Building a Context Engineering Workflow

Context engineering involves creating dynamic systems that provide LLMs with the right information, tools, and format to complete tasks effectively. This tutorial demonstrates building a multi-agent research assistant that gathers context from four sources: documents, memory, web search, and ArXiv. The workflow uses Tensorlake for document processing, Milvus for vector storage, Zep for memory management, and Firecrawl for web scraping, orchestrated through CrewAI agents that filter and synthesize responses.