{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-memtensor--memos","slug":"memtensor--memos","name":"MemOS","type":"mcp","url":"https://memos.openmem.net","page_url":"https://unfragile.ai/memtensor--memos","categories":["mcp-servers"],"tags":["agent","agent-memory","clawdbot","llm","llm-memory","long-term-memory","memory","memory-agent","memory-management","memory-operating-system","memory-retrieval","memory-scheduling","moltbot","openclaw","rag","retrieval-augmented-generation","skill-memory","skills"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-memtensor--memos__cap_0","uri":"capability://memory.knowledge.multi.tenant.memory.cube.allocation.and.lifecycle.management","name":"multi-tenant memory cube allocation and lifecycle management","description":"Allocates isolated memory cubes (GeneralMemCube instances) per user/tenant with independent lifecycle management, enabling parallel memory operations across multiple agents without cross-contamination. Uses MOSProduct and UserManager to orchestrate cube creation, access control, and garbage collection through a layered OS-like abstraction that mirrors traditional process management.","intents":["I need to isolate memory state for different users in a multi-agent system without shared context leakage","I want to manage memory lifecycle (creation, persistence, cleanup) for multiple concurrent agents automatically","I need to enforce per-tenant memory quotas and access boundaries in a shared deployment"],"best_for":["teams building multi-user LLM agent platforms","SaaS providers deploying MemOS for multiple customers","enterprises requiring strict data isolation between departments or projects"],"limitations":["No built-in cross-tenant memory sharing or federation — each cube is completely isolated","Cube lifecycle tied to user session; long-term persistence requires explicit checkpoint/restore logic","Scaling beyond 1000+ concurrent cubes requires careful database connection pooling configuration"],"requires":["Python 3.9+","FastAPI for API layer integration","PostgreSQL or PolarDB for multi-tenant metadata storage","Neo4j or compatible graph database for memory graph storage"],"input_types":["user_id (string)","cube_config (JSON with memory type, storage backend, embedding model)"],"output_types":["cube_instance (GeneralMemCube object with unique ID)","access_token (for subsequent memory operations)"],"categories":["memory-knowledge","multi-tenancy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_1","uri":"capability://memory.knowledge.graph.based.memory.storage.with.semantic.relationship.indexing","name":"graph-based memory storage with semantic relationship indexing","description":"Stores memories as nodes in a property graph (Neo4j backend) with edges representing semantic relationships (causality, temporal sequence, entity co-occurrence), enabling structured traversal and context-aware retrieval. TreeTextMemory and BaseGraphDB implement hierarchical memory organization where facts are decomposed into atomic nodes and linked by relationship types, supporting both keyword and semantic graph queries.","intents":["I need to store agent memories with explicit relationships so I can traverse context (e.g., 'what events led to this decision?')","I want to deduplicate memories by detecting semantic equivalence across different phrasings","I need to query memories using graph patterns (e.g., 'find all skills used in tasks involving user X')"],"best_for":["agents requiring causal reasoning over memory (e.g., debugging decision chains)","systems needing to detect and merge semantically equivalent memories","teams building knowledge graphs from agent interactions"],"limitations":["Graph traversal queries add 50-200ms latency per relationship hop; deep queries (5+ hops) become expensive","Neo4j requires separate infrastructure and licensing for production deployments","Memory deduplication relies on embedding similarity thresholds that may miss subtle duplicates or create false positives"],"requires":["Neo4j 4.4+ or compatible graph database","Embedding model (OpenAI, Anthropic, or local) for semantic relationship detection","Python 3.9+ with neo4j-driver package"],"input_types":["memory_text (string)","memory_type (enum: fact, skill, event, entity)","relationships (list of {type, target_node_id})"],"output_types":["graph_node (with node_id, properties, embedding vector)","relationship_edges (list of typed edges to other nodes)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_10","uri":"capability://search.retrieval.internet.search.integration.for.memory.augmentation","name":"internet search integration for memory augmentation","description":"Integrates web search (via configurable search APIs) to augment agent memory with real-time information, enabling agents to retrieve current facts not in their memory store. Search results are processed through the multi-modal extraction pipeline and stored as time-stamped memory nodes with source attribution.","intents":["I want my agent to search the web for current information when memory doesn't have an answer","I need to augment agent memory with real-time facts (news, prices, weather) without manual updates","I want to