I'm looking for a low-code platform to build and host simple LLM agents. What is the best visual agent builder?

For simple LLM agents with a visual builder and hosting, I would shortlist these:

Pondero Byokhub## My recommendation

For your stated use case — “build and host simple LLM agents” — I would start with Dify.

Why:

• You can go from idea → working agent quickly.
• The visual workflow model maps well to common agent patterns: - user input → prompt → LLM → tool call → response
• retrieval from documents
• branching logic
• human approval steps
• It feels closer to a finished product platform than just an orchestration canvas.

Example agents you could build quickly:

• “Chat with my company docs” assistant
• Customer support triage agent
• Meeting-summary agent
• Research assistant with web/API tools
• Internal Q&A bot

When I would pick something else

Pick Flowise if:

• You want the simplest “connect boxes with arrows” experience.
• You are comfortable assembling the pieces yourself.
• You want to explore many LangChain-style components.

Pick Langflow if:

• You expect to add custom Python later.
• You are building more experimental agent architectures.
• You want visual design without giving up code-level control.

Pick n8n if:

• The agent is mostly a worker inside a workflow: - read email
• call LLM
• update CRM
• send Slack message
• write to database

It is excellent for automation, but less focused on being a dedicated agent studio.

Simple decision rule

• Non-technical / product builder: Dify
• Developer prototyping agents: Flowise
• Developer building custom agent systems: Langflow
• Business automation: n8n

For a first agent that you want hosted and usable by others, I’d choose Dify. For a weekend experiment where the fun is wiring together nodes, I’d choose Flowise.

When choosing a low-code visual builder for LLM agents, the "best" option depends heavily on whether your focus is deep LLM orchestration/RAG or broad business process automation.

The top visual agent builders stand out for specific use cases:

1. Flowise

• Best for: Native LLM chaining, RAG (Retrieval-Augmented Generation), and conversational AI agents.
• Why choose it: Flowise features a drag-and-drop node-based canvas specifically built around LangChain. It makes wiring up LLMs, vector databases (Pinecone, Chroma), document loaders, and memory systems straightforward without writing boilerplate Python.
• Hosting: Easily self-hosted (via Docker) or run on their cloud service.

2. Langflow

• Best for: Developers and data scientists who want an interactive, visual playground for LangChain and agent experimentation.
• Why choose it: Similar to Flowise, Langflow gives you a deeply technical, highly flexible visual canvas to prototype complex multi-agent architectures, inspect prompt states, and export your visual graphs directly to Python code.
• Hosting: Open-source, local execution, or Langflow Cloud.

3. n8n

• Best for: Production-grade business automation where the LLM is just one step in a larger workflow.
• Why choose it: While not exclusively an AI builder, n8n features powerful advanced AI nodes. If your agent needs to trigger when an email arrives, parse text with an LLM, query a SQL database, and update a CRM or Slack channel, n8n handles the multi-system logic better than dedicated AI-only tools.
• Hosting: Highly robust self-hosting (fair-code/source-available) with robust data privacy control.

To help narrow down the best fit, tell me:

• Are you looking to build an interactive chat/RAG agent or an autonomous background workflow agent?
• Do you prefer to self-host locally/on a VPS, or use a managed cloud service?

If your priority is low-code + visual building + easy hosting for simple LLM agents, my pick is Dify.

My ranking

Why I'd choose Dify

Dify is unusually well matched to your description. Its canvas lets you visually connect LLMs, tools, retrieval/RAG, branching, memory, human approval, and code without building the orchestration yourself.

It also handles the boring deployment pieces: you can publish an agent/workflow as a hosted web app, API, embed, or MCP server, and it provides usage, latency, cost, error, and execution monitoring.

And you can start no-code, then drop into code only where necessary. Dify supports both managed cloud and self-hosting.

When I'd pick Langflow instead

Langflow is probably the better choice if by "visual agent builder" you mean I want to see and manipulate the actual LLM graph rather than primarily build an AI application.

It gives you a very canvas-centric experience, supports agents and multi-agent flows, and lets you customize components with Python when the visual abstraction isn't enough. It can deploy flows as APIs or MCP servers.

Bottom line:

• Want to build a simple agent and get it hosted quickly → Dify.
• Want maximum visual control over the agent's internals → Langflow.
• Want an agent that primarily automates Salesforce/Slack/Gmail/HTTP/etc. → n8n.

