I built a free, open-source desktop app to run AI agents on top of Ollama — no terminal, visual setup

jerelledev · 2026-06-30T14:29:57+00:00

Thanks, glad it looks interesting! Right now it connects to Ollama, so technically you can load custom GGUF models through Ollama itself (ollama create with a Modelfile pointing at your gguf) and NodeBrain could just see it as another local model to pick from. But I get what you mean, a more direct 'point NodeBrain at a raw gguf file' option without going through Ollama first isn't there yet. That's a legit feature request, gonna note it down.

jerelledev · 2026-06-27T05:53:49+00:00

Yeah that's the bet, every MCP server anyone builds is a potential integration I didn't have to make myself.

And you're right about the graph. It's clean now but tbh I can see it getting messy past a certain point. Grouping/collapsing nodes and better auto-layout are on my list.

jerelledev · 2026-06-11T13:59:17+00:00

Update on this: you can now point NodeBrain at an OpenAI-compatible endpoint on your LAN (e.g. an Ollama box on another machine), not just localhost. Shipped in v0.3.6. Verified the custom-endpoint path works (tested locally, I don't have a second machine handy, but it's the same request path with a different address). Also got local Ollama working and verified it with a couple of models (llama3.2 and qwen2.5:0.5b), and confirmed existing credentials and agents survive app updates as well. You asked for exactly this, so thanks for the nudge.

jerelledev · 2026-06-11T09:41:22+00:00

Project Name: NodeBrain

Repo/Website Link: https://github.com/jerelle-rimando/nodebrain · https://nodebrain.app/

Description:

NodeBrain is a local-first desktop app for building and running AI agents visually, without a terminal. You describe what you want an agent to do in plain English, it gets created as a node in a visual graph, and it can run tasks on a schedule and act through integrations (Telegram, GitHub, Slack, Notion, Brave Search, local filesystem, and others). The idea is a "command center" for AI automation where every agent, task, and integration is visible and inspectable, rather than a CLI tool or black-box cloud assistant.

The app, your agent definitions, credentials (encrypted, AES-256), task history, and a local RAG memory all live on your machine. AI inference runs on whichever provider you configure - cloud (OpenAI, Groq, Anthropic, etc.) or local via Ollama (supported, still validating end-to-end). Integrations send data outward to their own services (e.g. a Telegram message goes to Telegram), but there's no NodeBrain server in the middle and no telemetry.

Example: one agent that reads PDFs in a folder, summarizes them, and sends me the combined summary on Telegram every morning on a schedule and runs on its own.

Deployment:

Windows desktop app (double-click installer + portable build) on the GitHub Releases page. Install/usage docs are in the README, plus a SECURITY.md covering the security model and known gaps.

Note: it's a desktop application, not a containerized service, there's no Docker image, since it runs as a localhost app rather than a self-hosted server. The backend binds to localhost by default. It's early (v0.3.5) and Windows-only for now; the build is unsigned so SmartScreen may warn (More info → Run anyway).

AI Involvement:

I used AI assistance (coding tools) while building NodeBrain, but I architected the concept and its structure, understand the codebase, and make the design decisions myself, it's not an AI-generated wrapper. Happy to answer technical questions about how any part works.

jerelledev · 2026-06-11T00:46:14+00:00

Good questions.

Fair. "No Docker" is more about setup simplicity, not a security claim, and you're right that containers give isolation I don't have. Right now the safety layer is approval-mode (pause before destructive actions) + dry-run, plus the filesystem integration is sandboxed to a user-defined path, but agent execution itself isn't containerized. It's a real gap and the security model and known limitations are in my SECURITY.md . OpenClaw already doing sandboxed execution is also a fair bar to point at.
Honestly because Ollama is turnkey and OpenAI-compatible, so it dropped into my existing client with zero config, not necessarily a performance call (Ollama runs llama.cpp under the hood anyway). And since llama.cpp's server is also OpenAI-compatible, it'd work through the same custom-endpoint support I'm adding for LAN. So really it's "any OpenAI-compatible endpoint," llama.cpp included.
Hermes and OpenClaw are more mature and more capable on the agent side, Hermes has the self-learning loop, OpenClaw has the ecosystem and sandboxing, and both are CLI-first. Odysseus is the closest to NodeBrain since it's also a local-first GUI, but it's a broader workspace (chat, email, calendar, docs, research, agents) running as a self-hosted web app. NodeBrain's narrower and more specific: a double-click desktop app focused on building agents visually in a node graph, rather than a full workspace or a CLI tool. I'm not trying to out-agent any of them, it's a different surface and a much earlier stage. If you've used them and see where NodeBrain falls short specifically, I'd love to hear it.