track information sources and provide citations for web-sourced memories"],"best_for":["agents answering questions requiring current information (news, prices, weather)","systems needing to supplement static memory with dynamic web data","teams building fact-checking or research agents"],"limitations":["Web search adds 1-5 second latency per query; not suitable for real-time response requirements","Search result quality varies; irrelevant or misleading results may be stored as memories","Search API costs accumulate; high-volume agents may incur significant expenses"],"requires":["Web search API (Google Custom Search, Bing Search, or open-source alternative)","API key and rate limit configuration","Python 3.9+ with search client library"],"input_types":["search_query (string)","search_config (num_results, result_types, time_filter)"],"output_types":["search_results (list of {title, url, snippet, timestamp})","stored_memories (memories created from search results with source attribution)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_11","uri":"capability://memory.knowledge.multi.cube.and.multi.user.pattern.support.with.shared.memory.access","name":"multi-cube and multi-user pattern support with shared memory access","description":"Enables multiple agents/users to operate on separate memory cubes while selectively sharing memories through explicit sharing policies and cross-cube references. Implements access control and memory federation patterns, allowing cubes to reference memories from other cubes with configurable read/write permissions.","intents":["I want multiple agents to maintain separate memories but share common facts (e.g., company knowledge base)","I need to implement team collaboration where agents can access shared memories with different permission levels","I want to build memory hierarchies where team memories aggregate individual agent memories"],"best_for":["multi-agent systems requiring memory sharing and collaboration","teams building hierarchical memory structures (individual → team → organization)","systems with complex access control requirements (read-only, read-write, admin)"],"limitations":["Cross-cube references add query complexity; shared memory access slower than local queries","Access control enforcement requires careful configuration; misconfiguration can leak sensitive memories","No built-in conflict resolution for concurrent writes to shared memories"],"requires":["Multi-cube deployment with shared database backend","Access control configuration (per-cube permissions, sharing policies)","Python 3.9+ with multi-cube orchestration logic"],"input_types":["cube_id (source cube)","shared_cube_id (target cube to share with)","sharing_policy (read_only, read_write, admin)"],"output_types":["shared_memory_reference (cross-cube reference with permissions)","access_log (audit trail of shared memory access)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_12","uri":"capability://automation.workflow.memory.operation.monitoring.and.scheduler.status.tracking","name":"memory operation monitoring and scheduler status tracking","description":"Provides real-time monitoring of memory operations and scheduler status through dedicated API endpoints and logging infrastructure (SchedulerLogger, Scheduler Status API). Tracks operation latency, success/failure rates, and resource usage, enabling observability and debugging of memory system health.","intents":["I want to monitor memory operation latency and identify bottlenecks","I need to track scheduler task status and detect failed memory operations","I want to debug memory system issues by inspecting operation logs and metrics"],"best_for":["production deployments requiring operational visibility","teams debugging memory system performance issues","systems with SLA requirements needing metrics and alerting"],"limitations":["Logging overhead adds CPU/disk usage; high-volume systems may require log sampling","Metrics are local to each instance; distributed deployments require centralized logging","No built-in alerting; teams must integrate with external monitoring systems"],"requires":["Logging infrastructure (file, syslog, or cloud logging service)","Metrics collection (Prometheus, CloudWatch, or custom)","Python 3.9+ with logging and metrics libraries"],"input_types":["operation_id (for querying specific operation status)","time_range (for historical metrics)"],"output_types":["operation_status (queued, processing, completed, failed)","metrics (latency_ms, success_rate, resource_usage)","logs (structured logs with operation details)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_13","uri":"capability://tool.use.integration.openclaw.plugin.integration.for.agent.framework.compatibility","name":"openclaw plugin integration for agent framework compatibility","description":"Integrates with OpenClaw agent framework (memos-local-openclaw, Cloud OpenClaw Plugin) through plugin architecture, enabling seamless memory integration into OpenClaw-based agents. Provides local and cloud deployment options with automatic memory cube provisioning and agent lifecycle management.","intents":["I want to add persistent memory to my OpenClaw agent without rewriting agent