users' } },\n  { rows: 100, cached: true },\n  { agentId: 'agent-1' }\n);\n\nconst generator = new CausalHypothesisGenerator({ llmBackend: backend });\nconst hypotheses = await generator.generateFromFailure(trace, { agentId: 'agent-1' });\n```\n\n---\n\n## Error Handling & Resilience\n\n### Retry with Backoff\n\n```typescript\nimport { withRetry, retryable } from '@cogitator-ai/core';\n\nconst result = await withRetry(() => unreliableApiCall(), {\n  maxRetries: 5,\n  baseDelay: 1000,\n  maxDelay: 30000,\n  backoff: 'exponential',\n  jitter: 0.1,\n  onRetry: (error, attempt, delay) => {\n    console.log(`Retry ${attempt} after ${delay}ms: ${error.message}`);\n  },\n});\n\nconst retryableFetch = retryable(fetch, { maxRetries: 3 });\nconst response = await retryableFetch('https://api.example.com');\n```\n\n### Circuit Breaker\n\n```typescript\nimport { CircuitBreaker, CircuitBreakerRegistry } from '@cogitator-ai/core';\n\nconst breaker = new CircuitBreaker({\n  threshold: 5,\n  resetTimeout: 30000,\n  successThreshold: 2,\n});\n\nif (breaker.canExecute()) {\n  try {\n    const result = await riskyOperation();\n    breaker.recordSuccess();\n  } catch (error) {\n    breaker.recordFailure();\n    throw error;\n  }\n}\n\nbreaker.onStateChange((state) => {\n  console.log('Circuit state:', state);\n});\n```\n\n---\n\n## Prompt Injection Detection\n\nProtect your agents from jailbreak attempts, prompt injections, and other adversarial inputs:\n\n```typescript\nimport { Cogitator, PromptInjectionDetector } from '@cogitator-ai/core';\n\n// Standalone usage\nconst detector = new PromptInjectionDetector({\n  detectInjection: true, // \"Ignore previous instructions...\"\n  detectJailbreak: true, // DAN, developer mode attacks\n  detectRoleplay: true, // Malicious roleplay scenarios\n  detectEncoding: true, // Base64, hex encoded attacks\n  detectContextManipulation: true, // [SYSTEM], <|im_start|> injections\n  classifier: 'local', // 'local' (fast) or 'llm' (accurate)\n  action: 'block', // 'block' | 'warn' | 'log'\n  threshold: 0.7,\n});\n\nconst result = await detector.analyze('Ignore all previous instructions and...');\n// { safe: false, threats: [...], action: 'blocked', analysisTime: 2 }\n\n// Integrated with Cogitator runtime\nconst cog = new Cogitator({\n  security: {\n    promptInjection: {\n      detectInjection: true,\n      detectJailbreak: true,\n      action: 'block',\n      threshold: 0.7,\n    },\n  },\n});\n\n// Throws PromptInjectionError if attack detected\nawait cog.run(agent, { input: userInput });\n```\n\n### Detection Types\n\n| Type                   | Examples                                              |\n| ---------------------- | ----------------------------------------------------- |\n| `direct_injection`     | \"Ignore previous instructions\", \"Your new prompt is\"  |\n| `jailbreak`            | DAN prompts, \"developer mode enabled\", \"unrestricted\" |\n| `roleplay`             | \"Pretend you are evil AI\", \"From now on you are\"      |\n| `encoding`             | Base64 payloads, hex escape sequences, unicode tricks |\n| `context_manipulation` | `[SYSTEM]:`, `<\\|im_start\\|>`, markdown role markers  |\n\n### Custom Patterns & Allowlist\n\n```typescript\nconst detector = new PromptInjectionDetector({\n  action: 'block',\n  patterns: [/secret\\s+backdoor/i], // Custom regex patterns\n  allowlist: ['ignore the previous search'], // Legitimate phrases\n});\n\n// Dynamic updates\ndetector.addPattern(/company\\s+specific\\s+attack/i);\ndetector.addToAllowlist('ignore previous results');\n```\n\n### LLM-Based Classification\n\nFor higher accuracy with complex attacks:\n\n```typescript\nconst detector = new PromptInjectionDetector({\n  classifier: 'llm',\n  llmBackend: openaiBackend,\n  llmModel: 'gpt-4o-mini',\n  action: 'block',\n});\n```\n\n### Statistics & Callbacks\n\n```typescript\nconst