Report Abuse

Basic Information

YC-Killer is an open source collection of enterprise-grade AI agent projects and templates intended to provide free alternatives to high-profile startup offerings. The repository catalogs multiple specialized agent applications such as research agents, a fully agentic quantitative hedge fund, a Jarvis-like executive assistant, an automated call center, an AI hospital, a tutoring professor agent, and an accounting firm, each maintained as its own sub-repository with dedicated setup instructions. The project aims to democratize access to advanced AI by publishing production-ready agent designs, deployment configurations, and operational practices so researchers, engineers, and organizations can run, study, and extend sophisticated multi-agent systems without large funding barriers. The README emphasizes modularity, real-time capabilities, containerized deployments, and community contributions.

Links

App Details

Features
Collection of distinct agent products with focused capabilities and infrastructure components. Agentic Deep Research supports recursive exploration, parallel processing, AI analysis, and markdown reports. The Quant Hedge Fund includes idea generation, alpha engineering, NumPy+Numba feature pipelines, Polars data engine, backtesting and DVC for data versioning. Jarvis provides calendar, email, task management, web search, real-time WebSocket updates and OAuth authentication. Call Center supports speech-to-text, text-to-speech, call recording, RLHF and admin analytics. AI Hospital implements multi-specialist coordination, insurance checks and contextual query analysis. Agentic Professor offers tutoring, whiteboard, diagram and TTS features. Cross-cutting technologies include TypeScript/Node.js, React/Next.js, Docker, Kubernetes, GPT-4 integration and documented agent-specific READMEs.
Use Cases
The repository lowers the barrier to deploying advanced multi-agent applications by providing open reference implementations and production-oriented tooling. Teams can reuse agent architectures, leverage provided data and backtesting patterns, and adopt recommended stacks for real-time and containerized deployments. Researchers gain reproducible examples for research workflows and automated strategy pipelines. Developers can iterate on modular agents tailored for domain use cases such as finance, healthcare, education, customer support and accounting. The project also fosters collaboration through contribution guidelines and individual agent READMEs so maintainers and contributors can set up, test, and deploy agents using Docker, Kubernetes and model integrations without building infrastructure from scratch.

Please fill the required fields*