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Supports in-memory, Redis (short-term), and PostgreSQL with pgvector (long-term semantic memory).\n\n## Installation\n\n```bash\npnpm add @cogitator-ai/memory\n\n# Optional peer dependencies\npnpm add ioredis  # For Redis adapter\npnpm add pg       # For PostgreSQL adapter\n```\n\n## Features\n\n- **Multiple Adapters** - In-memory, Redis, PostgreSQL with pgvector\n- **Thread Management** - Create, update, delete conversation threads\n- **Token Counting** - Estimate token usage without tiktoken dependency\n- **Context Builder** - Build token-aware conversation context\n- **Embedding Services** - OpenAI and Ollama embedding integration\n- **Semantic Search** - Vector similarity search with pgvector\n- **Hybrid Search** - BM25 + Vector with Reciprocal Rank Fusion\n- **Facts Storage** - Store and retrieve agent knowledge\n- **Knowledge Graph** - Entity-relationship memory with multi-hop traversal\n- **Zod Schemas** - Type-safe configuration validation\n\n---\n\n## Quick Start\n\n```typescript\nimport { InMemoryAdapter, ContextBuilder } from '@cogitator-ai/memory';\n\nconst memory = new InMemoryAdapter();\nawait memory.connect();\n\nconst threadResult = await memory.createThread('agent-1', { topic: 'greeting' });\nconst thread = threadResult.data!;\n\nawait memory.addEntry({\n  threadId: thread.id,\n  message: { role: 'user', content: 'Hello!' },\n});\n\nawait memory.addEntry({\n  threadId: thread.id,\n  message: { role: 'assistant', content: 'Hi there!' },\n});\n\nconst builder = new ContextBuilder(\n  { maxTokens: 4000, strategy: 'recent' },\n  { memoryAdapter: memory }\n);\n\nconst context = await builder.build({\n  threadId: thread.id,\n  agentId: 'agent-1',\n  systemPrompt: 'You are helpful.',\n});\n\nconsole.log(context.messages);\n```\n\n---\n\n## Memory Adapters\n\n### In-Memory Adapter\n\nFast, non-persistent storage for development and testing.\n\n```typescript\nimport { InMemoryAdapter } from '@cogitator-ai/memory';\n\nconst memory = new InMemoryAdapter({\n  maxEntries: 1000,\n});\n\nawait memory.connect();\n```\n\n### Redis Adapter\n\nPersistent short-term memory with TTL support.\n\n```typescript\nimport { RedisAdapter } from '@cogitator-ai/memory';\n\nconst memory = new RedisAdapter({\n  url: 'redis://localhost:6379',\n  keyPrefix: 'cogitator:',\n  ttl: 3600,\n});\n\nawait memory.connect();\n```\n\n### PostgreSQL Adapter\n\nLong-term storage with vector search via pgvector.\n\n```typescript\nimport { PostgresAdapter } from '@cogitator-ai/memory';\n\nconst memory = new PostgresAdapter({\n  connectionString: 'postgresql://localhost:5432/cogitator',\n  schema: 'public',\n  poolSize: 10,\n});\n\nawait memory.connect();\n```\n\n### SQLite Adapter\n\n```typescript\nimport { SQLiteAdapter } from '@cogitator-ai/memory';\n\nconst memory = new SQLiteAdapter({\n  provider: 'sqlite',\n  path: './cogitator.db',\n  walMode: true,\n});\n```\n\n### MongoDB Adapter\n\n```typescript\nimport { MongoDBAdapter } from '@cogitator-ai/memory';\n\nconst memory = new MongoDBAdapter({\n  provider: 'mongodb',\n  uri: 'mongodb://localhost:27017',\n  database: 'cogitator',\n});\n```\n\n### Qdrant Adapter (Embedding)\n\n```typescript\nimport { QdrantAdapter } from '@cogitator-ai/memory';\n\nconst embeddings = new QdrantAdapter({\n  provider: 'qdrant',\n  url: 'http://localhost:6333',\n  collection: 'cogitator',\n  dimensions: 1536,\n});\n```\n\n### Factory Function\n\n```typescript\nimport { createMemoryAdapter, createEmbeddingAdapter } from '@cogitator-ai/memory';\n\nconst memory = await createMemoryAdapter({ provider: 'memory' });\n\nconst redis = await createMemoryAdapter({\n  provider: 'redis',\n  url: 'redis://localhost:6379',\n});\n\nconst postgres = await createMemoryAdapter({\n  provider: 