{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mem0ai--mem0","slug":"mem0ai--mem0","name":"mem0","type":"agent","url":"https://mem0.ai","page_url":"https://unfragile.ai/mem0ai--mem0","categories":["ai-agents"],"tags":["agents","ai","ai-agents","application","chatbots","chatgpt","genai","llm","long-term-memory","memory","memory-management","python","rag","state-management"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mem0ai--mem0__cap_0","uri":"capability://memory.knowledge.multi.scope.persistent.memory.storage.with.llm.powered.fact.extraction","name":"multi-scope persistent memory storage with llm-powered fact extraction","description":"Stores conversational history, user preferences, and domain knowledge across user, agent, and session scopes using LLM-powered fact extraction to intelligently decompose unstructured text into queryable memory units. The system uses a configurable LLM (18+ providers via LlmFactory) to parse incoming text, extract semantic facts, and automatically determine memory relevance and structure before persisting to vector or graph stores. This approach eliminates manual memory management and enables context-aware retrieval without explicit tagging.","intents":["I want my AI agent to remember user preferences and conversation history across sessions without manual state management","I need to extract structured facts from unstructured conversational text and store them for later retrieval","I want to scope memory by user, agent, or session so different contexts don't pollute each other"],"best_for":["AI agent developers building multi-turn conversational systems","Teams building personalized chatbots that need long-term user context","Developers migrating from stateless LLM APIs to stateful agent architectures"],"limitations":["LLM-based fact extraction adds latency (typically 500ms-2s per memory add operation depending on LLM provider)","Extraction quality depends on LLM capability — weaker models may miss nuanced facts or over-extract noise","No built-in deduplication across similar facts — requires custom post-processing or similarity thresholds to avoid redundant storage","Memory scoping is logical only — requires application code to enforce scope boundaries, no database-level isolation"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Vector store backend (Pinecone, Weaviate, Qdrant, Milvus, etc.) or local SQLite for OSS mode","Embedding model provider (OpenAI, Hugging Face, Ollama, etc.)"],"input_types":["text (conversational messages, user statements)","structured metadata (user_id, session_id, agent_id for scoping)"],"output_types":["persisted memory records with embeddings","memory IDs for update/delete operations","metadata including extraction timestamp and LLM model used"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_1","uri":"capability://search.retrieval.semantic.memory.search.with.vector.and.graph.based.retrieval","name":"semantic memory search with vector and graph-based retrieval","description":"Retrieves stored memories using semantic similarity search across vector stores (24+ providers via VectorStoreFactory) and optionally augments results with graph-based entity and relationship queries. The system embeds user queries using the same embedding model as stored memories, performs vector similarity search with configurable thresholds, and can optionally traverse knowledge graphs to find related entities and relationships. Results are ranked and filtered by relevance, recency, and custom metadata filters.","intents":["I want to retrieve relevant memories from a user's history based on semantic similarity to their current query","I need to find memories related to specific entities or relationships in a knowledge graph","I want to combine vector similarity with graph traversal to find both direct and indirect memory connections"],"best_for":["Developers building context-aware chatbots that need semantic search over memory","Teams using graph databases to model domain knowledge and entity relationships","Applications requiring hybrid search (vector + graph) for complex memory queries"],"limitations":["Vector search quality depends on embedding model — weak embeddings produce poor semantic matching","Graph search requires pre-extracted entities and relationships — unstructured text alone won't populate the graph","Similarity threshold tuning is manual and domain-specific — no automatic threshold optimization","Cross-store consistency not guaranteed — vector and graph stores may diverge if updates fail partially"],"requires":["Vector store backend (Pinecone, Weaviate, Qdrant, Milvus, Chroma, etc.)","Embedding model provider (OpenAI, Hugging Face, Ollama, etc.)","Optional: Graph store backend (Neo4j, ArangoDB, etc.) for graph-based retrieval","Memories must be pre-stored via add() operation before search is possible"],"input_types":["text query (user question or context snippet)","optional metadata filters (user_id, session_id, time range)","optional graph traversal parameters (entity types, relationship types)"],"output_types":["ranked list of memory records with similarity scores","optional graph