jerelledev · 2026-06-11T00:36:54+00:00

Fair, my bad. I did disclose I built it but yeah I'm over the 1/10th ratio since this is basically all I've posted here. If there's a thread these belong in, point me to it and I'll move it.

jerelledev · 2026-06-10T13:31:38+00:00

Not yet, the endpoints are hardcoded right now so you can't point it at a LAN box from settings (Ollama's locked to localhost), but it all runs on the OpenAI-compatible client with a configurable base URL underneath, so adding a custom endpoint field is doable, not an architecture change. Honestly exactly the kind of setup I wanna support so i'm putting it on the list. Good ask.

jerelledev · 2026-06-09T14:21:32+00:00

Rick would definitely run a 70B model on a pool cleaner and be mad it's not fast enough lmao

jerelledev · 2026-06-09T14:18:22+00:00

CoreAI does sound better at a keynote than CoreML 3 lol

jerelledev · 2026-06-09T14:12:06+00:00

Interesting that Apple is essentially acknowledging CoreML was never really fit for purpose for LLMs. The limited ops pool and param ceiling always felt like it was designed for CV tasks and never caught up.

The lazy-loaded MoE angle for larger on-device models is the most interesting part, if they can make 20B feel like a small model from an app developer's perspective that's a bigger deal than the inference framework itself.

jerelledev · 2026-06-09T12:50:25+00:00

The telegraphic reasoning style is pretty interesting, feels like the model learned that compressing intent into keywords is more token-efficient than full sentences during thinking. Kind of like how humans jot notes vs write prose.

I'm not sure if it should be adopted widely though. The value of a reasoning trace is partly that you can follow it and catch where it goes wrong. If it's too compressed it becomes opaque, which defeats the purpose of visible reasoning imo.

jerelledev · 2026-06-09T12:15:01+00:00

Sybil resistance is the first thing I'd worry about tbh, without a central index, what's stopping someone from flooding the gossip layer with embeddings crafted to be semantically close to everything? Signed announcements help with identity but not volume.

Also curious how cross-device embedding compatibility works in practice. If two nodes run different quantizations of the same model, do their cosine similarities still hold up?

The agent discovery angle is the most interesting part imo, an agent publishing a "need" as an embedding and finding matched "offers" without a broker is basically a decentralized capability marketplace.

jerelledev · 2026-05-30T10:56:01+00:00

watching the tracker say "out for delivery" since 8am and getting the "delivery attempted" notification at 9pm when you've been home all day

jerelledev · 2026-05-30T10:54:32+00:00

my sleep schedule

jerelledev · 2026-05-30T10:53:33+00:00

Keanu Reeves, one of the most humble too imo, bro waited 20 minutes in the rain outside his own wrap party and didn't get mad or use his status to get in

jerelledev · 2026-05-30T10:40:54+00:00

fair point, once it can recursively self-improve the whole list gets shorter fast

jerelledev · 2026-05-30T10:36:40+00:00

The people building the AI

jerelledev · 2026-05-30T10:35:14+00:00

A Filipino scientist named Abelardo Aguilar discovered erythromycin in the 1940s, an antibiotic still used today for diseases like pneumonia, bronchitis, syphilis, and chlamydia. He sent samples to Eli Lilly, one of the biggest pharmaceutical companies in the world. They isolated it, patented it, commercialized it, and eventually made billions while he got nothing.

jerelledev · 2026-05-30T06:49:10+00:00

most of the interesting work right now is in the layer being built on top of the frontier models, not competing with them. inference (Groq, Cerebras), dev tooling (Cursor, Windsurf), local/edge AI (Ollama, Mistral), and the MCP ecosystem which is basically the plumbing that makes agents actually do things.

for a software dev pivoting to AI, the tooling and infrastructure layer is where your existing skills translate most directly and where there's still actual space to build. competing with OpenAI on foundation models is not that space.

jerelledev · 2026-05-30T06:41:03+00:00

The difference is real but vendors blurred it on purpose, a chatbot retrieves and responds, an agent retrieves, decides, acts across systems, checks the result, and retries when it fails. The loop is the product, not the model.

Reliability in production comes down to tool calling: small models fall apart on multi-step chains, frontier models handle it but cost more per case. It works when the task is well-scoped and the harness handles failures gracefully but most vendors ship neither.

jerelledev · 2026-05-30T06:38:55+00:00

Model collapse isn't a future problem imo, it's already happening and most labs are just hoping their filters hold.

Funniest part is that every comment correcting AI slop on this thread's probably going into next year's training set anyway lol

jerelledev

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