'postgres',\n  connectionString: 'postgresql://localhost:5432/db',\n});\n\nconst sqlite = await createMemoryAdapter({\n  provider: 'sqlite',\n  path: './cogitator.db',\n});\n\nconst qdrant = await createEmbeddingAdapter({\n  provider: 'qdrant',\n  dimensions: 1536,\n});\n```\n\n---\n\n## MemoryAdapter Interface\n\nAll adapters implement the `MemoryAdapter` interface:\n\n```typescript\ninterface MemoryAdapter {\n  readonly provider: MemoryProvider;\n\n  connect(): Promise<MemoryResult<void>>;\n  disconnect(): Promise<MemoryResult<void>>;\n\n  createThread(agentId: string, metadata?: Record<string, unknown>): Promise<MemoryResult<Thread>>;\n  getThread(threadId: string): Promise<MemoryResult<Thread | null>>;\n  updateThread(threadId: string, metadata: Record<string, unknown>): Promise<MemoryResult<Thread>>;\n  deleteThread(threadId: string): Promise<MemoryResult<void>>;\n\n  addEntry(entry: Omit<MemoryEntry, 'id' | 'createdAt'>): Promise<MemoryResult<MemoryEntry>>;\n  getEntries(options: MemoryQueryOptions): Promise<MemoryResult<MemoryEntry[]>>;\n  getEntry(entryId: string): Promise<MemoryResult<MemoryEntry | null>>;\n  deleteEntry(entryId: string): Promise<MemoryResult<void>>;\n  clearThread(threadId: string): Promise<MemoryResult<void>>;\n}\n```\n\n### Thread Operations\n\n```typescript\nconst result = await memory.createThread('agent-1', {\n  topic: 'support',\n  user: 'user-123',\n});\nconst thread = result.data!;\n\nconst threadResult = await memory.getThread(thread.id);\n\nawait memory.updateThread(thread.id, {\n  resolved: true,\n});\n\nawait memory.deleteThread(thread.id);\n```\n\n### Entry Operations\n\n```typescript\nconst entry = await memory.addEntry({\n  threadId: thread.id,\n  message: { role: 'user', content: 'Hello' },\n  tokenCount: 10,\n});\n\nconst entries = await memory.getEntries({\n  threadId: thread.id,\n  limit: 50,\n  includeToolCalls: true,\n});\n\nconst single = await memory.getEntry(entry.data!.id);\n\nawait memory.deleteEntry(entry.data!.id);\n\nawait memory.clearThread(thread.id);\n```\n\n---\n\n## Context Builder\n\nBuild token-aware conversation context from memory.\n\n### Configuration\n\n```typescript\ninterface ContextBuilderConfig {\n  maxTokens: number;\n  reserveTokens?: number;\n  strategy: 'recent' | 'relevant' | 'hybrid';\n  includeSystemPrompt?: boolean;\n  includeFacts?: boolean;\n  includeSemanticContext?: boolean;\n}\n```\n\n### Basic Usage\n\n```typescript\nimport { ContextBuilder } from '@cogitator-ai/memory';\n\nconst builder = new ContextBuilder(\n  {\n    maxTokens: 4000,\n    reserveTokens: 400,\n    strategy: 'recent',\n    includeSystemPrompt: true,\n  },\n  { memoryAdapter: memory }\n);\n\nconst context = await builder.build({\n  threadId: 'thread_123',\n  agentId: 'agent-1',\n  systemPrompt: 'You are a helpful assistant.',\n});\n\nconsole.log(context.messages);\nconsole.log(context.tokenCount);\nconsole.log(context.truncated);\n```\n\n### With Facts and Semantic Search\n\n```typescript\nconst builder = new ContextBuilder(\n  {\n    maxTokens: 8000,\n    strategy: 'recent',\n    includeFacts: true,\n    includeSemanticContext: true,\n  },\n  {\n    memoryAdapter: memory,\n    factAdapter: factStore,\n    embeddingAdapter: vectorStore,\n    embeddingService: embeddings,\n  }\n);\n\nconst context = await builder.build({\n  threadId: 'thread_123',\n  agentId: 'agent-1',\n  systemPrompt: 'You are an expert.',\n  currentInput: 'What is machine learning?',\n});\n\nconsole.log(context.facts);\nconsole.log(context.semanticResults);\n```\n\n### Built Context\n\n```typescript\ninterface BuiltContext {\n  messages: Message[];\n  facts: Fact[];\n  semanticResults: (Embedding & { score: number })[];\n  tokenCount: number;\n  truncated: boolean;\n  metadata: {\n    originalMessageCount: number;\n    includedMessageCount: number;\n    factsIncluded: number;\n    semanticResultsIncluded: number;\n  };\n}\n```\n\n---\n\n## Token Counting\n\nSimple token estimation without tiktoken dependency.