{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-doobidoo--mcp-memory-service","slug":"doobidoo--mcp-memory-service","name":"mcp-memory-service","type":"mcp","url":"https://github.com/doobidoo/mcp-memory-service","page_url":"https://unfragile.ai/doobidoo--mcp-memory-service","categories":["mcp-servers"],"tags":["agent-memory","agentic-ai","ai-agents","autogen","claude","crewai","knowledge-graph","langgraph","long-term-memory","mcp","mcp-server","memory","model-context-protocol","multi-agent","open-source","rag","semantic-search","sqlite-vec","vector-database","vector-storage"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-doobidoo--mcp-memory-service__cap_0","uri":"capability://search.retrieval.semantic.memory.retrieval.with.local.embeddings","name":"semantic-memory-retrieval-with-local-embeddings","description":"Performs sub-5ms vector similarity search over stored memories using ONNX-based local embeddings without external API calls. Implements a hybrid retrieval pipeline that combines dense vector search (via sqlite-vec) with optional ONNX-based re-ranking to surface contextually relevant memories from long-term storage. The system maintains embedding indices in SQLite or Cloudflare Vectorize, enabling instant semantic matching without cloud latency or token costs.","intents":["Retrieve relevant past conversations and facts when an agent needs context for a decision","Find semantically similar memories across multiple agent sessions without external embedding APIs","Build context windows by pulling the most relevant stored knowledge without manual query engineering"],"best_for":["Multi-agent systems (LangGraph, CrewAI, AutoGen) requiring persistent context across sessions","Teams building Claude integrations that need long-term memory without OpenAI/Anthropic embedding costs","Developers deploying agents in bandwidth-constrained or offline environments"],"limitations":["ONNX embeddings are fixed to a single model (typically sentence-transformers); switching models requires re-embedding entire corpus","Sub-5ms latency assumes local SQLite-Vec; remote Cloudflare backend adds network round-trip latency (~50-200ms)","Semantic search quality depends on embedding model choice; no built-in fine-tuning for domain-specific vocabularies"],"requires":["Python 3.9+","SQLite 3.45+ (for vec extension) OR Cloudflare account with D1/Vectorize","ONNX Runtime (auto-installed via pyproject.toml)","Minimum 512MB RAM for embedding model in memory"],"input_types":["text query (natural language or structured)","optional metadata filters (tags, timestamps, entity types)"],"output_types":["ranked list of memory objects with similarity scores","structured JSON with memory ID, content, metadata, and relevance score"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_1","uri":"capability://memory.knowledge.typed.knowledge.graph.storage.and.querying","name":"typed-knowledge-graph-storage-and-querying","description":"Maintains a typed, directed knowledge graph where memories are nodes and relationships (causes, fixes, contradicts, references, etc.) are edges with semantic meaning. The system stores relationships in a relational schema (likely using SQLAlchemy ORM based on architecture patterns) and supports graph traversal queries to infer indirect associations and build richer context. Relationships are typed to enable domain-aware reasoning (e.g., distinguishing causal links from contradictions).","intents":["Model complex relationships between facts (e.g., 'bug X is caused by issue Y, which was fixed by PR Z')","Perform multi-hop reasoning to surface indirect connections agents might miss with flat vector search","Build explainable memory chains where agents can trace how conclusions were derived from related facts"],"best_for":["Research and analysis agents that need to track causality and dependencies","Multi-agent systems where shared knowledge graphs enable cross-agent reasoning","Teams building knowledge-intensive applications (documentation systems, incident management, research tools)"],"limitations":["Graph traversal queries add latency (~10-50ms per hop); no built-in query optimization for deep traversals","Relationship inference is autonomous but not user-controllable; no explicit schema validation for relationship types","Scaling to millions of relationships requires careful indexing; no distributed graph database support (SQLite/D1 only)"],"requires":["Python 3.9+","SQLAlchemy 2.0+ (for ORM)","SQLite 3.45+ or Cloudflare D1 for storage","Memory objects must include entity type metadata for relationship inference"],"input_types":["memory objects with entity types and content","relationship type specifications (causes, fixes, contradicts, etc.)","optional graph traversal queries"],"output_types":["typed relationship edges with source/target memory IDs","graph traversal results (paths, connected