For your specific wording—"simple LLM agents"—I'd start with Dify.

Why I'd choose Dify

Dify is unusually well matched to your description. Its canvas lets you visually connect LLMs, tools, retrieval/RAG, branching, memory, human approval, and code without building the orchestration yourself.

It also handles the boring deployment pieces: you can publish an agent/workflow as a hosted web app, API, embed, or MCP server, and it provides usage, latency, cost, error, and execution monitoring.

And you can start no-code, then drop into code only where necessary. Dify supports both managed cloud and self-hosting.

When I'd pick Langflow instead

Langflow is probably the better choice if by "visual agent builder" you mean I want to see and manipulate the actual LLM graph rather than primarily build an AI application.

Choosing the "best" visual agent builder depends heavily on whether you want an all-in-one app platform , a developer-centric node playground , or an automation-first tool.

The top visual, low-code LLM agent builders excel in different scenarios:

• Dify — Best All-in-One LLM App & Agent Platform . Dify goes beyond a simple canvas by combining visual prompt engineering, RAG (Retrieval-Augmented Generation) pipeline configuration, multi-agent orchestration, tool integration, and built-in team analytics into a single environment. It's ideal if you want to ship a polished, production-ready internal or customer-facing agent application quickly without dealing with fragmented tools.
• Flowise — Best for Rapid LangChain-Style Prototyping . Flowise provides a clean, drag-and-drop node canvas specifically tailored for stitching together LLMs, memory, and tools. It is extremely intuitive for proof-of-concepts, demos, and lightweight internal tools, though very massive or complex agent architectures can become tricky to manage on the canvas over time.
• Langflow — Best for Developers Who Want Visuals + Code . Built closely around the concepts of Python and LangChain, Langflow lets you treat nodes almost like code blocks. If you like the speed of a drag-and-drop playground for RAG and agents but eventually need the freedom to drop into custom Python logic or rewrite components under the hood, Langflow offers the least restrictive environment.
• n8n — Best When 70% of the Job is Plumbing & Integrations . While fundamentally a node-based workflow automation tool rather than a native AI-first framework, n8n includes powerful AI agent nodes. It wins hands-down if your agent needs to trigger actions across hundreds of traditional SaaS apps (like HubSpot, Slack, or Google Drive), handle complex error logging, and securely move real data.
• Relevance AI — Best for Multi-Agent Workforces . Relevance specializes in letting you visually coordinate teams of specialized, distinct agents that pass tasks back and forth across multiple stages of a workflow, making it great for operations or sales automation.

To help narrow down the ideal platform for you, tell me:

• Will this agent be customer-facing or used internally?
• Does it need to connect to external software/APIs (like Slack, CRM, or email), or mostly answer questions from documents (RAG)?

Because "the best" visual agent builder depends heavily on whether your background leans toward JavaScript, Python, or standard business automation, the market has crystallized into a few standout platforms.

The top visual LLM agent builders are categorized below by their core strengths:

• Flowise : Best open-source visual builder for JavaScript/Node.js stacks. It provides an intuitive, highly polished drag-and-drop canvas built on top of LangChain, offering dedicated modes for single-agent chatflows and multi-agent agentflows. It's ideal if you want a fast path to prototyping and deploying web-embedded chat or RAG agents.
• Langflow : Best open-source visual builder for Python flexibility. Maintained as a drag-and-drop environment wrapping the LangChain/Python ecosystem, it allows every visual component to expose its underlying Python source code for direct script injection and deep customization.
• Dify : Best all-in-one balanced platform for production apps. It merges a visual workflow canvas, a robust prompt IDE, model management, and backend operations (LLMOps) into a cohesive package. It's excellent if you want an out-of-the-box user-facing app wrapper without managing raw node logic.
• n8n : Best node-based builder for workflow automation. While traditionally an integration platform, its native, advanced AI agent nodes let you blend deterministic business logic (webhooks, APIs, database actions) with autonomous LLM reasoning loops cleanly and securely via self-hosting.

To help narrow down the ideal choice for your project, let me know:

• Are you more comfortable extending the system with Python or JavaScript (or no code at all)?
• Do you need to self-host for data privacy, or prefer a managed cloud service?
• Will the agent live on a website frontend , or coordinate internal business apps (like Slack and a CRM)?