\n\n```typescript\nimport {\n  countTokens,\n  countMessageTokens,\n  countMessagesTokens,\n  truncateToTokens,\n} from '@cogitator-ai/memory';\n\nconst tokens = countTokens('Hello, world!');\n\nconst msgTokens = countMessageTokens({ role: 'user', content: 'Hello!' });\n\nconst totalTokens = countMessagesTokens([\n  { role: 'user', content: 'Hello!' },\n  { role: 'assistant', content: 'Hi there!' },\n]);\n\nconst truncated = truncateToTokens('Very long text...', 100);\n```\n\n---\n\n## Embedding Services\n\n### OpenAI Embeddings\n\n```typescript\nimport { OpenAIEmbeddingService, createEmbeddingService } from '@cogitator-ai/memory';\n\nconst embeddings = new OpenAIEmbeddingService({\n  apiKey: process.env.OPENAI_API_KEY!,\n  model: 'text-embedding-3-small',\n});\n\nconst embeddings = createEmbeddingService({\n  provider: 'openai',\n  apiKey: process.env.OPENAI_API_KEY!,\n});\n\nconst vector = await embeddings.embed('Hello, world!');\n\nconst vectors = await embeddings.embedBatch(['Hello', 'World']);\n```\n\n### Ollama Embeddings\n\n```typescript\nimport { OllamaEmbeddingService, createEmbeddingService } from '@cogitator-ai/memory';\n\nconst embeddings = new OllamaEmbeddingService({\n  model: 'nomic-embed-text',\n  baseUrl: 'http://localhost:11434',\n});\n\nconst embeddings = createEmbeddingService({\n  provider: 'ollama',\n  model: 'nomic-embed-text',\n});\n\nconst vector = await embeddings.embed('Hello, world!');\n```\n\n### Google Embeddings\n\n```typescript\nimport { GoogleEmbeddingService, createEmbeddingService } from '@cogitator-ai/memory';\n\nconst embeddings = new GoogleEmbeddingService({\n  apiKey: process.env.GOOGLE_API_KEY!,\n  model: 'gemini-embedding-001',\n  dimensions: 768,\n});\n\nconst embeddings = createEmbeddingService({\n  provider: 'google',\n  apiKey: process.env.GOOGLE_API_KEY!,\n});\n\nconst vector = await embeddings.embed('Hello, world!');\n```\n\n---\n\n## Hybrid Search\n\nCombine keyword search (BM25) with semantic search (vector embeddings) using Reciprocal Rank Fusion for best-of-both-worlds retrieval.\n\n### Configuration\n\n```typescript\nimport {\n  HybridSearch,\n  InMemoryEmbeddingAdapter,\n  OpenAIEmbeddingService,\n} from '@cogitator-ai/memory';\n\nconst embeddingService = new OpenAIEmbeddingService({\n  apiKey: process.env.OPENAI_API_KEY!,\n});\n\nconst embeddingAdapter = new InMemoryEmbeddingAdapter();\n\nconst search = new HybridSearch({\n  embeddingAdapter,\n  embeddingService,\n  keywordAdapter: embeddingAdapter, // PostgresAdapter also implements KeywordSearchAdapter\n  defaultWeights: { bm25: 0.4, vector: 0.6 },\n});\n```\n\n### Search Strategies\n\n```typescript\n// Pure vector search (semantic similarity)\nconst vectorResults = await search.search({\n  query: 'machine learning algorithms',\n  strategy: 'vector',\n  limit: 10,\n});\n\n// Pure keyword search (BM25)\nconst keywordResults = await search.search({\n  query: 'machine learning algorithms',\n  strategy: 'keyword',\n  limit: 10,\n});\n\n// Hybrid search (combines both with RRF)\nconst hybridResults = await search.search({\n  query: 'machine learning algorithms',\n  strategy: 'hybrid',\n  weights: { bm25: 0.4, vector: 0.6 },\n  limit: 10,\n});\n\n// Results include both scores\nhybridResults.data.forEach((result) => {\n  console.log(`${result.content} — score: ${result.score}`);\n  console.log(`  vector: ${result.vectorScore}, keyword: ${result.keywordScore}`);\n});\n```\n\n### Document Indexing\n\nFor keyword search, documents must be indexed:\n\n```typescript\n// Add documents to BM25 index\nsearch.indexDocument('doc-1', 'Machine learning is a subset of AI...');\nsearch.indexDocument('doc-2', 'Deep learning uses neural networks...');\n\n// Remove from index\nsearch.removeDocument('doc-1');\n\n// Clear entire index\nsearch.clearIndex();\n\n// Check index size\nconsole.log('Indexed documents:', search.indexSize);\n```\n\n### Why Hybrid Search?