components)","inferred associations with confidence scores"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_10","uri":"capability://automation.workflow.backup.restore.and.data.synchronization.utilities","name":"backup-restore-and-data-synchronization-utilities","description":"Provides command-line utilities for backing up memory to files, restoring from backups, and synchronizing memory between different storage backends or instances. Supports incremental backups to minimize storage overhead and includes validation checks to ensure data integrity during restore operations. Synchronization utilities enable replication of memory across multiple deployments (e.g., local to cloud, or between team members).","intents":["Back up memory data for disaster recovery and compliance","Restore memory from backups after data loss or corruption","Synchronize memory between local and remote deployments or between team members"],"best_for":["Teams requiring backup/restore for compliance or disaster recovery","Developers migrating memory between storage backends (SQLite to Cloudflare)","Organizations sharing memory across multiple agent deployments"],"limitations":["Backup/restore is offline; no live backup while agents are accessing memory","Incremental backups require tracking change history; no built-in deduplication across backups","Synchronization is one-way (source to destination); no bidirectional merge for conflicting changes"],"requires":["Python 3.9+","Access to source and destination storage backends","Sufficient disk space for backups (depends on memory size)","Command-line access to the memory service host"],"input_types":["backup destination path (local filesystem or cloud storage)","restore source path (backup file)","synchronization source and destination configurations"],"output_types":["backup files (compressed JSON or binary format)","restore logs with success/failure status","synchronization reports with records copied and conflicts detected"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_11","uri":"capability://data.processing.analysis.metadata.codec.and.quality.analytics.system","name":"metadata-codec-and-quality-analytics-system","description":"Encodes and decodes memory metadata (entity types, relationships, quality scores, access patterns) into a compact binary format for efficient storage and transmission. The system tracks quality metrics (access frequency, recency, consolidation status, confidence scores) and provides analytics to identify memory health issues (stale facts, low-confidence memories, orphaned relationships). Analytics can be queried to generate reports on memory quality and usage patterns.","intents":["Efficiently store memory metadata without bloating the database","Track memory quality metrics to identify facts that need consolidation or removal","Generate analytics reports on memory usage patterns and health"],"best_for":["Large memory stores (>100K memories) where metadata storage overhead is significant","Teams monitoring memory quality and needing visibility into memory health","Systems requiring detailed audit trails of memory access and modifications"],"limitations":["Binary codec is opaque; debugging metadata issues requires decoding tools","Quality analytics are computed on-demand; no real-time monitoring or alerting","Metadata schema is fixed; no support for custom quality metrics without code changes"],"requires":["Python 3.9+","SQLite 3.45+ or Cloudflare D1 for storage","ONNX embeddings for quality scoring"],"input_types":["memory metadata (entity types, relationships, quality scores)","access logs (read/write timestamps, user IDs)"],"output_types":["encoded metadata (binary format)","quality analytics reports (JSON or CSV)","health metrics (average quality score, stale memory count, orphaned relationships)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_12","uri":"capability://automation.workflow.docker.deployment.and.system.service.installation","name":"docker-deployment-and-system-service-installation","description":"Provides Docker containerization for easy deployment of the memory service in containerized environments (Kubernetes, Docker Compose, etc.) and system service installation scripts for running the service as a background daemon on Linux/macOS. Docker images include all dependencies (Python, ONNX, SQLite) and expose the REST API and MCP server ports. System service installation enables automatic startup on system boot and process supervision.","intents":["Deploy memory service in Docker for containerized agent systems","Run memory service as a background daemon on development machines","Ensure memory service survives system reboots and process crashes"],"best_for":["Teams deploying agents in Kubernetes or Docker Compose","Developers wanting memory service to run automatically on system startup","Organizations requiring containerized