alt Hacker NewsLaunch HN: Spine Swarm (YC S23) – AI agents that collaborate on a visual canvas
Hey HN! We're Ashwin and Akshay from Spine AI (https://www.getspine.ai). Spine Swarm is a multi-agent system that works on an infinite visual canvas to complete complex non-coding projects: competitive analysis, financial modeling, SEO audits, pitch decks, interactive prototypes, and more. Here's a video of it in action: https://www.youtube.com/watch?v=R_2-ggpZz0Q.
We've been friends for over 13 years. We took our first ML course together at NTU, in a part of campus called North Spine, which is where the name comes from. We went through YC in S23 and have spent about 3 years building Spine across many product iterations.
The core idea: chat is the wrong interface for complex AI work. It's a linear thread, and real projects aren't linear. Sure, you can ask a chatbot to reference the financial model from earlier in the thread, or run research and market sizing together, but you're trusting the model to juggle that context implicitly. There's no way to see how it's connecting the pieces, no way to correct one step without rerunning everything, and no way to branch off and explore two strategies side by side. ChatGPT was a demo that blew up, and chat stuck around as the default interface, not because it's the right abstraction. We thought humans and agents needed a real workspace where the structure of the work is explicit and user-controllable, not hidden inside a context window.
So we built an infinite visual canvas where you think in blocks instead of threads. Each block is our abstraction on top of AI models. There are dedicated block types for LLM calls, image generation, web browsing, apps, slides, spreadsheets, and more. Think of them as Lego bricks for AI workflows: each one does something specific, but they can be snapped together and composed in many different ways. You can connect any block to any other block, and that connection guarantees the passing of context regardless of block type. The whole system is model-agnostic, so in a single workflow you can go from an OpenAI LLM call, to an image generation mode like Nano Banana Pro, to Claude generating an interactive app, each block using whatever model fits best. Multiple blocks can fan out from the same input, analyzing it in different ways with different models, then feed their outputs into a downstream block that synthesizes the results.
The first version of the canvas was fully manual. Users entered prompts, chose models, ran blocks, and made connections themselves. It clicked with founders and product managers because they could branch in different directions from the same starting point: take a product idea and generate a prototype in one branch, a PRD in another, a competitive critique in a third, and a pitch deck in a fourth, all sharing the same upstream context. But new users didn't want to learn the interface. They kept asking us to build a chat layer that would generate and connect blocks on their behalf, to replicate the way we were using the tool. So we built that, and in doing so discovered something we didn't expect: the agents were capable of running autonomously for hours, producing complete deliverables. It turned out agents could run longer and keep their context windows clean by delegating work to blocks and storing intermediary context on the canvas, rather than holding everything in a single context window.
Here's how it works now. When you submit a task, a central orchestrator decomposes it into subtasks and delegates each to specialized persona agents. These agents operate on the canvas blocks and can override default settings, primarily the model and prompt, to fit each subtask. Agents pick the best model for each block and sometimes run the same block with multiple models to compare and synthesize outputs. Multiple agents work in parallel when their subtasks don't have dependencies, and downstream agents automatically receive context from upstream work. The user doesn't configure any of this. You can also dispatch multiple tasks at once and the system will queue dependent ones or start independent ones immediately.
Agents aren't fully autonomous by default. Any agent can pause execution and ask the user for clarification or feedback before continuing, which keeps the human in the loop where it matters. And once agents have produced output, you can select a subset of blocks on the canvas and iterate on them through the chat without rerunning the entire workflow.
The canvas gives agents something that filesystems and message-passing don't: a persistent, structured representation of the entire project that any agent can read and contribute to at any point. In typical multi-agent systems, context degrades as it passes between agents. The canvas addresses this because agents store intermediary results in blocks rather than trying to hold everything in memory, and they leave explicit structured handoffs designed to be consumed efficiently by the next agent in the chain. Every step is also fully auditable, so you can trace exactly how each agent arrived at its conclusions.
