Imagine you are sitting in front of a blank digital canvas, and instead of fighting with Python syntax or wrestling with API documentation, you just start dragging boxes. One box is a researcher, another is a critic, and a third is a publisher. You connect them with a few lines, hit a button, and suddenly you have a fully functioning AI workforce executing complex tasks in parallel. Today’s prompt from Daniel is about exactly that—a project called Sim Studio that is essentially trying to become the Figma for AI agents.
It is a massive project, Corn. I have been tracking the repository on GitHub, and it is exploding. We are looking at over twenty-seven thousand stars and nearly four thousand commits. That level of velocity in the open-source world usually means people have found a significant pain point, and in this case, the pain point is that building reliable, multi-step AI agents is still far too difficult for the average developer, let alone a non-technical person.
Well, and it is not just about making it easy, right? It is about making it visible. When you are writing agentic code in something like LangChain or even just raw Python, the logic is buried in layers of abstraction. You cannot easily "see" the flow of information. By the way, fun fact for the listeners—Google Gemini 3 Flash is actually writing our script today, which feels fitting given we are talking about the next layer of the AI stack.
It really does. And to your point about visibility, Sim Studio is an open-source, visual agent workflow builder. It is designed to help you build, deploy, and orchestrate these "AI workforces." While we have seen low-code tools before, like Zapier or Make dot com, Sim Studio is built from the ground up specifically for the unique challenges of LLMs—things like state management, long-running processes, and complex tool calling.
I think people hear "low-code" and they immediately think of simple triggers. Like, "If I get an email, put it in a spreadsheet." But Daniel's prompt here is pointing us toward something much more sophisticated. Is Sim Studio actually capable of replacing a custom-coded agentic framework, or is it just a prototyping tool that you eventually outgrow?
That is the big question. But before we get into the "why," we should probably talk about the "how." Under the hood, Sim Studio uses a node-based graph system. If you have ever used Blender or Unreal Engine’s Blueprints, it feels very familiar. Each node represents a specific action or a specific model call. But what makes it technically impressive is the execution engine. It is built on Node dot js and uses WebSockets for real-time updates. This means as the agent is "thinking" or searching the web, you see the data moving through the graph in real-time.
So it is not just a static map of what might happen. It is a live dashboard of what is happening. I love that because one of the biggest issues with agents is the "black box" problem. You run a script, it sits there for thirty seconds, and then it either spits out an answer or crashes with a cryptic error. With a visual graph, if the "Search" node fails because of a rate limit, you see the red light on that specific box immediately.
Precisely. And the way it handles state is where it gets really "nerdy." In traditional programming, you have to manually pass variables from one function to the next. In Sim Studio, the "context" flows through the edges of the graph. You can define what information gets passed along and how it gets transformed. For example, you might have an "Agent" node that takes in raw data from a "Web Scraper" node. You don't just dump the whole HTML into the LLM; you use a "Filter" node in between to clean the text, and you can see that transformation happening visually.
It sounds like it is democratizing the "architect" role. You still need to understand the logic of how an agent should work, but you don't need to be a senior engineer to implement it. Though, I have to wonder, Herman... does this make us lazy? If I can just drag a "Research" node onto a canvas, am I losing the granular control I get with code?
You might lose some of the absolute lowest-level control, but Sim Studio actually allows for "Custom Code" nodes. If the built-in blocks don't do what you need, you can drop into a JavaScript or Python environment within a single node to handle a specific edge case. It is more about removing the boilerplate. Think about how much time developers spend just setting up environment variables, handling API retries, and formatting JSON. Sim Studio handles all of that infrastructure so you can focus on the actual logic of the agent.
Let’s walk through a real-world example, because I think that makes it stick for people. If I wanted to build a "Market Research Assistant" using Sim Studio, what does that actually look like on the canvas?
Okay, let’s build it in our heads. First, you drag out an "Input" node where you type your research topic—let’s say, "The current state of solid-state battery manufacturing." That input connects to a "Search" node. This node is configured to use something like Tavily or Serper to crawl the web.
And I assume I can configure that node to say, "Give me the top ten results from the last six months," right?
High-level configuration, yes. Now, the output of that search is a big mess of text and links. So, you connect that to an "Iterator" node. This is a crucial concept in Sim Studio. The iterator says, "For every link you found, perform the following steps." Inside that loop, you have a "Scraper" node to pull the full text of the articles, and then a "Summarizer" node, which is just an LLM call with a specific system prompt.
This is where it gets cool. Because once you have those summaries, you don't just want a list. You want a cohesive report.
