Comparison · Local Models · Self-Hosted
TheYgent vs Dify, n8n, Langflow & Flowise
All five let you build AI workflows on a visual canvas, and all five can be self-hosted. They differ in who runs the models, what the software sends home by default, and what the license lets you do. If your requirement is local models only and data that stays on hardware you control, those differences decide it.
Checked against each project’s own source code and documentation on October 12, 2026 — Dify 1.17.1, n8n 2.42.7, Langflow 1.12.5, Flowise 3.1.4. Spotted something out of date? Open an issue and we’ll fix it.
The Short Answer
Choose TheYgent when the models have to run on your own hardware and you’d rather not operate a separate model server. TheYgent installs models from an in-app catalog and runs them on its own engines — llama.cpp on any machine, MLX on Apple Silicon, vLLM (experimental) on NVIDIA GPUs — and it sends no telemetry.
Choose one of the others when you need a large integration catalog, a mature ecosystem, or a more permissive license, and you’re happy to run local models in a separate server that the builder calls over HTTP. Each of them is years ahead of TheYgent on community and integrations.
Side by Side
| Feature | TheYgent | Dify | n8n | Langflow | Flowise |
|---|---|---|---|---|---|
| Built for | AI agents on local models: a no-code builder with its own inference plane | LLM apps: agentic workflows, RAG pipelines, a collaborative workspace | Workflow automation with AI nodes and 400+ integrations | AI agents and workflows, extensible in Python | AI agents and chat flows, built in TypeScript |
| Visual, no-code canvas | Yes | Yes | Yes | Yes | Yes |
| Self-hosted | Yes — bare metal, Docker Compose, or Kubernetes | Yes | Yes | Yes | Yes |
| Runs local models itself | Yes — manages llama.cpp, MLX, and vLLM (experimental): starts an engine on first call, unloads it when idle | No — connects to a model server you run, e.g. through its Ollama plugin | No — connects to a model server you run, e.g. through its Ollama nodes | No — connects to a model server you run, e.g. through its Ollama component | No — connects to a model server you run, e.g. through its ChatOllama node |
| Download models in the app | Yes — Hugging Face catalog with a RAM fit check for every variant | Done in the separate model server | Done in the separate model server | Done in the separate model server | Done in the separate model server |
| Local speech & images | Built-in engines for speech-to-text, text-to-speech, and image generation (image generation needs extra setup) | Through a model provider you connect | Through a model provider you connect | Through a model provider you connect | Through a model provider you connect |
| Telemetry by default | None — one startup request, carrying no data | No analytics keys set; checks updates.dify.ai and its plugin marketplace |
Anonymous diagnostics on — opt out with N8N_DIAGNOSTICS_ENABLED=false |
On — opt out with DO_NOT_TRACK=true |
Off unless a PostHog key is configured |
| MCP tools | Yes — stdio and HTTP, a registry browser, and OpenAPI/GraphQL specs turned into MCP servers | Yes | Yes | Yes | Yes |
| Human-in-the-loop | Yes — an approval node on the durable runtime, so a pending approval survives a restart | Yes — Human Input node | Yes — human review for tools | Yes — Human Input component | Yes — Human Input node |
| License | Sustainable Use License (fair-code) | Modified Apache 2.0 — multi-tenant use needs a commercial license | Sustainable Use License (fair-code), plus enterprise-licensed files | MIT | Apache 2.0, plus an enterprise-licensed directory |
| Maturity | New — 1.0 released October 2026 | Established — about 158k GitHub stars | Established — about 207k GitHub stars | Established — about 156k GitHub stars | Established — about 55k GitHub stars |
“Connects to a model server you run” means the model runs in separate software you install and operate, and the builder calls it over HTTP. Star counts are from GitHub on October 12, 2026. Sources for every row are listed at the end of this page.
Where the Differences Are
Local inference: built in, or a separate server
Every builder here can call a local model. The difference is whose job it is to run it. With Dify, n8n,
Langflow, and Flowise you install a model server, download models into it, keep it running, and point the
builder at its URL. TheYgent’s inference plane does that itself: install a model from the Hugging Face
catalog (each variant shows whether it fits your RAM), and the right engine starts on the first call, stays
warm while it’s used, and unloads after 15 idle minutes. Models are addressed by a logical ID such as
triage-fast, so moving one from MLX on a Mac to llama.cpp on a Linux box never touches the
agent graph.
What leaves your machine by default
TheYgent collects no analytics and has no update check, crash reporter, or license server. Runs, traces, and documents stay in your own Postgres, and trace export over OpenTelemetry is off until you point it at a collector you run. The one automatic request — a public model price list fetched at startup by the inference plane’s model client library — carries none of your data, and it falls back to a bundled copy when you’re offline. Every network touchpoint is listed here.
n8n and Langflow send usage telemetry until you opt out; Dify checks for updates and loads its plugin marketplace by default; Flowise’s server stays quiet unless an analytics key is configured. All of it can be switched off — the question is whether it starts on or off.
