mem0ai

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.

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.

Mem0 implements background memory consolidation that periodically analyzes stored interactions, identifies redundant or outdated memories, and creates summarized versions to reduce storage and retrieval latency.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.

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.

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.

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.

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).

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.