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.

Sherpa by CartographAI is a Python-based agent framework designed to streamline the creation of intelligent assistants and automated workflows. It enables developers to define agents that can interpret user input, select appropriate LLM endpoints or external APIs, and orchestrate complex tasks such as document summarization, data retrieval, and conversational Q&A. With its plugin architecture, Sherpa supports easy integration of custom tools, memory stores, and routing strategies to optimize response relevance and cost. Users can configure multi-step pipelines where each module performs a distinct function—like semantic search, text analysis, or code generation—while Sherpa manages context propagation and fallback logic. This modular approach accelerates prototype development, improves maintainability, and empowers teams to build scalable AI-driven solutions for diverse applications.

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.

Graphium is a knowledge graph and LLM orchestration framework that supports ingestion of structured data, creation of semantic embeddings, and hybrid retrieval for Q&A and chat. It integrates with popular LLMs, graph databases, and vector stores to enable explainable, graph-powered AI agents. Users can visualize graph structures, query relationships, and employ multi-hop reasoning. It provides RESTful APIs, SDKs, and a web UI for managing pipelines, monitoring queries, and customizing prompts, making it ideal for enterprise knowledge management and research applications.