llmstack.ai

LLMStack supports importing your own data from Web URLs, Sitemaps, PDFs, Audio, PPTs, Google Drive, and Notion. Once imported, that data can be connected to LLM models to ground your generative AI apps and chatbots. This is part of LLMStack's no-code workflow, so you don't need to write ingestion code to get your content into an app.

Supported Data Sources

Based on LLMStack's own description of its "Bring your own Data" capability, the platform lists the following sources:

The listing uses "including" and "etc.," which signals these are representative examples rather than a guaranteed exhaustive list. If you need a source that isn't named, treat it as unconfirmed rather than assumed to work.

How Imported Data Connects to Models

The purpose of importing data is to "connect it to LLM models to supercharge your generative AI applications and chatbots." In practice this means your imported content becomes the grounding material an app or chatbot draws on, rather than relying only on a model's built-in knowledge.

LLMStack also states it supports all major model providers, including OpenAI, Cohere, Stability AI, and Hugging Face. So the data-connection step and the model-choice step are separate: you bring content from the sources above, then attach it to whichever supported model you're using.

What This Means for a No-Code Build

Because data import sits inside a no-code flow, the practical sequence is:

1. Choose a source from the supported list (for example, a PDF or a Notion page).
2. Import that content into LLMStack.
3. Connect the imported data to an LLM model.
4. Build the app or chatbot on top of that grounded data.

You can also share the resulting app publicly or restrict access using LLMStack's permission model, with viewer and collaborator roles for building together.

Choosing a Source: Practical Considerations

• Broad site coverage: A Sitemap is the more efficient option when you want many pages from one site rather than adding URLs one at a time.
• Structured documents: PDFs and PPTs suit content that already exists as files.
• Living workspaces: Google Drive and Notion imports make sense when your source material changes in those tools.
• Audio: Useful when your knowledge lives in recordings rather than text.

What Isn't Specified

The available information does not state limits on file size, number of sources, refresh or sync frequency, or whether imports update automatically when the original source changes. It also doesn't detail authentication requirements for Google Drive or Notion. If any of these matter for your use case, verify them directly with LLMStack before committing.

LLMStack lets you share a built app either publicly with anyone on the internet or privately with specific people, and it separates those people into viewer and collaborator roles so multiple users can modify and build the app together. This applies to apps you have already assembled in the no-code builder; the sharing and permission controls described here govern who can open the app and who can change it.

Two ways to share an app

LLMStack's permission model gives you a binary choice at the top level:

• Public sharing — the app is available to everyone on the internet. Useful for demos, internal tools you want anyone to reach, or a chatbot you want to distribute widely.
• Restricted access — the app is limited to certain individuals you name. Useful when the app touches private data or is meant for a defined team.

The distinction matters because LLMStack apps are often connected to your own data — web URLs, sitemaps, PDFs, audio, PPTs, Google Drive, or Notion imports. If an app is grounded in material you would not publish, restricted access is the setting that keeps it from being reachable by anyone with the link.

Roles: viewer vs. collaborator

Within the people you grant access to, LLMStack distinguishes two roles:

The practical consequence is that sharing an app and co-building an app are separate grants. You can hand someone a working app without handing them the ability to alter its prompts, model chain, or data connections. Conversely, a collaborator is someone you are treating as a co-builder, so grant that role only to people you would let edit the app directly.

Building collaboratively

Because collaborator access allows multiple users to modify and build the same app, LLMStack is set up for team construction rather than a single author publishing a finished artifact. The platform is built on React and describes itself as a no-code way to build generative AI apps, chatbots, and agents, so the people you invite as collaborators do not need to write code to participate in the build.

A reasonable division of labor looks like this:

1. One person assembles the first working version — model chain, data sources, and the app surface.
2. That person shares it as a collaborator with teammates who will refine prompts, swap or add model providers, or adjust which data sources feed the app.
3. Everyone iterates on the same app rather than passing files around.
4. When the app is ready for an audience that should not edit it, those users are added as viewers or the app is made public.

Choosing a sharing setting

• Use public sharing when the app contains nothing sensitive and you want zero-friction access — a public demo, a marketing chatbot, a general-purpose helper.
• Use restricted access with viewer role when the app should be used but not changed, and only by a known set of people.
• Use restricted access with collaborator role when the person is genuinely part of the build and you are comfortable with them changing the app.

What to check before you share

• What data the app is connected to. LLMStack supports importing your own data from web URLs, sitemaps, PDFs, audio, PPTs, Google Drive, and Notion. Public sharing exposes whatever the app can surface from those sources.
• Which model providers the app chains. LLMStack supports major providers including OpenAI, Cohere, Stability AI, and Hugging Face; sharing an app shares access to its behavior, so confirm the chain is one you are willing to expose.
• Who actually needs edit rights. Default to viewer unless the person is building with you.

The site's own framing — "share apps publicly with everyone on the internet or restrict access to only certain individuals using our granular permission model" — is the whole mechanism: one public/restricted switch, plus a role assignment per person. Everything else about collaboration follows from those two decisions.

