I put a fully local, open-source coding stack through its paces to see if it can stand in for Claude Code. The combo pairs Block’s Goose agent with Ollama and the Qwen3-coder model, promising zero cloud dependency, $0 in usage fees, and private, on-device development. Here’s how it works and what I learned.
What I Tested and Why It Matters for Local Coding Agents
Goose, developed by Block, is an open-source agent framework designed to operate on your local files and tools—more like a junior developer that edits your codebase than a chat-only assistant. I connected it to Ollama, a local LLM server, and used Qwen3-coder, a coding-focused model from the Qwen team widely associated with Alibaba Cloud’s research efforts.
- What I Tested and Why It Matters for Local Coding Agents
- Setup in Practice: Installing Ollama and Goose Locally
- How the Agent Loop Works for Coding with Your Repo
- Hands-On Results from Building a Simple WordPress Plugin
- Privacy, Cost, and Trade-Offs for a Fully Local Stack
- Bottom Line: Can a Free Local Stack Replace Paid Agents?
The draw is clear: your code never leaves your machine, you avoid usage caps, and you’re not paying for tokens. That’s attractive at a time when paid AI coding tiers commonly run $20–$100+ per month per user. GitHub’s published studies show developers can complete certain coding tasks up to 55% faster with AI pair-programming, which raises the stakes for having a capable assistant you control.
Setup in Practice: Installing Ollama and Goose Locally
I started with Ollama. After installing the desktop app, I selected qwen3-coder:30b from the model list. The download triggered on my first prompt and landed at roughly 17GB, so plan your storage accordingly. I enabled “Expose to network” so other apps could reach the local API and set a 32K context window to balance memory and responsiveness on a high-RAM Mac.
Next, I installed Goose. In Provider Settings, I chose Ollama, pointed it to the local endpoint, and selected qwen3-coder:30b. I set a working directory so the agent could read and write code files. Once configured, Goose recognized the model and was ready to operate like a coding partner with filesystem access and an iterative loop.
If you’re on constrained hardware, consider a smaller model (for example, 7B or 14B variants) or a more aggressive quantization. On 16GB machines, large models can feel sluggish; unified memory on M-series Macs helps, but you’ll still trade speed for size. The upside is you can swap models at will without changing your workflow.
How the Agent Loop Works for Coding with Your Repo
Unlike a simple chatbot, Goose runs an agentic loop: plan, edit, run, and verify. It reads your repo, makes changes, writes files, and can execute commands or tests you approve. That loop is the key difference from chat-only coding—feedback is grounded in the actual codebase, so corrections accumulate rather than reset with each prompt.
In practical terms, you describe the goal, provide constraints, and let the agent attempt a solution. You review diffs, run tests, and nudge it when it veers off-spec. The model’s context window governs how much it can “remember” from your code at once; for multi-file projects, thoughtful scoping and incremental tasks improve accuracy.
Hands-On Results from Building a Simple WordPress Plugin
For a smoke test, I asked Goose to build a simple WordPress plugin with a small UI and a deterministic behavior. The first attempt installed cleanly but didn’t meet the functional requirements. After several targeted corrections—spelling out edge cases and file structure—the agent converged. It took five iterations to deliver a working version that matched the brief.
The iterative gains were tangible because Goose edits the actual files, not just a suggested snippet. Each round tightened the implementation, and the final output required only minor manual touch-ups. That mirrors my experience with cloud agents, which often need similar cycles to nail details.
Performance-wise, running locally felt competitive for prompt-to-edit turnaround on a high-RAM machine. With multiple heavy apps open, response latency stayed reasonable, and the model handled a 32K context without hiccups. On lower-spec systems, expect slower generation and consider smaller models for a better speed-to-quality ratio.
Privacy, Cost, and Trade-Offs for a Fully Local Stack
Everything here runs on-device, which is a strong default for teams with sensitive code or strict compliance. No sign-in is required, and nothing leaves your machine unless you explicitly enable cloud features. For solo developers and startups, the $0 marginal cost is compelling versus ongoing subscription fees.
The trade-off is operational overhead. You manage models, storage, and compatibility yourself, and larger models demand serious memory. While the 30B coder model proved capable, complex refactors or cross-language projects still benefit from careful prompt engineering, unit tests, and a human reviewing diffs—exactly as you would with commercial tools.
Bottom Line: Can a Free Local Stack Replace Paid Agents?
Can a free, local stack replace a paid coding agent today? For many day-to-day tasks—new features in a modest repo, utility scripts, plugin scaffolding—the Goose + Ollama + Qwen3-coder setup is remarkably close, with solid privacy and no usage bills.
If you need maximum speed, guardrails, or enterprise integrations out of the box, a commercial agent still has advantages. But the gap is narrowing, and this fully local approach is already good enough to be a serious option—especially if you value control over your data and your costs.