traversal results (related entities, relationship paths)","metadata including memory source, extraction timestamp, relevance score"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_10","uri":"capability://memory.knowledge.self.hosted.oss.deployment.with.local.configuration.and.no.cloud.dependencies","name":"self-hosted oss deployment with local configuration and no cloud dependencies","description":"Provides open-source Memory class for self-hosted deployments where developers manage their own vector stores, LLM providers, and graph stores. Configuration is specified via YAML or Python dict, and the system instantiates all components locally using factory patterns. No cloud dependencies or API calls to Mem0 servers — all processing happens on-premise. Supports both sync (Memory) and async (AsyncMemory) variants.","intents":["I want to run Mem0 entirely on-premise without cloud dependencies","I need to use my own LLM provider (local Ollama, on-premise Anthropic) and vector store","I want to avoid vendor lock-in and maintain full control over memory data"],"best_for":["Organizations with data residency or security requirements (healthcare, finance)","Teams with existing infrastructure (Kubernetes, on-premise databases)","Developers building cost-sensitive applications (using free/open-source components)"],"limitations":["Infrastructure management burden — developers must set up and maintain vector stores, LLM providers, graph stores","No managed multi-tenancy — developers must implement their own tenant isolation","Scaling is manual — no auto-scaling like cloud platforms","No built-in monitoring, logging, or alerting — developers must add observability","Configuration complexity — many moving parts (LLM, embedder, vector store, graph store) to configure"],"requires":["Python 3.9+","Vector store backend (Qdrant, Milvus, Chroma, etc.) running locally or on-premise","LLM provider (Ollama, vLLM, on-premise Anthropic, etc.)","Embedding model (Ollama, Hugging Face, etc.)","Optional: Graph store (Neo4j, ArangoDB, etc.)","YAML configuration file or Python dict specifying all components"],"input_types":["configuration (YAML or Python dict with provider names, endpoints, credentials)","same memory operations as hosted platform (add, search, update, delete)"],"output_types":["same as hosted platform (memory records, search results)","local logs and metrics (no cloud observability)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_11","uri":"capability://memory.knowledge.batch.memory.operations.with.concurrent.processing","name":"batch memory operations with concurrent processing","description":"Supports batch operations (add multiple memories, search multiple queries, update multiple records) with concurrent processing to improve throughput. Batch operations are submitted as lists and processed in parallel using async concurrency or thread pools, reducing total execution time compared to sequential operations. Useful for bulk imports, batch indexing, and high-throughput scenarios.","intents":["I want to import a large number of memories (e.g., conversation history) efficiently","I need to search for multiple queries concurrently without waiting for each to complete","I want to update many memories in parallel to reduce total execution time"],"best_for":["Bulk data imports (migrating from other systems, importing conversation history)","High-throughput applications with many concurrent memory operations","Batch processing pipelines (nightly indexing, periodic memory consolidation)"],"limitations":["Batch operations may hit rate limits on LLM providers or vector stores","Error handling in batches is complex — partial failures require retry logic","Memory usage scales with batch size — very large batches may exhaust memory","Ordering is not guaranteed — batch results may be returned out of order","No built-in progress tracking or cancellation — developers must implement these"],"requires":["Async support (AsyncMemory) or thread pool (Memory with threading)","Vector store and LLM provider with sufficient rate limits for concurrent requests","Error handling code to manage partial batch failures"],"input_types":["list of memory records (for batch add)","list of queries (for batch search)","list of memory IDs and updates (for batch update)"],"output_types":["list of results (one per input item)","optional: error list for failed items"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_12","uri":"capability://automation.workflow.telemetry.analytics.and.performance.monitoring","name":"telemetry, analytics, and performance monitoring","description":"Provides built-in telemetry and analytics tracking memory operations (add, search, update, delete) with metrics like latency, token usage, cost, and error rates. Metrics are collected and can be exported to monitoring systems (Datadog, New Relic, etc.) or analyzed locally. Enables performance optimization by identifying bottlenecks (slow LLM calls, slow vector store queries, etc.) and cost tracking by monitoring token usage and API calls.","intents":["I want to monitor memory operation latency and identify performance bottlenecks","I