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.

NeuralGPT is designed to simplify AI Agent development by offering modular components and standardized pipelines. At its core, it features customizable Agent classes, retrieval-augmented generation (RAG), and memory layers to maintain conversational context. Developers can integrate vector databases (e.g., Chroma, Pinecone, Qdrant) for semantic search and define tool agents to execute external commands or API calls. The framework supports multiple LLM backends such as OpenAI, Hugging Face, and Azure OpenAI. NeuralGPT includes a CLI for quick prototyping and a Python SDK for programmatic control. With built-in logging, error handling, and extensible plugin architecture, it accelerates deployment of intelligent assistants, chatbots, and automated workflows.