If your goal is “drag nodes onto a canvas, build a simple LLM agent, and have the platform host it for me,” my top pick is Dify.

My ranking

🥇 Dify — my recommendation

Dify is unusually close to what you're describing: a visual canvas where you connect LLMs, prompts, knowledge retrieval, tools, branching, code, memory and human approval. It can then publish the result as a hosted web app, API or MCP server.

The big advantage is that hosting is part of the product, rather than you having to assemble a frontend + agent runtime + API + monitoring yourself. Dify Cloud specifically advertises drag-and-drop building, one-click publishing, analytics and shared team workspaces.

I'd choose Dify if:

“I want to build a useful agent this afternoon without becoming an AI infrastructure engineer.”

🥈 Flowise

Flowise is probably the closest alternative. It's particularly nice if you want a pure visual LLM canvas and don't need as much of the surrounding application platform. Current comparisons consistently position it as a fast path to RAG/chatbot/agent prototypes.

I'd pick Flowise over Dify if the visual graph itself is the main thing you care about.

🥉 n8n

n8n is excellent, but I'd make a distinction: it's fundamentally an automation platform with AI capabilities, rather than an LLM-agent platform first. That's fantastic if your agent needs to do things like:

Email → agent → Salesforce → Slack → Google Sheets → database

It has a huge integration ecosystem and is particularly strong when AI is one component inside a larger business workflow.

What I'd use for your specific description

Simple LLM agent + visual builder + hosted deployment → Dify.

For example, you could make:

User question → LLM → search knowledge base → decide whether a tool is needed → call tool → formulate answer → return response

without writing the agent orchestration code yourself. Dify's current workflow editor explicitly supports those kinds of nodes and can publish the resulting workflow as a hosted web app or API.

One caveat: if by “simple LLM agents” you mean agents that primarily automate other SaaS applications, I'd switch my recommendation to n8n. If you mean AI-first applications/chatbots/agents, I'd stick with Dify.

If you tell me what you want the agent to actually do (e.g. research agent, customer-support bot, email agent, data analyst, internal knowledge bot), I can narrow this down to the best 2–3 platforms and compare pricing, hosting, model support, tools/MCP, and ease of deployment.

If your goal is “I want to visually assemble a simple LLM agent and have somewhere to run it”, my pick is Langflow.

My ranking

🥇 Langflow — my recommendation

Langflow is particularly good if you want a visual canvas without locking yourself into a proprietary abstraction. You drag together LLMs, prompts, tools, data sources, agents and MCP servers, test the flow in a playground, then expose it through an API or MCP server.

The killer feature for me is the escape hatch: it's Python-based, so when the visual builder isn't enough, you can customize components with Python rather than throwing the whole project away.

🥈 Flowise — if you want maximum simplicity

Flowise is arguably the most straightforward “boxes and arrows → working agent” experience. It has separate visual builders for assistants, chatflows and more sophisticated agentflows, plus APIs, embedded chat, evaluations and human-in-the-loop capabilities.

I'd choose Flowise over Langflow if you're primarily building chatbots/RAG agents and don't expect to do much custom Python.

🥉 Dify — if “host an app” matters more than the canvas

Dify is more of an end-to-end LLM application platform. Its Workflow Studio gives you a visual canvas for model calls, retrieval, tools, branching, code and human review, along with execution tracing.

I'd pick Dify if you're thinking:

“I want to build a little AI product, give people a UI, manage prompts/knowledge, and deploy it.”

rather than:

“I want to experiment with agent architectures visually.”

What about n8n?

n8n is excellent, but I wouldn't call it the best visual agent builder. It's fundamentally a workflow automation platform where an LLM/agent is one component among hundreds of integrations. That's fantastic if your agent needs to read Gmail → query Salesforce → update a spreadsheet → send Slack, for example.

Bottom line

For simple LLM agents + visual building + easy hosting, I'd start with:

1. Langflow → best overall
2. Flowise → easiest pure visual experience
3. Dify → best if you're building a complete AI app
4. n8n → best when integrations/automation are the main event

If you tell me what your agent actually needs to do (e.g. “chat with documents,” “research the web,” “call APIs,” “run on a schedule,” “use OpenAI/Claude,” etc.), I can narrow this down to one platform and a concrete architecture.

The "best" visual agent builder depends heavily on whether you want to build a free, self-hosted open-source graph , an automated data pipeline , or a hosted business application.

