Agent Library and Testing Hub for Managing Multiple AI Assistants

Added Jun 2025 3 design docs

As organizations and learners build more AI agents, they run into an organizational problem before a technical one: agents accumulate in scattered scripts and notebooks with no catalog, no versioned configurations, and no consistent way to compare how different agents handle the same task. Without a management layer, useful agents get rebuilt from scratch and poor ones never get retired. The intern builds a platform that brings order to this sprawl. Using Python and FastAPI, the intern implements an agent registry where each agent is stored with its configuration, description, and capabilities, and a collections feature that groups related agents into curated libraries for topics such as research, summarization, and answer-engine style question answering. LangChain provides the execution layer and OpenAI models power the agents themselves. The centerpiece is a testing playground where a user selects any registered agent, submits a query, and inspects the response alongside runs of the same query against other agents, making behavioral differences concrete and comparable. Delivering the platform demonstrates skills in API and schema design, agent lifecycle management, LangChain integration, and evaluation thinking: the intern learns to treat agents as managed assets with measurable behavior rather than disposable experiments.

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