{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-mem0ai","slug":"pypi-mem0ai","name":"mem0ai","type":"mcp","url":"https://pypi.org/project/mem0ai/","page_url":"https://unfragile.ai/pypi-mem0ai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-mem0ai__cap_0","uri":"capability://memory.knowledge.multi.provider.memory.persistence.with.abstracted.storage.backends","name":"multi-provider memory persistence with abstracted storage backends","description":"Mem0 provides a pluggable storage abstraction layer that decouples memory data from specific persistence backends, supporting multiple providers (PostgreSQL, Pinecone, Weaviate, Qdrant) through a unified interface. The architecture uses a provider registry pattern where each backend implements standardized CRUD operations, allowing agents to switch storage systems without code changes. Memory records are stored with embeddings, metadata, and temporal versioning to enable semantic retrieval and historical tracking.","intents":["I want my AI agent to remember user interactions across sessions without being locked into a specific database vendor","I need to migrate memory storage from one backend to another without rewriting agent code","I want to support both vector databases and traditional SQL for different use cases in the same application"],"best_for":["teams building multi-tenant AI agents requiring flexible storage strategies","developers prototyping with local storage who later need cloud-scale vector databases","enterprises with existing database infrastructure wanting to integrate agent memory"],"limitations":["Provider implementations vary in consistency guarantees — some backends lack ACID transactions for concurrent memory updates","Switching backends requires data migration; no built-in migration tooling provided","Performance characteristics differ significantly between providers (PostgreSQL ~50ms, Pinecone ~200ms for semantic search)"],"requires":["Python 3.8+","At least one supported backend installed (psycopg2 for PostgreSQL, pinecone-client, weaviate-client, or qdrant-client)","Valid credentials/connection strings for chosen provider"],"input_types":["text (conversation turns, user messages)","structured metadata (user_id, session_id, tags)","embeddings (pre-computed or generated on-the-fly)"],"output_types":["memory records with embeddings","retrieval results ranked by relevance","structured metadata for filtering"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_1","uri":"capability://memory.knowledge.semantic.memory.retrieval.with.hybrid.search","name":"semantic memory retrieval with hybrid search","description":"Mem0 implements semantic search by converting memory queries into embeddings and retrieving similar past interactions using vector similarity, with optional hybrid search combining keyword matching and semantic relevance. The system supports configurable embedding models (OpenAI, Ollama, local models) and ranking strategies to balance precision and recall. Retrieved memories are ranked by relevance score and can be filtered by metadata (user, session, time range) before being returned to the agent.","intents":["I want my agent to find relevant past conversations without exact keyword matching","I need to retrieve memories that are semantically similar to the current user query","I want to combine keyword search with semantic similarity for better recall"],"best_for":["conversational AI agents needing context-aware responses based on history","customer service bots that must reference past interactions semantically","personalization systems requiring nuanced memory matching beyond keyword search"],"limitations":["Embedding quality depends on chosen model; smaller models (384-dim) may miss nuanced semantic relationships","Hybrid search requires maintaining both vector and keyword indexes, increasing storage overhead by 30-50%","Relevance ranking is deterministic but may require tuning of similarity thresholds per use case"],"requires":["Python 3.8+","Embedding model access (OpenAI API key, or local Ollama instance, or HuggingFace model)","Vector-capable backend (Pinecone, Weaviate, Qdrant, or PostgreSQL with pgvector extension)"],"input_types":["text query (user message or agent prompt)","optional metadata filters (user_id, session_id, date range)"],"output_types":["ranked list of memory records with similarity scores","filtered by metadata constraints","formatted for agent context injection"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_2","uri":"capability://memory.knowledge.automatic.memory.consolidation.and.summarization","name":"automatic memory consolidation and summarization","description":"Mem0 implements background memory consolidation that periodically analyzes stored interactions, identifies redundant or outdated memories, and creates summarized versions to reduce storage and retrieval latency. The system uses LLM-based summarization to merge similar memories while preserving key facts, and can be configured with policies for retention (e.g., keep detailed memories for 7 days, then summarize). Consolidation runs asynchronously and maintains version history for audit trails.","intents":["I want my agent's memory to stay efficient as conversation history grows over weeks or months","I need to automatically merge duplicate or similar memories to reduce noise in retrieval","I want to keep detailed recent memories but summarized older ones for cost efficiency"],"best_for":["long-running agents serving individual users over extended periods","cost-sensitive deployments where LLM token usage for memory retrieval is a concern","systems requiring audit trails and version history of memory evolution"],"limitations":["Summarization is lossy — fine-grained details may be lost during consolidation","LLM-based consolidation adds latency (typically 2-5 seconds per consolidation batch) and token costs","Consolidation policies are global; per-user or per-session policies require custom implementation","No built-in mechanism to prevent consolidation of memories that should remain distinct"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or local Ollama for summarization)","Background task runner (optional but recommended for async consolidation)"],"input_types":["stored memory records with timestamps and metadata","consolidation policies (retention periods, similarity thresholds)"],"output_types":["consolidated memory