LLMStack supports all major model providers — including OpenAI, Cohere, Stability AI, and Hugging Face — and lets you combine them through a no-code feature called model chaining. You can use these models to build generative AI apps, chatbots, and agents without writing code. This applies if you want to mix models from different vendors in a single workflow; it does not mean every provider's full model catalog is available, since the platform describes support at the provider level.

Which model providers are supported

According to the platform's own description, LLMStack supports "all major model providers," and names these specifically:

• OpenAI
• Cohere
• Stability AI
• Hugging Face
• and more (the source lists these as examples rather than an exhaustive list)

The practical takeaway: if your app depends on a model from one of these named providers, LLMStack is designed to let you use it directly. Because the source says "and more" rather than listing every provider, treat the list as representative — verify any specific provider you need before committing to a build.

What model chaining means here

Model chaining is the ability to combine multiple models into one application flow. Instead of picking a single model to handle everything, you connect models so the output of one feeds into another.

The platform frames this as an ease-of-use feature: "Easy to Use Model Chaining." The point is that you get the flexibility of a multi-model pipeline without writing the glue code yourself.

A concrete example

Suppose you want a chatbot that answers questions about your own documents:

1. A retrieval step pulls relevant text from your imported data.
2. A language model (for example, from OpenAI or Cohere) generates an answer grounded in that text.
3. An image model (for example, from Stability AI) could generate a visual if the answer calls for one.

Each step can come from a different provider, and chaining is what connects them into a single app.

How this fits with your own data

Model chaining becomes more useful when combined with your own data. LLMStack lets you import data and connect it to LLM models to "supercharge" generative AI apps and chatbots. Supported data sources include:

• Web URLs
• Sitemaps
• PDFs
• Audio
• PPTs
• Google Drive
• Notion imports

So a typical build is: bring in your data, chain the models that process and generate from it, and ship the result as an app or chatbot.

Building and sharing

LLMStack is a no-code platform, so the chaining and model selection happen through its interface rather than through code. Once built, apps can be shared publicly with anyone on the internet, or restricted to specific individuals using a granular permission model. Viewer and collaborator roles let multiple people modify and build an app together.

What to check before you commit

• Provider coverage: The named providers are OpenAI, Cohere, Stability AI, and Hugging Face. If you need a provider outside this list, confirm support first.
• Pricing and access: The available source material does not state pricing or login requirements, so don't assume the platform is free or usable without an account.
• Data source fit: If your content lives in a format not listed above, verify it can be imported before designing your pipeline around it.

LLMStack is a no-code platform for building generative AI apps, chatbots, and agents. You use it when you want to connect your own data to large language models and ship a working app without writing a frontend or backend yourself. It supports model chaining across providers such as OpenAI, Cohere, Stability AI, and Hugging Face, and it lets you share what you build publicly or restrict it to specific people.

What LLMStack actually does

The platform sits between your data and the model providers you already use. Instead of wiring up API calls, retrieval, and a UI by hand, you assemble those pieces in a no-code builder.

Three capabilities define the product:

• Model chaining — Combine multiple providers in one app. A single workflow can call OpenAI for text, Stability AI for images, and Hugging Face for a specialized model, rather than locking you into one vendor.
• Bring your own data — Import your own data and connect it to LLM models to power generative AI applications and chatbots.
• Collaborative building on React — Apps are built on React, and you can share them publicly or restrict access with a granular permission model.

What you can build

The clearest fit is anything that needs your data plus a language model behind a simple interface.

A concrete example: a support bot that answers from your help center. You import the source pages, connect them to a model, and share the app with your team as collaborators while keeping it private from the public.

Data sources you can connect

LLMStack accepts a wide variety of data sources, so you are not limited to plain text files:

• Web URLs and sitemaps
• PDFs and PPTs
• Audio
• Google Drive
• Notion imports

This matters because most real projects start with data that already exists in one of these places. If your knowledge lives in Notion or a Drive folder, you can point LLMStack at it rather than reformatting everything first.

Sharing and access control

Once an app works, distribution is a permissions question. LLMStack gives you two ends of a spectrum:

• Public — Share the app with everyone on the internet.
• Restricted — Limit access to certain individuals using the granular permission model.

It also separates roles: viewers can use the app, while collaborators can modify and build it together. That distinction is what makes it usable for a team rather than a single builder.

Who it is for

LLMStack fits you if:

• You have data you want a model to work over, and you would rather not build the plumbing yourself.
• You want to mix providers instead of committing to one.
• You need to hand a working app to teammates or a limited audience.

It is a weaker fit if you need full control over every layer of the stack, or if your use case has no data component and is just a thin wrapper around a single model call.

Getting started

1. Pick the data source that holds your content (URL, sitemap, PDF, Notion, and so on).
2. Import it and connect it to the model you want to use.
3. Chain additional models if the app needs more than text generation.
4. Set sharing and roles — public, or restricted to specific viewers and collaborators.

The fastest way to judge whether it suits you is to take one real dataset you already have and try to get a working chatbot out of it. If the import and model connection go smoothly, the rest of the platform's value — chaining, permissions, collaboration — is available to build on.