We ran benchmarks to validate what we were seeing. On Google DeepMind's DeepSearchQA, which is 900 questions spanning 17 fields, each structured as a causal chain where each step depends on completing the previous one, Spine Swarm scored 87.6% on the full dataset with zero human intervention. For the benchmark we used a subset of block types relevant to the questions (LLM calls, web browsing, table) and removed irrelevant ones like document, spreadsheet, and slide generation. We also disabled human clarification so agents ran fully independently. The agents were not just auditable but also state of the art. The auditability also exposed actual errors in an older benchmark (GAIA Level 3), cases where the expected answer was wrong or ambiguous, which you'd never catch with a black-box pipeline. We detail the methodology, architecture, and benchmark errors in the full writeup: https://blog.getspine.ai/spine-swarm-hits-1-on-gaia-level-3-...
Benchmarks measure accuracy on closed-ended questions. Turns out the same architecture also leads to better open-ended outputs like decks, reports, and prototypes with minimal supervision. We've seen early users split into two camps: some watch the agents work and jump in to redirect mid-flow, others queue a task and come back to a finished deliverable. Both work because the canvas preserves the full chain of work, so you can audit or intervene whenever you want.
A good first task to try: give it your website URL and ask for a full SEO analysis, competitive landscape, and a prioritized growth roadmap with a slide deck. You'll see multiple agents spin up on the canvas simultaneously. People have also used it for fundraising pitch decks with financial models, prototyping features from screenshots and PRDs, competitive analysis reports and deep-dive learning plans that research a topic from multiple angles and produce structured material you can explore further.
Pricing is usage-based credits tied to block usage and the underlying models used. Agents tend to use more credits than manual workflows because they're tuned to get you the best possible outcome, which means they pick the best blocks and do more work. Details here: https://www.getspine.ai/pricing. There's a free tier, and one honest caveat: we sized it to let you try a real task, but tasks vary in complexity. If you run out before you've had a proper chance to explore, email us at [email protected] and we'll work with you.
We'd love your feedback on the experience: what worked, what didn't, and where it fell short. We're also curious how others here approach complex, multi-step AI work beyond coding. What tools are you using, and what breaks first? We'll be in the comments all day.
Comments
Calling it a 'canvas' makes me think that this tool is about AI agents doing some kind of collaborative drawing. Looking at the vid though, it seems more like an environment for visually organizing and managing agentic work (which seems very cool, and quite a bit more than just a canvas).
I like the overall idea and presentation. In trying it out, I hit the token cap before my trial task was able to complete and show me the end result. I'm sure your free-tier token costs are non-trivial but it was definitely a bummer that I couldn't even see one initial run's output to decide if I wanted to pay.
I decided to gamble the one month fee to let it continue, but the payment defaulting to annual was jarring. I can see it lets you advertise a lower price but that only made me more tempted to leave altogether when I saw the price go up on the final screen.
Why is the landing page “paged”? Feels odd on mobile. Some titles are cut off because of it.
I didn’t read the post, I checked out the website just like 99% of the people will do.
Simple advice, if you are selling a product with a selling point of being visual, show it on your website. Not in a YouTube video but actual screenshots, short cut 10 sec video/gif
The persistence layer is probably more valuable here than the visual canvas itself. When running multi-agent workflows on complex tasks, the hard part isn't the agents working in parallel — it's maintaining coherent state when something fails mid-run or needs revision. A canvas that preserves intermediate results and lets you surgically re-run specific branches without starting over would solve a real pain point. Curious how Spine handles partial failures — does the whole workflow restart or can it pick up from the last valid checkpoint?
I have not seen my final report yet for my query around machinist.com. However I would like to say my initial impression is very positive. At least in terms of the digestion of my somewhat nebulous request. I like the way your app was able to burrow down to pain points I have experienced and am trying to work out in terms of product market fit for the domain. I look forward to exploring more and giving you more feedback when I see the final report. I will also add that I am looking forward to using your product to explore other opportunities that I'm sure are out there in this age of AI.
I got dizzy from the star effect when scrolling the website.
"I make AI output lots of stuff" is not an intrinsically valuable thing. I can run the same thing on Claude in research mode and get a report with cited sources in a more digestable format on my phone. What's the eval here on if any of this is good? Is it even possible to test (ie, you cant really AB test startup ideas)?
Super cool!
I'm completely sold on the canvas layer. Embracing non linearity is such a boon when you're on the ideas stage. When you have verified it though, moving it to another medium (a document, presentation or just code) is often the best choice.
Do you see the canvases created with Spine as "one off" that you discard when you have got your deliverable, or as something living that you keep around?
I'm building a side project for running SQL on a canvas (kavla.dev), so I'm thinking about canvas workflows all the time!