records with reduced token count","version history linking original and summarized memories","consolidation metadata (timestamp, summarization model used)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_3","uri":"capability://memory.knowledge.multi.user.memory.isolation.with.role.based.access.control","name":"multi-user memory isolation with role-based access control","description":"Mem0 provides memory isolation at the user/session level, ensuring that memories from one user cannot be accessed by another without explicit sharing. The system implements role-based access control (RBAC) where agents can be configured with permissions to read, write, or delete memories for specific users or groups. Memory records are tagged with ownership metadata (user_id, organization_id) and queries are automatically scoped to the authenticated user's memories unless cross-user access is explicitly granted.","intents":["I want to ensure user A's conversation history is never visible to user B in a multi-user system","I need to implement admin access to review user memories for moderation or support","I want to allow selective memory sharing between users (e.g., team members sharing context)"],"best_for":["multi-tenant SaaS platforms with AI agents serving multiple customers","enterprise systems requiring data isolation and compliance (GDPR, HIPAA)","collaborative AI systems where selective memory sharing is a feature"],"limitations":["RBAC implementation is basic; no attribute-based access control (ABAC) for complex policies","Memory isolation is enforced at query time, not at storage layer — requires careful query construction","No built-in audit logging for memory access; compliance audits require external logging","Cross-user memory sharing requires manual policy configuration per pair"],"requires":["Python 3.8+","Authentication system providing user_id and role information","Backend supporting metadata-based filtering (all supported providers)"],"input_types":["authenticated user context (user_id, role, organization_id)","memory records with ownership metadata","access control policies (role → permissions mapping)"],"output_types":["scoped memory records (only accessible to authenticated user)","access control decisions (allow/deny)","audit logs (optional, requires external integration)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_4","uri":"capability://memory.knowledge.agent.agnostic.memory.api.with.llm.integration","name":"agent-agnostic memory api with llm integration","description":"Mem0 provides a unified memory API that works with any LLM or agent framework (LangChain, AutoGen, custom agents) through a simple Python interface. The system handles embedding generation, storage, and retrieval transparently, exposing methods like add(), get(), search(), and delete() that abstract away backend complexity. Memory can be automatically injected into agent prompts as context, or manually retrieved and formatted by the agent.","intents":["I want to add memory to my existing agent without rewriting its core logic","I need a simple API to store and retrieve memories that works with any LLM framework","I want to automatically include relevant memories in my agent's system prompt"],"best_for":["developers integrating memory into existing LLM agents with minimal refactoring","teams using multiple agent frameworks (LangChain, AutoGen, custom) who need a unified memory layer","rapid prototyping where memory should be added incrementally without architectural changes"],"limitations":["API is synchronous by default; async support requires manual coroutine wrapping","Memory context injection is manual — no automatic prompt engineering or token budgeting","No built-in integration with specific agent frameworks (e.g., LangChain memory interface); requires adapter code","Memory retrieval is stateless — no conversation-level context tracking without manual session management"],"requires":["Python 3.8+","Mem0 package installed (pip install mem0ai)","Configured storage backend and embedding model","LLM API access (for embedding generation and optional summarization)"],"input_types":["text (messages, interactions to store)","structured metadata (user_id, session_id, tags)","query strings (for retrieval)"],"output_types":["memory IDs (for tracking stored memories)","retrieved memory records with metadata","formatted context strings for prompt injection"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_5","uri":"capability://memory.knowledge.temporal.memory.versioning.and.history.tracking","name":"temporal memory versioning and history tracking","description":"Mem0 maintains a complete version history of memory records, tracking when each memory was created, updated, and accessed. The system stores temporal metadata (timestamps, version numbers) and allows querying memories as they existed at specific points in time. This enables agents to understand how user preferences or facts have evolved, and supports rollback to previous memory states if needed. Version history is queryable through the standard memory API.","intents":["I want to see how a user's preferences have changed over time","I need to understand what my agent knew about a user at a specific past date","I want to rollback a memory update if it was incorrect or corrupted"],"best_for":["long-running personalization systems tracking preference evolution","compliance-heavy applications requiring audit trails of memory changes","debugging scenarios where understanding memory state at a specific time is critical"],"limitations":["Version history storage increases database size by 2-5x depending on update frequency","Querying historical memory states requires explicit time parameters; no automatic temporal reasoning","Rollback is manual — no automatic conflict resolution if newer versions exist","Version history is immutable once created; cannot be edited or deleted for compliance"],"requires":["Python 3.8+","Backend supporting versioning (all supported providers, though PostgreSQL is most efficient)","Sufficient storage for version history (estimate 2-5x base memory size)"],"input_types":["memory records with update operations","optional timestamp parameters for historical queries"],"output_types":["versioned memory records with creation/update timestamps","version history lists showing all changes to a memory","point-in-time memory