Right. So, after the loop finishes, all those summaries flow into a "Synthesizer" node. This is a different LLM—maybe you use a "heavy" model like Claude three point five Sonnet or GPT-four-o here—to take all those fragments and write a structured five-page report. Finally, you connect that to a "File System" node that saves it as a PDF or an "Email" node that sends it to your boss.
The part that blows my mind is that in a traditional script, managing that "loop" and making sure the summaries don't exceed the context window of the final synthesis node is a huge pain. You’re writing recursive functions or managing complex arrays. In Sim Studio, it’s just a loop icon on the canvas.
And if you want to add a human-in-the-loop step? You just drop an "Approval" node before the final email is sent. The execution pauses, sends you a notification, you look at the draft, hit "Approve," and then the graph continues. Doing that in code requires setting up a database to save the state, a frontend for the user to see the draft, and a webhook to restart the process. Sim Studio gives you all of that out of the box.
You mentioned human-in-the-loop, and I think that is the "killer feature" for business use. Most companies are terrified of letting an AI agent loose on their Twitter account or their client emails. But if the "architect" can build in these checkpoints visually, it becomes much more palatable. It’s like having a junior employee who does all the legwork but always brings the final draft to you for a signature.
It changes the economics of building these tools. If it takes three weeks to code an agent, you only do it for high-value tasks. If it takes three hours to "draw" one in Sim Studio, you start automating everything. You start building little agents for your personal finances, for your workout tracking, for your grocery lists.
I want to push back a little on the "it’s easy" narrative. Even with a visual tool, agentic logic is hard. We have talked about MiroFish before—that massive simulation engine—and the complexity there wasn't the code, it was the "emergent behavior." When you have multiple nodes talking to each other, don't you still run into issues where the agent gets stuck in a loop or hallucinates something that breaks the downstream nodes?
You absolutely do. Sim Studio doesn't fix the underlying limitations of LLMs. If your prompt is bad, your agent is bad. But what it does do is give you "Observability." When an agent gets stuck in a loop in a script, it just hangs or eats up your API credits until you kill the process. In Sim Studio, you can literally see the "tokens" bouncing back and forth between two nodes. You can see the conversation history growing. It makes debugging a visual process rather than a forensic one.
That’s a great point. It’s the difference between looking at a car engine and seeing smoke coming out of one specific pipe, versus just having a car that won't start and no idea why.
And let's talk about the "Open Source" aspect. This is why Daniel is so excited about it. Because Sim Studio is open-source, you aren't locked into a specific provider. You can run it locally. You can plug in local models using Ollama or LM Studio. If you’re a company with sensitive data, you can run the whole Sim Studio stack on your own servers, use a local Llama three model, and your data never leaves your building. That is a huge differentiator from the "SaaS" versions of these tools.
Yeah, the "Death of SaaS" is a theme we keep coming back to. Why pay twenty dollars a month for a specialized AI writing tool when you can build a better version of it yourself in an afternoon and host it for free?
It’s the "Bespoke AI" movement. We are moving away from "one size fits all" software and toward "custom-built workflows" that fit your specific brain and your specific business. Sim Studio is the workshop where those tools are built.
Let’s talk about the "Agent Node" specifically, because that is the heart of the system. In the GitHub repo, they talk a lot about "Skills." What does that mean in the context of Sim Studio?
This is one of the more powerful abstractions they have. A "Skill" is essentially a pre-defined tool that an agent can use. It might be "Search Google," "Read Database," or "Generate Image." But in Sim Studio, a "Skill" can also be another graph.
Wait, so it’s recursive? I can build a "Research Agent" graph, save it, and then use that entire graph as a single node inside a larger "Company Strategy" graph?
Yes! It’s modular. This is how you handle complexity. You don't build one giant, messy graph with a thousand nodes. You build a library of small, reliable "Sub-agents." You have a "Fact Checker" sub-agent, a "Copywriter" sub-agent, and a "Data Analyst" sub-agent. Then, your main "Manager" agent just calls those skills as needed.
That sounds like a proper software engineering architecture, just represented visually. It’s like using libraries in coding. You’re not reinventing the wheel every time; you’re just importing your own custom-built "Wheels."
And because it’s open-source, we are already seeing a community-driven "Skill Store" emerging. You can go to GitHub, find a skill that someone else built for, say, "Interacting with the Salesforce API," and just import it into your Sim Studio instance. It’s the "GitHub of Agents" idea coming to life.
I can see why it has twenty-seven thousand stars. That kind of ecosystem play is incredibly powerful. But let's get into the second-order effects. If building agents becomes this easy, what happens to the job market for "AI Engineers"? Are they going the way of the web designer after Squarespace came out?