deployments for security and isolation"],"limitations":["Docker images are pre-built; no support for custom configurations without rebuilding","System service installation is Linux/macOS only; no Windows support","Process supervision is basic (systemd or launchd); no advanced orchestration features"],"requires":["Docker 20.10+ (for Docker deployment) OR systemd/launchd (for system service)","Python 3.9+ (for system service installation)","Sufficient disk space for Docker images (~500MB)"],"input_types":["Docker Compose configuration (environment variables, volume mounts)","system service configuration (port, storage backend, logging)"],"output_types":["running Docker container with exposed ports","system service status (active, inactive, failed)","logs from Docker container or system service"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_2","uri":"capability://automation.workflow.autonomous.memory.consolidation.with.decay.and.clustering","name":"autonomous-memory-consolidation-with-decay-and-clustering","description":"Implements a background consolidation system inspired by biological memory consolidation that automatically clusters similar memories, compresses redundant information, and applies time-decay to less-relevant facts. The system runs asynchronously (likely via background tasks or scheduled jobs) to analyze memory access patterns, identify semantic clusters, and merge or archive memories to manage context window limits. Decay functions reduce the relevance scores of older memories, simulating natural forgetting while preserving important facts.","intents":["Automatically manage memory growth so agents don't exceed context window limits over long sessions","Compress redundant memories (e.g., multiple mentions of the same fact) into consolidated summaries","Deprioritize stale information while preserving critical long-term knowledge"],"best_for":["Long-running agent systems (CrewAI, AutoGen) that accumulate memories over days/weeks","Teams needing automatic memory hygiene without manual intervention","Applications where context window is a bottleneck (e.g., Claude with 200K token limit)"],"limitations":["Consolidation is autonomous and non-deterministic; no user control over clustering thresholds or decay rates without code changes","Decay functions are time-based only; no semantic importance weighting (e.g., critical facts decay slower)","Clustering adds computational overhead (~100-500ms per consolidation cycle); no built-in cost estimation for large memory stores"],"requires":["Python 3.9+","Background task runner (APScheduler or similar, auto-configured)","SQLite 3.45+ or Cloudflare D1","ONNX embeddings for clustering (same as semantic search)"],"input_types":["memory corpus with timestamps and access frequency metadata","optional consolidation configuration (decay rate, clustering threshold)"],"output_types":["consolidated memory objects with merged content","archived/deleted memory records with consolidation reason","metadata updates reflecting decay scores and cluster assignments"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_3","uri":"capability://tool.use.integration.mcp.protocol.server.with.remote.http.support","name":"mcp-protocol-server-with-remote-http-support","description":"Exposes memory capabilities as a Model Context Protocol (MCP) server compatible with Claude Desktop, IDEs, and other MCP clients. Implements both native MCP (stdio-based) and Remote MCP via Streamable HTTP with mDNS discovery, enabling agents to access memory through standardized tool calls. The HTTP bridge allows remote clients to communicate with the MCP server over the network with OAuth 2.1 authentication, supporting multi-client scenarios without requiring local installation.","intents":["Integrate memory service with Claude Desktop or IDE extensions via native MCP protocol","Enable remote agents (running on different machines) to access shared memory via HTTP","Discover and connect to memory services on the local network using mDNS without manual configuration"],"best_for":["Claude Desktop users wanting persistent memory without custom Python setup","Teams deploying agents across multiple machines needing centralized memory","Developers building MCP-compatible tools that need memory integration"],"limitations":["Native MCP (stdio) requires local Python environment; Remote MCP adds network latency (~50-200ms per call)","mDNS discovery only works on local networks; no built-in support for cloud-based service discovery","MCP tool schema is fixed; no dynamic tool registration based on memory types or custom operations"],"requires":["Python 3.9+","Claude Desktop 0.4+ (for native MCP) OR any HTTP client (for Remote MCP)","Node.js 18+ (optional, for IDE integrations)","OAuth 2.1 provider configuration (for Remote MCP authentication)"],"input_types":["MCP tool calls with parameters (e.g., 'store_memory', 