LLMStack Review: No-Code AI Agent Builder | hokai.io
LLMStack is an open-source no-code platform for building AI agents and RAG workflows. Integrates 20+ LLMs (OpenAI, Hugging Face), supports self-hosting. Free tier, Pro $50/month.
LLMStack is an open-source, no-code platform for building AI agents, chatbots, and RAG workflows without coding. Integrates 20+ language models including OpenAI and Hugging Face. Self-host on Kubernetes or use managed cloud service. Free tier available; Pro $50/month. Built by Promptly for teams and enterprises.
Pricing
Free tier: unlimited development with limited deployments and API calls. Pro tier: $50/month with higher API rate limits and production deployments. Annual plans available with discounts. Enterprise pricing available for self-hosted deployments.
Frequently Asked Questions
What is LLMStack and what does it do?
LLMStack is an open-source, no-code platform developed by Promptly that enables users to build AI agents, chatbots, and workflows by visually connecting language models (LLMs) to their business data without writing code. It supports 20+ LLM providers including OpenAI, Hugging Face, Cohere, and Stability AI. Users can create model chains, retrieval-augmented generation (RAG) workflows, and customer support agents that integrate with Slack, Discord, or custom APIs.
How much does LLMStack cost?
LLMStack offers a free tier for development and experimentation with limited API calls and deployments. The Pro tier starts at $50/month with higher API rate limits and full production deployment capabilities. Annual plans are available with discounts. Self-hosted deployments are free but require infrastructure costs for running Kubernetes, PostgreSQL, and vector databases. LLM token costs (OpenAI, Cohere, etc.) are passed through separately.
What are the main features of LLMStack?
Key features include: (1) Model chaining to visually orchestrate multi-step LLM workflows, (2) Data integration supporting PDFs, CSVs, Google Drive, Notion, and websites with automatic indexing and RAG, (3) Flexible deployment to cloud or on-premises infrastructure, (4) API-first architecture allowing export as production HTTP APIs, and (5) Collaboration tools with role-based access control for team development. The platform also integrates with Slack and Discord for workflow triggering.
Is LLMStack free to use?
Yes, LLMStack offers a free tier suitable for development, prototyping, and small-scale usage with unlimited development environments but limited API calls and deployments. The free tier is ideal for exploring the platform and learning how to build workflows. For production applications with higher throughput, the Pro tier starts at $50/month. Self-hosted deployment is also free but requires managing your own infrastructure.
What are the best alternatives to LLMStack?
Top alternatives include AnythingLLM (local-first, offline RAG), Dify (LLMOps-focused with visual RAG), LangChain (developer framework for Python/JavaScript), Flowise (open-source drag-and-drop UI), and crewAI (multi-agent orchestration). Choose AnythingLLM if you need local data privacy and offline operation. Choose Dify for enterprise-grade LLMOps features. Choose LangChain or crewAI for custom agent development with code.
Who is LLMStack best for?
LLMStack is ideal for business analysts, product managers, and non-technical teams building custom AI applications on proprietary data. It suits enterprises implementing AI-powered customer support agents, data teams rapidly prototyping RAG systems, and teams needing flexible deployment (cloud or on-premises) for data privacy. It is less suitable for ML engineers requiring fine-tuned model control or solo freelancers building simple chatbots.
Does LLMStack support multiple language models?
Yes, LLMStack integrates 20+ language model providers including OpenAI (GPT-4, GPT-4 Turbo, GPT-3.5), Cohere, Hugging Face, Stability AI, Anthropic Claude, and others. This multi-model flexibility allows users to switch between providers mid-project, avoid vendor lock-in, and experiment with different models without rebuilding workflows. Token costs for each model are passed through at actual provider rates.