LlamaIndex is a developer-focused Python library designed to bridge the gap between large language models and private or domain-specific data. It offers multiple index types—such as vector, tree, and keyword indices—along with adapters for databases, file systems, and web APIs. The framework includes tools for slicing documents into nodes, embedding those nodes via popular embedding models, and performing smart retrieval to supply context to an LLM. With built-in caching, query schemas, and node management, LlamaIndex streamlines building retrieval-augmented generation, enabling highly accurate, context-rich responses in applications like chatbots, QA services, and analytics pipelines.

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.