\n\n| Search Type        | Strengths                                            | Weaknesses                      |\n| ------------------ | ---------------------------------------------------- | ------------------------------- |\n| **Vector**         | Finds semantically similar content, handles synonyms | Misses exact keyword matches    |\n| **Keyword (BM25)** | Exact term matching, fast                            | Misses synonyms and paraphrases |\n| **Hybrid**         | Best of both worlds                                  | Slightly more computation       |\n\n**Example:** Query \"ML algorithms\"\n\n- Vector search finds \"machine learning methods\" (semantic match)\n- Keyword search finds \"ML algorithms comparison\" (exact match)\n- Hybrid returns both, ranked by combined relevance\n\n### Reciprocal Rank Fusion (RRF)\n\nHybrid search uses RRF to combine rankings from both search methods:\n\n```\nRRF_score(d) = Σ 1 / (k + rank_i(d))\n```\n\nWhere `k` is a constant (default 60) and `rank_i(d)` is the rank of document `d` in result set `i`. This produces stable rankings even when individual scores are on different scales.\n\n---\n\n## Zod Schemas\n\nType-safe configuration validation:\n\n```typescript\nimport {\n  MemoryProviderSchema,\n  InMemoryConfigSchema,\n  RedisConfigSchema,\n  PostgresConfigSchema,\n  MemoryAdapterConfigSchema,\n  ContextStrategySchema,\n  ContextBuilderConfigSchema,\n  EmbeddingProviderSchema,\n  OpenAIEmbeddingConfigSchema,\n  OllamaEmbeddingConfigSchema,\n  EmbeddingServiceConfigSchema,\n} from '@cogitator-ai/memory';\n\nconst config = MemoryAdapterConfigSchema.parse({\n  provider: 'redis',\n  url: 'redis://localhost:6379',\n  ttl: 3600,\n});\n\nconst builderConfig = ContextBuilderConfigSchema.parse({\n  maxTokens: 4000,\n  strategy: 'recent',\n});\n```\n\n---\n\n## Type Reference\n\n```typescript\nimport type {\n  MemoryType,\n  Thread,\n  MemoryEntry,\n  Fact,\n  Embedding,\n  MemoryProvider,\n  MemoryAdapterConfig,\n  RedisAdapterConfig,\n  PostgresAdapterConfig,\n  InMemoryAdapterConfig,\n  MemoryResult,\n  MemoryQueryOptions,\n  SemanticSearchOptions,\n  MemoryAdapter,\n  FactAdapter,\n  EmbeddingAdapter,\n  EmbeddingService,\n  EmbeddingProvider,\n  EmbeddingServiceConfig,\n  OpenAIEmbeddingConfig,\n  OllamaEmbeddingConfig,\n  ContextBuilderConfig,\n  ContextStrategy,\n  BuiltContext,\n  MemoryConfig,\n} from '@cogitator-ai/memory';\n```\n\n---\n\n## Examples\n\n### Conversation History\n\n```typescript\nimport { InMemoryAdapter } from '@cogitator-ai/memory';\n\nconst memory = new InMemoryAdapter();\nawait memory.connect();\n\nconst thread = await memory.createThread('chatbot');\n\nconst messages = [\n  { role: 'user' as const, content: 'What is AI?' },\n  { role: 'assistant' as const, content: 'AI is artificial intelligence...' },\n  { role: 'user' as const, content: 'Tell me more' },\n];\n\nfor (const msg of messages) {\n  await memory.addEntry({\n    threadId: thread.data!.id,\n    message: msg,\n  });\n}\n\nconst history = await memory.getEntries({\n  threadId: thread.data!.id,\n  limit: 10,\n});\n```\n\n### Token-Limited Context\n\n```typescript\nimport { InMemoryAdapter, ContextBuilder, countMessagesTokens } from '@cogitator-ai/memory';\n\nconst memory = new InMemoryAdapter();\nawait memory.connect();\n\nconst builder = new ContextBuilder(\n  { maxTokens: 2000, strategy: 'recent' },\n  { memoryAdapter: memory }\n);\n\nconst context = await builder.build({\n  threadId: 'thread_123',\n  