Comparison · Dify Alternative · Local Models

TheYgent vs Dify: Which Fits Local Models?

Dify is a mature platform for building LLM apps — agentic workflows, RAG pipelines, and a collaborative workspace. TheYgent is a newer, local-first agent builder that runs the models itself. If you are looking for a Dify alternative for private, self-hosted AI on local models, here is how they compare, including where Dify is the better pick.

Checked against Dify 1.17.1’s source and documentation on October 12, 2026. Report an inaccuracy.

At a Glance

TheYgent Dify
Built for AI agents on local models, with its own inference plane LLM apps — agentic workflows, RAG pipelines, a collaborative workspace
Local models Runs them itself: llama.cpp, MLX, and vLLM (experimental), started and unloaded on demand Connects to a model server you run, e.g. through its Ollama plugin
Getting a model In-app Hugging Face catalog with a RAM fit check per variant Download it in the model server, then add it as a provider model
Outbound by default No telemetry; one startup request carrying no data No analytics keys set; checks updates.dify.ai and connects to its plugin marketplace
MCP Stdio and HTTP servers, a registry browser, OpenAPI/GraphQL turned into MCP Imports tools from MCP servers; can publish apps as MCP servers
Human-in-the-loop Approval node; a pending approval survives a restart Human Input node
License Sustainable Use License (fair-code): no selling, no hosted service Modified Apache 2.0: commercial use allowed; multi-tenant use needs a commercial license; frontend logo must stay
Maturity New — 1.0 in October 2026 Established — about 158k GitHub stars

The Main Differences

Who runs the model

Dify is model-agnostic by design: models arrive through provider plugins, and for local inference you run a separate server — Ollama is the common choice — and register its URL. That keeps Dify flexible, but the model server is yours to install, update, size, and keep running. TheYgent puts the engines inside the product. You install a model from the catalog, and the inference plane starts llama.cpp or MLX on the first call, keeps at most two engines resident by default, and unloads idle ones after 15 minutes. Speech-to-text, text-to-speech, embeddings, and vision run on built-in engines too; image generation does as well, after some extra setup.

What the software does on its own

A default Dify install has no analytics keys configured, but its .env.example enables an update check against updates.dify.ai and the plugin marketplace at marketplace.dify.ai; both are settings you can change. TheYgent has no telemetry, update check, or marketplace connection. The model catalog and MCP hub browser reach Hugging Face and the MCP registries only when you open them. Here is the full list.

Licensing

Dify’s license is the more permissive of the two for commercial use: you may run Dify as a backend for your own products as long as you don’t operate a multi-tenant service or strip its branding. TheYgent’s Sustainable Use License allows internal business, personal, and non-commercial use, but not selling TheYgent or offering it as a hosted service.

Agents as versioned artifacts

In TheYgent the graph is the agent, and publishing it mints an immutable, content-addressed version you can invoke by API, roll back to, or put behind a token endpoint, an HMAC-signed webhook, or a cron schedule. Every run leaves a span waterfall — per-node inputs, outputs, timing, and token usage — in your own Postgres.

Which One Should You Pick?

Choose Dify if…

  • You want a mature platform with a large community and a plugin marketplace.
  • Several teams build and publish LLM apps in shared workspaces.
  • You mostly call hosted model APIs, or already operate a model server you like.
  • You need a license that allows commercial single-tenant use.

Choose TheYgent if…

  • The models must run on your own hardware, without a separate model server to operate.
  • You want no telemetry and a short, documented list of network touchpoints.
  • You run Apple Silicon and want native MLX, or mix local and hosted models per node.
  • Runs must survive crashes, and you want a per-node trace of every run.

Questions

Asked and Answered.

Is TheYgent a Dify alternative for local models?

Yes, if local models are the priority. Both are self-hosted visual builders for AI workflows and agents with RAG, MCP tools, and human approvals. The difference is that TheYgent runs local models on its own engines — llama.cpp, MLX, and vLLM (experimental) — while Dify connects to a model server you run separately. Dify is the more mature platform, with a larger community and a plugin marketplace.

Can Dify run local models?

Dify uses local models through model-provider plugins that connect to a model server you run, such as Ollama. Dify itself does not download or execute model weights; the model server does.

Does Dify send data home by default?

As of Dify 1.17.1, its Docker .env.example sets no analytics keys, but enables an update check against updates.dify.ai and the plugin marketplace at marketplace.dify.ai. Both can be changed in configuration. TheYgent has no telemetry or update check.

Sources