ResearchGPT automates end-to-end academic research workflows by integrating paper retrieval, PDF parsing, NLP-based text extraction, and GPT-4 powered summarization. Starting with a user-defined research topic, it queries Semantic Scholar and arXiv APIs to gather relevant papers, downloads and parses PDF content, and employs GPT-4 to distill key concepts, methodologies, and results. The agent compiles individual paper insights into a cohesive, structured report, supporting exports in Markdown or PDF formats. Advanced configuration options allow users to tailor search filters, define custom summarization prompts, and adjust output styles. By orchestrating these steps, ResearchGPT reduces manual effort, accelerates literature reviews, and ensures comprehensive coverage of academic sources.

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.

Rags provides a modular pipeline to build retrieval-augmented generative applications. It integrates with popular vector stores (e.g., FAISS, Pinecone), offers configurable prompt templates, and includes memory modules to maintain conversational context. Developers can switch between LLM providers like Llama-2, GPT-4, and Claude2 through a unified API. Rags supports streaming responses, custom preprocessing, and evaluation hooks. Its extensible design enables seamless integration into production services, allowing automated document ingestion, semantic search, and generation tasks for chatbots, knowledge assistants, and document summarization at scale.