code","I need to deploy MemOS alongside OpenClaw agents in cloud or local environments","I want to share memory between OpenClaw agents in a team deployment"],"best_for":["teams already using OpenClaw framework seeking memory capabilities","deployments requiring tight integration between agent framework and memory system","systems needing cloud-native memory deployment with OpenClaw"],"limitations":["Plugin architecture couples MemOS to OpenClaw; switching frameworks requires re-integration","Cloud deployment adds operational complexity; local deployment requires infrastructure management","No automatic memory migration between local and cloud deployments"],"requires":["OpenClaw framework installed and configured","memos-local-openclaw or Cloud OpenClaw Plugin package","Python 3.9+ with OpenClaw compatibility"],"input_types":["agent_config (OpenClaw agent configuration)","memory_config (cube type, storage backend)"],"output_types":["integrated_agent (OpenClaw agent with memory capabilities)","memory_cube_instance (provisioned and ready for use)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_14","uri":"capability://planning.reasoning.evaluation.framework.and.benchmark.support","name":"evaluation framework and benchmark support","description":"Provides evaluation infrastructure for measuring memory system performance (Evaluation Framework, Evaluation Benchmarks) including metrics for retrieval accuracy, skill extraction quality, and memory efficiency. Supports running standardized benchmarks and custom evaluation scripts to assess MemOS performance on agent tasks.","intents":["I want to measure how well my memory system is performing on agent tasks","I need to benchmark skill extraction quality and cross-task reuse effectiveness","I want to compare different memory configurations and optimization strategies"],"best_for":["teams optimizing memory system performance for their use case","researchers evaluating MemOS capabilities and limitations","systems requiring performance validation before production deployment"],"limitations":["Benchmarks are task-specific; results may not generalize to different agent domains","Evaluation requires labeled ground truth; creating benchmarks is labor-intensive","No automated performance regression detection; manual monitoring required"],"requires":["Evaluation dataset with ground truth labels","Benchmark configuration (metrics, evaluation tasks)","Python 3.9+ with evaluation framework"],"input_types":["evaluation_config (benchmark_name, metrics, task_list)","agent_traces (interaction logs for evaluation)"],"output_types":["evaluation_results (metrics: retrieval_accuracy, skill_quality, memory_efficiency)","benchmark_report (detailed analysis and recommendations)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_2","uri":"capability://search.retrieval.hybrid.vector.graph.search.with.multi.modal.embedding.support","name":"hybrid vector-graph search with multi-modal embedding support","description":"Combines vector similarity search (via embeddings) with graph pattern matching to retrieve memories, supporting multi-modal inputs (text, images, structured data) through pluggable embedding models. The Searcher component executes dual-path queries: semantic vector search for relevance ranking and graph traversal for relationship-based filtering, merging results with configurable fusion strategies.","intents":["I want to search memories using natural language and get results ranked by semantic relevance plus relationship context","I need to retrieve memories from images or mixed-media inputs (text + image descriptions)","I want to filter search results by relationship type (e.g., 'skills used in marketing tasks only')"],"best_for":["agents processing multi-modal inputs (chat + documents + images)","systems requiring both semantic relevance and structural filtering","teams needing explainable retrieval (showing relationship paths alongside results)"],"limitations":["Multi-modal embedding requires separate encoders per modality; no unified embedding space across text/image/code","Fusion of vector and graph results requires tuning weights; no automatic optimization","Large-scale vector search (>1M memories) requires approximate nearest neighbor (ANN) indexing; exact search becomes prohibitive"],"requires":["Embedding model supporting target modalities (e.g., CLIP for text+image, CodeBERT for code)","Vector index (built-in or external: Faiss, Milvus, Weaviate)","Graph database for relationship queries","Python 3.9+ with sentence-transformers or similar"],"input_types":["query_text (string)","query_image (optional, base64 or file path)","filter_relationships (optional, list of relationship types to include/exclude)"],"output_types":["ranked_memories (list of {memory_id, text, relevance_score, relationship_paths})","search_metadata (query_embedding, graph_traversal_depth, fusion_strategy_used)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_3","uri":"capability://automation.workflow.asynchronous.memory.scheduling.and.batch.processing","name":"asynchronous memory scheduling and batch processing","description":"Schedules memory operations (extraction, deduplication, consolidation) asynchronously