AI Agent Builder Pack
6 n8n workflows that take you from knowledge base setup to a production-ready AI agent with quality evaluation
What This Pack Does
Provides the complete workflow stack for building production AI agents: document ingestion, vector knowledge base, RAG implementation, web search, modular agent design, quality evaluation, and WhatsApp deployment.
Who It's For
Developers, AI engineers, and technical founders who want to build and deploy reliable AI agents with proper knowledge grounding and quality evaluation.
Expected Outcome
Ship a production-ready AI agent with RAG, web search, and quality evaluation in days instead of weeks
Highlights
- Complete agent lifecycle: Knowledge Base → RAG → Web Search → Multi-Function → Evaluate → Deploy
- Vector knowledge base setup with Supabase and Pinecone
- Retrieval-augmented generation for accurate, grounded responses
- Web-connected agent with Brave Search integration
- Response quality evaluation with cosine similarity scoring
- Production-ready WhatsApp deployment for any business
Included Workflows(6)
Knowledge Base
RAG
Web Search
Multi-Function
Quality
Deployment
Setup Requirements
An n8n instance, OpenAI API key, Supabase or Pinecone for vector storage, Google Drive for document ingestion, and WhatsApp Business API for production deployment.
Example Use Case
A SaaS company builds a customer support agent by indexing their documentation into Supabase vectors. The RAG workflow grounds responses in actual docs, the web search workflow handles questions about recent changes, and the modular agent routes billing questions to one handler and technical questions to another. The quality evaluation workflow scores response relevance, and the WhatsApp deployment makes it accessible to customers 24/7 — all built and shipped in under a week.
Primary Integrations
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