{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-jean-memory","slug":"jean-memory","name":"Jean Memory","type":"repo","url":"https://github.com/jonathan-politzki/your-memory","page_url":"https://unfragile.ai/jean-memory","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-jean-memory__cap_0","uri":"capability://memory.knowledge.llm.based.memory.extraction.and.structuring","name":"llm-based memory extraction and structuring","description":"Automatically extracts and structures contextual memories from unstructured user interactions using LLM-powered analysis. The system sends conversation context to configurable LLM providers (OpenAI, Anthropic, Gemini) via a factory pattern, which parse interactions and extract key facts, preferences, and relationships. Extracted memories are then normalized and stored in vector embeddings for semantic retrieval, enabling the system to learn and retain user context across sessions without manual annotation.","intents":["I want my AI agent to automatically learn and remember key facts about users from conversations without explicit tagging","I need to extract structured memories from unstructured chat history and store them for future context","I want to build an AI app that improves its responses over time by remembering user preferences and history"],"best_for":["AI agent builders implementing long-term user context","Teams building conversational AI with memory requirements","Developers creating personalized AI assistants"],"limitations":["LLM extraction quality depends on prompt engineering and model capability — hallucinations possible with low-quality models","Extraction latency adds 500ms-2s per interaction depending on LLM provider and context window size","Requires external LLM API calls, increasing per-interaction costs and introducing rate-limiting constraints","No built-in deduplication of semantically similar memories — requires post-processing or manual curation"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, Google Gemini, or Ollama)","Vector storage backend (Qdrant, Pinecone, Weaviate, or local FAISS)","Embedding model provider (OpenAI, Hugging Face, or local)","Python 3.9+ or Node.js 16+ for client SDKs"],"input_types":["conversation text","user interactions","chat messages","unstructured narrative"],"output_types":["structured memory objects","vector embeddings","memory metadata (timestamps, relevance scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_1","uri":"capability://memory.knowledge.multi.backend.vector.storage.with.semantic.search","name":"multi-backend vector storage with semantic search","description":"Provides unified vector storage abstraction supporting Qdrant, Pinecone, Weaviate, Azure Cognitive Search, Vertex AI Vector Search, and local FAISS via a factory-based provider pattern. Memories are stored as embeddings with metadata, enabling semantic similarity search across stored memories. The system handles embedding generation, vector indexing, and retrieval through a consistent API regardless of underlying storage backend, with configurable distance metrics and filtering.","intents":["I want to store memories in a vector database that supports semantic search without rewriting code if I switch backends","I need to retrieve contextually relevant memories from a large corpus based on semantic similarity to current conversation","I want to use local vector storage (FAISS) for development but scale to managed services (Qdrant, Pinecone) in production"],"best_for":["Teams building memory systems with multi-cloud or hybrid deployment requirements","Developers needing cost-flexible vector storage (local FAISS for dev, managed for prod)","Organizations with existing vector database infrastructure wanting to integrate memory"],"limitations":["Vector store abstraction adds ~50-100ms latency per search due to factory instantiation and network calls","Metadata filtering capabilities vary by backend — complex queries may not be portable across stores","Embedding dimension and distance metric must be consistent across all stored memories — schema migrations are manual","No built-in sharding or partitioning — large-scale deployments require external load balancing"],"requires":["At least one vector store configured (Qdrant, Pinecone, Weaviate, FAISS, Azure, or Vertex AI)","Embedding model provider API key or local model","Python 3.9+ or Node.js 16+","Network access to vector store (or local FAISS for offline mode)"],"input_types":["vector embeddings (float arrays)","memory metadata (JSON objects)","query text (converted to embeddings)"],"output_types":["ranked memory results with similarity scores","memory metadata and content","vector IDs for updates/deletes"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_10","uri":"capability://tool.use.integration.python.and.typescript.client.sdks.with.consistent.apis","name":"python and typescript client sdks with consistent apis","description":"Provides official client libraries for Python (MemoryClient, AsyncMemoryClient) and