need to track token usage and costs across different LLM providers","I want to analyze memory system behavior (e.g., which memories are accessed most frequently)"],"best_for":["Production applications requiring observability and performance monitoring","Teams optimizing cost and latency of memory operations","Developers debugging memory system issues"],"limitations":["Telemetry collection adds overhead (typically 5-10% latency increase)","Token counting is approximate — actual costs may vary by provider","No built-in alerting — developers must set up alerts in external monitoring systems","Telemetry data is not persisted by default — requires external storage","Privacy considerations — telemetry may include sensitive memory content"],"requires":["Optional: monitoring system (Datadog, New Relic, Prometheus, etc.) for exporting metrics","Configuration to enable telemetry collection"],"input_types":["memory operations (add, search, update, delete)","optional: custom metrics or tags"],"output_types":["metrics (latency, token usage, cost, error rate)","logs (operation details, errors)","optional: exported metrics to monitoring system"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_13","uri":"capability://memory.knowledge.custom.prompt.templates.for.memory.extraction.and.reasoning","name":"custom prompt templates for memory extraction and reasoning","description":"Allows developers to customize LLM prompts used for fact extraction, entity extraction, relationship extraction, and deduplication reasoning. Custom prompts enable domain-specific memory processing — e.g., extracting medical facts differently than customer support facts. Prompts are specified in configuration and can include variables (e.g., {{memory_content}}, {{entity_types}}) that are substituted at runtime.","intents":["I want to customize how facts are extracted from text for my specific domain","I need to extract domain-specific entities (medical terms, product names) that generic extraction misses","I want to control deduplication logic to handle domain-specific equivalence rules"],"best_for":["Domain-specific applications (healthcare, finance, legal) with specialized memory requirements","Teams optimizing memory quality for their use case","Developers fine-tuning memory behavior without code changes"],"limitations":["Prompt engineering is an art — custom prompts require iteration and testing","LLM quality still depends on model capability — weak models won't follow complex prompts","No built-in prompt validation — bad prompts may silently produce poor results","Prompt changes require re-processing existing memories for consistency","No A/B testing framework — comparing prompt versions requires manual setup"],"requires":["Configuration file or code to specify custom prompts","Understanding of prompt engineering best practices","LLM provider with sufficient capability to follow custom instructions"],"input_types":["custom prompt templates (text with variable placeholders)","optional: prompt variables (memory_content, entity_types, etc.)"],"output_types":["LLM responses following custom prompt instructions","extracted facts, entities, or relationships in custom format"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_14","uri":"capability://memory.knowledge.history.and.audit.trails.for.memory.mutations","name":"history and audit trails for memory mutations","description":"Maintains complete history of memory mutations (add, update, delete) with timestamps, user information, and change details. Enables auditing, debugging, and rollback of memory changes. History is stored in a dedicated backend (database, file system) and can be queried to understand how memories evolved over time. Useful for compliance, debugging, and understanding memory system behavior.","intents":["I want to audit who changed a memory and when for compliance purposes","I need to debug memory issues by reviewing the history of changes","I want to rollback a memory to a previous version if a bad update occurred"],"best_for":["Regulated industries (healthcare, finance) requiring audit trails","Applications with strict data governance requirements","Developers debugging memory system issues"],"limitations":["History storage adds overhead — every mutation creates a history entry","History queries may be slow for memories with many mutations","No automatic retention policy — history grows indefinitely without cleanup","Rollback is manual — no built-in rollback API","History doesn't include reasoning — why a memory was changed is not recorded"],"requires":["History backend (database, file system, or cloud storage)","Configuration to enable history tracking","Optional: retention policy to clean up old history"],"input_types":["memory mutations (add, update, delete operations)","optional: user information for audit trail"],"output_types":["history entries (timestamp, operation, user, change details)","history queries (list of changes for a memory)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_15","uri":"capability://tool.use.integration.framework.integrations.with.agent.frameworks.and.vercel.ai.sdk","name":"framework integrations