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Uses configurable embedding providers and supports similarity-based ranking to surface the most relevant results.","intents":["Find semantically similar memories or documents without exact keyword matching","Prevent duplicate embeddings from cluttering the vector index","Retrieve contextually relevant information for RAG pipelines","Build semantic search into memory management systems without external vector DB setup"],"best_for":["developers building LLM agents with memory systems","teams implementing RAG pipelines with deduplication requirements","applications needing semantic retrieval without managing separate vector databases"],"limitations":["Deduplication strategy and similarity thresholds are not configurable in the public API — uses fixed defaults","No built-in support for hybrid search combining semantic and keyword matching","Embedding quality depends entirely on the configured embedding provider; no local fallback"],"requires":["Embedding provider API key (OpenAI, Anthropic, or compatible service)","Node.js 16+ for runtime","@membank/core package installed"],"input_types":["text strings","document chunks","conversation turns"],"output_types":["ranked search results with similarity scores","deduplicated vector entries","metadata-enriched matches"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_1","uri":"capability://tool.use.integration.pluggable.embedding.provider.abstraction","name":"pluggable embedding provider abstraction","description":"Provides a provider-agnostic interface for embedding generation that abstracts away the specifics of different embedding APIs (OpenAI, Anthropic, local models, etc.). Developers configure a provider once and the system handles API calls, token counting, batching, and error handling transparently. The abstraction allows swapping providers without modifying application code, enabling cost optimization or model switching.","intents":["Switch between embedding providers (OpenAI, Anthropic, local) without code changes","Avoid vendor lock-in by abstracting embedding API details","Batch embed multiple texts efficiently with provider-specific optimizations","Handle provider-specific rate limits and error modes transparently"],"best_for":["teams wanting to avoid embedding provider lock-in","developers prototyping with multiple embedding models","applications needing cost-optimized embedding strategies"],"limitations":["No built-in provider auto-selection or fallback logic — requires explicit configuration","Batching behavior and optimization strategies vary by provider; no unified batching API","Local embedding models require separate setup and are not bundled"],"requires":["API credentials for at least one embedding provider","Node.js 16+","@membank/core package"],"input_types":["text strings","arrays of text for batch embedding"],"output_types":["embedding vectors (float arrays)","provider metadata (model name, token usage)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_2","uri":"capability://memory.knowledge.in.memory.and.persistent.storage.abstraction","name":"in-memory and persistent storage abstraction","description":"Provides a unified storage interface that supports both in-memory and persistent backends (file-based, database, etc.) for storing embeddings and metadata. The abstraction allows applications to start with in-memory storage for development and switch to persistent storage for production without code changes. Handles serialization, deserialization, and basic CRUD operations across different storage backends.","intents":["Store embeddings and memories in-memory for fast prototyping","Persist memories to disk or database for production use","Switch storage backends without rewriting application code","Manage memory lifecycle (create, read, update, delete) through a unified API"],"best_for":["developers prototyping memory systems and wanting to defer persistence decisions","teams migrating from in-memory to persistent storage","applications with variable storage requirements (dev vs. prod)"],"limitations":["No built-in transaction support or ACID guarantees across storage backends","Persistence layer does not include automatic backups or replication","Storage backend selection is compile-time or startup-time; no runtime switching"],"requires":["Node.js 16+","@membank/core package","Optional: file system access or database connection for persistent backends"],"input_types":["memory objects with embeddings and metadata","serializable JavaScript objects"],"output_types":["stored memory records","retrieval results with metadata","storage statistics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_3","uri":"capability://memory.knowledge.metadata.enriched.memory.indexing","name":"metadata-enriched memory indexing","description":"Indexes memories with associated metadata (timestamps, source, tags, custom attributes) alongside embeddings, enabling filtering and contextual retrieval beyond pure semantic similarity. The system stores metadata in a queryable format and allows filtering search results by metadata predicates (e.g., 'memories from the last 24 hours' or 'memories tagged as critical'). Metadata is preserved through storage and retrieval cycles.","intents":["Filter semantic search results by time range, source, or custom tags","Build context-aware memory systems that understand memory provenance","Implement memory expiration or lifecycle management based on metadata","Support multi-dimensional memory queries combining semantic and metadata filters"],"best_for":["agents needing