easyagents.dev
Think your local LLM isn’t powerful enough
to write commercial-grade code?
You’re asking the wrong question.
The problem isn’t your model. It’s the size of the task you’re handing it. Break the work down properly and a free local model can ship production software — while the big cloud APIs handle only what they’re actually worth paying for.
I spent three months convinced my hardware wasn’t good enough.
I was wrong.
I run a reasonably decent home server. Not a monster rig — but a solid machine with a GPU that could handle a 13B or 30B parameter model without breaking a sweat. I had Ollama installed. I had models downloaded. I had everything set up.
And every time I tried to use it for real development work, I got frustrated and went back to the cloud APIs.
The local model would start confidently, get halfway through a complex task, then veer off course. It would forget context. It would hallucinate function signatures. It would write code that looked right but didn’t compile. I concluded the model wasn’t good enough and kept paying $200+ a month in API bills instead.
What I didn’t understand — and what took me an embarrassingly long time to figure out — is that no model handles massive, open-ended tasks well. Not the local ones. Not the cloud ones either, if you’re honest about it. The difference is that GPT-4 and Claude fail more gracefully on large context, so you don’t notice until you check the output carefully.
The real problem was how I was using these models. I was throwing entire features at them in one shot. “Add a complete Stripe payment system with webhooks, subscription management, and a billing dashboard.” Even a brilliant human developer wouldn’t tackle that as a single unbroken task.
When I started breaking that down — genuinely atomically, into 12 to 15 specific, isolated steps — something shifted. The local model handled each step cleanly. It had enough context. It knew exactly what success looked like. It compiled on the first try far more often than I expected.
My API bill didn’t drop gradually. It fell off a cliff. I went from $230 a month to under $15. The $15 is for planning only — a quick call to a smarter cloud model to decompose the work. The actual implementation runs entirely on my local machine, for free, 24 hours a day.
A few developers I knew saw what I was doing and asked if I had tooling for it. I didn’t, at the time. So I built it.
— The Founder
It’s not the model. It’s the task size.
Even if a model can technically read 32K tokens, its effective reasoning degrades badly as context grows. Giving it your whole codebase and a vague instruction is setting it up to fail.
Open-ended tasks have too many valid interpretations. A smaller model picks one path and sticks to it confidently — often the wrong one. Atomic tasks have only one valid output. Smaller models excel at those.
Routine tasks — adding a field, writing a test, renaming a function — don’t need GPT-4. You’re paying premium rates for work a free local model handles perfectly well.
When a local model fails a task, most tools give up or loop forever. What’s actually needed is a build-gate check and a targeted debug pass — not a fresh attempt at the same broken prompt.
Give your local model work it can actually win at.
A single call to a capable cloud model (or your own if you prefer) breaks your feature into 10–20 atomic steps. Each step has a clear target file, exact old snippet, new snippet, and acceptance criteria. The expensive model does planning — the cheap local model does implementation.
Each step is handed only the files it needs. Your local model sees 2–3 relevant files, not 200. It knows exactly what to change. The probability of a correct output jumps dramatically.
After each change, EasyAgents runs your build. If it fails, the error is fed back to the model for a targeted fix — not a re-run of the whole task. Most local models fix a specific compiler error in one shot.
Bring your Ollama, LM Studio, GPT4All, or vLLM instance. No data sent to the cloud during code generation. Your code stays on your hardware. You just expose a tunnel URL and point EasyAgents at it.
Pay for intelligence. Run implementation free.
The two-tier approach: a brief call to a smart model for planning, then everything else runs locally at zero cost.
Reads your task, scans relevant files, decomposes into atomic steps. Typical cost: $0.02 – $0.08 per feature. Runs once per task.
Executes each atomic step against a single targeted file. Runs the build. Fixes errors. Commits the change. Cost: $0.00. Every time.
Already running a local model? You’re ready to go.
EasyAgents works with any OpenAI-compatible server.
If it has a /v1/chat/completions endpoint, it works.
Just expose a tunnel URL (Cloudflare Tunnel or ngrok — both free) and paste it into your settings. Done.
“I thought my 3090 was overkill for this. Now it’s my main dev tool.”
“I had qwen2.5-coder running locally and assumed it wasn’t good enough for the e-commerce platform I was building. The whole cart, checkout, and inventory system — EasyAgents broke it into 47 steps. Local model nailed 44 of them without any help. The 3 it got wrong were fixed with one debug pass. $0 in API fees for the implementation.”
“I use a 7B model on an M2 MacBook. Everyone told me I needed at least a 70B for production code. That’s not true if you’re handing it the right sized tasks. I shipped a full REST API with auth, rate limiting, and Stripe webhooks last week. Paid maybe 30 cents total — just for the planning step.”
“Our company had a strict no-cloud-code policy, so I couldn’t use Copilot or ChatGPT for the actual implementation. EasyAgents let me use a local model for all the code generation and only call out for planning. Legal were happy. I was happy. The code quality was better than I expected from a local runner.”
What it actually costs per feature.
| Approach | Typical cost / feature | Code stays private? | Works on large codebases? |
|---|---|---|---|
| Cloud-only (ChatGPT/Claude direct) | $1.50 – $6.00 | No | Degrades |
| Local model, unstructured | $0.00 (but poor results) | Yes | No |
| EasyAgents + local model | $0.02 – $0.08 | Yes | Yes ✓ |
Built for real development work.
Your local model is more capable than you think.
It just needs the right architecture around it.
Stop paying cloud rates for implementation work your local model can handle for free. Start using what you already have.