LLMStack
LLMStack is an open-source no-code RAG chatbot and AI agent builder you can self-host.
Pick LLMStack when you want a grounded, document-backed assistant running without hiring an engineer, and you care about swapping model providers or self-hosting. The documented Gemini Pro, Claude-2 (100K context), and HeyGen Realtime Avatars support give it more reach than the average no-code chatbot builder. Pass if you need to tune retrieval internals or run heavy production traffic on your own dime.
Verified 4d ago · liveness 74/100 · cite: rightaichoice.com/tools/llmstack
- Business users building a no-code RAG chatbot from company documents
- Teams whose knowledge base already lives in Google Drive or Notion
- Prototypers who want to swap or chain multiple model providers in one app
- Organizations that need a self-hosted assistant for document privacy
- Teams that need model fine-tuning rather than retrieval grounding
- Developers who want step-level control over chunking, embeddings, or reranking
- High-volume production workloads without dedicated infrastructure
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Skip LLMStack if you need to fine-tune models or control retrieval at the step level, since the no-code builder will push you back into the underlying framework for anything the UI doesn't expose.
Realtime avatar apps bill through HeyGen on top of whatever you pay for LLMStack, so a popular avatar bot adds a second vendor invoice.
LLMStack competes on being open source rather than on being cheap: the self-hosted edition is free to run if you supply the infrastructure and maintain it, while the managed cloud shifts that to a subscription plus your model providers' API bills. Compared with a direct OpenAI or Anthropic API build, you trade some per-call margin for not having to build the retrieval and permissions layers yourself. Compare against Flowise if you want the free open-source route with more code-level control.
In short
LLMStack — LLMStack is an open-source no-code RAG chatbot and AI agent builder you can self-host. Best for Business users building a no-code RAG chatbot from company documents, Teams whose knowledge base already lives in Google Drive or Notion, Prototypers who want to swap or chain multiple model providers in one app. Free to start; paid plans from $15/mo.
What's new in LLMStack
Checked 3 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.
Average across the 3 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- No-code drag-and-drop builder for AI agents, workflows, and chat apps
- Model chaining across OpenAI, Cohere, Stability AI, and Hugging Face
- Built-in RAG pipeline to ground chatbot answers in your own documents
- Import content from web URLs and sitemaps
- Import PDF and audio files as data sources
- Import PowerPoint decks into the retrieval pipeline
- Google Drive and Notion data source imports
- Google Gemini Pro model support
- Anthropic Claude-2 with 100K context window support
- HeyGen Realtime Avatars for realtime video answers from documents
- Granular permissions with viewer and collaborator roles
- Share apps publicly or restrict to specific individuals
- Real-time collaborative editing for multiple builders
- Self-host the open-source edition on your own infrastructure
- Managed cloud offering for teams that don't want to run infra
About LLMStack
LLMStack is an open-source platform from MakerDojo, Inc. for building AI agents, workflows, and chat apps without writing pipeline code. You assemble apps in a drag-and-drop builder, chain models from OpenAI, Cohere, Stability AI, and Hugging Face, and ground answers in your own content through a built-in RAG pipeline. Documented data sources span web URLs, sitemaps, PDFs, audio, PowerPoint decks, Google Drive, and Notion imports, so the retrieval layer is mostly configuration rather than engineering. Access control separates viewer and collaborator roles, letting some users edit an app while others only consume it, and apps can be shared publicly or locked to specific individuals. The platform also documents Gemini Pro support, Anthropic Claude-2 with its 100K context window, and a HeyGen Realtime Avatars integration that generates realtime video of an avatar answering questions from your documents. That combination matters for two audiences: business users who want an internal assistant shipped this week, and teams that need an assistant grounded in private documents they are unwilling to hand to a closed SaaS. Because the open-source edition can be deployed on your own infrastructure, the second group gets a real alternative to a cloud-only build. Where LLMStack differentiates itself is multi-provider chaining under a no-code surface. Single-model builders force you to pick one vendor up front; here you can swap or sequence providers as prices and capabilities shift. The tradeoff is depth: this is a platform for grounded assistants, not a workbench for step-level retrieval experimentation. Positioned against Flowise on one side and hand-built OpenAI or Anthropic API pipelines on the other, LLMStack trades maximum control for less wiring. If you want a grounded chatbot running on your documents without owning the retrieval stack, it is a reasonable starting point.
