At its core, Memary provides a modular memory management system tailored for large language model agents. By abstracting memory interactions through a common API, it supports multiple storage backends, including in-memory dictionaries, Redis for distributed caching, and vector stores like Pinecone or FAISS for semantic search. Users define schema-based memories (episodic, semantic, or long-term) and leverage embedding models to populate vector stores automatically. Retrieval functions allow contextually relevant memory recall during conversations, enhancing agent responses with past interactions or domain-specific data. Designed for extensibility, Memary can integrate custom memory backends and embedding functions, making it ideal for developing robust, stateful AI applications such as virtual assistants, customer service bots, and research tools requiring persistent knowledge over time.

Memor offers a memory subsystem for language model agents, allowing them to store embeddings of past events, user preferences, and contextual data in vector databases. It supports multiple backends such as FAISS, ElasticSearch, and in-memory stores. Using semantic similarity search, agents can retrieve relevant memories based on query embeddings and metadata filters. Memor’s customizable memory pipelines include chunking, indexing, and eviction policies, ensuring scalable, long-term context management. Integrate it within your agent’s workflow to enrich prompts with dynamic historical context and boost response relevance over multi-session interactions.