OpenCode - The Open Source Agent for the Local Fortress

In my last article about my AI workflow, I already celebrated OpenCode as the "game changer" for deep local development. It's the tool that takes over where visual builders like v0 reach their limits. But one topic keeps coming up in conversations with CTOs and Lead Devs: "How do we use such power tools without our code wandering off into the cloud?"

The answer has often been unsatisfactory: "You don't" or "Wait for the Enterprise On-Premise solution in Q4 2027".

But the landscape is changing. With OpenCode, a player enters the field that doesn't just have "Open Source" in its name but takes code sovereignty seriously.

OpenCode is primarily a Terminal-First AI Coding Agent, though it now also offers a promising desktop app (currently in beta, which I use myself). Unlike Copilot or Cursor, which primarily live as "autocomplete on steroids" in the editor, OpenCode acts like a real colleague in the command line or its own window.

You give it a task: "Refactor the Auth-Service to use JWTs instead of Sessions, and adapt all tests."

OpenCode:

1. Reads the code and understands dependencies (LSP integration).
2. Plans the steps.
3. Executes changes file by file.
4. Runs tests to verify its work.
5. Corrects itself if tests fail.

This alone is impressive. But the real killer feature is its Model Agnosticism.

Let's imagine a scenario: A defense contractor or a bank. Strictest compliance. Not a single byte of code is allowed to leave the corporate infrastructure. GitHub Copilot is not an option here.

This is where OpenCode shines. Since it is open source and can talk to any LLM endpoint, we can build a completely isolated "air-gapped" solution.

We need three components:

1. The Brain: A local inference server (e.g., Ollama or vLLM) running on a strong internal GPU machine.
2. The Interface: OpenCode CLI on developer laptops.
3. The Connection: An internal network.

1. Set up Inference Server We use Ollama as a simple example. On the server (e.g., with an NVIDIA H100 or just a strong Mac Studio):

   bash
   ollama serve
   ollama pull deepseek-coder:33b

   The server now listens on port 11434.

2. Configure OpenCode On the developer laptop, we install OpenCode:

   bash
   curl -fsSL https://opencode.ai/install | bash

   Instead of logging in to Anthropic or OpenAI, we configure the local endpoint in ~/.opencode/config.json:

   json
   {
     "models": [
       {
         "name": "company-internal-model",
         "provider": "openai-compatible",
         "apiBase": "http://internal-ai-server:11434/v1",
         "apiKey": "not-needed",
         "contextWindow": 32000
       }
     ]
   }

3. Get Started

   bash
   opencode --model company-internal-model

For the developer, it feels like magic. They have an intelligent agent that knows their code, refactors, and explains.

For the security department, it's a dream:

• Data Residency: The code never leaves the internal network.
• Auditability: Every prompt and every response runs through your own server and can be logged.
• Cost Control: No pro-user license fees. Costs are fixed (hardware + electricity).

OpenCode also offers an Enterprise Version for larger teams. This adds things admins love:

• Centralized Config Management: Distribute LLM settings to all devs.
• SSO Integration: Login via Okta or Active Directory.
• Compliance Logs: Who generated what and when?

The time when we had to decide between "good AI" (Cloud) and "secure AI" (dumb & local) is over. OpenCode proves that modern agentic workflows are possible even in the most secure environments.

Anyone still using security concerns as an excuse not to use AI has lost their last argument with OpenCode.

• OpenCode Official Website

  The official entry point for downloads and documentation.

• Setting Up OpenCode with Local Models

  Detailed guide by Rami Krispin on local usage.

• OpenCode GitHub Repository

  The source code of the agent.