I think it shifts. The "AI Engineer" of the future won't be the person who knows how to call an API—everyone will know how to do that. The value will be in "Agent Architecture." Knowing how to decompose a complex problem into the right series of nodes. Knowing how to "Red-Team" your own agent to make sure it doesn't do something stupid. It becomes more about "System Design" and less about "Syntax."
It’s funny, we keep seeing this pattern where the "boring" part of tech gets automated, and we are left with the "hard" part, which is actually thinking. I’m okay with that. I’d rather spend my time thinking about the logic of a market research flow than debugging why my JSON parser is failing because of a stray trailing comma.
And let's look at the "Enterprise" implications. Right now, if a marketing department wants an AI tool, they have to go through IT. IT has to vet the vendor, check the security, and then maybe, six months later, the marketing team gets a tool that is already outdated. With something like Sim Studio, IT can set up a secure, self-hosted instance, and then give the marketing team the keys to build their own tools within a "sandboxed" environment.
It’s the "Shadow IT" problem, but solved with a better platform. Instead of people using random, unvetted AI websites, they are building tools on a company-sanctioned, open-source platform. That is a huge win for security-conscious organizations.
And let's not forget the "Cost" side of things. When you are using a visual builder, you can easily swap between models. If you realize that your "Summarizer" node doesn't actually need a high-end model, you can switch it to a cheaper, faster model like Gemini three Flash or a local Llama model with literally two clicks. In a hard-coded system, that might involve changing libraries or refactoring code. Here, it’s just a dropdown menu.
I can see a world where you have a "Cost-Optimizer" agent that monitors your Sim Studio workflows and automatically swaps nodes to the cheapest model that still maintains a certain quality score.
People are already building those! That is the "Agentic Meta-Layer." You have agents that manage other agents, agents that optimize the performance of the system, and agents that monitor for errors. It starts to look less like "software" and more like a "digital ecosystem."
Okay, let's talk about the downsides. There has to be a catch. What are the limitations of Sim Studio right now?
The biggest hurdle is still "Reliability." Even with the best visual builder, LLMs are non-deterministic. You can build a beautiful graph that works perfectly nine times out of ten, and then on the tenth time, the LLM decides to output its response in a weird format and the whole thing cascades into a failure. Sim Studio has tools for "Retries" and "Error Handling," but it still requires a lot of testing to make an agent truly "Production-Ready."
Is there a "Test Suite" functionality? Like, can I run a hundred inputs through my graph and see the success rate?
They are adding that. It’s part of the "Evaluations" or "Evals" framework. You can define what a "good" answer looks like, and Sim Studio will run your graph against a dataset and give you a score. But again, it’s a new field. We are still figuring out how to "Unit Test" a thought process.
That’s a wild sentence. "Unit testing a thought process." We really are in the future, aren't we?
We are. Another limitation is "Latency." When you have a complex graph with twenty nodes, and each node is an LLM call that takes two to three seconds... suddenly your "instant" assistant takes a full minute to get back to you. Sim Studio tries to mitigate this with parallel execution—running nodes at the same time if they don't depend on each other—but the "Speed of Thought" is still limited by the "Speed of the API."
I suppose as models get faster, that problem goes away. If we get to sub-one-hundred-millisecond response times for "reasoning" models, then a twenty-node graph still feels fast enough for a real-time conversation.
That’s the dream. And honestly, for most "Batch" tasks—like writing a report or analyzing a spreadsheet—latency doesn't matter that much. If it takes five minutes to do an hour's worth of work, that’s still a massive win.
So, if I’m a listener right now, and I’m thinking, "This sounds cool, but where do I start?" What is the "Hello World" of Sim Studio?
The easiest way is to go to their GitHub—simstudioai slash sim—and look at the "Quick Start." You can actually run a version of it in a Docker container with one command. Once you’re in, I’d suggest building a simple "News Curator."
Walk me through it.
One "Input" node for a keyword, like "Artificial Intelligence." One "Search" node to find the latest news. One "Filter" node to remove sponsored content. One "Summarizer" node to give you the gist of each story. And one "Markdown" node to format it into a nice newsletter. It takes about ten minutes to set up, and it’s a great way to understand how data flows through the system.
It’s like the modern version of building a "To-Do List" app when you’re learning to code. It’s the foundational project that teaches you the "Grammar" of agents.
And once you have that, you start seeing everything as a graph. You look at your email inbox and think, "That’s just an input node." You look at your calendar and think, "That’s a scheduling tool." You start "Agentizing" your entire digital life.
I love the idea of "Agentizing." It’s a bit scary, though. If everything is an agent, where does the human go? We’ve talked about agentic UI testing before—where AI agents act as "Model Users" to test apps. If agents are building the apps, and agents are testing the apps, and agents are using the apps... are we just the ones paying the electricity bill?