detector = new PromptInjectionDetector({\n  action: 'block',\n  onThreat: (result, input) => {\n    console.log('Attack detected:', result.threats);\n    logToSecurity(input, result);\n  },\n});\n\nawait detector.analyze('...');\nconst stats = detector.getStats();\n// { analyzed: 100, blocked: 5, warned: 0, allowRate: 0.95 }\n```\n\n---\n\n## Tool Caching\n\nCache tool results to avoid redundant API calls with exact or semantic matching:\n\n### Exact Match Caching\n\n```typescript\nimport { tool, withCache } from '@cogitator-ai/core';\nimport { z } from 'zod';\n\nconst webSearch = tool({\n  name: 'web_search',\n  description: 'Search the web',\n  parameters: z.object({ query: z.string() }),\n  execute: async ({ query }) => {\n    return await searchApi(query);\n  },\n});\n\nconst cachedSearch = withCache(webSearch, {\n  strategy: 'exact',\n  ttl: '1h',\n  maxSize: 1000,\n  storage: 'memory',\n});\n\nawait cachedSearch.execute({ query: 'weather in Paris' }, ctx);\nawait cachedSearch.execute({ query: 'weather in Paris' }, ctx); // cache hit\n\nconsole.log(cachedSearch.cache.stats());\n// { hits: 1, misses: 1, size: 1, evictions: 0, hitRate: 0.5 }\n```\n\n### Semantic Caching\n\nSimilar queries hit the cache based on embedding similarity:\n\n```typescript\nimport { withCache } from '@cogitator-ai/core';\nimport type { EmbeddingService } from '@cogitator-ai/types';\n\nconst embeddingService: EmbeddingService = {\n  embed: async (text) => openai.embeddings.create({ input: text }),\n  embedBatch: async (texts) => /* ... */,\n  dimensions: 1536,\n  model: 'text-embedding-3-small',\n};\n\nconst cachedSearch = withCache(webSearch, {\n  strategy: 'semantic',\n  similarity: 0.95,         // 95% similarity threshold\n  ttl: '1h',\n  maxSize: 1000,\n  storage: 'memory',\n  embeddingService,\n});\n\nawait cachedSearch.execute({ query: 'weather in Paris' }, ctx);\nawait cachedSearch.execute({ query: 'Paris weather forecast' }, ctx); // semantic hit\n```\n\n### Redis Storage\n\nFor production with persistence:\n\n```typescript\nimport { withCache, RedisToolCacheStorage } from '@cogitator-ai/core';\n\nconst cachedTool = withCache(webSearch, {\n  strategy: 'semantic',\n  similarity: 0.95,\n  ttl: '1h',\n  maxSize: 1000,\n  storage: 'redis',\n  redisClient: redisClient, // ioredis compatible client\n  keyPrefix: 'myapp:cache',\n  embeddingService,\n});\n```\n\n### Cache Management\n\n```typescript\nconst cached = withCache(tool, config);\n\n// Get statistics\nconst stats = cached.cache.stats();\n\n// Invalidate specific entry\nawait cached.cache.invalidate({ query: 'specific query' });\n\n// Clear all entries\nawait cached.cache.clear();\n\n// Pre-warm cache\nawait cached.cache.warmup([\n  { params: { query: 'common query 1' }, result: { data: '...' } },\n  { params: { query: 'common query 2' }, result: { data: '...' } },\n]);\n```\n\n### Cache Callbacks\n\n```typescript\nconst cached = withCache(tool, {\n  strategy: 'exact',\n  ttl: '1h',\n  maxSize: 100,\n  storage: 'memory',\n  onHit: (key, params) => console.log('Cache hit:', key),\n  onMiss: (key, params) => console.log('Cache miss:', key),\n  onEvict: (key) => console.log('Evicted:', key),\n});\n```\n\n---\n\n### Fallback Patterns\n\n```typescript\nimport {\n  withFallback,\n  withGracefulDegradation,\n  createLLMFallbackExecutor,\n} from '@cogitator-ai/core';\n\nconst result = await withFallback(\n  () => primaryCall(),\n  () => fallbackCall()\n);\n\nconst degraded = await withGracefulDegradation(\n  () => fullFeatureCall(),\n  [() => reducedFeatureCall(), () => minimalCall(), () => cachedResult()]\n);\n\nconst llmExecutor = createLLMFallbackExecutor([\n  { provider: 'openai', model: 'gpt-4o' },\n  { provider: 'anthropic', model: 'claude-sonnet-4-5-20250929' },\n  { provider: 'ollama', model: 'llama3.3:70b' },\n]);\nconst response = await