'retrieve_memory')","HTTP requests with Bearer token (for Remote MCP)"],"output_types":["MCP tool results (JSON-serialized memory objects)","HTTP responses with memory data and metadata"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_4","uri":"capability://tool.use.integration.rest.api.with.oauth.2.1.authentication","name":"rest-api-with-oauth-2-1-authentication","description":"Provides a FastAPI-based REST API for memory operations (store, retrieve, update, delete) with OAuth 2.1 PKCE and Dynamic Client Registration (DCR) for secure team collaboration. The API supports both local (development) and remote (production) deployments, with token-based authentication and optional role-based access control. Implements standard REST conventions with JSON payloads and HTTP status codes, making it compatible with any HTTP client (Python, JavaScript, Go, etc.).","intents":["Build custom integrations with memory service from non-Python languages or frameworks","Enable secure multi-user access to shared memory with OAuth 2.1 authentication","Deploy memory service as a microservice accessible to distributed agent systems"],"best_for":["Teams building polyglot agent systems (Python + Node.js + Go, etc.)","Organizations requiring OAuth 2.1 compliance for security audits","Developers deploying memory as a standalone microservice in Kubernetes or Docker"],"limitations":["REST API adds network latency compared to in-process Python calls (~50-200ms per request)","OAuth 2.1 setup requires external identity provider (Okta, Auth0, etc.) or self-hosted OIDC server","No built-in rate limiting or quota management; requires external API gateway (Kong, Envoy) for production"],"requires":["Python 3.9+","FastAPI 0.100+","OAuth 2.1 provider (Okta, Auth0, Keycloak, etc.)","HTTPS in production (self-signed certs acceptable for development)"],"input_types":["JSON payloads with memory objects, queries, and metadata","OAuth 2.1 Bearer tokens in Authorization header","Query parameters for filtering and pagination"],"output_types":["JSON responses with memory objects, search results, or status messages","HTTP status codes (200, 201, 400, 401, 404, 500)","OAuth 2.1 tokens and OIDC discovery endpoints"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_5","uri":"capability://data.processing.analysis.document.ingestion.pipeline.with.chunking.and.metadata.extraction","name":"document-ingestion-pipeline-with-chunking-and-metadata-extraction","description":"Processes unstructured documents (text, markdown, PDFs) by chunking them into semantic units, extracting metadata (entity types, tags, timestamps), and storing them as memories with embeddings. The pipeline uses configurable chunking strategies (sliding window, sentence-based, or semantic) to preserve context while respecting embedding model limits. Metadata extraction likely uses regex patterns or LLM-based extraction to identify entities, relationships, and tags from document content.","intents":["Ingest documentation, research papers, or knowledge bases into the memory system for agent retrieval","Automatically extract structured metadata (entities, relationships) from unstructured text","Chunk large documents intelligently to preserve semantic meaning while fitting embedding model limits"],"best_for":["Research and analysis agents that need to index large document collections","Teams building knowledge bases or documentation systems with AI search","Developers migrating from static RAG systems to dynamic agent memory"],"limitations":["Chunking strategy is fixed per configuration; no adaptive chunking based on document structure or content type","Metadata extraction is heuristic-based (regex or simple LLM calls); no fine-tuned NER models for domain-specific entities","Large document ingestion is synchronous; no built-in batching or streaming for multi-gigabyte corpora"],"requires":["Python 3.9+","Document parsers (PyPDF2 for PDFs, markdown parser for .md files)","ONNX embeddings for chunking validation","Optional: LLM API key (OpenAI, Anthropic) for advanced metadata extraction"],"input_types":["text files (.txt, .md)","PDF documents (.pdf)","structured data (JSON, CSV) with optional schema hints"],"output_types":["memory objects with chunked content, embeddings, and extracted metadata","ingestion report with chunk count, metadata coverage, and processing time"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_6","uri":"capability://tool.use.integration.claude.hooks.integration.for.session.memory","name":"claude-hooks-integration-for-session-memory","description":"Integrates with Claude's conversation hooks (session start/end) to automatically retrieve relevant memories at the beginning of a conversation and consolidate new memories at the end. The system intercepts Claude API calls to inject context from the memory service and capture new facts from conversation