with agent frameworks and vercel ai sdk","description":"Provides native integrations with popular agent frameworks (LangChain, LlamaIndex, OpenClaw) and the Vercel AI SDK, enabling seamless memory integration into existing agent systems. Integrations handle memory context injection, automatic memory updates from agent interactions, and framework-specific optimizations. Developers can use Mem0 as a drop-in memory layer without rewriting agent code.","intents":["I want to add persistent memory to my LangChain or LlamaIndex agent without rewriting code","I need to integrate Mem0 with Vercel AI SDK for my Next.js application","I want automatic memory updates from agent interactions without manual memory management"],"best_for":["Developers using LangChain, LlamaIndex, or Vercel AI SDK who want to add memory","Teams migrating existing agents to use persistent memory","Rapid prototyping with popular agent frameworks"],"limitations":["Framework integrations may lag behind framework updates — compatibility issues possible","Integration abstractions add complexity — debugging requires understanding both Mem0 and the framework","Not all framework features are supported — some advanced features may not integrate","Performance overhead from integration layer — typically 10-20% latency increase","Limited customization — integration may not support all Mem0 features"],"requires":["Specific agent framework (LangChain, LlamaIndex, OpenClaw) or Vercel AI SDK","Mem0 (hosted or self-hosted)","Framework-specific configuration"],"input_types":["agent interactions (messages, tool calls, responses)","framework-specific context"],"output_types":["memory-augmented agent responses","automatic memory updates"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_16","uri":"capability://search.retrieval.reranking.and.relevance.scoring.for.search.results","name":"reranking and relevance scoring for search results","description":"Applies reranking algorithms to search results to improve relevance beyond vector similarity. Reranking uses LLM-based scoring (e.g., cross-encoder models) or statistical methods to re-score vector search results, promoting more relevant memories to the top. Useful when vector similarity alone produces poor ranking or when combining multiple search strategies (vector + graph).","intents":["I want to improve search result relevance beyond vector similarity","I need to combine vector and graph search results with intelligent ranking","I want to use LLM-based reranking to understand semantic relevance"],"best_for":["Applications where vector similarity alone produces poor ranking","Hybrid search systems combining vector and graph results","High-quality memory retrieval where relevance is critical"],"limitations":["Reranking adds latency (typically 200-500ms per search) due to LLM calls","Reranking quality depends on LLM capability — weak models may not improve ranking","No built-in reranking strategy selection — developers must choose algorithm","Reranking is applied post-search — cannot reduce initial search result set","Cost increases with reranking — additional LLM calls add up"],"requires":["LLM provider for reranking (OpenAI, Anthropic, Cohere, etc.)","Initial search results from vector or graph store","Configuration specifying reranking strategy"],"input_types":["search results (list of memories with similarity scores)","original query (for context-aware reranking)"],"output_types":["reranked results (same memories, different order)","reranking scores (confidence in new ranking)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_2","uri":"capability://memory.knowledge.intelligent.memory.update.and.consolidation.with.llm.driven.deduplication","name":"intelligent memory update and consolidation with llm-driven deduplication","description":"Updates existing memories by detecting semantic duplicates or related memories, merging conflicting information, and consolidating redundant facts using LLM-powered reasoning. When a new fact is added, the system searches for similar existing memories, uses an LLM to determine if they should be merged or updated, and performs the consolidation while preserving history. This prevents memory bloat and ensures the memory store remains coherent and non-redundant.","intents":["I want to update a memory when new information about a user arrives, without creating duplicate entries","I need to consolidate conflicting information in memory (e.g., user changed their preference)","I want to prevent memory bloat by automatically merging similar facts"],"best_for":["Long-running agents that accumulate memories over weeks or months","Applications with high-frequency memory updates from the same user","Teams building memory systems that must remain coherent and non-redundant"],"limitations":["LLM-based deduplication adds significant latency (1-3s per update) due to similarity search + LLM reasoning","Merge decisions are LLM-dependent — weaker models may incorrectly merge unrelated facts or fail to merge true duplicates","History tracking adds storage overhead — all previous versions are retained, requiring periodic cleanup","No automatic