TypeScript (MemoryClient) with identical APIs, enabling developers to use the same memory operations across language ecosystems. Clients handle authentication, request serialization, error handling, and retry logic transparently. Both SDKs support local and remote memory backends, enabling seamless development-to-production transitions.","intents":["I want to use the same memory API in both my Python backend and TypeScript frontend without learning different interfaces","I need a type-safe client library that catches errors at compile time (TypeScript) or provides IDE autocomplete (Python)","I want to switch between local and remote memory backends without changing my application code"],"best_for":["Full-stack teams using Python and TypeScript","Developers wanting type safety and IDE support","Applications transitioning from local to cloud memory"],"limitations":["Maintaining API parity across languages adds development overhead — features may lag in one language","TypeScript client requires Node.js 16+ — not available for browser-only applications","Python client requires Python 3.9+ — older projects may need upgrades","SDK versioning must be coordinated across languages — version mismatches can cause compatibility issues"],"requires":["Python 3.9+ (for Python SDK) or Node.js 16+ (for TypeScript SDK)","Memory backend configured (local or remote)","API key if using remote backend"],"input_types":["memory objects","configuration objects","query parameters"],"output_types":["memory results","operation confirmations","error objects"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_11","uri":"capability://automation.workflow.self.hosted.deployment.with.docker.and.kubernetes.support","name":"self-hosted deployment with docker and kubernetes support","description":"Provides Docker containerization and Kubernetes manifests for self-hosted deployments of the full Jean Memory stack (backend API, MCP server, frontend UI). Deployment includes environment-based configuration for memory backends, LLM providers, and authentication. Kubernetes support enables horizontal scaling, automatic failover, and resource management for production deployments.","intents":["I want to run Jean Memory on my own infrastructure without relying on a hosted service","I need to deploy memory to Kubernetes for high availability and auto-scaling","I want to keep user data on-premises for compliance or privacy reasons"],"best_for":["Enterprise teams with on-premises requirements","Organizations with strict data residency requirements","Teams wanting full control over infrastructure and scaling"],"limitations":["Self-hosting adds operational overhead — requires DevOps expertise for production deployments","Kubernetes complexity increases with cluster size — small deployments may be over-engineered","Resource requirements vary by workload — memory backends and LLM calls can be expensive","Security hardening is user's responsibility — misconfiguration can expose sensitive data"],"requires":["Docker 20.10+ or container runtime","Kubernetes 1.20+ (for K8s deployments) or Docker Compose for single-machine","Memory backend infrastructure (Qdrant, Postgres, etc.)","LLM provider API keys","TLS certificates for HTTPS"],"input_types":["Docker Compose files","Kubernetes manifests","environment variables","configuration files"],"output_types":["running containers","Kubernetes pods","service endpoints","logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_12","uri":"capability://memory.knowledge.conversation.memory.context.injection.for.ai.responses","name":"conversation memory context injection for ai responses","description":"Automatically retrieves relevant memories from the vector store based on current conversation context and injects them into the LLM prompt before generating responses. The system performs semantic search on the query, ranks results by relevance, and formats memories as context blocks in the system prompt. This enables AI models to provide personalized, contextually-aware responses without explicit memory management by the application.","intents":["I want my AI to automatically remember and reference past conversations without me explicitly loading memories","I need the AI to provide personalized responses based on learned user preferences and history","I want to improve response quality by giving the AI relevant context from past interactions"],"best_for":["Conversational AI applications requiring personalization","Chatbots that need to maintain context across sessions","Customer service AI that should remember customer history"],"limitations":["Memory injection adds 200-500ms latency per response due to vector search and ranking","Injected memories consume LLM context tokens — reduces space for user input and response","Irrelevant memories can degrade response quality — requires careful ranking and filtering","Memory injection is non-deterministic — same query may retrieve different memories depending on vector store state"],"requires":["Vector store with