transcripts. This enables Claude to maintain continuity across separate conversations without explicit memory management by the user.","intents":["Automatically load relevant context when starting a new Claude conversation","Capture and store new facts from Claude conversations without manual extraction","Enable Claude to reference past conversations and build on previous reasoning"],"best_for":["Claude API users building long-running assistant applications","Teams using Claude for research or analysis that spans multiple sessions","Developers wanting transparent memory integration without modifying Claude prompts"],"limitations":["Hooks are Claude-specific; no support for other LLM providers (GPT-4, Gemini, etc.)","Memory retrieval happens at conversation start; no mid-conversation memory updates","Fact extraction from conversation transcripts is heuristic-based; no guarantee of capturing all relevant information"],"requires":["Python 3.9+","Claude API key (Anthropic)","Memory service running locally or remotely","Claude API version 2024+ (for hooks support)"],"input_types":["Claude conversation transcripts (user messages, assistant responses)","session metadata (user ID, conversation ID, timestamp)"],"output_types":["injected context (relevant memories) in system prompt","new memory objects extracted from conversation","consolidation report with facts captured and relationships inferred"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_7","uri":"capability://data.processing.analysis.onnx.based.local.ranking.and.quality.scoring","name":"onnx-based-local-ranking-and-quality-scoring","description":"Implements a local ONNX-based re-ranker that scores and ranks search results based on relevance and quality metrics without external API calls. The system computes quality scores based on metadata (access frequency, recency, consolidation status) and uses an ONNX model to re-rank semantic search results. Async scoring allows quality computation to run in the background without blocking retrieval operations.","intents":["Improve search result quality by re-ranking semantic matches based on relevance and metadata","Score memory quality to identify stale or low-confidence facts for consolidation","Compute relevance scores locally without external ranking APIs (Cohere, etc.)"],"best_for":["Applications where search result quality is critical (research, analysis, decision-making)","Teams wanting to avoid external ranking API costs and latency","Systems with heterogeneous memory quality (mix of high-confidence and uncertain facts)"],"limitations":["Re-ranking adds latency (~50-200ms per query); no built-in caching of ranking results","Quality scoring is based on fixed metadata features; no custom scoring functions or domain-specific weights","ONNX re-ranker model is fixed; no fine-tuning on domain-specific relevance judgments"],"requires":["Python 3.9+","ONNX Runtime","Pre-trained ONNX re-ranker model (e.g., cross-encoder from Hugging Face)","Memory objects with quality metadata (access frequency, timestamps, confidence scores)"],"input_types":["search results from semantic retrieval (memory objects with similarity scores)","optional quality metadata (access frequency, recency, consolidation status)"],"output_types":["re-ranked memory objects with quality scores","quality analytics (average score, distribution, outliers)","async scoring reports for background processing"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_8","uri":"capability://automation.workflow.hybrid.storage.backend.with.sqlite.and.cloudflare.support","name":"hybrid-storage-backend-with-sqlite-and-cloudflare-support","description":"Abstracts storage operations behind a unified interface that supports both local SQLite (with vec extension) and remote Cloudflare Workers (D1 database, Vectorize embeddings, R2 object storage). The system automatically selects the appropriate backend based on configuration and provides transparent fallback/synchronization between backends. Hybrid mode enables local caching with remote persistence, reducing latency while maintaining durability.","intents":["Deploy memory service locally for development with automatic migration to Cloudflare for production","Use local SQLite for fast development iteration without cloud infrastructure","Synchronize memory between local and remote deployments for hybrid architectures"],"best_for":["Teams wanting to start with local SQLite and scale to Cloudflare without code changes","Developers building edge-deployed agents using Cloudflare Workers","Organizations needing hybrid local/cloud deployments for compliance or performance reasons"],"limitations":["Hybrid mode requires manual synchronization logic; no built-in conflict resolution for divergent local/remote states","Cloudflare backend has different performance characteristics (D1 is slower than local SQLite); no automatic query