via GeneralScheduler and SchedulerDispatcher, enabling background processing of memory updates without blocking agent interactions. Uses task queues and configurable scheduling policies to batch memory writes, compress old memories, and trigger skill extraction on a separate execution timeline from agent queries.","intents":["I want memory updates to happen in the background so agent responses aren't delayed by memory I/O","I need to periodically consolidate and compress old memories to manage storage growth","I want to extract reusable skills from memory asynchronously and make them available for future tasks"],"best_for":["long-running agents where memory operations must not block inference","systems with high memory write volume requiring batching for efficiency","teams needing background skill extraction and memory consolidation"],"limitations":["Asynchronous scheduling introduces eventual consistency — newly written memories may not be immediately searchable","Scheduler failure or crash can lose in-flight memory operations; requires persistent task queue","Scheduling policies are static; no dynamic adjustment based on memory growth or query patterns"],"requires":["Task queue backend (Redis, RabbitMQ, or in-memory for single-instance deployments)","Scheduler configuration (batch size, scheduling interval, consolidation thresholds)","Python 3.9+ with async/await support"],"input_types":["memory_operation (enum: write, extract_skill, consolidate, deduplicate)","operation_payload (memory_text, memory_type, priority)","scheduling_policy (batch_size, max_delay_seconds, consolidation_threshold)"],"output_types":["task_id (for tracking operation status)","scheduler_status (queued, processing, completed, failed)","operation_result (memory_id, skill_extracted, consolidation_summary)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_4","uri":"capability://memory.knowledge.skill.memory.extraction.and.cross.task.reuse","name":"skill memory extraction and cross-task reuse","description":"Automatically extracts reusable skills from agent interactions (via LLM-based skill detection) and stores them in a skill memory layer, enabling agents to discover and apply learned skills across different tasks. The skill extraction pipeline analyzes memory traces, identifies generalizable patterns, and creates skill nodes in the graph with preconditions, execution steps, and success metrics.","intents":["I want my agent to learn a skill from one task and automatically apply it to similar tasks later","I need to extract generalizable procedures from agent memory and make them discoverable","I want to track skill effectiveness and refine skills based on success/failure patterns"],"best_for":["agents performing repeated task categories (e.g., data extraction, customer support)","systems where skill reuse directly improves performance or reduces latency","teams building skill libraries from agent interactions"],"limitations":["Skill extraction relies on LLM-based pattern detection; false positives (overgeneralization) or false negatives (missed patterns) are common","Skill applicability matching requires semantic similarity; no guarantee extracted skill will work in new context","Skill versioning and deprecation not built-in; old skills may persist even if superseded"],"requires":["LLM with function calling (OpenAI, Anthropic, or local model) for skill extraction","Memory traces with sufficient detail (action sequences, inputs, outputs)","Graph database for skill node storage and relationship indexing","Python 3.9+ with skill extraction pipeline configured"],"input_types":["task_trace (sequence of {action, input, output, success_flag})","skill_extraction_config (min_generalization_score, min_success_rate, skill_categories)"],"output_types":["skill_node (skill_id, name, preconditions, steps, postconditions, success_metrics)","skill_applicability_score (relevance to new task, confidence)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_5","uri":"capability://memory.knowledge.tree.structured.hierarchical.memory.organization","name":"tree-structured hierarchical memory organization","description":"Organizes memories in a tree hierarchy (TreeTextMemory) where high-level summaries branch into detailed sub-memories, enabling efficient compression and selective retrieval at different abstraction levels. Memories are decomposed into atomic facts at leaf nodes, aggregated into topic clusters at intermediate nodes, and summarized at root nodes, supporting both top-down (summary-first) and bottom-up (detail-first) traversal.","intents":["I want to store large memory traces compactly by summarizing at multiple levels of abstraction","I need to retrieve memories at different detail levels (summary vs. full details) depending on context","I want to efficiently prune old memories by removing leaf nodes while preserving summaries"],"best_for":["agents with long interaction histories requiring memory compression","systems needing multi-level memory abstraction (executive summary → detailed facts)","teams managing memory growth through hierarchical pruning"],"limitations":["Tree construction requires LLM-based summarization at each level; adds latency and