Licensing
If an OSI-approved open-source license matters, Langflow (MIT) and Flowise (Apache 2.0, outside its enterprise directory) are the permissive options. TheYgent and n8n share the Sustainable Use License: free to use, modify, and self-host for internal business, personal, and non-commercial purposes, but not to sell or run as a hosted service. Dify’s modified Apache 2.0 allows commercial use but requires a commercial license for multi-tenant deployments.
Where the others are ahead
TheYgent reached 1.0 in October 2026; the others have years of production use and communities in the tens or hundreds of thousands. n8n connects to 400+ services out of the box. Dify has a plugin marketplace and multi-user workspaces built for teams shipping LLM apps. Langflow lets you write custom components in Python. Flowise builds on the LangChain.js ecosystem. If those matter more than where the models run, pick the tool that has them.
One Comparison at a Time
TheYgent vs Dify
An LLM-app platform with a plugin marketplace, versus a local-first builder that runs its own engines.
Read the comparison → vs n8nTheYgent vs n8n
Broad workflow automation with AI nodes, versus AI agents built around local inference.
Read the comparison → vs langflowTheYgent vs Langflow
An MIT-licensed, Python-extensible flow builder, versus a builder with its own inference plane.
Read the comparison → vs flowiseTheYgent vs Flowise
A TypeScript agent builder on LangChain.js, versus local engines and durable runs built in.
Read the comparison →Questions
Asked and Answered.
Which visual AI agent builder is best when I can only use local models?
If the requirement is local models only, the deciding question is who runs the models. TheYgent ships its own managed engines — llama.cpp on any machine, MLX on Apple Silicon, and vLLM (experimental) on NVIDIA GPUs — and installs models from an in-app catalog. Dify, n8n, Langflow, and Flowise connect to a model server you install and operate separately. All five can be self-hosted and all five can work with local models; TheYgent is the one where the inference engine is part of the product.
Do I need Ollama or LM Studio to run local models with TheYgent?
No. TheYgent runs local models with its own engines: llama.cpp on any machine, MLX on Apple Silicon, and vLLM (experimental) on NVIDIA GPUs. make engines installs the engine binaries once; after that you install models from the in-app Hugging Face catalog, and TheYgent starts each engine on the first call, keeps it warm, and unloads it when idle. There is no separate model server to run or keep alive.
Which of these platforms send telemetry by default?
As of October 12, 2026: TheYgent has no telemetry. n8n enables anonymous diagnostics by default (set
N8N_DIAGNOSTICS_ENABLED=false to opt out). Langflow sends telemetry by default (set
DO_NOT_TRACK=true to opt out). Flowise’s server sends usage events only when a
PostHog key is configured. Dify ships with no analytics keys set, but by default checks
updates.dify.ai for new versions and connects to its plugin marketplace.
Which of these platforms are open source?
Langflow is MIT-licensed. Flowise is Apache 2.0, except an enterprise directory under a commercial license. Dify uses a modified Apache 2.0 license that requires a commercial license for multi-tenant use and forbids removing its logo from the frontend. TheYgent and n8n both use the Sustainable Use License — fair-code and source-available, not OSI open source: free to use, modify, and self-host for internal business, personal, and non-commercial purposes, but not to sell or offer as a hosted service.
Can I move my flows from Dify, n8n, Langflow, or Flowise to TheYgent?
There is no automatic importer — flows are rebuilt on TheYgent’s canvas. The building blocks map closely: LLM, tool, MCP, retrieval, router, transform, guardrail, and human-approval nodes, plus subgraphs, loops, and maps. MCP servers you already use connect as they are, and a REST API you call today can become a tool node or an MCP server generated from its OpenAPI spec.
Sources
Checked on October 12, 2026. Telemetry and license rows cite each project’s own source files; feature rows cite its documentation.
- Dify — LICENSE · docker/.env.example (
CHECK_UPDATE_URL,MARKETPLACE_ENABLED) · Ollama plugin · MCP · Human Input node - n8n — LICENSE.md · diagnostics.config.ts · Ollama Chat Model node · MCP Client Tool node · Human-in-the-loop for tools
- Langflow — LICENSE · telemetry settings · Ollama component · MCP client · Human-in-the-loop
- Flowise — LICENSE.md · telemetry.ts · ChatOllama node · Tools & MCP · Human in the loop
- TheYgent — LICENSE.md · The engines · MCP · Human-in-the-loop · What touches the network
Try It on Your Own Hardware.
Clone the repo, run make engines and make up, and install a model from the catalog. No vendor account, no cloud dependency.