Comparison · Flowise Alternative · Local Models

TheYgent vs Flowise: Visual Agents, Local Engines

Flowise builds AI agents visually on the LangChain.js ecosystem, under an Apache 2.0 license. TheYgent is a local-first agent builder that runs its own inference engines. If you are looking for a Flowise alternative for private AI agents on local models, here is how they differ, and when Flowise is the better choice.

Checked against Flowise 3.1.4’s source and documentation on October 12, 2026. Report an inaccuracy.

At a Glance

TheYgent Flowise
Built for AI agents on local models, with its own inference plane AI agents and chat flows on LangChain.js components
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 ChatOllama node
Telemetry by default None — one startup request, carrying no data Off unless a PostHog key is configured
MCP Stdio and HTTP servers, a registry browser, OpenAPI/GraphQL turned into MCP MCP tools through its MCP nodes
Human-in-the-loop Approval node; a pending approval survives a restart Human Input node in Agentflow
Built with Python and React, on Postgres with pgvector TypeScript and Node.js
License Sustainable Use License (fair-code) Apache 2.0, plus an enterprise-licensed directory
Maturity New — 1.0 in October 2026 Established — about 55k GitHub stars

The Main Differences

Who runs the model

Flowise reaches local models through chat-model nodes such as ChatOllama, which call a model server you operate. TheYgent ships the engines: install a model from the in-app Hugging Face catalog — each variant shows whether it fits your RAM — and llama.cpp, MLX, or vLLM (experimental) starts on the first call and unloads when idle. Local embeddings, vision, speech-to-text, and text-to-speech run on the same built-in engines.

Runs that survive a crash

TheYgent can run any published agent on its durable runtime, which checkpoints every node. Kill the process mid-run and the run resumes from the last completed node — including loops, subgraphs, and a human approval that has been waiting for days. Every run also leaves a span waterfall with per-node inputs, outputs, timing, and token usage in your own Postgres, exportable over OpenTelemetry if you want it.

Licensing

Flowise’s Apache 2.0 license is the more permissive outside its enterprise directory, and it allows commercial and hosted use. TheYgent’s Sustainable Use License allows internal business, personal, and non-commercial use, but not selling TheYgent or offering it as a hosted service.

Which One Should You Pick?

Choose Flowise if…

  • You want an Apache 2.0 license, including for products you sell or host.
  • Your team works in TypeScript and the LangChain.js ecosystem.
  • You want an established project with a sizable community.
  • Running a separate model server for local models is fine.

Choose TheYgent if…

  • The models must run on your hardware without a separate server to operate.
  • You run Apple Silicon and want native MLX for chat, vision, and speech.
  • Runs must resume after a crash, with a per-node trace of each run.
  • You want agents as immutable, content-addressed versions you can roll back.

Questions

Asked and Answered.

Is TheYgent a Flowise alternative for local models?

Yes, when local models are the priority. Both are visual builders for AI agents with MCP tools and human approvals. TheYgent runs local models on its own engines — llama.cpp, MLX, and vLLM (experimental) — while Flowise calls a model server you run, such as Ollama. Flowise is Apache 2.0-licensed and built on LangChain.js; TheYgent uses the Sustainable Use License.

Can Flowise run local models?

Flowise uses local models by connecting to a model server you run — for example Ollama through its ChatOllama node. Flowise itself does not download or execute model weights.

Sources