GenAI Processors provides a library of reusable, configurable processors to build end-to-end generative AI workflows. Developers can ingest documents, break them into semantic chunks, generate embeddings, store and query vectors, apply retrieval strategies, and dynamically construct prompts for large language model calls. Its plug-and-play design allows easy extension of custom processing steps, seamless integration with Google Cloud services or external vector stores, and orchestration of complex RAG pipelines for tasks such as question answering, summarization, and knowledge retrieval.

Python AGNO Memory Agent provides a structured approach to agent memory by organizing memories via an AGNO framework. It leverages embedding models to convert textual memories into vector representations and stores them in configurable vector stores like ChromaDB, FAISS, or SQLite. Agents can add new memories, query relevant past events, update outdated entries, or delete irrelevant data. The library offers timeline tracking, namespaced memory stores for multi-agent scenarios, and customizable similarity thresholds. It integrates easily with popular LLM frameworks and can be extended with custom embedding models to suit diverse AI agent applications.