I had to read this text in order to understand what this tool does, because I could not know from the website (without watching a video). You should use Spine to improve your website. ;-)
Congrats on the launch! Meta comment, but I just ain't reading all of the above. You need to be able to explain this in about 20% the number of words or you'll lose people, especially VC.
My advice is to start with "Spine Swarm solves _____" then how, then why you're different. 3 short paragraphs, preferably 1-2 sentences each.
What does it mean to say 30,000 monthly credits and 1500 daily refresh credits? If my project takes 7000 credits (the way your demo does) then does that mean I couldn’t actually do it on the lowest available pricing plan because I couldn’t use 7000 credits in one run? If this is the case, what am abysmal pricing model!
In the demo video you shared (yt link) how many credits did that whole project take? What is the prices to fix elements of it (for example of you dislike a minor aspect of the generated spreadsheet do follow up instructions utilize only the narrow subset of agents that has been demoed to that subtask, or does it create new agents who have to create new context in the narrow follow up task?)
Interesting idea, I wanted to see an example of the agents working on a canvas when I opened your page. I saw nothing of the sort. Sorry, but immediate fail.
This may be too harsh, but you need to make it immediately clear to someone today why they can't just have Claude Code one shot your app!
the 'chat is the wrong interface' framing is interesting but i think the real friction is context fragility, not linearity. long chats break because models lose the thread, not because you can't branch. the canvas fixes that by making context explicit and persistent — you can see what the agents are actually holding.
curious how the orchestrator handles conflicts when parallel agents produce contradictory outputs. does the downstream synthesizer just pick, or does it surface the divergence to the user?
Just as a tiny first piece of feedback, the main marketing website is very hard to understand or grok without a demo of how the tool works. Even just the quick YouTube video that you added in your post here, if embedded, would make a difference.
There are so many "agentic tools" out there that it's really hard to see what differentiates this just based on the website.
Why do I need a canvas to visualize the work that the agents are doing? I don't want to see their thought process, I just want the end product like how ChatGPT or Claude currently work.
whoa congrats on the launch. lol I launched my visual canvas for agents today too. I went in a more of a collaborative canvas IDE, agent orchestration direction. But very cool to see your take on it
https://getmesa.dev is mine
Is it possible to build self-improving swarm loops? (ie swarm x builds a thing, swarm y critiques and improved x’s work, repeat…)
excuse my memory at this point, arent there like a 100 of these posted on HN every month that all have something to do with multi agent collaboration that support 1000 models?
Dark UI pattern: pretends that it is immediately usable only to redirect for sign-up.
Is the value prop that I can see what the agent is doing? This is not the way: https://youtu.be/R_2-ggpZz0Q?t=158
How am I supposed to get anything out of this? Consider that agents are going to get faster and run more and more tasks in parallel. This is not manageable for a human to follow in real time. I can barely keep up with one agent in real-time, let alone a swarm.
What I could see being useful is if you monitored the agents and notified me when one is in the middle of something that deserves my attention.
[dead]
[dead]
[flagged]
[dead]
I think this is really neat. You should probably take it as a compliment that the biggest criticisms so far are about the website landing page. ;)
I like canvases in general, and I especially like them for mentally organizing and referring to this sort of broad work. (Honestly, I think zoomable canvases would make a better window manager in general, but I digress)
One small piece of friction: My default mouse-based ways of dragging the canvas around (that work in most canvases like Figma) aren't working. I saw that you had a tutorial, and I have learned to hold space now, but I prefer the "hold middle mouse button to drag my canvas view around".
I've got a couple of research tasks running now, and my current open questions as a very new user are: 1) How easy will it be to store the outputs into a Github repository. 2) How easy will it be to refer back to this later? 3) Can I build upon it manually or automatically? 4) Can I (securely) share it with someone else for them to see and build upon it? 5) Can I do something "locally" with it? Not necessarily the model, but my preferred interface for LLMs at this point is Claude Code. Could I have a Claude Code instance running in one of these boxes somehow? 6) What if I want to do private stuff with it and don't like the traffic going through Spine's servers? Could I pay them for the interface, but bring my own keys? (Related: Can I self host somehow?) 7) When this is done, each artifact it found (screenshot, webpage, etc), is going to be helpful. The data-hoarder in me wants to make sure I can search these later. Heck, if I could do that, this would become my preferred "web browser". (But again, I digress.)