indexed memories","Embedding model for query encoding","LLM provider for response generation","Memory ranking/filtering logic"],"input_types":["user query/message","conversation history","user/session ID"],"output_types":["formatted memory context","ranked memory results","LLM prompt with injected memories"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_13","uri":"capability://data.processing.analysis.memory.deduplication.and.consolidation","name":"memory deduplication and consolidation","description":"Identifies semantically similar or duplicate memories using vector similarity and LLM-powered comparison, then consolidates them into single authoritative memories. The system runs periodic deduplication jobs that cluster similar memories, merge metadata, and update relationships. This prevents memory bloat from repeated extraction of the same facts and improves retrieval efficiency.","intents":["I want to prevent my memory system from storing duplicate facts extracted from multiple conversations","I need to consolidate similar memories to reduce storage and improve search performance","I want to merge conflicting memories into a single authoritative version"],"best_for":["Long-running AI systems with high conversation volume","Applications where memory accuracy is critical","Cost-conscious deployments wanting to minimize storage"],"limitations":["Deduplication adds computational overhead — requires periodic batch jobs or background processing","Similarity thresholds are tunable but imperfect — may merge distinct memories or miss duplicates","Consolidation can lose nuance — merging 'user likes coffee' and 'user prefers tea' loses preference specificity","Deduplication is irreversible — merged memories cannot be split without audit trail"],"requires":["Vector store with similarity search","LLM provider for semantic comparison","Batch processing infrastructure (Celery, Airflow, etc.)","Configurable similarity thresholds"],"input_types":["memory corpus","similarity threshold","consolidation rules"],"output_types":["deduplicated memory set","merge operations log","consolidated memory objects"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_2","uri":"capability://automation.workflow.async.first.memory.operations.with.batch.processing","name":"async-first memory operations with batch processing","description":"Provides AsyncMemoryClient for non-blocking memory operations and batch APIs for bulk memory creation, updates, and deletion. The system uses Python asyncio patterns to handle concurrent memory operations without blocking, enabling high-throughput scenarios. Batch endpoints accept arrays of memory objects and process them transactionally, reducing API overhead and enabling efficient bulk imports or synchronization across multiple AI agents.","intents":["I want to add memories to my AI system without blocking the main conversation thread","I need to bulk import historical conversation data into memory storage efficiently","I want to update or delete multiple memories in a single API call to reduce latency"],"best_for":["High-throughput AI applications handling multiple concurrent conversations","Data migration and bulk import scenarios","Multi-agent systems requiring synchronized memory updates"],"limitations":["Async operations require Python 3.7+ with asyncio event loop — not available in synchronous-only environments","Batch operations have size limits (typically 100-1000 items per request) — very large imports require pagination","Error handling in batch operations is all-or-nothing by default — partial failures require custom retry logic","Async debugging is more complex than synchronous code, increasing development friction"],"requires":["Python 3.7+ with asyncio support","AsyncMemoryClient initialization with async context manager","Event loop running in application (FastAPI, aiohttp, or custom asyncio loop)"],"input_types":["memory objects (JSON/dict)","arrays of memories for batch operations","memory IDs for batch updates/deletes"],"output_types":["async operation results","batch operation status","memory IDs and timestamps"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_3","uri":"capability://memory.knowledge.graph.based.memory.relationships.and.reasoning","name":"graph-based memory relationships and reasoning","description":"Implements MemoryGraph class that models memories as nodes in a knowledge graph with edges representing relationships (e.g., 'user prefers X', 'X is related to Y'). The system uses LLM-powered reasoning to infer relationships between extracted memories and stores them as graph edges, enabling multi-hop reasoning and contextual memory retrieval. Graph traversal can retrieve not just direct memories but related context, improving response relevance by understanding memory relationships.","intents":["I want my AI to understand relationships between different memories and use that context for better responses","I need to perform multi-hop reasoning across memories (e.g., 'user likes coffee' + 'coffee