LLMGraph - Visual AI Workflows with Built-In RAG

Build AI workflows visually or with chat, ship in seconds - no code, no infra.

Unclaimed

Updated Jul 2026 · Added Jul 2026

llmgraph.ai

Automation PlatformsFreeautomation-platforms

Visit Llmgraph

Share: X in LinkedIn

Add your screenshot here

Image or video shown in this spot

Follow Llmgraph

What is Llmgraph?

LLMGraph lets teams build and deploy AI workflows - RAG systems, support bots, and agentic apps - without code or infrastructure. Describe workflows in plain English or drag-and-drop nodes on a visual canvas, then ship instantly to a REST endpoint or embeddable chat widget. Built-in RAG grounds answers in your documents with citations; six LLMs to choose from.

Explore Llmgraph

Need help implementing LLMGraph - Visual AI Workflows with Built-In RAG?

Find verified specialists who work with LLMGraph - Visual AI Workflows with Built-In RAG

Browse specialists

Key Features of Llmgraph

Visual graph builder with drag-and-drop nodes
Natural language workflow description via chat
Built-in RAG with PDF, Word, and markdown support
Six foundation models (Claude Sonnet/Haiku, Llama, Mistral, Amazon Nova)
REST API endpoint deployment
Embeddable chat widget
Production-ready templates (RAG, Agent Chat)
Error handling with catch blocks and choice nodes
Dev/staging/prod environments with version-pinned state machines
Source citations for document-grounded answers
Stripe-shaped API keys with origin gating and daily budget caps
Token-credit metering and rate limiting

Who Should Use Llmgraph?

Build RAG systems grounded in company handbooks or documentation

Deploy customer support bots with intent routing

Create internal knowledge-base chat tools

Route support tickets by intent classification

Embed AI assistance directly into products

Scale agentic workflows across REST and browser surfaces

Llmgraph: Pros & Cons

✓Pros

No code required - build visually or with chat
No infrastructure to manage - ships to endpoint/widget in one click
Multi-model support in a single workflow
Built-in document grounding with automatic citations
First-class error handling with catch blocks
Production-grade deployments with dev/staging/prod environments
Version pinning and one-click promotion/rollback
Security-first API keys with origin gating and per-key budgets

Frequently Asked Questions about Llmgraph

What models does LLMGraph support?

LLMGraph supports six foundation models including Claude Sonnet 4.5, Claude Haiku 4.5, Llama 3.1 70B and 8B, Mistral Large, and Amazon Nova Pro. You can mix different model tiers in a single workflow.

Do I need to set up a vector database?

No. LLMGraph has built-in RAG that grounds answers in your documents without requiring a separate vector database setup.

How do I deploy my workflow?

Every workflow deploys to a REST API endpoint or an embeddable chat widget with one click. You can use either surface in customer support, internal tools, or your product.

Can I build workflows without coding?

Yes. You can describe what you want in plain English via chat and watch the graph build itself, or wire it together visually on the canvas.

What environments are supported?

LLMGraph provides dev, staging, and production environments with version-pinned state machines. You can promote with one click and rollback by re-pinning.