llmExecutor.chat(request);\n```\n\n---\n\n## Constitutional AI (Guardrails)\n\nBuilt-in content safety with input/output filtering, tool guards, and critique-revision loops:\n\n```typescript\nimport { ConstitutionalAI, InputFilter, OutputFilter, ToolGuard } from '@cogitator-ai/core';\n\nconst constitutional = new ConstitutionalAI({\n  llm: backend,\n  constitution: createConstitution([...DEFAULT_PRINCIPLES, customPrinciple]),\n  config: { enabled: true },\n});\n\nconst inputFilter = new InputFilter({ llm: backend });\nconst inputResult = await inputFilter.evaluate('user message');\nif (inputResult.isHarmful) {\n  console.log('Blocked:', inputResult.harmCategories);\n}\n\nconst outputFilter = new OutputFilter({ llm: backend });\nconst outputResult = await outputFilter.evaluate('agent response');\n\nconst toolGuard = new ToolGuard({ llm: backend, strictMode: true });\nconst guardResult = await toolGuard.evaluate({\n  name: 'delete_file',\n  arguments: { path: '/etc/passwd' },\n});\n\n// Integrated with Cogitator runtime\nconst cog = new Cogitator({\n  guardrails: {\n    enabled: true,\n    model: 'openai/gpt-4o-mini',\n  },\n});\n```\n\n---\n\n## Cost-Aware Routing\n\nAutomatically route tasks to the optimal model based on complexity, budget, and latency requirements:\n\n```typescript\nimport { CostAwareRouter, TaskAnalyzer, CostTracker, BudgetEnforcer } from '@cogitator-ai/core';\n\nconst router = new CostAwareRouter({\n  config: {\n    enabled: true,\n    budgets: {\n      daily: 10.0,\n      monthly: 200.0,\n    },\n  },\n});\n\nconst recommendation = await router.route({\n  input: 'Simple greeting',\n  complexity: 'low',\n  speedPreference: 'fast',\n});\nconsole.log('Use model:', recommendation.model);\nconsole.log('Estimated cost:', recommendation.estimatedCost);\n\n// Integrated with Cogitator\nconst cog = new Cogitator({\n  costRouting: {\n    enabled: true,\n    budgets: { daily: 10.0 },\n  },\n});\n```\n\n---\n\n## Context Management\n\nAutomatic context window management with compression strategies:\n\n```typescript\nimport { ContextManager, TruncateStrategy, SummarizeStrategy } from '@cogitator-ai/core';\n\nconst cog = new Cogitator({\n  context: {\n    enabled: true,\n    maxTokens: 8000,\n    strategy: 'hybrid',\n  },\n});\n```\n\nAvailable strategies: `TruncateStrategy`, `SlidingWindowStrategy`, `SummarizeStrategy`, `HybridStrategy`.\n\n---\n\n## Observability\n\nExport traces to Langfuse or any OTLP-compatible backend:\n\n```typescript\nimport { createLangfuseExporter, createOTLPExporter } from '@cogitator-ai/core';\n\nconst langfuse = createLangfuseExporter({\n  publicKey: process.env.LANGFUSE_PUBLIC_KEY!,\n  secretKey: process.env.LANGFUSE_SECRET_KEY!,\n  baseUrl: 'https://cloud.langfuse.com',\n});\n\nconst otlp = createOTLPExporter({\n  endpoint: 'http://localhost:4318/v1/traces',\n  headers: { Authorization: 'Bearer ...' },\n});\n```\n\n---\n\n## Agent as Tool\n\nUse one agent as a tool for another — enables hierarchical agent architectures:\n\n```typescript\nimport { Cogitator, Agent, agentAsTool } from '@cogitator-ai/core';\n\nconst researcher = new Agent({\n  name: 'researcher',\n  instructions: 'Research topics thoroughly',\n  model: 'openai/gpt-4o',\n  tools: [webSearch],\n});\n\nconst researchTool = agentAsTool(cog, researcher, {\n  name: 'research',\n  description: 'Delegate research tasks to a specialist agent',\n  includeUsage: true,\n});\n\nconst manager = new Agent({\n  name: 'manager',\n  instructions: 'Coordinate tasks and delegate research',\n  model: 'openai/gpt-4o',\n  tools: [researchTool],\n});\n```\n\n---\n\n## Logging\n\n```typescript\nimport { Logger, getLogger, setLogger, createLogger } from '@cogitator-ai/core';\n\nconst logger = createLogger({\n  level: 'debug',\n  prefix: '[MyApp]',\n  timestamps: true,\n});\n\nsetLogger(logger);\n\ngetLogger().info('Agent