to filter memories by source or recency","applications with complex memory lifecycle requirements","systems tracking memory provenance and audit trails"],"limitations":["Metadata query language is limited — no complex boolean expressions or nested filters","Metadata indexing adds storage overhead; no optimization for sparse metadata","No built-in metadata schema validation or enforcement"],"requires":["Node.js 16+","@membank/core package","Structured metadata objects (JSON-serializable)"],"input_types":["memory text","metadata objects (timestamps, tags, custom fields)","filter predicates"],"output_types":["filtered and ranked search results","metadata-annotated memories","query statistics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_4","uri":"capability://data.processing.analysis.batch.embedding.and.indexing.with.error.recovery","name":"batch embedding and indexing with error recovery","description":"Processes multiple texts in batches for embedding generation and indexing, with built-in error handling and retry logic for failed embeddings. The system groups texts into provider-appropriate batch sizes, handles partial failures gracefully, and allows resuming failed batches without re-processing successful entries. Provides progress tracking and detailed error reporting for debugging batch operations.","intents":["Embed large document collections efficiently without hitting API rate limits","Handle transient API failures during batch embedding without losing progress","Monitor batch processing status and identify which items failed","Resume failed batch jobs without re-processing already-embedded items"],"best_for":["teams bulk-importing documents into memory systems","applications with periodic batch embedding jobs","systems needing reliable batch processing with failure recovery"],"limitations":["Retry logic uses fixed backoff; no exponential backoff or adaptive strategies","No distributed batch processing — all batches run on a single process","Failed items are logged but not automatically re-queued; manual intervention required"],"requires":["Node.js 16+","@membank/core package","Embedding provider API with batch support"],"input_types":["arrays of text strings","batch configuration (size, timeout, retry count)"],"output_types":["batch processing results with success/failure status","indexed embeddings","error logs with item-level details"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_5","uri":"capability://memory.knowledge.memory.context.window.management.for.llm.integration","name":"memory context window management for llm integration","description":"Manages the selection and ordering of retrieved memories to fit within an LLM's context window constraints. The system ranks retrieved memories by relevance, truncates or summarizes to stay within token limits, and provides formatted context strings ready for injection into LLM prompts. Supports configurable context window sizes and prioritization strategies (e.g., recency vs. relevance).","intents":["Automatically select the most relevant memories that fit in an LLM's context window","Format retrieved memories into prompt-ready context strings","Balance memory relevance against context window constraints","Integrate memory retrieval seamlessly into LLM agent loops"],"best_for":["developers building LLM agents with memory systems","applications needing to manage context efficiently across multiple LLM calls","systems with variable context window sizes (different models)"],"limitations":["No built-in memory summarization — truncates rather than compresses long memories","Prioritization strategies are limited to simple heuristics; no learned ranking","Token counting relies on the embedding provider's estimates; actual LLM tokenization may differ"],"requires":["Node.js 16+","@membank/core package","LLM model context window specification"],"input_types":["retrieved memory objects with embeddings and metadata","context window size (tokens)","prioritization strategy configuration"],"output_types":["selected memories (subset of retrieved results)","formatted context string for LLM prompt","token usage statistics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_6","uri":"capability://data.processing.analysis.similarity.based.memory.deduplication.with.configurable.thresholds","name":"similarity-based memory deduplication with configurable thresholds","description":"Detects and removes semantically similar memories using embedding similarity scores and configurable thresholds, preventing redundant information from accumulating in the memory store. The system compares new memories against existing ones using cosine similarity or other distance metrics, and either rejects duplicates or merges them based on configuration. Deduplication runs automatically on insertion or can be triggered manually on existing memory stores.","intents":["Prevent semantically duplicate memories from being stored","Merge similar memories to consolidate information","Configure sensitivity of duplicate detection (strict vs. lenient)","Clean up existing memory stores by removing near-duplicates"],"best_for":["systems with high-volume memory ingestion prone to duplicates","applications needing to control memory store size and quality","teams building memory systems with strict deduplication requirements"],"limitations":["Similarity thresholds are global; no per-category or context-specific thresholds","Deduplication is one-way (new vs. existing); no bidirectional merging strategies","No built-in conflict resolution when merging duplicate memories with different