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Handles batch processing and caches embeddings to avoid redundant API calls.","intents":["I need to convert my conversation history into searchable vectors for semantic recall","I want to find similar past interactions based on meaning, not keyword matching","I need to build a memory system that understands context across multiple conversations"],"best_for":["AI agents and chatbots requiring persistent semantic memory","Teams building RAG systems with OpenAI as the embedding provider","Developers implementing cognitive architectures with vector-based recall"],"limitations":["Depends on OpenAI API availability and rate limits (3,500 requests/minute for text-embedding-3)","Embedding quality bounded by OpenAI model capabilities; no fine-tuning support","Requires network calls for each embedding generation unless caching is implemented","Vector dimensionality fixed by model choice (1536 for text-embedding-3-small, 3072 for large)"],"requires":["OpenAI API key with embeddings model access","Node.js 14+ runtime","@engram-mem/core package for memory integration","Network connectivity to OpenAI API endpoints"],"input_types":["plain text","conversation transcripts","document excerpts","query strings"],"output_types":["float32 vectors (1536 or 3072 dimensions)","embedding metadata with model name and timestamp"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-engram-mem-openai__cap_1","uri":"capability://text.generation.language.text.summarization.with.extractive.and.abstractive.modes","name":"text summarization with extractive and abstractive modes","description":"Leverages OpenAI's language models to produce summaries of long-form text in both extractive (selecting key sentences) and abstractive (generating new summary text) modes. Integrates with Engram's memory to compress conversation history and long documents into concise representations while preserving semantic meaning. Supports configurable summary length and style parameters.","intents":["I need to compress long conversation histories to fit within token limits while preserving key information","I want to generate executive summaries of research documents for quick recall","I need to reduce memory footprint of stored interactions without losing critical context"],"best_for":["Chatbot systems managing long-running conversations with token budget constraints","Research and knowledge management systems requiring document compression","Agents building hierarchical memory structures with summaries at multiple levels"],"limitations":["Abstractive summarization quality depends on OpenAI model capability; may hallucinate or omit nuanced details","Extractive mode limited to selecting existing sentences; cannot paraphrase or synthesize","Summarization adds latency (typically 1-3 seconds per document depending on length)","No domain-specific fine-tuning; generic summaries may miss specialized terminology importance"],"requires":["OpenAI API key with GPT-3.5-turbo or GPT-4 access","Text input under 128,000 tokens (model context limit)","@engram-mem/core for memory integration","Node.js 14+ runtime"],"input_types":["plain text documents","conversation transcripts","markdown content","structured text with metadata"],"output_types":["summarized text (variable length)","summary metadata (compression ratio, mode used, timestamp)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-engram-mem-openai__cap_2","uri":"capability://data.processing.analysis.named.entity.extraction.and.cognitive.tagging","name":"named entity extraction and cognitive tagging","description":"Extracts structured entities (people, organizations, locations, concepts, dates) from unstructured text using OpenAI's language understanding capabilities. Automatically tags memories with extracted entities to enable entity-based retrieval and relationship mapping. Supports custom entity schemas and hierarchical entity relationships.","intents":["I want to automatically tag conversations with mentioned people, companies, and topics for later filtering","I need to build a knowledge graph of entities mentioned across multiple interactions","I want to retrieve all memories mentioning a specific person or organization without keyword search"],"best_for":["Conversational AI systems requiring entity-aware memory indexing","Knowledge management systems building entity-centric views of information","CRM and customer intelligence applications tracking mentioned entities across interactions"],"limitations":["Entity extraction accuracy varies by entity type and context; proper nouns more reliable than abstract concepts","No built-in entity disambiguation; homonyms (e.g., 'Apple' company vs fruit) require context resolution","Custom entity schemas require manual definition; no automatic schema learning","Extraction adds ~500ms-1s latency per interaction depending on text length"],"requires":["OpenAI API key with GPT-3.5-turbo or GPT-4 access","@engram-mem/core for memory integration","Optional: custom entity schema definition in JSON format","Node.js 14+ runtime"],"input_types":["plain text","conversation transcripts","documents with metadata","structured text with context"],"output_types":["structured entity objects with type, value, confidence score","entity relationship graph (optional)","tagged memory records with entity references"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-engram-mem-openai__cap_3","uri":"capability://search.retrieval.cross.encoder.semantic.reranking.for.retrieval.refinement","name":"cross-encoder semantic reranking for retrieval refinement","description":"Applies OpenAI-powered cross-encoder models to rerank retrieved memories based on semantic relevance to a query. 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Uses embeddings, entity relationships, and summarization to fit the most valuable information within token budgets. Implements a multi-level memory hierarchy (working memory, episodic memory, semantic memory) with intelligent promotion/demotion based on access patterns.","intents":["I need to fit relevant memories into my agent's context window without losing critical information","I want to automatically prioritize recent and frequently accessed memories over older ones","I need to balance memory diversity (covering multiple topics) with relevance to the current query"],"best_for":["Long-running agents with unbounded conversation history","Multi-turn dialogue systems with strict token budgets","Agents managing multiple concurrent conversations with shared memory"],"limitations":["Context selection is heuristic-based; no guarantee of optimal information selection for all query types","Recency bias may deprioritize older but highly relevant information","Token counting approximations may underestimate