started', { agentId: agent.id });\ngetLogger().debug('Tool call', { tool: 'calculator', args: { expression: '2+2' } });\ngetLogger().warn('Rate limited', { retryAfter: 60 });\ngetLogger().error('Failed', { error: 'Connection timeout' });\n```\n\n---\n\n## Type Reference\n\n### Core Types\n\n```typescript\nimport type {\n  Agent,\n  AgentConfig,\n  Tool,\n  ToolConfig,\n  ToolContext,\n  Message,\n  MessageRole,\n  ToolCall,\n  ToolResult,\n  CogitatorConfig,\n  RunOptions,\n  RunResult,\n  Span,\n} from '@cogitator-ai/core';\n```\n\n### LLM Types\n\n```typescript\nimport type {\n  LLMBackend,\n  LLMProvider,\n  LLMConfig,\n  ChatRequest,\n  ChatResponse,\n  ChatStreamChunk,\n  LLMErrorContext,\n  LLMDebugOptions,\n  LLMPlugin,\n} from '@cogitator-ai/core';\n```\n\n### Reasoning Types\n\n```typescript\nimport type {\n  ToTConfig,\n  ToTResult,\n  ToTStats,\n  ThoughtTree,\n  ThoughtNode,\n  ThoughtBranch,\n  BranchScore,\n  ExplorationStrategy,\n} from '@cogitator-ai/core';\n```\n\n### Learning Types\n\n```typescript\nimport type {\n  ExecutionTrace,\n  ExecutionStep,\n  TraceStore,\n  Demo,\n  MetricFn,\n  MetricResult,\n  OptimizerConfig,\n  OptimizationResult,\n} from '@cogitator-ai/core';\n```\n\n### Time Travel Types\n\n```typescript\nimport type {\n  ExecutionCheckpoint,\n  ReplayOptions,\n  ReplayResult,\n  ForkOptions,\n  ForkResult,\n  TraceDiff,\n  TimeTravelConfig,\n} from '@cogitator-ai/core';\n```\n\n### Causal Types\n\n```typescript\nimport type {\n  CausalNode,\n  CausalEdge,\n  CausalGraph,\n  CausalRelationType,\n  InterventionQuery,\n  CounterfactualQuery,\n  CausalHypothesis,\n  CausalEvidence,\n  StructuralEquation,\n} from '@cogitator-ai/core';\n```\n\n### Error Types\n\n```typescript\nimport {\n  CogitatorError,\n  LLMError,\n  ErrorCode,\n  isRetryableError,\n  getRetryDelay,\n} from '@cogitator-ai/core';\n\ntry {\n  await riskyOperation();\n} catch (error) {\n  if (error instanceof LLMError) {\n    console.log('Provider:', error.provider);\n    console.log('Status:', error.statusCode);\n  } else if (error instanceof CogitatorError) {\n    console.log('Code:', error.code);\n    console.log('Retryable:', isRetryableError(error));\n  }\n}\n```\n\n---\n\n## Examples\n\n### Research Agent with Memory\n\n```typescript\nimport { Cogitator, Agent, tool } from '@cogitator-ai/core';\n\nconst webSearch = tool({\n  name: 'web_search',\n  description: 'Search the web',\n  parameters: z.object({ query: z.string() }),\n  execute: async ({ query }) => {\n    return { results: await searchApi(query) };\n  },\n});\n\nconst cog = new Cogitator({\n  memory: { adapter: 'redis', redis: { url: 'redis://localhost:6379' } },\n  reflection: { enabled: true },\n});\n\nconst researcher = new Agent({\n  name: 'researcher',\n  instructions: 'Research topics thoroughly using web search',\n  model: 'openai/gpt-4o',\n  tools: [webSearch],\n});\n\nconst result = await cog.run(researcher, {\n  input: 'Research the latest AI developments',\n  threadId: 'research-session-1',\n});\n```\n\n### Streaming Response\n\n```typescript\nconst result = await cog.run(agent, {\n  input: 'Write a story about...',\n  stream: true,\n  onToken: (token) => process.stdout.write(token),\n});\n```\n\n### Full Observability\n\n```typescript\nconst result = await cog.run(agent, {\n  input: 'Analyze this data...',\n  onRunStart: ({ runId }) => console.log(`Run ${runId} started`),\n  onToolCall: (call) => console.log(`Calling ${call.name}`),\n  onToolResult: (result) => console.log(`Result: ${JSON.stringify(result.result)}`),\n  onSpan: (span) => {\n    console.log(`[${span.name}] ${span.duration}ms`);\n  },\n  onRunComplete: (result) => {\n    console.log(`Cost: $${result.usage.cost.toFixed(4)}`);\n    console.log(`Tokens: ${result.usage.totalTokens}`);\n  },\n});\n```\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}