metadata"],"requires":["Node.js 16+","@membank/core package","Embedding provider for similarity computation"],"input_types":["new memory text","existing memory store","similarity threshold (0-1 scale)"],"output_types":["deduplication decision (accept/reject/merge)","merged memory record (if applicable)","similarity scores for rejected items"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_7","uri":"capability://memory.knowledge.memory.expiration.and.lifecycle.management","name":"memory expiration and lifecycle management","description":"Automatically manages memory lifecycle by tracking creation/access timestamps and removing or archiving memories based on configurable expiration policies. The system supports time-based expiration (e.g., delete memories older than 30 days), access-based expiration (e.g., delete unused memories), and custom lifecycle hooks. Expired memories can be archived rather than deleted for audit trails or later recovery.","intents":["Automatically clean up old or stale memories to control storage growth","Implement memory decay where older memories become less relevant over time","Archive memories for compliance or audit purposes before deletion","Configure different expiration policies for different memory types or sources"],"best_for":["long-running agents with unbounded memory growth","systems with compliance or audit requirements","applications needing to balance memory freshness against storage costs"],"limitations":["Expiration policies are global; no per-memory or per-category policies","No built-in archival storage — archived memories are not automatically backed up","Lifecycle hooks are synchronous; no async cleanup operations"],"requires":["Node.js 16+","@membank/core package","Persistent storage backend (in-memory storage does not support expiration)"],"input_types":["expiration policy configuration (time-based, access-based, custom)","memory records with timestamps"],"output_types":["expired memory records (deleted or archived)","lifecycle event logs","storage statistics after cleanup"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-membank-core__cap_8","uri":"capability://code.generation.editing.typescript.first.type.safe.memory.api","name":"typescript-first type-safe memory api","description":"Provides a fully typed TypeScript API with generics for memory objects, metadata, and query results, enabling compile-time type checking and IDE autocomplete for memory operations. The system uses TypeScript generics to allow applications to define custom memory schemas and metadata types, with full type safety throughout the API. Type definitions are exported for use in application code, reducing runtime errors.","intents":["Get IDE autocomplete and type checking for memory operations","Define custom memory schemas with type safety","Catch memory-related type errors at compile time","Share memory type definitions across application code"],"best_for":["TypeScript-first teams building memory systems","applications with complex memory schemas needing type safety","developers wanting to avoid runtime type errors in memory operations"],"limitations":["TypeScript-only; no Python or other language SDKs","Type safety is compile-time only; runtime validation is not enforced","Generic type constraints may be complex for deeply nested memory schemas"],"requires":["TypeScript 4.5+","Node.js 16+","@membank/core package"],"input_types":["TypeScript type definitions for memory objects","custom metadata type definitions"],"output_types":["type-safe memory objects","typed query results","exported type definitions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Embedding provider API key (OpenAI, Anthropic, or compatible service)","Node.js 16+ for runtime","@membank/core package installed","API credentials for at least one embedding provider","Node.js 16+","@membank/core package","Optional: file system access or database connection for persistent backends","Structured metadata objects (JSON-serializable)","Embedding provider API with batch support","LLM model context window specification"],"failure_modes":["Deduplication strategy and similarity thresholds are not configurable in the public API — uses fixed defaults","No built-in support for hybrid search combining semantic and keyword matching","Embedding quality depends entirely on the configured embedding provider; no local fallback","No built-in provider auto-selection or fallback logic — requires explicit configuration","Batching behavior and optimization strategies vary by provider; no unified batching API","Local embedding models require separate setup and are not bundled","No built-in transaction support or ACID guarantees across storage backends","Persistence layer does not include automatic backups or replication","Storage backend selection is compile-time or startup-time; no runtime switching","Metadata query language is limited — no complex boolean expressions or nested filters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1669316880566112,"quality":0.28,"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":2335,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=membank-core","compare_url":"https://unfragile.ai/compare?artifact=membank-core"}},"signature":"Mb6HX4Kjb7eE+OAEYA6UoIxqhVXS1k/EfK5zEB0EOGkfUw3O51Id0TUwh13EOjp8GdFAL+gtSkkALmPm7yuiCA==","signedAt":"2026-06-23T04:41:54.521Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/membank-core","artifact":"https://unfragile.ai/membank-core","verify":"https://unfragile.ai/api/v1/verify?slug=membank-core","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"}}