conflict resolution strategy — contradictory facts may be stored with conflicting values"],"requires":["Existing memories in the store (from prior add() operations)","LLM provider for deduplication reasoning (OpenAI, Anthropic, Ollama, etc.)","Vector store with similarity search capability","Optional: History/audit trail backend for tracking memory mutations"],"input_types":["text (new fact or updated information)","memory_id (optional, to explicitly update a specific memory)","metadata (user_id, session_id, timestamp)"],"output_types":["updated memory record with new content and timestamp","merge/consolidation metadata (which memories were merged, deduplication score)","history entry documenting the change"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_3","uri":"capability://memory.knowledge.graph.based.entity.and.relationship.extraction.with.configurable.similarity.thresholds","name":"graph-based entity and relationship extraction with configurable similarity thresholds","description":"Automatically extracts entities and relationships from stored memories and builds a knowledge graph using LLM-powered extraction and configurable graph store backends (Neo4j, ArangoDB, etc.). The system identifies named entities (people, organizations, concepts) and their relationships, stores them in a graph database, and enables traversal-based queries. Similarity thresholds control entity deduplication — entities with names or embeddings above the threshold are merged into single nodes, preventing fragmentation.","intents":["I want to build a knowledge graph of entities and relationships from conversational memories","I need to query memories by entity (e.g., 'all memories about Alice') rather than just semantic similarity","I want to find indirect connections between memories through shared entities or relationships"],"best_for":["Developers building knowledge-intensive agents (customer support, research assistants, domain experts)","Teams needing structured entity-relationship queries on top of unstructured memories","Applications requiring graph-based reasoning over memory (e.g., social networks, organizational hierarchies)"],"limitations":["Entity extraction quality depends on LLM capability — weak models may miss entities or extract noise","Similarity threshold tuning is manual and domain-specific — no automatic optimization","Graph store consistency requires careful transaction handling — partial failures can leave graph in inconsistent state","Entity deduplication is one-way — merging entities in the graph doesn't automatically update vector store references","Relationship extraction is limited to what the LLM can infer from text — implicit relationships may be missed"],"requires":["Graph store backend (Neo4j, ArangoDB, or other supported provider)","LLM provider for entity and relationship extraction","Embedding model for entity similarity matching (optional, for semantic deduplication)","Memories must be pre-stored before graph extraction"],"input_types":["text (conversational memories or documents)","optional entity type hints (person, organization, concept, etc.)","similarity threshold configuration (0.0-1.0)"],"output_types":["graph nodes (entities with properties: name, type, embedding, metadata)","graph edges (relationships with properties: type, confidence, source memory_id)","graph traversal results (paths, connected components, entity neighborhoods)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_4","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.configurable.model.selection.and.fallback","name":"multi-provider llm integration with configurable model selection and fallback","description":"Abstracts LLM provider selection through a factory pattern (LlmFactory) supporting 18+ providers (OpenAI, Anthropic, Ollama, Cohere, Groq, etc.) with unified configuration and fallback logic. Developers specify an LLM provider in config, and the system instantiates the appropriate client, handles authentication, manages rate limits, and supports fallback to alternative providers if the primary fails. This enables memory operations (fact extraction, deduplication reasoning, entity extraction) to work with any LLM without code changes.","intents":["I want to use a specific LLM provider (OpenAI, Anthropic, local Ollama) for memory operations without rewriting code","I need to switch LLM providers or add fallback providers for reliability","I want to use open-source or local LLMs to avoid cloud dependencies and API costs"],"best_for":["Teams evaluating different LLM providers for memory operations","Developers building cost-sensitive applications (using cheaper models like Groq or local Ollama)","Organizations with data residency requirements (using local or on-premise LLMs)"],"limitations":["LLM quality varies significantly by provider — cheaper models (Groq, Cohere) may produce lower-quality fact extraction","API rate limits and quotas are provider-specific — no built-in rate limiting or queue management","Fallback logic is basic — no intelligent provider selection based on latency or cost","Configuration is static at initialization — switching providers