Behind the Verdict
LLMStack is the tool we'd reach for when the request is 'build me a chatbot on our docs' and there is no ML engineer in the room. The drag-and-drop builder plus the documented import path for PDFs, sitemaps, audio, PowerPoint, Google Drive, and Notion covers the unglamorous part of RAG: getting the content in. Permissions with viewer and collaborator roles mean you can ship an internal assistant without inventing an access model. When to pick it: you want to chain models across OpenAI, Cohere, Stability AI, Hugging Face, Gemini Pro, or Claude-2 without writing provider glue. Multi-model flexibility is the reason to choose LLMStack over a single-vendor no-code builder, and self-hosting is the reason to choose it over a closed cloud product when the documents are sensitive. When to pass: if your problem is retrieval quality, not retrieval plumbing. Teams that want to swap chunkers, rerankers, or embedding strategies at the step level will hit the ceiling of a UI-driven pipeline quickly. Fine-tuning is out of scope here; this is a grounding tool, not a training platform. Closest alternative: Flowise. Flowise gives developers more node-level control; LLMStack gives business users more finished UI. If your team is comfortable in code, Flowise may be the better long-term home. If not, LLMStack gets you further before you hit the wall. One caveat on the integration list: Gemini Pro, Claude-2 with 100K context, and HeyGen Realtime Avatars are recent additions, and the HeyGen piece is genuinely unusual for this category. Expect the model roster to keep moving. On cost: we could not verify current published tiers in this refresh, so treat any pricing you see quoted elsewhere as unconfirmed until you check the vendor directly.
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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 the Drive folders, the Notion policy pages, and the HR handbook PDF, then publish a chat app restricted to staff and share the link in Slack.
Outcome: Staff answer their own policy questions instead of asking HR, and the answer trail points back to the source documents.
Point LLMStack at the site's sitemap to pull documentation pages, wire the RAG pipeline to OpenAI, test answers against real support tickets, then switch the same app to Gemini Pro to compare output on long pages.
Outcome: A working grounded bot in days, with provider comparison done before committing to one model.
Load the product FAQ and launch PDFs, attach the HeyGen Realtime Avatars integration, and publish an avatar that answers visitor questions from those documents.
Outcome: A talking FAQ that handles the repetitive questions and frees the team for the ones that need a human.
Use Cases
- Build a RAG chatbot that answers questions from your company PDFs and documents.
- Import a documentation site via sitemap and publish an assistant your team can query.
- Chain OpenAI and Claude-2 in a single app for multi-step content generation.
- Deploy a realtime avatar chatbot that speaks answers pulled from your knowledge base.
- Share a restricted AI app with named colleagues so only they can use it.
- Use Gemini Pro for tasks that involve long source texts.
- Give a team of editors collaborator access to the same app while everyone else gets view-only.
Models Under the Hood
as of 2026-10-17
Limitations
- The no-code builder is the ceiling as well as the selling point: anything the UI doesn't expose pushes you into the underlying framework.
- Realtime avatar functionality depends on third-party HeyGen, which sits outside LLMStack's control.
- Because you chain other vendors' models, answer quality is bounded by those APIs, and running the managed cloud at high volume is subject to that service's own usage terms.
- Self-hosting moves the operational burden to you, and LLMStack's own docs are thin in the pages available here.
as of 2026-10-07
Verification history
We have re-verified LLMStack 8 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-checked, vendor evidence unchanged
- — 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
Showing the 6 most recent of 8 verification passes.
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.
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 competes on being open source rather than on being cheap: the self-hosted edition is free to run if you supply the infrastructure and maintain it, while the managed cloud shifts that to a subscription plus your model providers' API bills. Compared with a direct OpenAI or Anthropic API build, you trade some per-call margin for not having to build the retrieval and permissions layers yourself. Compare against Flowise if you want the free open-source route with more code-level control.
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.
Business users importing from Google Drive or Notion can get a first grounded chat app working in an afternoon, since those sources are first-class and the RAG pipeline is built in. Prototypers comparing providers should budget an extra day or two to wire models like Gemini Pro and Claude-2 and evaluate answers. Self-hosted deployments add real time on top: you own provisioning, upgrades, and
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 a hand-rolled OpenAI API chatbot: import your source documents as data sources and let the RAG pipeline replace your retrieval glue.
- →From document Q&A built on manual chunking scripts: move the source files into Google Drive or Notion imports and rebuild the app in the no-code builder.
- →From a single-provider no-code bot: recreate the app and add a second model such as Gemini Pro or Claude-2 to the chain for comparison.
- ↗To Flowise: rebuild the flow with more code-level control over each pipeline step.
- ↗To a direct OpenAI or Anthropic API build: export your source documents and reimplement retrieval and permissions in your own stack.
- ↗To a self-hosted LLMStack deployment: take the same app definition off the managed cloud and run it on your own infrastructure for document privacy.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “LLMStack”, and we withheld 6: 6 could not be judged, because “LLMStack” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about LLMStack.
Official links
Tools that pair well with LLMStack
Common stack mates teams adopt alongside LLMStack, with the specific reason each pairing earns its keep.
Dify
Dify is an open-source visual builder for agentic workflows and RAG pipelines, deployable on cloud, VPC, or self-hosted Docker.
MaxKB
MaxKB is Fit2Cloud's open-source, self-hosted enterprise agent platform for RAG knowledge bases, visual workflows, and AI assistants on
Ruoyi Ai
Open-source, self-hosted AI assistant and agent framework with unified model routing, a full RAG chain, and local/remote MCP tooling.
Featured Head-to-Head Comparisons
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 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 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.
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Frequently Asked Questions
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