Compare Sequential, Agent-Based, and Parallel LLM Processing with Claude 3.7

This workflow demonstrates three distinct approaches to chaining LLM operations using Claude 3.7 Sonnet. Connect to any section to experience the differences in implementation, performance, and capabilities. What you'll find: 1️⃣ Naive Sequential Chaining The simplest but least efficient approach - connecting LLM nodes in a direct sequence. Easy to set up for beginners but becomes unwieldy and slow as your chain grows. 2️⃣ Agent-Based Processing with Memory Process a list of instructions through a single AI Agent that maintains conversation history. This structured approach provides better context management while keeping your workflow organized. 3️⃣ Parallel Processing for Maximum Speed Spl

Help me set up the n8n workflow "Compare Sequential, Agent-Based, and Parallel LLM Processing with Claude 3.7" (https://n8n.io/workflows/3527). It uses: HTTP Request, AI Agent, Basic LLM Chain, Anthropic Chat Model, Simple Memory, Chat Memory Manager. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.