snapshots"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_6","uri":"capability://memory.knowledge.configurable.embedding.model.selection.with.local.and.cloud.options","name":"configurable embedding model selection with local and cloud options","description":"Mem0 abstracts embedding generation through a pluggable model interface, supporting both cloud-based embeddings (OpenAI, Cohere) and local models (Ollama, HuggingFace). The system allows switching embedding models without reprocessing stored memories, storing model metadata with each embedding for compatibility tracking. Embedding generation is configurable per memory instance, enabling different models for different use cases (e.g., fast local embeddings for real-time retrieval, high-quality cloud embeddings for consolidation).","intents":["I want to use local embeddings to avoid cloud API costs and latency","I need to switch from OpenAI embeddings to a cheaper provider without losing existing memories","I want to use different embedding models for different parts of my application"],"best_for":["cost-conscious teams wanting to minimize API calls with local embeddings","privacy-focused applications requiring on-premise embedding generation","teams experimenting with different embedding models to optimize quality/cost tradeoff"],"limitations":["Local embedding models are slower (100-500ms per embedding vs 50-100ms for cloud APIs)","Embedding quality varies significantly between models; smaller local models may have lower semantic accuracy","Switching embedding models mid-deployment requires re-embedding existing memories for consistency","No built-in mechanism to compare embedding quality across models"],"requires":["Python 3.8+","For cloud embeddings: API key (OpenAI, Cohere, etc.)","For local embeddings: Ollama running locally or HuggingFace model downloaded (~500MB-2GB disk space)"],"input_types":["text to embed (memory content)","embedding model configuration (provider, model name, parameters)"],"output_types":["embedding vectors (384-1536 dimensions depending on model)","model metadata (provider, model name, dimension count)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-mem0ai__cap_7","uri":"capability://memory.knowledge.memory.filtering.and.querying.with.metadata.based.constraints","name":"memory filtering and querying with metadata-based constraints","description":"Mem0 supports complex memory queries combining semantic search with metadata filtering, allowing agents to retrieve memories matching specific criteria (user, session, date range, custom tags). The system implements a filter DSL that works across all backends, translating high-level filter expressions into backend-specific query syntax (SQL WHERE clauses, vector DB metadata filters). Filters can be combined with semantic search to narrow results before ranking by relevance.","intents":["I want to retrieve memories only from a specific conversation session","I need to find memories from the past week that match a semantic query","I want to exclude certain memory types (e.g., system messages) from retrieval"],"best_for":["agents managing multiple concurrent conversations requiring session-scoped memory","systems with complex memory organization (tags, categories, time-based retention)","applications needing fine-grained control over which memories are considered for context"],"limitations":["Filter DSL is basic; complex boolean logic requires manual query construction","Metadata filtering is applied after semantic search in some backends, reducing efficiency","Custom metadata fields require schema definition; no dynamic field support","Filter performance varies by backend (PostgreSQL is fastest, vector DBs may require full scan)"],"requires":["Python 3.8+","Memories tagged with queryable metadata (user_id, session_id, timestamp, custom tags)"],"input_types":["semantic query string","metadata filter expressions (user_id=X, session_id=Y, created_after=Z)","optional ranking parameters"],"output_types":["filtered and ranked memory records","metadata for each result (relevance score, filter match details)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","At least one supported backend installed (psycopg2 for PostgreSQL, pinecone-client, weaviate-client, or qdrant-client)","Valid credentials/connection strings for chosen provider","Embedding model access (OpenAI API key, or local Ollama instance, or HuggingFace model)","Vector-capable backend (Pinecone, Weaviate, Qdrant, or PostgreSQL with pgvector extension)","LLM API access (OpenAI, Anthropic, or local Ollama for summarization)","Background task runner (optional but recommended for async consolidation)","Authentication system providing user_id and role information","Backend supporting metadata-based filtering (all supported providers)","Mem0 package installed (pip install mem0ai)"],"failure_modes":["Provider implementations vary in consistency guarantees — some backends lack ACID transactions for concurrent memory updates","Switching backends requires data migration; no built-in migration tooling provided","Performance characteristics differ significantly between providers (PostgreSQL ~50ms, Pinecone ~200ms for semantic search)","Embedding quality depends on chosen model; smaller models (384-dim) may miss nuanced semantic relationships","Hybrid search requires maintaining both vector and keyword indexes, increasing storage overhead by 30-50%","Relevance ranking is deterministic but may require tuning of similarity thresholds per use case","Summarization is lossy — fine-grained details may be lost during consolidation","LLM-based consolidation adds latency (typically 2-5 seconds per consolidation batch) and token costs","Consolidation policies are global; per-user or per-session policies require custom implementation","No built-in mechanism to prevent consolidation of memories that should remain distinct","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:24.098Z","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=pypi-mem0ai","compare_url":"https://unfragile.ai/compare?artifact=pypi-mem0ai"}},"signature":"r5hhn4ahxRtExy3E4n3gRTv0jM1vtP2zVMFunput07W2JlQ4xhL5hgAD3sQZaLx970BG9l4JljBrUztTnXPoDg==","signedAt":"2026-06-20T18:57:35.676Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-mem0ai","artifact":"https://unfragile.ai/pypi-mem0ai","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-mem0ai","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"}}