actual token usage (varies by tokenizer)","No support for cross-conversation memory merging; each conversation maintains separate context"],"requires":["@engram-mem/core for memory integration","Configured token budget (typically 2000-4000 tokens for context)","Memory records with embeddings and metadata","Node.js 14+ runtime"],"input_types":["query or current interaction context","memory database with embeddings and scores","token budget constraint"],"output_types":["prioritized memory list within token budget","context composition metadata (number of memories, coverage by type)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-engram-mem-openai__cap_5","uri":"capability://data.processing.analysis.conversation.to.memory.transformation.pipeline","name":"conversation-to-memory transformation pipeline","description":"Converts raw conversation transcripts into structured memory artifacts by applying embeddings, summarization, entity extraction, and metadata enrichment in a coordinated pipeline. Handles multi-turn conversations, speaker attribution, and context preservation. Stores results in Engram's memory format with full indexing for later retrieval.","intents":["I want to automatically ingest conversation logs into my agent's memory system","I need to extract structured insights (entities, topics, decisions) from unstructured chat history","I want to make past conversations searchable and retrievable by semantic meaning"],"best_for":["Chatbot systems archiving conversation history for future reference","Customer service platforms building institutional memory from support interactions","Research teams analyzing interview transcripts and focus group discussions"],"limitations":["Pipeline latency scales with conversation length (typically 2-5 seconds for 10k-token conversation)","Requires sequential processing of pipeline stages; no parallelization across stages","Speaker attribution must be provided in input; no automatic speaker diarization","Conversation context may be lost if turns are processed independently"],"requires":["OpenAI API key with embeddings, GPT, and entity extraction capabilities","@engram-mem/core for memory storage","Conversation transcript in structured format (JSON with speaker/text pairs)","Node.js 14+ runtime"],"input_types":["conversation transcript (JSON array of turns)","speaker metadata (optional)","conversation metadata (date, participants, topic)"],"output_types":["memory records with embeddings, summaries, entities","conversation index with turn-level and conversation-level metadata","relationship graph connecting entities across turns"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-engram-mem-openai__cap_6","uri":"capability://tool.use.integration.multi.provider.memory.adapter.interface","name":"multi-provider memory adapter interface","description":"Provides abstraction layer allowing Engram to work with different embedding, summarization, and extraction providers (OpenAI, Anthropic, local models) through a unified interface. Enables switching providers without changing agent code. Handles provider-specific API differences, error handling, and fallback strategies.","intents":["I want to use OpenAI for embeddings but Anthropic for summarization based on cost/quality tradeoffs","I need to switch providers if one API becomes unavailable without rewriting my agent","I want to use local models for sensitive data while keeping OpenAI for general processing"],"best_for":["Teams with multi-provider strategies (cost optimization, redundancy, compliance)","Enterprises requiring local processing for sensitive data with cloud fallback","Developers building portable agents that work across different LLM ecosystems"],"limitations":["Abstraction adds ~50-100ms overhead per capability call due to adapter dispatch","Provider-specific features (e.g., OpenAI's specific embedding dimensions) may not be portable","Error handling must account for provider-specific failure modes and rate limits","No automatic cost optimization; developers must manually configure provider selection"],"requires":["@engram-mem/core for adapter registration","API keys for configured providers (OpenAI, Anthropic, etc.)","Provider-specific adapter implementations","Node.js 14+ runtime"],"input_types":["capability request (embed, summarize, extract)","input data (text, etc.)","provider configuration"],"output_types":["normalized capability output (embeddings, summaries, entities)","provider metadata (model used, latency, cost)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with embeddings model access","Node.js 14+ runtime","@engram-mem/core package for memory integration","Network connectivity to OpenAI API endpoints","OpenAI API key with GPT-3.5-turbo or GPT-4 access","Text input under 128,000 tokens (model context limit)","@engram-mem/core for memory integration","Optional: custom entity schema definition in JSON format","Pre-retrieved candidate set (typically 10-100 items from embedding search)","Configured token budget (typically 2000-4000 tokens for context)"],"failure_modes":["Depends on OpenAI API availability and rate limits (3,500 requests/minute for text-embedding-3)","Embedding quality bounded by OpenAI model capabilities; no fine-tuning support","Requires network calls for each embedding generation unless caching is implemented","Vector dimensionality fixed by model choice (1536 for text-embedding-3-small, 3072 for large)","Abstractive summarization quality depends on OpenAI model capability; may hallucinate or omit nuanced details","Extractive mode limited to selecting existing sentences; cannot paraphrase or synthesize","Summarization adds latency (typically 1-3 seconds per document depending on length)","No domain-specific fine-tuning; generic summaries may miss specialized terminology importance","Entity extraction accuracy varies by entity type and context; proper nouns more reliable than abstract concepts","No built-in entity disambiguation; homonyms (e.g., 'Apple' company vs fruit) require context resolution","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1402433346219312,"quality":0.39,"ecosystem":0.6000000000000001,"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":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1263,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=engram-mem-openai","compare_url":"https://unfragile.ai/compare?artifact=engram-mem-openai"}},"signature":"OdU8BcMfQd4cfLqBZokiwWyYZm+MuwtZNbW5w+Kymikz6e1bT8Yr6BAlhZS3ZrQ3b1a1rNf6H2Oea9Gx7A2lAQ==","signedAt":"2026-06-20T21:24:59.975Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/engram-mem-openai","artifact":"https://unfragile.ai/engram-mem-openai","verify":"https://unfragile.ai/api/v1/verify?slug=engram-mem-openai","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"}}