cost","Summarization introduces information loss; fine-grained details may be irretrievable from summaries alone","Tree rebalancing after memory updates is not automatic; manual reorganization may be needed"],"requires":["LLM for summarization (OpenAI, Anthropic, or local model)","Graph database supporting hierarchical queries","Python 3.9+ with tree construction and traversal logic"],"input_types":["memory_sequence (list of {text, timestamp, metadata})","tree_config (branching_factor, summarization_depth, leaf_node_size)"],"output_types":["tree_structure (root_node with children, each containing summary and detail_pointers)","memory_at_level (summary at requested abstraction level)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_6","uri":"capability://data.processing.analysis.multi.modal.memory.content.processing.and.extraction","name":"multi-modal memory content processing and extraction","description":"Processes diverse input modalities (text, images, documents, structured data) through MultiModalStructMemReader, extracting semantic content and converting to unified memory representations. Supports OCR for images, document parsing for PDFs, and structured data extraction from tables/JSON, with configurable extraction pipelines per modality.","intents":["I want to ingest agent interactions from multiple sources (chat logs, uploaded documents, images) into memory","I need to extract structured information from unstructured inputs (e.g., entities from images, tables from PDFs)","I want to normalize diverse input formats into a unified memory representation"],"best_for":["agents processing multi-modal user inputs (text + images + documents)","systems ingesting data from multiple sources (APIs, file uploads, web scraping)","teams needing flexible input handling without custom preprocessing"],"limitations":["OCR accuracy varies by image quality; low-quality images produce garbled text","Document parsing requires format-specific handlers; unsupported formats fail silently or require custom parsers","Extraction quality depends on modality-specific models; no unified quality guarantee across modalities"],"requires":["OCR engine (Tesseract, EasyOCR, or cloud API)","Document parser (PyPDF2, pdfplumber for PDFs; python-docx for Word)","Vision model for image understanding (CLIP, GPT-4V, or local model)","Python 3.9+ with pillow, pdf libraries"],"input_types":["text (string)","image (base64, file path, or URL)","document (PDF, DOCX, TXT file)","structured_data (JSON, CSV, table HTML)"],"output_types":["unified_memory_representation (text + extracted_entities + metadata)","extraction_confidence (per-modality confidence scores)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_7","uri":"capability://memory.knowledge.memory.quality.assurance.and.deduplication","name":"memory quality assurance and deduplication","description":"Detects and merges duplicate or semantically equivalent memories using embedding similarity and graph-based relationship analysis, maintaining memory quality and preventing redundant storage. Deduplication runs asynchronously via the scheduler, comparing new memories against existing ones using configurable similarity thresholds and merging strategies (keep newest, keep highest-quality, merge summaries).","intents":["I want to prevent duplicate memories from accumulating and bloating storage","I need to detect when the same fact is stored in different phrasings and merge them","I want to maintain memory quality by removing low-confidence or contradictory memories"],"best_for":["long-running agents where memory duplication is inevitable","systems with high memory write volume requiring deduplication","teams needing memory quality metrics and cleanup"],"limitations":["Deduplication relies on embedding similarity; subtle differences may be missed or false positives created","Merging strategies are static; no learning-based optimization of merge decisions","Deduplication latency scales with memory size; large memory stores require sampling or approximate matching"],"requires":["Embedding model for similarity comparison","Similarity threshold configuration (typically 0.85-0.95 cosine similarity)","Merge strategy selection (keep_newest, keep_highest_quality, merge_summaries)","Python 3.9+ with scipy or similar for similarity computation"],"input_types":["memory_to_deduplicate (text, embedding, metadata)","dedup_config (similarity_threshold, merge_strategy, quality_metrics)"],"output_types":["dedup_result (merged_memory_id, duplicates_removed_count, merge_summary)","quality_metrics (storage_saved, dedup_confidence)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_8","uri":"capability://tool.use.integration.rest.api.with.request.response.schema.validation","name":"rest api with request/response schema validation","description":"Exposes MemOS operations through FastAPI REST endpoints with strict request/response schema validation, enabling client integration and monitoring. API layer (product_router.py) handles memory CRUD operations, search queries, scheduler status, and multi-cube management with automatic request validation and error handling.","intents":["I want to integrate MemOS into my agent framework via HTTP API