agentId: 'agent-1',\n  systemPrompt: 'You are a concise assistant.',\n});\n\nconsole.log(`Using ${context.tokenCount} tokens`);\nconsole.log(`Truncated: ${context.truncated}`);\nconsole.log(\n  `Included ${context.metadata.includedMessageCount} of ${context.metadata.originalMessageCount} messages`\n);\n```\n\n### Semantic Memory with PostgreSQL\n\n```typescript\nimport { PostgresAdapter, OpenAIEmbeddingService } from '@cogitator-ai/memory';\n\nconst memory = new PostgresAdapter({\n  connectionString: process.env.DATABASE_URL!,\n});\n\nconst embeddings = new OpenAIEmbeddingService({\n  apiKey: process.env.OPENAI_API_KEY!,\n});\n\nawait memory.connect();\n\nconst content = 'Machine learning is a subset of AI...';\nconst vector = await embeddings.embed(content);\n```\n\n---\n\n## Knowledge Graph\n\nEntity-relationship memory with multi-hop traversal and semantic reasoning.\n\n### PostgresGraphAdapter\n\n```typescript\nimport { PostgresGraphAdapter } from '@cogitator-ai/memory';\n\nconst graph = new PostgresGraphAdapter({\n  connectionString: process.env.DATABASE_URL!,\n});\n\nawait graph.connect();\n\nconst person = await graph.addNode({\n  agentId: 'agent-1',\n  type: 'person',\n  name: 'Alice',\n  description: 'Software engineer',\n  properties: { role: 'developer' },\n  confidence: 1.0,\n  source: 'user',\n});\n\nconst company = await graph.addNode({\n  agentId: 'agent-1',\n  type: 'organization',\n  name: 'TechCorp',\n  confidence: 1.0,\n  source: 'extracted',\n});\n\nawait graph.addEdge({\n  agentId: 'agent-1',\n  sourceNodeId: person.id,\n  targetNodeId: company.id,\n  type: 'works_at',\n  weight: 1.0,\n  confidence: 0.95,\n  source: 'extracted',\n});\n```\n\n### Multi-hop Traversal\n\n```typescript\nconst result = await graph.traverse({\n  startNodeId: person.id,\n  maxDepth: 3,\n  direction: 'outgoing',\n  edgeTypes: ['works_at', 'knows', 'located_in'],\n});\n\nconsole.log('Visited nodes:', result.visitedNodes);\nconsole.log('Paths found:', result.paths);\n```\n\n### Shortest Path\n\n```typescript\nconst path = await graph.findShortestPath(startNodeId, endNodeId);\nif (path) {\n  console.log('Path:', path.nodes.map((n) => n.name).join(' -> '));\n  console.log('Total weight:', path.totalWeight);\n}\n```\n\n### Semantic Node Search\n\n```typescript\nconst similar = await graph.searchNodesSemantic({\n  agentId: 'agent-1',\n  query: 'machine learning engineer',\n  limit: 10,\n  threshold: 0.7,\n});\n```\n\n### LLM Entity Extraction\n\n```typescript\nimport { LLMEntityExtractor } from '@cogitator-ai/memory';\n\nconst extractor = new LLMEntityExtractor(llmBackend, {\n  minConfidence: 0.7,\n  maxEntitiesPerText: 20,\n});\n\nconst result = await extractor.extract(\n  'Alice works at TechCorp in San Francisco. She knows Bob from the AI conference.'\n);\n\nconsole.log('Entities:', result.entities);\nconsole.log('Relations:', result.relations);\n```\n\n### Graph Inference Engine\n\n```typescript\nimport { GraphInferenceEngine } from '@cogitator-ai/memory';\n\nconst engine = new GraphInferenceEngine(graph, {\n  minConfidence: 0.5,\n  maxInferredEdges: 100,\n});\n\nconst inferred = await engine.infer('agent-1');\nconsole.log('Inferred edges:', inferred.edges);\nconsole.log('Rules applied:', inferred.rulesApplied);\n```\n\n### Graph Context Builder\n\n```typescript\nimport { GraphContextBuilder } from '@cogitator-ai/memory';\n\nconst contextBuilder = new GraphContextBuilder(graph, embeddingService, {\n  maxNodes: 20,\n  maxEdges: 50,\n  includeInferred: true,\n});\n\nconst context = await contextBuilder.buildContext({\n  agentId: 'agent-1',\n  query: 'Tell me about Alice and her work',\n});\n\nconsole.log('Relevant nodes:', context.nodes);\nconsole.log('Relationships:', context.edges);\n```\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}