LLMStack
No-code platform to build AI agents with your data
A capable open-source no-code tool for quickly building AI agents with your own data. It's not for advanced model training, but its data source integrations and collaborative features make it a solid pick for teams looking to prototype internal tools or customer-facing chatbots.
- Business users building no-code AI agents
- Teams needing RAG with custom data
- Prototypers and innovators in generative AI
- Organizations wanting self-hosted AI platform
- Teams requiring advanced custom code workflows
- Users needing fine-tuning or model training
- High-throughput production without self-hosting
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In short
LLMStack — No-code platform to build AI agents with your data. Best for Business users building no-code AI agents, Teams needing RAG with custom data, Prototypers and innovators in generative AI. Free to use.
What's new in LLMStack
Checked 3 days agoAcross the latest 4 updates: 4 feature updates.
Realtime Avatars support with HeyGen
LLMStack adds HeyGen realtime avatar integration for video generation.
Build AI Apps with Google's Gemini Pro
LLMStack adds support for Google Gemini Pro API.
100K Context Window with Anthropic
LLMStack adds Claude-2 Model with 100K context window.
Retrieval Augmented Generation (RAG): What, Why and How?
Explaining RAG architecture and its integration with LLMStack.
What independent users actually report about LLMStack
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 3 sources (YouTube, Bluesky, GitHub).
- +No-code multi-agent framework lowers barrier for AI app building.
- +Supports chaining multiple models from various providers.
- +Built-in RAG pipeline with data from web, PDFs, Google Drive.
- +Open-source self-hosting gives full data control.
- +Collaborative editing and granular permission model.
- −Fails to start on fresh install due to database migration bugs.
- −Users report numerous bugs in chat and agent functionality.
- −No native support for local models from Hugging Face.
- −Postgres connectivity issues plague initial setup.
- −CLI lacks options like --host/--port, requiring config edits.
- • Cloud infrastructure costs for self-hosting
- • Potential managed tier pricing not disclosed
Viability Score
How likely is LLMStack to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- No-code multi-agent framework
- Open-source self-hosting
- Model chaining with OpenAI, Cohere, Stability AI, Hugging Face
- Bring your own data (Web URLs, PDFs, Audio, PPTs, Google Drive, Notion)
- Built-in RAG pipeline
- Real-time avatar generation with HeyGen
- Granular permission model (viewer/collaborator roles)
- App sharing (public or private)
- Realtime video avatar chatbot
- Built-in vector store for retrieval
- Drag-and-drop interface powered by React
- Collaborative editing
About LLMStack
LLMStack is an open-source platform that lets teams build AI agents, workflows, and applications with their own data—all without writing code. It supports multiple model providers (OpenAI, Cohere, Stability AI, Hugging Face, etc.) and a wide range of data sources including web URLs, PDFs, audio, PPTs, Google Drive, and Notion. Powered by React, the builder offers a drag-and-drop interface for chaining models, setting up RAG pipelines, and building chatbots. Apps can be shared publicly or restricted with granular permissions (viewer/collaborator roles), and multiple users can collaborate in real-time. LLMStack is ideal for business users, developers, and organizations that need to prototype and deploy generative AI apps quickly, either via the managed cloud or self-hosted open-source core. Its strength lies in making multi-agent orchestration and data integration approachable without deep technical expertise.
Behind the Verdict
LLMStack fills a specific niche: teams that want to build AI agents fast without coding but need to ground them in their own data. The multi-model chaining and pre-built RAG pipeline are genuinely useful—you can connect a PDF, a Google Drive folder, or a website sitemap, and have a chatbot answering from that content in minutes. The open-source aspect is a big plus for organizations that need self-hosting for compliance or cost control. That said, this isn't a platform for fine-tuning models or building custom training pipelines. It's a rapid prototyping and deployment layer, not a deep AI development kit. If your team needs custom code logic or heavy API workflows, you'll hit limits. Compared to something like LangChain's orchestration (which requires coding), LLMStack is far more approachable but less flexible. For business users, product managers, or solution architects who need to demonstrate AI capabilities quickly, it's a strong choice. Just don't expect to train or fine-tune models here.
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Use Cases
- Build a RAG chatbot that answers from your company's PDFs and documents
- Create a multi-agent workflow that chains OpenAI and Claude-2 for content generation
- Deploy a realtime avatar chatbot that speaks answers from your knowledge base
- Share a private AI app with team members to automate email responses
- Integrate Gemini Pro into your app for 100K context window tasks
Models Under the Hood
as of 2026-07-17
Limitations
- The platform's free tier may have limits on usage or model access.
- Self-hosting requires infrastructure setup.
- Advanced features like real-time avatars are tied to third-party integrations, and the managed cloud may introduce latency.
Resources & Guides
Official links
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