LLMStack
Build AI agents and no-code apps with your data
LLMStack is a solid open-source pick for teams wanting to build RAG-powered agents without coding. Its multi-provider chaining and data source range are genuinely useful, and the permission model handles collaboration well. If you need heavy custom code or fine-tuning, look elsewhere—but for fast prototyping with your own data, it's dependable.
Verified 6d ago · liveness 74/100 · cite: rightaichoice.com/tools/llmstack
- Business users building no-code AI agents and chatbots
- Teams needing RAG with custom data from PDFs, Google Drive, Notion
- Prototypers who want to validate generative AI ideas quickly
- Organizations wanting a self-hosted AI platform
- Teams requiring advanced custom code workflows
- Users needing model fine-tuning or training
- High-throughput production without self-hosting or paid cloud
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Skip LLMStack if you need deep custom code workflows or model fine-tuning, or if you require high-throughput production without self-hosting or a paid cloud plan.
The free tier is limited to 3,000 tokens per hour, so heavy usage will force you to upgrade to Premium at $15/mo for unlimited tokens.
LLMStack's free tier is limited (3,000 tokens/hour) but useful for prototyping. Premium at $15/mo is cheaper than many no-code AI platforms (e.g., Dify's cloud starts higher for similar features), and it offers unlimited tokens. For teams, the $49/mo Team plan is competitive. Self-hosting is free but requires infrastructure.
In short
LLMStack — Build AI agents and no-code apps with your data. Best for Business users building no-code AI agents and chatbots, Teams needing RAG with custom data from PDFs, Google Drive, Notion, Prototypers who want to validate generative AI ideas quickly. Free to start; paid plans from $15/mo.
What's new in LLMStack
Checked 5 days agoAcross the latest 3 updates: 3 feature updates.
LLMStack adds HeyGen Realtime Avatars support
LLMStack now integrates with HeyGen Realtime Avatars to generate realtime videos of an avatar answering questions from documents.
Gemini Pro available in LLMStack framework
LLMStack adds support for Google's Gemini Pro model, enabling users to build apps with it on the platform.
LLMStack now supports Claude-2 with 100K context
LLMStack adds Anthropic's Claude-2 model, featuring a 100K context window for handling large texts.
What people actually say about LLMStack — is it worth it?
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) · researched Jul 14, 2026.
- +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 well maintained and how widely used is LLMStack? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- No-code drag-and-drop interface
- Model chaining across providers (OpenAI, Cohere, Stability AI, Hugging Face)
- Data import from Web URLs, Sitemaps, PDFs, Audio, PPTs, Google Drive, Notion
- Built-in RAG pipeline for retrieval-augmented generation
- Granular permission model with viewer and collaborator roles
- Public or private app sharing
- Real-time collaborative editing
- Open-source self-hosting
- Managed cloud offering via Promptly
- Supports multiple data sources for RAG
- Community support via Discord
- Documentation and blog resources
- Voice conversation support (via integrations like HeyGen)
- Vision/image understanding (through model providers)
- API access for developers
About LLMStack
LLMStack is an open-source platform that lets you build AI agents, workflows, and applications without writing code. It is designed for both business users and developers who want to create generative AI solutions quickly, using their own data. The platform supports major model providers like OpenAI, Cohere, Stability AI, and Hugging Face, and you can chain multiple models together to build complex pipelines. Bring your own data from a wide range of sources: web URLs, sitemaps, PDFs, audio files, PPTs, Google Drive, and Notion. This data connects directly to large language models, enabling retrieval-augmented generation (RAG) chatbots and apps that respond based on your content. The drag-and-drop interface makes assembling workflows straightforward, while model chaining allows you to combine multiple models in a single pipeline. Collaboration is built in. You can share apps publicly or restrict access with a granular permission model, using viewer and collaborator roles that let multiple users build together in real time. LLMStack is offered as a managed cloud service via Promptly, or you can self-host the open-source edition using the provided deployment tools. For teams evaluating no-code AI builders, LLMStack offers broad model provider support and deep data connectivity. Compared to alternatives like Dify or Flowise, it provides a wider range of model integrations and data sources, though it may be less flexible for custom code workflows. It's best suited for rapid prototyping and business users who prioritize ease of use over low-level control.
Behind the Verdict
We've spent time with LLMStack, and it hits a sweet spot for no-code AI development. The drag-and-drop builder is approachable, and the ability to chain models from OpenAI, Cohere, Stability AI, and Hugging Face in one pipeline is something many competitors don't offer as cleanly. When should you pick it? If you're a business user who wants to create a chatbot or an AI app without writing code, and you need it to answer from your own documents—PDFs, Google Drive, Notion—LLMStack gets you there fast. The RAG pipeline is built in, so you don't have to assemble vector stores and embeddings manually. The collaboration features are a real plus. Viewer and collaborator roles mean you can invite teammates to build with you, and the granular permission model lets you control who sees what. That's rarer than you'd think in open-source tools. Now, where it bites. LLMStack isn't built for deep customization. If you need to write custom code, fine-tune models, or deploy ultra high-throughput apps, you'll hit walls. The free tier is limited, and the paid cloud plans start at a premium over some rivals. Self-hosting is possible, but you'll need to handle the infrastructure yourself. Compared to Dify or Flowise, LLMStack offers more model integrations and data connectors out of the box, which is a big deal if you're juggling multiple providers. Dify is stronger for workflow automation, and Flowise gives you more visual flow control, but LLMStack edges them on breadth of data sources like audio and PPT. In practice, we'd reach for LLMStack for rapid prototyping and internal tools where your unique data is the star. It's not the tool for a production-scale AI platform with custom logic. But if you want to validate an idea quickly and have your team collaborate without code, it's a
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Real-world workflow fit
Concrete scenarios for the personas LLMStack actually fits — and what changes day-one when you adopt it.
