{"name":"ReAct Multi-Agent System","description":"A production-grade ReAct agent with tool calling, exponential backoff retries, and a multi-agent orchestrator (supervisor → research / doc-generation / general). Handles web research, professional document generation (PDF/PPTX/DOCX), and Q&A.","url":"http://agent.naveenb.dev","version":"1.0.0","capabilities":{"streaming":false,"pushNotifications":false},"defaultInputModes":["text"],"defaultOutputModes":["text"],"skills":[{"id":"research","name":"Web Research","description":"Search the web via Tavily/Serper/DuckDuckGo and synthesise findings from multiple sources with citations.","inputModes":["text"],"outputModes":["text"],"examples":["Search for the latest AI market trends in 2026","Find current IPL 2026 standings and match results"]},{"id":"document-generation","name":"Document Generation","description":"Generate styled PDF, PPTX, or DOCX files using Python libraries (reportlab, python-pptx, python-docx). Can research first then generate.","inputModes":["text"],"outputModes":["text"],"examples":["Create a PDF report on climate change","Make a PPTX presentation on AI trends with current data"]},{"id":"general-qa","name":"General Q&A","description":"Answer factual questions, perform mathematical calculations, and read files from the data sandbox.","inputModes":["text"],"outputModes":["text"],"examples":["What is 15 * 7?","Summarise what you know about retrieval-augmented generation"]}]}