Validate AI agent concepts with free tools for LLM orchestration, data storage, and workflow automation.
As a small startup, we need to quickly build and test AI agents using LLM APIs and vector databases, but we lack the budget for expensive tools and need to ensure our initial setup is secure and can be easily managed without dedicated DevOps.
With this stack, your team will be able to visually design and deploy AI agents, store and retrieve relevant data efficiently, and automate agent workflows. You'll have a centralized knowledge base for all project documentation and a clear path to scale as your needs grow, all while keeping initial costs extremely low.
Visually build and manage LLM-powered AI agents and RAG pipelines.
Provide a serverless PostgreSQL database with vector capabilities for RAG and agent memory.
Automate complex workflows, connect AI agents to external services, and manage data flows.
Host and deploy frontend applications or API endpoints for AI agents.
Centralize documentation, project plans, and agent design specifications.
Flowise can expose API endpoints that n8n AI can consume to trigger and orchestrate AI agent workflows. Both have APIs.
Flowise can connect to Neon as a vector database for RAG, leveraging Neon's pgvector capabilities. Both have APIs.
Vercel applications can easily connect to Neon databases for backend data storage and retrieval. Both have APIs.
This stack focuses on leveraging free tiers and open-source solutions to build and test AI agents with minimal financial outlay. It prioritizes essential features for LLM orchestration, vector data storage, and basic workflow automation, allowing a small team to rapidly prototype and validate their AI automation use case.