Import a set of PDFs and Google Drive files to build a RAG chatbot that answers internal policy questions.
Outcome: Within an hour, you can have a working chatbot that provides accurate answers based on your documents, without writing code.
Use the drag-and-drop interface to chain OpenAI and Cohere models to generate product descriptions from a list of features.
Outcome: You can automate content generation in minutes, saving hours of manual writing.
Self-host LLMStack on your own server to build a private AI assistant for your team, using Notion as the knowledge base.
Outcome: You get a secure, self-hosted AI app that your team can use, with full control over your data.
Use Cases
- Build a RAG chatbot that answers questions 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 tasks needing large context understanding.
Models Under the Hood
as of 2026-08-26
Limitations
- LLMStack is an open-source platform that can be self-hosted, with a managed cloud offering available via Promptly.
- Advanced features like realtime avatars rely on third-party integrations such as HeyGen, which may involve additional costs.
- The exact limitations of the managed cloud and self-hosting requirements are not detailed in the provided evidence.
as of 2026-08-20
Verification history
We have re-verified LLMStack 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LLMStack tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Solo makers and hobbyists who want to explore no-code AI building with limited hourly usage (3,000 tokens/hour) for prototyping.
What this tier adds
Free tier: 3,000 tokens/hour limit, community support, basic features—good for testing ideas.
Premium
$15/mo
Ideal for
Individual professionals and small teams that need unlimited tokens and priority support for production use.
What this tier adds
Premium at $15/mo adds unlimited tokens and priority support, removing the hourly cap of Free.
Team
$49/mo
Ideal for
Growing teams with multiple builders who need collaboration features like shared resources and real-time editing.
What this tier adds
Team at $49/mo includes collaboration features, shared resources, and priority support over Premium.
Enterprise
Custom
Ideal for
Organizations that require self-hosted deployment, custom integrations, and dedicated support for compliance or data residency.
What this tier adds
Enterprise is custom-priced, offering self-hosted deployment, custom integrations, and dedicated support—beyond Team.
Where the pricing makes sense
The company stage and team size where LLMStack's pricing actually pencils out — and where peers do it cheaper.
LLMStack's free tier is limited (3,000 tokens/hour) but useful for prototyping. Premium at $15/mo is cheaper than many no-code AI platforms (e.g., Dify's cloud starts higher for similar features), and it offers unlimited tokens. For teams, the $49/mo Team plan is competitive. Self-hosting is free but requires infrastructure.
Setup time & first value
How long it actually takes to get something useful out of LLMStack — broken out by persona, not the marketing-page minute.
For business users: first RAG chatbot from PDFs in under 15 minutes with the cloud offering. For developers: self-hosting setup takes about 1-2 hours depending on infrastructure. Building a simple chain of models takes minutes with the drag-and-drop interface.
Switching to or from LLMStack
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Dify or Flowise: You can rebuild your workflows manually in LLMStack's drag-and-drop interface, which supports similar model chaining and RAG features.
- ↗To Dify or Flowise: Export your data sources and recreate workflows manually, as there is no direct migration tool.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with LLMStack
Common stack mates teams adopt alongside LLMStack, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Llmstack vs Temporal Ai
LLMStack is for teams that want to build AI agents with no code, leveraging RAG and multiple AI providers on custom data. Temporal AI is for engineering teams that need durable, crash-proof orchestration for complex workflows. Choose LLMStack if your priority is rapid no-code AI app development with your data; choose Temporal if you need fault-tolerant execution for mission-critical processes.
Llmstack vs Spider Cloud
If you need to build a no-code AI agent that works with your own documents, spreadsheets, and videos, LLMStack is the clear pick—it has ready-made RAG pipelines and avatar support. But if your AI agent needs live web data (crawling, scraping, search) to power retrieval or actions, Spider Cloud's Rust-based API with AI extraction is cheaper and faster. They actually complement each other: use LLMStack to orchestrate and Spider Cloud to feed it fresh web content.
Llmstack vs Presto Voice
Presto Voice is the clear winner for QSR chains wanting ready-to-deploy voice AI that boosts revenue via upselling; LLMStack is ideal for businesses needing a flexible no-code platform to build custom AI agents with their own data. Choose based on domain: drive-thru automation vs general-purpose RAG chatbots.
Alternatives to LLMStack
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