is a beverage' → recommend beverages)","I want to visualize and understand the knowledge graph of user preferences and relationships"],"best_for":["AI systems requiring deep contextual reasoning across multiple memory domains","Knowledge management applications with complex entity relationships","Personalization engines needing to infer user preferences from relationship graphs"],"limitations":["Graph relationship inference adds 1-3s latency per memory extraction due to LLM reasoning","Graph storage backends (Neo4j, etc.) add operational complexity and cost","Relationship inference quality depends on LLM capability — may miss subtle or domain-specific relationships","Graph traversal can explode in complexity with large graphs — deep multi-hop queries become expensive"],"requires":["Graph storage backend (Neo4j, or SQL-based graph representation)","LLM provider for relationship inference","Vector storage for semantic memory lookup","Python 3.9+"],"input_types":["extracted memories","relationship inference prompts","graph traversal queries"],"output_types":["graph edges (relationships)","multi-hop memory results","relationship metadata (confidence scores, types)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_4","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.for.ai.tool.integration","name":"mcp (model context protocol) server for ai tool integration","description":"Implements a Model Context Protocol server that exposes memory operations as tools callable by Claude and other MCP-compatible AI models. The server provides standardized tool definitions for memory add, retrieve, update, and delete operations, allowing AI agents to autonomously manage their own memory without explicit API calls. Tools are discoverable via MCP protocol, enabling seamless integration with Claude's tool-use capabilities and other MCP clients.","intents":["I want Claude to automatically save important facts from conversations to its memory without me managing the API","I need to expose memory operations as discoverable tools that any MCP-compatible AI can use","I want to build an AI agent that can autonomously manage its own memory as part of its reasoning process"],"best_for":["Claude users wanting to add persistent memory to conversations","Teams building MCP-compatible AI agents with memory requirements","Developers creating AI systems where agents manage their own context"],"limitations":["MCP protocol overhead adds ~100-200ms per tool invocation compared to direct API calls","Tool discovery and schema validation happens at runtime — complex memory operations may exceed MCP message size limits","Claude's tool-use capability is non-deterministic — agent may not always choose to use memory tools","Requires MCP client support — not all AI models or platforms support MCP yet"],"requires":["MCP server running (Python implementation provided)","MCP-compatible AI client (Claude, or other MCP implementations)","Memory backend configured (vector store, LLM provider)","Network connectivity between MCP client and server"],"input_types":["MCP tool calls with memory operations","tool arguments (memory content, queries, IDs)","MCP protocol messages"],"output_types":["MCP tool results","memory operation confirmations","retrieved memory content"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_5","uri":"capability://tool.use.integration.multi.llm.provider.abstraction.with.configurable.prompts","name":"multi-llm provider abstraction with configurable prompts","description":"Implements LlmFactory that abstracts LLM provider selection (OpenAI, Anthropic, Google Gemini, Ollama) through a unified configuration interface. Each provider is wrapped with consistent method signatures for memory extraction and relationship inference, while supporting provider-specific optimizations (e.g., token counting for OpenAI, streaming for Anthropic). Prompts are configurable via YAML/JSON, enabling fine-tuning of memory extraction behavior without code changes.","intents":["I want to switch between different LLM providers (OpenAI, Anthropic, Gemini) without rewriting my memory extraction code","I need to optimize costs by using cheaper models for some operations and premium models for others","I want to customize the prompts used for memory extraction to match my domain-specific requirements"],"best_for":["Teams evaluating multiple LLM providers for memory extraction","Cost-conscious builders wanting to mix cheap and premium models","Domain-specific applications requiring custom extraction prompts"],"limitations":["Provider abstraction hides model-specific capabilities — advanced features (vision, function calling) may not be portable","Prompt quality varies significantly across models — same prompt may produce different extraction results","Token counting and rate limiting are provider-specific — cost optimization requires manual tuning per provider","Fallback logic for provider failures must be implemented by user"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Google, or