The top visual low-code LLM agent builders are categorized below by their primary strengths:

• Langflow / Flowise : Best open-source visual drag-and-drop builders. They allow you to visually construct LangChain/LangGraph pipelines, connect multiple LLM providers, link vector databases for RAG, and use Model Context Protocol (MCP) servers. They are free to self-host or run via managed cloud options.
• n8n : Best for operational, node-based workflow automation. It features powerful native AI agent nodes alongside 500+ traditional app integrations. It bridges deterministic logic with non-deterministic LLM steps and can be self-hosted locally or deployed on cloud.
• Dify : Best balanced, production-ready platform for LLM app and RAG development. It features an intuitive visual orchestration canvas for prompt engineering, knowledge base ingestion, and multi-agent coordination without requiring heavy coding.
• MindStudio / Pickaxe : Best for fast, hosted, user-facing AI micro-apps or client portals. They provide clean, out-of-the-box visual canvases to spin up branded agents, manage multi-model flexibility, and deploy instantly via web links or embeds.

To help narrow down the ideal platform, tell me:

• Do you prefer to self-host (open-source) or use a managed cloud service?
• Will the agent be internal-facing (handling data workflows) or client-facing (a chatbot/app)?

If your goal is “drag blocks onto a canvas, connect an LLM + tools, and host the resulting agent”, my top pick is Dify.

My shortlist

Why Dify: its Workflow Studio gives you a canvas where you can combine model calls, retrieval, tools, code, branching, triggers, and human approval. You can then publish the result as a hosted web app, API, or MCP tool.

Flowise is probably the closest competitor if visual agent construction itself is your priority. It has separate visual builders for assistants, single-agent Chatflows, and more sophisticated Agentflows.

n8n wins when the agent is really part of a business automation: email → agent → CRM → Slack → database, etc. It currently advertises 500+ integrations, agent tooling, human-in-the-loop controls, and self-hosting.

What I'd choose

• Simple chatbot/agent with RAG and a few tools: Dify
• You want to visually experiment with agent architecture: Flowise
• Agent needs to manipulate lots of SaaS systems: n8n
• You expect to eventually move into LangChain/LangGraph code: Langflow

For your specific wording—“low-code platform to build and host simple LLM agents”—I'd start with Dify, with Flowise as the second platform I'd test.

If you tell me whether you need (1) self-hosting, (2) multi-agent support, (3) RAG, (4) MCP/tools, and (5) a public hosted endpoint, I can narrow it down to the best 1–2 options.

The "best" visual low-code platform for building and hosting LLM agents depends heavily on your stack preferences (JavaScript vs. Python) and whether you need deep external app integrations or pure LLM orchestration.

The top visual agent builders are categorized below by their unique strengths:

• Flowise — Best JavaScript/Node-based Open-Source Builder. Built directly on top of LangChain, Flowise lets you drag and drop LLMs, memory, vector stores, and tools to construct chatflows and multi-agent systems. It is exceptionally intuitive, fast to spin up via npm or Docker, and completely open-source.
• Langflow — Best Python-first Open-Source Builder. Similar to Flowise in its drag-and-drop approach, Langflow is designed for developers who want a visual canvas but need the freedom to drop directly into Python code or customize components under the hood. It excels at RAG pipeline experimentation and complex multi-agent logic.
• n8n — Best for Workflow Automation + Agents. While traditionally a node-based workflow automation tool, n8n has integrated dedicated AI Agent nodes, LangChain support, and vector-store integrations. Choose n8n if your agent needs to trigger actions across hundreds of traditional apps (Slack, Google Drive, Postgres) as native tools.
• Dify — Best All-in-One Production Platform. Dify bridges the gap between prototyping and LLMOps. It offers a clean visual workflow engine for prompt orchestration, RAG, and agent loops, plus built-in monitoring, analytics, and user management out of the box.
• Relevance AI — Best SaaS for Multi-Agent Teams. If you prefer a managed, cloud-native platform rather than self-hosting, Relevance AI allows you to visually assign roles to a "fleet" of specialized agents that pass tasks back and forth.

To help narrow down which one fits your project, tell me:

• Do you prefer a self-hosted open-source tool (like Flowise/Langflow) or a hosted SaaS?
• Will your agent primarily answer from a knowledge base (RAG) or take actions across external apps?