{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-kuindji-memory-domain","slug":"kuindji-memory-domain","name":"@kuindji/memory-domain","type":"repo","url":"https://github.com/kuindji/memory-domain#readme","page_url":"https://unfragile.ai/kuindji-memory-domain","categories":["data-pipelines"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-kuindji-memory-domain__cap_0","uri":"capability://memory.knowledge.domain.driven.graph.based.memory.storage.with.semantic.indexing","name":"domain-driven graph-based memory storage with semantic indexing","description":"Stores memories as nodes in a directed graph structure with domain-driven design principles, enabling relationships between memory entities to be explicitly modeled and traversed. Uses embedding vectors to index memory content semantically, allowing memories to be retrieved not just by exact match but by conceptual similarity. The graph structure persists relationships between domain entities (e.g., users, conversations, events) as first-class citizens rather than denormalized fields.","intents":["I need to store conversational context where entities and their relationships matter as much as the text itself","I want to query memories by semantic meaning rather than keyword matching","I need to build a knowledge graph where domain concepts connect across multiple conversations or sessions"],"best_for":["AI agents and chatbots requiring persistent, relationship-aware context","Teams building knowledge management systems with domain-specific entity models","Developers implementing multi-turn dialogue systems where entity relationships drive conversation flow"],"limitations":["Graph traversal performance degrades with very large graphs (>100k nodes) without proper indexing strategy","Embedding-based retrieval requires pre-computed vectors; real-time embedding generation adds latency per query","No built-in distributed graph storage — single-instance deployments only without external graph database integration"],"requires":["Node.js 14+ or compatible JavaScript runtime","Embedding model provider (OpenAI, Hugging Face, or local embeddings library)","Storage backend (in-memory, file-based, or external database adapter)"],"input_types":["text (memory content)","structured objects (domain entities with properties)","embedding vectors (pre-computed or generated on-the-fly)"],"output_types":["graph nodes with metadata","traversal paths between entities","ranked memory results with similarity scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_1","uri":"capability://search.retrieval.semantic.similarity.search.with.embedding.based.retrieval","name":"semantic similarity search with embedding-based retrieval","description":"Implements vector similarity search by computing embeddings for memory queries and comparing them against stored memory embeddings using distance metrics (cosine, Euclidean). Returns ranked results ordered by semantic relevance rather than keyword overlap. Supports configurable embedding models and distance functions, allowing swapping between different embedding providers without changing query logic.","intents":["I want to find memories semantically similar to a user query without exact keyword matching","I need to retrieve the most relevant context for an LLM prompt from a large memory store","I want to find memories about a concept even if the exact wording differs"],"best_for":["LLM-powered agents needing context retrieval that understands intent, not just keywords","Conversational AI systems where user queries vary in phrasing but refer to the same concepts","Knowledge base systems serving non-technical users who may not know exact terminology"],"limitations":["Embedding quality depends entirely on the embedding model used; poor embeddings produce poor retrieval","Requires pre-computing embeddings for all stored memories, adding storage overhead (~1-2KB per memory for typical embeddings)","Semantic search cannot distinguish between homonyms or context-dependent meanings without additional metadata","Distance metric choice (cosine vs Euclidean) affects ranking; no automatic optimization for domain-specific relevance"],"requires":["Embedding model with vector output (OpenAI text-embedding-3-small, Sentence Transformers, etc.)","Vector storage or in-memory index for similarity computation","Query text to embed (minimum 1 character, practical minimum ~5 words for meaningful embeddings)"],"input_types":["text query","embedding vector (if pre-computed)","configuration object specifying embedding model and distance metric"],"output_types":["ranked array of memory objects with similarity scores","filtered results based on score threshold"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_2","uri":"capability://memory.knowledge.configurable.memory.persistence.with.pluggable.storage.adapters","name":"configurable memory persistence with pluggable storage adapters","description":"Provides an abstraction layer for memory persistence that decouples the domain model from storage implementation. Developers can implement custom storage adapters (file-based, database, cloud storage) by conforming to a standard interface, enabling memories to be persisted to any backend without changing application code. Supports both synchronous and asynchronous persistence operations.","intents":["I want to switch from in-memory storage to a database without refactoring my memory queries","I need to