local Ollama)","Configuration file specifying LLM provider and model","Prompt templates (default provided, customizable)","Python 3.9+"],"input_types":["LLM provider configuration","prompt templates (text)","conversation context","extraction instructions"],"output_types":["extracted memories","LLM reasoning traces","token usage metrics"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_6","uri":"capability://tool.use.integration.rest.api.with.authentication.and.rate.limiting","name":"rest api with authentication and rate limiting","description":"Provides a FastAPI-based REST API exposing memory operations (add, retrieve, update, delete, search) with JWT-based authentication and configurable rate limiting. The API supports both synchronous and asynchronous endpoints, request validation via Pydantic schemas, and OpenAPI documentation. Rate limiting is enforced per API key, preventing abuse and enabling fair-use policies for multi-tenant deployments.","intents":["I want to expose memory operations as a REST API that my frontend and other services can call","I need to authenticate API requests and track usage per user or application","I want to prevent abuse by rate-limiting memory operations per API key"],"best_for":["Web application backends integrating memory into user-facing features","Multi-tenant SaaS platforms offering memory as a service","Teams building microservices that need to share memory across services"],"limitations":["REST API adds network latency (50-200ms) compared to in-process library calls","Rate limiting is per-key, not per-user — shared keys bypass rate limits","JWT tokens require secure storage and rotation — token leakage enables unauthorized access","OpenAPI schema may expose sensitive information about memory structure"],"requires":["FastAPI server running (Python 3.9+)","JWT secret key for authentication","Memory backend configured","HTTPS for production (TLS certificate)"],"input_types":["JSON request bodies","URL parameters","JWT tokens in Authorization header","query strings for search/filter"],"output_types":["JSON responses","HTTP status codes","OpenAPI schema","rate limit headers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_7","uri":"capability://memory.knowledge.memory.versioning.and.audit.trail","name":"memory versioning and audit trail","description":"Tracks all memory modifications (create, update, delete) with timestamps, user IDs, and change diffs, enabling full audit trails and version history. Each memory object maintains a version number and linked list of previous versions, allowing rollback to prior states. Audit logs are immutable and queryable, supporting compliance requirements and debugging memory-related issues.","intents":["I need to track who modified which memories and when for compliance and debugging","I want to rollback a memory to a previous version if extraction was incorrect","I need to understand how a user's profile evolved over time based on memory changes"],"best_for":["Regulated industries requiring audit trails (healthcare, finance)","Teams debugging memory extraction issues","Applications where memory accuracy is critical"],"limitations":["Version history storage increases database size by 2-5x depending on update frequency","Rollback operations require careful handling to avoid breaking dependent memories or relationships","Audit log queries can be slow on large datasets — requires indexing on timestamp and user_id","Immutable audit logs prevent deletion of sensitive data — requires separate PII redaction logic"],"requires":["Persistent storage backend supporting versioning (SQL database or document store)","User authentication to track who made changes","Timestamp synchronization across distributed systems"],"input_types":["memory objects","modification operations (create/update/delete)","user context"],"output_types":["versioned memory objects","audit log entries","change diffs","version history"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_8","uri":"capability://data.processing.analysis.embedding.model.provider.abstraction","name":"embedding model provider abstraction","description":"Provides EmbedderFactory supporting multiple embedding providers (OpenAI, Hugging Face, Azure OpenAI, Vertex AI, local models) with unified embedding generation interface. Each provider is wrapped with consistent method signatures for converting text to vector embeddings, while supporting provider-specific features (batch processing, dimension selection, model variants). Embeddings are cached to reduce redundant API calls and improve performance.","intents":["I want to generate embeddings for memories using different providers without changing my code","I need to optimize embedding costs by using cheaper local models for development and premium APIs for production","I want to cache embeddings to avoid redundant API calls and reduce latency"],"best_for":["Teams evaluating embedding providers for cost and quality","Developers building hybrid systems with local and cloud