requires restarting the application","Token counting and cost estimation are not built-in — developers must track costs manually"],"requires":["API key or endpoint URL for at least one LLM provider","Python 3.9+","Network access to LLM provider (or local Ollama instance for self-hosted)","Configuration file or environment variables specifying LLM provider and model name"],"input_types":["configuration (provider name, model name, API key, endpoint URL)","optional: fallback provider list"],"output_types":["LLM client instance (provider-specific)","LLM responses (text completions, structured extractions)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_5","uri":"capability://data.processing.analysis.multi.backend.embedding.generation.with.configurable.embedding.models","name":"multi-backend embedding generation with configurable embedding models","description":"Abstracts embedding model selection through EmbedderFactory supporting 11+ providers (OpenAI, Hugging Face, Ollama, Cohere, etc.) with unified configuration. Developers specify an embedding provider in config, and the system generates embeddings for stored memories and search queries using the selected model. Embeddings are cached and reused across vector store operations, reducing redundant API calls. Supports both cloud-based and local embedding models.","intents":["I want to generate embeddings for memories using a specific embedding model without managing API clients","I need to use open-source or local embedding models (Ollama, Hugging Face) to avoid cloud dependencies","I want to switch embedding models without rewriting memory search code"],"best_for":["Teams optimizing embedding quality vs. cost (e.g., switching from OpenAI to Hugging Face)","Applications with data residency requirements (using local embedding models)","Developers building multi-modal systems (text + image embeddings)"],"limitations":["Embedding quality varies by model — cheaper models may produce lower-quality semantic representations","Embedding dimension varies by provider (OpenAI: 1536, Hugging Face: 384-768) — switching models requires re-embedding all stored memories","No built-in caching across requests — embeddings are regenerated for identical queries","Batch embedding is not optimized — large-scale embedding operations may be slow","No automatic model selection based on query type or domain"],"requires":["API key or endpoint URL for at least one embedding provider","Vector store backend compatible with embedding dimensions (e.g., Pinecone supports 1536-dim OpenAI embeddings)","Configuration specifying embedding provider and model name"],"input_types":["text (memories, queries to embed)","configuration (provider name, model name, API key)"],"output_types":["dense vector embeddings (float arrays, dimension varies by model)","embedding metadata (model name, dimension, generation timestamp)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_6","uri":"capability://memory.knowledge.multi.backend.vector.store.abstraction.with.24.provider.support","name":"multi-backend vector store abstraction with 24+ provider support","description":"Abstracts vector database selection through VectorStoreFactory supporting 24+ backends (Pinecone, Weaviate, Qdrant, Milvus, Chroma, Supabase, etc.) with unified CRUD API. Developers specify a vector store provider in config, and the system handles connection pooling, index creation, similarity search, and metadata filtering. Supports both cloud-hosted (Pinecone, Weaviate Cloud) and self-hosted (Qdrant, Milvus, Chroma) deployments with identical code.","intents":["I want to store and search embeddings without managing vector database clients directly","I need to switch vector stores (e.g., from Pinecone to Qdrant) without rewriting memory code","I want to use a self-hosted vector store to avoid cloud vendor lock-in"],"best_for":["Teams evaluating different vector databases for cost, latency, or feature set","Developers building multi-tenant systems (using vector store namespaces or indexes)","Organizations with data residency requirements (using self-hosted vector stores)"],"limitations":["Vector store feature parity is incomplete — some providers support metadata filtering, others don't","Index configuration is provider-specific — optimal settings vary by backend (e.g., Pinecone index type vs. Qdrant distance metric)","Scaling characteristics differ — some providers (Pinecone) auto-scale, others (Milvus) require manual tuning","Consistency guarantees vary — some are eventually consistent, others strongly consistent","Migration between vector stores requires re-indexing all embeddings"],"requires":["Vector store backend (Pinecone, Weaviate, Qdrant, Milvus, Chroma, etc.)","API key or connection string for the chosen vector store","Embeddings to store (generated via EmbedderFactory)","Configuration specifying vector store provider and connection details"],"input_types":["embeddings (dense vectors with metadata)","search queries (query embeddings + metadata filters)","CRUD operations (add, update, delete, search)"],"output_types":["search results (ranked list of vectors with similarity scores and