persist memories to multiple backends simultaneously (e.g., local cache + cloud backup)","I want to implement custom storage logic (e.g., encryption, compression, archival) without modifying the core framework"],"best_for":["Teams building multi-tenant systems where storage requirements vary per tenant","Developers prototyping with in-memory storage but planning production database migration","Projects requiring custom persistence logic (encryption, versioning, audit trails)"],"limitations":["Adapter pattern adds abstraction overhead; direct database access may be faster for simple use cases","No built-in transaction support across multiple adapters; consistency guarantees depend on adapter implementation","Async adapter operations require careful error handling; failures in one adapter don't automatically rollback others","No migration tooling provided; switching storage backends requires manual data migration"],"requires":["Storage backend (file system, database, cloud service, or in-memory store)","Adapter implementation conforming to the framework's storage interface","Node.js runtime with async/await support"],"input_types":["memory objects (domain entities)","query filters (for retrieval)","adapter configuration (connection strings, credentials)"],"output_types":["persistence confirmation (success/failure)","retrieved memory objects","storage statistics (count, size)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_3","uri":"capability://memory.knowledge.memory.relationship.modeling.and.graph.traversal","name":"memory relationship modeling and graph traversal","description":"Allows defining typed relationships between memory entities (e.g., 'mentions', 'references', 'contradicts') and traversing the graph to discover connected memories. Relationships are first-class objects with properties, enabling rich semantic connections beyond simple foreign keys. Supports depth-limited traversal, filtering by relationship type, and aggregating results across multiple paths.","intents":["I want to find all memories related to a topic by following relationship chains","I need to detect when a new memory contradicts or reinforces existing memories","I want to build a knowledge graph where concepts are connected through multiple relationship types"],"best_for":["Knowledge management systems where entity relationships are as important as content","Reasoning engines that need to traverse memory graphs to answer complex queries","Conversational AI that tracks entity relationships across multiple turns"],"limitations":["Graph traversal can be expensive for deeply connected graphs; no automatic query optimization or cost estimation","Circular relationships require explicit cycle detection to avoid infinite traversal","No built-in graph visualization or analysis tools; developers must implement their own","Relationship type validation is optional; no enforcement of relationship cardinality constraints"],"requires":["Memory entities with unique identifiers","Relationship type definitions (schema or runtime)","Graph storage backend capable of storing edges"],"input_types":["source entity ID","relationship type filter (optional)","traversal depth limit","traversal direction (outbound, inbound, bidirectional)"],"output_types":["array of connected entities","traversal paths with relationship chains","relationship metadata"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_4","uri":"capability://memory.knowledge.memory.lifecycle.management.with.temporal.tracking","name":"memory lifecycle management with temporal tracking","description":"Tracks memory creation, modification, and access timestamps, enabling time-based queries and memory aging strategies. Supports marking memories as archived, deleted, or expired, and provides hooks for lifecycle events (on-create, on-update, on-access). Enables implementing memory decay (older memories ranked lower) and retention policies without manual cleanup.","intents":["I want to deprioritize old memories in search results while keeping them available","I need to implement automatic memory expiration (e.g., forget temporary context after a session)","I want to audit when memories were created and modified for compliance or debugging"],"best_for":["Conversational AI systems that need to balance long-term context with recent relevance","Privacy-conscious applications implementing data retention policies","Systems requiring audit trails for memory changes"],"limitations":["Temporal queries add complexity; no built-in time-series optimization","Memory decay scoring requires tuning decay functions; no automatic optimization","Lifecycle hooks are synchronous; long-running operations block memory operations","No built-in garbage collection; expired memories must be explicitly cleaned up"],"requires":["System clock for timestamp generation","Storage backend supporting timestamp fields","Optional: lifecycle hook implementations"],"input_types":["memory object","timestamp (auto-generated or provided)","lifecycle event type (create, update, access, delete)"],"output_types":["memory with temporal metadata","time-filtered memory results","lifecycle event logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_5","uri":"capability://memory.knowledge.domain.model.definition.with.type.safe.memory.schemas","name":"domain model definition with type-safe memory schemas","description":"Provides a framework for defining domain models (entities, value objects, aggregates) with type safety, enabling developers to structure memories according to domain concepts rather than generic key-value pairs. Supports validation, serialization, and custom methods on domain objects. Type definitions enable IDE autocomplete and compile-time checking for memory operations.","intents":["I want to define memory schemas that match my domain concepts (e.g., User, Conversation, Decision)","I need type safety when reading and writing memories to catch errors early","I want to add custom logic to memory objects (e.g., methods to compute derived properties)"],"best_for":["TypeScript projects where type safety is a priority","Teams with well-defined domain models that should drive memory structure","Systems requiring validation and serialization of complex memory objects"],"limitations":["Type definitions add boilerplate; simple use cases may not justify the overhead","Runtime validation is optional; developers must explicitly enable it","Serialization/deserialization requires custom logic for complex types (dates, nested objects)","Type definitions don't automatically generate database schemas; manual migration required"],"requires":["TypeScript 4.0+ (for type definitions)","Optional: validation library (Zod, Joi, class-validator)"],"input_types":["domain model class definitions","validation rules (optional)","serialization strategies (optional)"],"output_types":["typed memory objects","validation errors","serialized JSON"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_6","uri":"capability://memory.knowledge.batch.memory.operations.with.transaction.like.semantics","name":"batch memory operations with transaction-like semantics","description":"Supports performing multiple memory operations (create, update, delete, relate) as a logical unit with rollback on failure. Implements optimistic concurrency control or pessimistic locking depending on configuration. Enables efficient bulk operations without individual round-trips to storage, useful for syncing large memory sets or performing complex multi-step memory updates.","intents":["I want to create multiple related memories atomically (all succeed or all fail)","I need to update a memory and all its related memories in one operation","I want to bulk-import memories from an external source efficiently"],"best_for":["Systems performing complex multi-step memory updates","Bulk data import/sync operations","Applications requiring strong consistency guarantees"],"limitations":["Transaction support depends on storage adapter; not all adapters support transactions","Rollback semantics are adapter-specific; no guarantee of atomicity across multiple adapters","Large batch operations may consume significant memory; no automatic batching or pagination","Locking strategies add complexity; deadlock detection not built-in"],"requires":["Storage adapter with transaction support","Batch operation API","Optional: locking strategy configuration"],"input_types":["array of memory operations (create, update, delete, relate)","transaction options (isolation level, timeout)"],"output_types":["transaction result (success/failure)","array of operation results","rollback confirmation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_7","uri":"capability://search.retrieval.memory.query.language.with.filtering.and.aggregation","name":"memory query language with filtering and aggregation","description":"Provides a query API for filtering memories by properties, relationships, and temporal criteria, with support for aggregation operations (count, group-by, statistics). Queries are composable and can be combined with semantic search. Supports both simple property filters and complex nested queries on related entities.","intents":["I want to find all memories about a topic created in the last week","I need to count memories by category and get statistics","I want to find memories that match multiple criteria (property filters + semantic similarity)"],"best_for":["Systems with complex memory filtering requirements","Analytics and reporting on memory stores","Applications combining structured and semantic queries"],"limitations":["Query performance depends on storage backend; no automatic query optimization","Complex nested queries can be slow without proper indexing","Aggregation operations are in-memory; large result sets may cause memory issues","No query language (SQL-like); API-based queries only"],"requires":["Memory objects with queryable properties","Query API","Optional: indexing strategy for performance"],"input_types":["filter criteria (property values, ranges, patterns)","aggregation operations (count, group-by, sum, avg)","sort order","pagination parameters"],"output_types":["filtered memory array","aggregation results","pagination metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_8","uri":"capability://memory.knowledge.memory.context.window.management.for.llm.integration","name":"memory context window management for llm integration","description":"Automatically selects and ranks relevant memories to fit within an LLM's context window, using a combination of semantic similarity, recency, and importance scores. Provides utilities to format selected memories for LLM prompts and manage token budgets. Supports context prioritization strategies (e.g., prioritize recent memories, prioritize high-importance memories).","intents":["I want to automatically select the most