embeddings","High-throughput applications where embedding caching is critical"],"limitations":["Embedding dimension varies by provider (OpenAI: 1536, Hugging Face: 384-1024) — dimension mismatch breaks vector search","Embedding quality varies significantly — cheaper models may produce poor semantic representations","Caching adds memory overhead (1-10GB for large memory stores) — requires external cache store for distributed systems","Provider API rate limits constrain embedding throughput — batch processing required for bulk operations"],"requires":["At least one embedding provider configured (OpenAI, Hugging Face, Azure, Vertex AI, or local)","API keys for cloud providers","Memory for embedding cache (local) or cache backend (Redis, etc.)","Python 3.9+"],"input_types":["text to embed","batch of texts","embedding provider configuration"],"output_types":["vector embeddings (float arrays)","embedding metadata (model, dimension)","cache hit/miss indicators"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-jean-memory__cap_9","uri":"capability://automation.workflow.web.ui.for.memory.management.and.visualization","name":"web ui for memory management and visualization","description":"Provides a React-based web interface for viewing, searching, editing, and deleting memories with real-time updates. The UI displays memory content, metadata (timestamps, relevance scores), and relationships in a searchable, filterable interface. Users can manually edit memories, view extraction history, and visualize the knowledge graph of relationships. The interface integrates with the backend API and supports both local and cloud deployments.","intents":["I want to see what memories my AI system has learned about me and verify they're accurate","I need to manually edit or delete memories that were extracted incorrectly","I want to visualize the relationships between my memories to understand what the AI knows about me"],"best_for":["End users wanting transparency into AI memory","Developers debugging memory extraction issues","Teams managing shared memory systems with multiple users"],"limitations":["Web UI adds deployment complexity (frontend hosting, CORS, authentication)","Real-time updates require WebSocket or polling — adds server load","Graph visualization can be slow with >1000 nodes — requires optimization or pagination","Manual memory editing bypasses extraction validation — users can introduce inconsistencies"],"requires":["React 18+ or compatible framework","Backend API running and accessible","Authentication system (JWT, OAuth, etc.)","Node.js 16+ for development"],"input_types":["memory search queries","memory edit forms","filter/sort parameters"],"output_types":["rendered memory list","graph visualization","memory detail views","edit confirmations"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API key for at least one LLM provider (OpenAI, Anthropic, Google Gemini, or Ollama)","Vector storage backend (Qdrant, Pinecone, Weaviate, or local FAISS)","Embedding model provider (OpenAI, Hugging Face, or local)","Python 3.9+ or Node.js 16+ for client SDKs","At least one vector store configured (Qdrant, Pinecone, Weaviate, FAISS, Azure, or Vertex AI)","Embedding model provider API key or local model","Python 3.9+ or Node.js 16+","Network access to vector store (or local FAISS for offline mode)","Python 3.9+ (for Python SDK) or Node.js 16+ (for TypeScript SDK)","Memory backend configured (local or remote)"],"failure_modes":["LLM extraction quality depends on prompt engineering and model capability — hallucinations possible with low-quality models","Extraction latency adds 500ms-2s per interaction depending on LLM provider and context window size","Requires external LLM API calls, increasing per-interaction costs and introducing rate-limiting constraints","No built-in deduplication of semantically similar memories — requires post-processing or manual curation","Vector store abstraction adds ~50-100ms latency per search due to factory instantiation and network calls","Metadata filtering capabilities vary by backend — complex queries may not be portable across stores","Embedding dimension and distance metric must be consistent across all stored memories — schema migrations are manual","No built-in sharding or partitioning — large-scale deployments require external load balancing","Maintaining API parity across languages adds development overhead — features may lag in one language","TypeScript client requires Node.js 16+ — not available for browser-only applications","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jean-memory","compare_url":"https://unfragile.ai/compare?artifact=jean-memory"}},"signature":"pNqI5Nx2kRj0Af7R8+7eipmsf31kWpLfD0zvdvFoCfdywFnQurHgqLmbd2E7oE4Xc8sQO+QY7szxQbWblEbYCg==","signedAt":"2026-06-21T11:44:21.161Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jean-memory","artifact":"https://unfragile.ai/jean-memory","verify":"https://unfragile.ai/api/v1/verify?slug=jean-memory","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"}}