metadata)","operation confirmations (success/failure for add, update, delete)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_7","uri":"capability://memory.knowledge.session.scoped.and.filter.based.memory.isolation","name":"session-scoped and filter-based memory isolation","description":"Enables memory isolation across users, agents, and sessions through configurable scoping and metadata-based filtering. When adding or searching memories, developers specify scope parameters (user_id, agent_id, session_id) which are stored as metadata and used to filter results. The system supports complex filters (AND, OR, NOT) on metadata fields, enabling fine-grained memory access control without requiring separate databases or indexes per user.","intents":["I want to isolate memories by user so one user's memories don't leak to another","I need to scope memories by agent or session so different conversation contexts don't interfere","I want to query memories with complex filters (e.g., 'memories from user X in the last 7 days')"],"best_for":["Multi-tenant SaaS applications with strict data isolation requirements","Multi-agent systems where different agents need separate memory contexts","Applications with complex memory access patterns (e.g., shared team memories + personal memories)"],"limitations":["Scoping is logical only — no database-level isolation, so application bugs could leak memories across scopes","Filter performance depends on vector store metadata filtering capability — some providers (Pinecone) have limited filtering","No built-in access control — scoping relies on application code to enforce boundaries","Complex filters (nested AND/OR) may not be supported by all vector store backends","Scope parameters must be specified on every operation — no default or implicit scoping"],"requires":["Vector store with metadata filtering support (Weaviate, Qdrant, Milvus support this; Pinecone has limited filtering)","Application code to specify scope parameters (user_id, agent_id, session_id) on add/search operations","Consistent scope naming convention across the application"],"input_types":["scope parameters (user_id, agent_id, session_id)","optional: complex filter expressions (AND, OR, NOT on metadata fields)","optional: time range filters (created_at, updated_at)"],"output_types":["filtered memory results (only memories matching the specified scope and filters)","metadata indicating which scope the memory belongs to"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_8","uri":"capability://memory.knowledge.asynchronous.memory.operations.with.async.await.support","name":"asynchronous memory operations with async/await support","description":"Provides AsyncMemory and AsyncMemoryClient classes enabling non-blocking memory operations (add, search, update, delete) using Python async/await. All I/O operations (LLM calls, vector store queries, graph database operations) are non-blocking, allowing high-throughput applications to handle multiple memory operations concurrently without thread pools. Async operations are fully compatible with async frameworks (FastAPI, aiohttp, asyncio).","intents":["I want to perform memory operations without blocking my async application (FastAPI, aiohttp)","I need to handle multiple concurrent memory requests from different users","I want to optimize throughput by batching async memory operations"],"best_for":["High-throughput web services (FastAPI, Starlette) handling many concurrent users","Real-time applications requiring low-latency memory operations","Developers building async-first agent systems"],"limitations":["Async operations add complexity — debugging async code is harder than sync code","Some vector store clients may not have full async support — fallback to sync-in-async may be needed","Batch operations are not optimized — concurrent operations may hit rate limits or connection pool exhaustion","Error handling in async contexts requires careful exception management","Async overhead may be negligible for low-throughput applications"],"requires":["Python 3.9+ with async/await support","Async-compatible vector store client (most modern providers support this)","Async-compatible LLM provider client","Application framework with async support (FastAPI, aiohttp, asyncio, etc.)"],"input_types":["same as sync operations (text, metadata, filters)","optional: batch operation lists for concurrent execution"],"output_types":["same as sync operations (memory records, search results)","coroutines that must be awaited"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mem0ai--mem0__cap_9","uri":"capability://tool.use.integration.hosted.platform.api.with.rest.endpoints.and.multi.tenant.support","name":"hosted platform api with rest endpoints and multi-tenant support","description":"Provides a cloud-hosted Mem0 Platform with REST API (MemoryClient communicates with https://api.mem0.ai) supporting multi-tenant deployments with organizations, projects, and API key management. The platform handles infrastructure (vector stores, graph stores, LLM calls), authentication, rate limiting, and billing. 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