relevant memories to include in an LLM prompt","I need to respect token limits while maximizing context relevance","I want to prioritize certain memories (e.g., recent or important) in the context window"],"best_for":["LLM-powered agents and chatbots needing context management","Systems with large memory stores but limited LLM context windows","Applications requiring fine-grained control over what context reaches the LLM"],"limitations":["Token counting is approximate; actual token usage depends on LLM tokenizer","Prioritization strategies are heuristic-based; no learning or optimization","Context selection is greedy; may not find optimal subset for complex queries","No support for context compression or summarization"],"requires":["Memory store with semantic search","Token counting utility (approximate or LLM-specific)","LLM context window size"],"input_types":["query or user message","context window size (tokens)","prioritization strategy","memory store"],"output_types":["selected memories (ranked)","formatted prompt text","token usage estimate"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-kuindji-memory-domain__cap_9","uri":"capability://memory.knowledge.memory.deduplication.and.conflict.resolution","name":"memory deduplication and conflict resolution","description":"Detects semantically similar or duplicate memories and provides strategies for merging or resolving conflicts. Supports fuzzy matching on properties and embedding-based similarity detection. Allows defining custom conflict resolution rules (e.g., keep newest, merge properties, flag for manual review). Prevents memory bloat from repeated information.","intents":["I want to detect when a new memory is a duplicate of an existing one","I need to merge similar memories to reduce storage and improve retrieval","I want to handle conflicting information in memories (e.g., different values for the same property)"],"best_for":["Systems ingesting memories from multiple sources (user input, external APIs, logs)","Long-running agents that accumulate memories over time","Applications requiring clean, non-redundant memory stores"],"limitations":["Deduplication is probabilistic; false positives/negatives depend on similarity thresholds","Merging strategies are heuristic; may lose information or create incorrect merged entities","Conflict resolution requires domain knowledge; generic strategies may not work for all domains","Deduplication adds latency to memory creation"],"requires":["Embedding model for similarity detection","Deduplication strategy (similarity threshold, merge rules)","Conflict resolution rules"],"input_types":["new memory object","existing memory store","similarity threshold","merge/conflict resolution strategy"],"output_types":["deduplication result (duplicate/unique)","merged memory (if duplicate)","conflict resolution result"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or compatible JavaScript runtime","Embedding model provider (OpenAI, Hugging Face, or local embeddings library)","Storage backend (in-memory, file-based, or external database adapter)","Embedding model with vector output (OpenAI text-embedding-3-small, Sentence Transformers, etc.)","Vector storage or in-memory index for similarity computation","Query text to embed (minimum 1 character, practical minimum ~5 words for meaningful embeddings)","Storage backend (file system, database, cloud service, or in-memory store)","Adapter implementation conforming to the framework's storage interface","Node.js runtime with async/await support","Memory entities with unique identifiers"],"failure_modes":["Graph traversal performance degrades with very large graphs (>100k nodes) without proper indexing strategy","Embedding-based retrieval requires pre-computed vectors; real-time embedding generation adds latency per query","No built-in distributed graph storage — single-instance deployments only without external graph database integration","Embedding quality depends entirely on the embedding model used; poor embeddings produce poor retrieval","Requires pre-computing embeddings for all stored memories, adding storage overhead (~1-2KB per memory for typical embeddings)","Semantic search cannot distinguish between homonyms or context-dependent meanings without additional metadata","Distance metric choice (cosine vs Euclidean) affects ranking; no automatic optimization for domain-specific relevance","Adapter pattern adds abstraction overhead; direct database access may be faster for simple use cases","No built-in transaction support across multiple adapters; consistency guarantees depend on adapter implementation","Async adapter operations require careful error handling; failures in one adapter don't automatically rollback others","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05185139398778875,"quality":0.3,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":165,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kuindji-memory-domain","compare_url":"https://unfragile.ai/compare?artifact=kuindji-memory-domain"}},"signature":"y1bB7v3mFB2b70vQ0F1FXF5FwLU4CV2u9zzhpCprqY9J6A9jMkmGmwhMaNcS9U9BRjAmYr/xzjiDe2YARS+aDg==","signedAt":"2026-06-22T08:54:35.918Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kuindji-memory-domain","artifact":"https://unfragile.ai/kuindji-memory-domain","verify":"https://unfragile.ai/api/v1/verify?slug=kuindji-memory-domain","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}