optimization per backend","Storage abstraction adds complexity; debugging storage issues requires understanding both backends"],"requires":["Python 3.9+","SQLite 3.45+ (for local backend) OR Cloudflare account with D1/Vectorize/R2 (for remote backend)","SQLAlchemy 2.0+ (for ORM abstraction)","Cloudflare API credentials (for remote backend)"],"input_types":["memory objects with content, embeddings, and metadata","storage backend configuration (local vs. Cloudflare)"],"output_types":["persisted memory objects with storage location metadata","synchronization reports (local/remote state, conflicts, resolution actions)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-doobidoo--mcp-memory-service__cap_9","uri":"capability://automation.workflow.web.dashboard.for.memory.visualization.and.management","name":"web-dashboard-for-memory-visualization-and-management","description":"Provides a single-page application (SPA) dashboard for visualizing memory contents, searching memories, managing relationships, and monitoring consolidation status. The dashboard connects to the REST API to display memory objects, knowledge graph relationships, and quality metrics in an interactive interface. Supports filtering, tagging, and manual memory editing for administrative tasks.","intents":["Visualize memory contents and knowledge graph relationships to understand what agents remember","Search and filter memories manually for debugging or auditing","Monitor consolidation status and memory health metrics"],"best_for":["Teams managing large memory stores and needing visibility into memory contents","Developers debugging agent behavior by inspecting stored memories","Administrators auditing memory for compliance or quality assurance"],"limitations":["Dashboard is read-mostly; no real-time updates (requires page refresh to see new memories)","Large knowledge graphs (>10K relationships) may render slowly due to browser limitations","No built-in access control; dashboard inherits authentication from REST API but has no role-based UI restrictions"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","REST API running and accessible","OAuth 2.1 credentials (for Remote MCP deployments)"],"input_types":["memory search queries (text, filters, date ranges)","manual memory edits (content, tags, relationships)"],"output_types":["rendered memory objects with metadata","knowledge graph visualization (nodes and edges)","consolidation and quality metrics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","SQLite 3.45+ (for vec extension) OR Cloudflare account with D1/Vectorize","ONNX Runtime (auto-installed via pyproject.toml)","Minimum 512MB RAM for embedding model in memory","SQLAlchemy 2.0+ (for ORM)","SQLite 3.45+ or Cloudflare D1 for storage","Memory objects must include entity type metadata for relationship inference","Access to source and destination storage backends","Sufficient disk space for backups (depends on memory size)","Command-line access to the memory service host"],"failure_modes":["ONNX embeddings are fixed to a single model (typically sentence-transformers); switching models requires re-embedding entire corpus","Sub-5ms latency assumes local SQLite-Vec; remote Cloudflare backend adds network round-trip latency (~50-200ms)","Semantic search quality depends on embedding model choice; no built-in fine-tuning for domain-specific vocabularies","Graph traversal queries add latency (~10-50ms per hop); no built-in query optimization for deep traversals","Relationship inference is autonomous but not user-controllable; no explicit schema validation for relationship types","Scaling to millions of relationships requires careful indexing; no distributed graph database support (SQLite/D1 only)","Backup/restore is offline; no live backup while agents are accessing memory","Incremental backups require tracking change history; no built-in deduplication across backups","Synchronization is one-way (source to destination); no bidirectional merge for conflicting changes","Binary codec is opaque; debugging metadata issues requires decoding tools","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4955902588201383,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:56:59.049Z","last_commit":"2026-05-03T11:59:31Z"},"community":{"stars":1768,"forks":269,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=doobidoo--mcp-memory-service","compare_url":"https://unfragile.ai/compare?artifact=doobidoo--mcp-memory-service"}},"signature":"snZPFShrBCGxkWy1W4jf8gfebFHJ+brqu6ZrQV9dUk9qTAxcrZVQa3KlPVQvcfXGYhgOgbjzEo5cAAybyXN0Aw==","signedAt":"2026-06-21T03:19:46.507Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/doobidoo--mcp-memory-service","artifact":"https://unfragile.ai/doobidoo--mcp-memory-service","verify":"https://unfragile.ai/api/v1/verify?slug=doobidoo--mcp-memory-service","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"}}