Stacks / 6 workflow stages

Set Up an Open / Local AI Workbench

Run models locally, add a usable interface, experiment with agents, and keep the stack less dependent on one hosted provider.

Best for

Developers experimenting with local models, privacy-conscious workflows, or provider-independent AI tooling.

Outcome

A local-first environment for running models, interacting with them, and building simple agentic workflows.

Not for

Teams that need guaranteed enterprise support, managed scale, or production SLAs from a single hosted platform.

Use this as a decision framework, not a mandatory shopping list. Swap or skip layers based on your existing stack, constraints, and risk profile.

01

1. Run models locally

Ollama

Start with a straightforward local model runtime that makes downloading and serving supported models easy.

Code & Build 179k stars
02

2. Add a usable interface

Open WebUI

Give local models a practical browser interface for everyday experimentation and testing.

Agents & Automation 149.3k stars
03

3. Add a coding workflow

OpenCode

Use an open coding-agent workflow when local/provider-flexible development is part of the goal.

AlternativesAider, Cline

Code & Build 199.3k stars
04

4. Build visual AI workflows

Dify

Prototype model and agent workflows with less custom application code.

AlternativesLangflow

Agents & Automation 153k stars
05

5. Discover models

Hugging Face Models

Explore open model options and their ecosystem before committing to a runtime choice.

AlternativesOpenRouter Models

06

6. Compare model tradeoffs

Artificial Analysis

Use independent performance and cost signals to understand where local/open options sit relative to hosted models.

AlternativesOpenRouter Rankings

Need this workflow built and running, not just linked? EE Solutions implements agentic stacks like this one. EE Solutions is a senior technology team for private capital firms and their portfolio companies.

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