Semantic Scholar FastMCP Server is designed to streamline academic research by exposing a RESTful API that sits between your application and the Semantic Scholar database. It orchestrates multiple prompt chains (MCP) in parallel—such as metadata retrieval, abstract summarization, citation extraction, and question answering—to produce fully processed results in a single response. Developers can configure each chain’s parameters, swap out language models, or add custom handlers, enabling rapid deployment of literature review assistants, research chatbots, and domain-specific knowledge pipelines without building complex orchestration logic from scratch.

Haystack is designed to help developers easily create custom search solutions that leverage the latest advancements in machine learning. With its components like document stores, retrievers, and readers, Haystack can connect to various data sources and effectively process queries. Its modular architecture supports mixed search strategies, including semantic search and traditional keyword-based search, making it a versatile tool for enterprises looking to enhance their search capabilities.

Summative Info Researcher Agents offers a modular framework of AI-driven agents designed to perform end-to-end research tasks. It automates web searches, scrapes content, extracts relevant data points, and synthesizes findings into clear, structured summaries. Built atop popular LLMs and extensible via plugin tools, the project allows users to define multi-step workflows, chain agents together, and adjust settings for domain-specific queries. Its flexible architecture supports integration with custom APIs, database connectors, and scheduling systems to fit academic, business, or personal research needs.