LLMGraph

What is LLMGraph?

LLMGraph is a no-code LLM workflow builder for teams that want to assemble LLM apps as graphs instead of writing backend glue. The app is built around visually wiring nodes on a canvas or describing the workflow in chat, then deploying the same graph as a REST API or an embeddable chat widget.

It is designed for RAG chatbots, support bots, and agent-style workflows. The product description emphasizes that you can ground answers in uploaded content, connect models and tools, and ship the finished flow to different surfaces without rebuilding it each time. In that sense, LLMGraph is both a no-code LLM workflow builder and a deployment layer for those workflows.

The platform also highlights a 14-day trial, version-pinned environments, and built-in controls for usage and rate limits. That makes it closer to a production workflow tool than a simple demo builder.

Key features

LLMGraph centers the no-code LLM workflow builder experience around a visual graph and a chat-based authoring mode.

• Visual graph builder: create nodes on a canvas and connect them into a workflow.
• Chat-based authoring: describe the workflow in plain English and have the graph sketched for you.
• RAG support: upload PDFs, Word documents, or markdown to ground responses in your own content.
• Citations: answers can include links back to the source passage or page.
• Multi-surface deployment: publish one workflow as a REST endpoint or an embeddable widget.
• Environment separation: deploy to dev, staging, and prod with version-pinned state machines.
• Branching and recovery: use choice nodes and catch blocks for conditional routing and failure handling.
• Model mix: the site lists Claude Sonnet 4.5, Claude Haiku 4.5, Llama 3.1 70B/8B, Mistral Large, and Amazon Nova Pro.
• Usage controls: secret and publishable API keys, origin gating, per-key daily budget caps, token-credit accounting, and GCRA rate limiting.

The no-code LLM workflow builder also supports a canvas run mode, so you can test a single node or the full graph before deployment.

How to use it

1. Start by describing the workflow in chat, or open the canvas and wire it manually.
2. Add a knowledge base by uploading PDFs, Word docs, or markdown if you want grounded RAG answers.
3. Choose the model or mix of models you want to use in the graph.
4. Run the workflow on the canvas to test outputs live before you deploy.
5. Promote the graph to dev, staging, or prod when you are ready.
6. Publish the result as a REST API endpoint or an embeddable chat widget.
7. Use the generated endpoint in your backend or the widget on a webpage, internal tool, or product surface.

Because the no-code LLM workflow builder handles both authoring and shipping, the same graph can serve as a support assistant, a product assistant, or an internal workflow.

Who it is for

LLMGraph is best for builders who need a no-code LLM workflow builder but still care about deployment details and operational control.

It may fit:

• product teams that want embedded AI support or help widgets
• developers who need a REST API for an LLM workflow without assembling the whole stack
• ops or internal-tool teams that need document-grounded assistants
• founders who want to prototype RAG apps and agent flows quickly
• teams that need a visual graph builder with branching, retries, and environment promotion

It is especially relevant when you want to turn an existing knowledge base into a chatbot, or when you need one workflow to power both a backend API and a browser widget.

What to know before you use it

This no-code LLM workflow builder is powerful, but it is not a replacement for source-of-truth systems.

• Grounded answers depend on the documents you upload, so outdated PDFs or incomplete docs will produce weak results.
• Citations help trace answers back to source material, but they do not guarantee perfect factual accuracy.
• The site mentions a REST endpoint and embeddable widget, but it does not provide full public details on limits, pricing tiers, or all integration options in the harvested text.
• If you need official policy language, legal language, or compliance-critical information, use the underlying source documents or an official system of record to verify the output.
• The platform supports production-style controls such as environments and rate limits, but you should still test workflows carefully before exposing them to users.

For teams that want a no-code LLM workflow builder with RAG, agents, and deployment in one place, LLMGraph is positioned as a practical way to move from idea to a shippable graph. If you need a visual LLM workflow builder that can publish to an API and widget, start here and verify critical answers against your source material.