Ha! Maybe. But I think the reality is more interesting. We become the "Directors." Instead of playing the violin, we are conducting the orchestra. Sim Studio is the conductor’s baton. It allows us to orchestrate complex behaviors without getting bogged down in the mechanics of each individual instrument.
That’s a nice way to look at it. It’s about leverage. One human with a well-built Sim Studio workforce can do the work of an entire department. That is either incredibly exciting or terrifying, depending on which side of that "leverage" you are on.
It’s why staying informed is so critical. The people who understand how to build and manage these agents will be the ones who thrive in the next decade. And open-source tools like Sim Studio are making that knowledge accessible to everyone, not just the "Priests of Code" at the big tech companies.
I’m curious about the "Deployment" side. Once I’ve built my perfect agent in the visual builder, how do I actually "Use" it in the real world? Does it stay inside Sim Studio, or can I export it?
You have a few options. You can use the Sim Studio API to trigger your workflows from other apps. So, you could have a button in your custom CRM that says "Run Research Agent," and it calls the Sim Studio backend. Or, you can deploy the agent as a standalone "Bot" on platforms like Discord, Slack, or Telegram. Sim Studio has built-in connectors for those.
So I can build a "Research Bot" in Sim Studio and then just invite it to my team’s Slack channel? That is incredibly seamless.
It really is. And they are working on "Edge Deployment," where you could potentially export the logic of the graph into a lightweight runtime that runs on a phone or an IoT device. We aren't quite there yet for complex agents, but that’s the direction the industry is moving.
It makes me think about the "One Million LLMs" episode we did. If you can deploy these agents that easily, then simulating a massive, interconnected workforce becomes a real possibility. You could have a thousand Sim Studio agents all working on different parts of a huge engineering project, all communicating through a central "Coordination" graph.
The "Central Intelligence Layer." That is literally the phrase they use in their GitHub description. "Sim is the central intelligence layer for your AI workforce." They are positioning themselves as the "Operating System" for agents.
It’s a bold claim, but with twenty-seven thousand stars, they have the momentum to back it up. I’m impressed by the focus on "Orchestration." A lot of tools focus on the "Brain" (the LLM), but the "Nervous System" (the workflow) is just as important.
And the "Memory." We haven't even talked about "Long-Term Memory" nodes. Sim Studio allows you to connect nodes to Vector Databases like Pinecone or Milvus. This means your agent can "Remember" things from previous runs. If you tell your "Researcher" agent that you hate a certain source, it can store that in its "Memory" node and never use that source again.
That is the "Personalization" piece. An agent that learns your preferences over time. That is the difference between a "Tool" and a "Partner."
And because it’s your own instance, that memory is yours. It’s not being used to train a global model; it’s just sitting in your database, making your agents smarter.
I think we have covered a lot of ground here, Herman. We’ve gone from the "Figma for AI" metaphor to the technical details of node-based execution, and finally to the broader implications of an "AI Workforce." What are the big takeaways for people?
First, if you are a developer, stop writing boilerplate agent code and go check out Sim Studio. It will speed up your prototyping by a factor of ten. Even if you eventually move to a custom-coded solution, the visual clarity you get from building a graph first is invaluable.
And for the non-developers?
Don't be intimidated! If you can use a flowchart or a mind-map, you can use Sim Studio. It is the best way to "Get your hands dirty" with AI without needing a Computer Science degree. Go to their GitHub, look at the examples, and try to automate one small, annoying part of your day.
I think my takeaway is that the "Visual" aspect is more than just a convenience—it’s a new way of thinking. Representing logic as a graph makes you realize where the bottlenecks are, where the loops are, and where you need a human to step in. It’s a "Sense-Making" tool for the AI age.
Well said. It’s about clarity. In a world of increasingly complex AI, we need tools that help us see the "Big Picture."
I’m definitely going to be playing around with this over the weekend. I want to see if I can build an agent that automatically finds the best "weird prompts" for Daniel to send us.
Ha! That might put Daniel out of a job. I’m not sure he’d appreciate that.
Or it just gives him more time to hang out with Ezra and Hannah while his "Prompt-Generating Agent" does the heavy lifting. It’s all about that leverage, Herman.
True. Well, I think that is a wrap on Sim Studio. It is a phenomenal project, and it’s a perfect example of why the open-source AI community is so vibrant right now.
Agreed. Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power this show—if you’re building AI apps, Modal is the place to do it.
This has been My Weird Prompts. If you enjoyed this deep dive, we’d love it if you could leave us a review on Apple Podcasts or Spotify—it really does help new listeners find the show.
We will be back next time with another prompt from Daniel. Until then, keep experimenting, keep building, and maybe try drawing your next AI instead of coding it.
Goodbye, everyone.
Catch you later.