without direct Python imports","I need to validate incoming memory operations and return structured error responses","I want to monitor memory operations and scheduler status through API endpoints"],"best_for":["teams integrating MemOS into polyglot agent systems (non-Python clients)","systems requiring HTTP-based memory access for security/isolation","deployments needing API monitoring and rate limiting"],"limitations":["HTTP overhead adds 10-50ms latency per request vs. direct Python calls","Large memory payloads (>10MB) may exceed HTTP request size limits","Schema validation adds CPU overhead; complex schemas slow request processing"],"requires":["FastAPI 0.95+","Python 3.9+ with Pydantic for schema validation","HTTP client library (requests, httpx, or language-specific equivalents)"],"input_types":["JSON request body with memory operation parameters","URL path/query parameters for filtering and pagination"],"output_types":["JSON response with operation result and metadata","HTTP status codes (200, 400, 404, 500) with error details"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-memtensor--memos__cap_9","uri":"capability://tool.use.integration.configurable.llm.and.embedding.model.integration","name":"configurable llm and embedding model integration","description":"Supports pluggable LLM and embedding model backends (OpenAI, Anthropic, local models) through configuration, enabling agents to choose models based on cost/performance trade-offs. Configuration system (LLM and Embedder Configuration) allows runtime model switching without code changes, with fallback strategies and batch processing support.","intents":["I want to use different LLMs for different operations (e.g., GPT-4 for skill extraction, GPT-3.5 for summarization)","I need to switch embedding models without reindexing existing memories","I want to use local models for privacy-sensitive deployments without cloud API calls"],"best_for":["teams optimizing cost/performance by model selection per operation","systems requiring model flexibility for A/B testing or gradual migration","deployments with privacy constraints requiring local model support"],"limitations":["Model switching requires re-embedding existing memories for vector search; expensive operation","Local models require significant compute resources; inference latency 5-10x higher than cloud APIs","No automatic model selection; teams must manually configure optimal models per operation"],"requires":["API keys for cloud models (OpenAI, Anthropic) or local model setup (Ollama, vLLM)","Configuration file specifying model endpoints and parameters","Python 3.9+ with model-specific client libraries"],"input_types":["model_config (model_name, endpoint_url, api_key, parameters like temperature)","operation_type (skill_extraction, summarization, embedding)"],"output_types":["model_response (text, embedding vector, or structured output)","usage_metadata (tokens_used, latency, cost_estimate)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastAPI for API layer integration","PostgreSQL or PolarDB for multi-tenant metadata storage","Neo4j or compatible graph database for memory graph storage","Neo4j 4.4+ or compatible graph database","Embedding model (OpenAI, Anthropic, or local) for semantic relationship detection","Python 3.9+ with neo4j-driver package","Web search API (Google Custom Search, Bing Search, or open-source alternative)","API key and rate limit configuration","Python 3.9+ with search client library"],"failure_modes":["No built-in cross-tenant memory sharing or federation — each cube is completely isolated","Cube lifecycle tied to user session; long-term persistence requires explicit checkpoint/restore logic","Scaling beyond 1000+ concurrent cubes requires careful database connection pooling configuration","Graph traversal queries add 50-200ms latency per relationship hop; deep queries (5+ hops) become expensive","Neo4j requires separate infrastructure and licensing for production deployments","Memory deduplication relies on embedding similarity thresholds that may miss subtle duplicates or create false positives","Web search adds 1-5 second latency per query; not suitable for real-time response requirements","Search result quality varies; irrelevant or misleading results may be stored as memories","Search API costs accumulate; high-volume agents may incur significant expenses","Cross-cube references add query complexity; shared memory access slower than local queries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6446535797661281,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:29.527Z","last_commit":"2026-05-03T13:07:00Z"},"community":{"stars":8872,"forks":789,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=memtensor--memos","compare_url":"https://unfragile.ai/compare?artifact=memtensor--memos"}},"signature":"UqdRFC2a2a7OMDSn2x0HobHC/7YKNvv3aHc7wiemOOl0Hm03+m4TvNEkaQcVRzlt3mCHTR/ozc3PoMpHrHk8BQ==","signedAt":"2026-06-21T17:20:02.738Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/memtensor--memos","artifact":"https://unfragile.ai/memtensor--memos","verify":"https://unfragile.ai/api/v1/verify?slug=memtensor--memos","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"}}