Show HN: Sim Studio – Open-Source Agent Workflow GUI
62 comments
·April 28, 2025monatron
Hi y'all. Love the idea and congratulations on your launch. I've used [n8n](https://github.com/n8n-io/n8n) for similar use cases in the past. Any differences in Sim Studio that you'd like to call out?
ekarabeg
Thank you! n8n has done really well over the last few years to simplify the workflow building process. I responded to this in a previous comment, but we believe the agent building process should be more open, meaning fewer abstractions between the interface and the model provider. We want our platform to be as lightweight as possible.
How this translates in the application is through features like allowing for custom tool calling with code execution, JSON schema input for response format, etc. I'd love to hear your thoughts using Sim Studio - let us know how we compare to the other workflow builders!
skeeter2020
> Building reliable, multi-step agent systems with current frameworks often gets complicated fast.
In my experience so far it's not just complicated, but effectively impossible. I struggle to get a single agent to reliably & consistently use tools, and adding n+1 agents is a error multiplier.
waleedlatif1
on our platform (for the providers that allow granular tool use control), you can actually 'force' certain tool calls and have the agent dynamically select others. this was a painpoint that we faced ourselves and were confused why any frameworks didn't allow granular tool use control if the provider allows it. try it out and let us know what you think
simple10
Congrats on the launch! Looking forward to playing with it.
Do you mind elaborating on what differentiates Sim Studio from n8n, Flowise, RAGFlow and other open source flow based AI automation platforms?
ekarabeg
Thanks! The main difference between Sim Studio and other open-source AI agent workflow builders is the level of abstraction used when creating agents.
For instance, n8n has a "memory" parameter, which is not an inherent parameter of LLMs. You can inject your agent's memories into the agent message history (or system prompt) - which is the most common scenario - but we give you control over that. We want to provide visibility, so everything that's exposed on the workflow canvas is exactly what's being executed in the background. Also, we think it's faster and more intuitive to get your workflow up and running in Sim Studio. I'd love your feedback, though! What do you think?
all2
This sounds like execution/variable resolution scopes in programming languages. I wonder if there are ideas from programming languages you could pick up and use?
ekarabeg
Yes exactly! A lot of our platform was inspired by programming languages - for loops, for each loops, custom variables, and environment variables in settings. If you have any more concepts, we'd love to hear them!
ddon
Would be amazing to be able to design the workflow using your builder, and then export to code (and choose the language) and then copy paste the code into the project... just an idea.
waleedlatif1
this is something we've been looking into. curious, would you rather export the code into an existing agentic framework like crewai/langgraph, or have it exported as raw code? also, would you prefer if the code was exported block-by-block or the entire workflow altogether?
Akhiljp
Love the launch, is there a way to add observability tools like langtrace into this so that we know what kind of prompts and inputs are going into the flows?
rahimnathwani
The UI looks lovely.
If I run Sim Studio with docker compose, how do I point it to the existing `ollama serve` instance running on the host?
I looked in settings (in the workspace UI) but don't see anywhere to configure the ollama endpoint.
waleedlatif1
thank you!
for ollama running on your host machine, you'll need to modify the docker configuration since by default it's looking at http://localhost:11434 which points to localhost inside the container, not your host. you can either add `extra_hosts` as `host.docker.internal:host-gateway` to your docker compose and set the OLLAMA_HOST envvar to `OLLAMA_HOST=http://host.docker.internal:11434`, or just run `docker compose up --profile local-cpu -d --build --network=host` when running the compose command.
will add this to the readme and add in some UI locally so its easily configurable! let me know if you have any issues
rahimnathwani
I modified the docker-compose.yml thus:
...
services:
simstudio:
build:
context: .
dockerfile: Dockerfile
ports:
- "3000:3000"
volumes:
- ./sim:/app
- /app/node_modules
- /app/.next
extra_hosts:
- "host.docker.internal:host-gateway"
environment:
- OLLAMA_HOST=http://host.docker.internal:11434
- NODE_ENV=development
- DATABASE_URL=postgresql://postgres:postgres@db:5432/simstudio
...
Then I went to localhost:3000/w/
Then I added an Agent block. I expected ollama (or my ollama models) to show up in the drop-down, but I only see the hosted models.
I even tried editing `sim/providers/ollama/index.ts`:
const OLLAMA_HOST = 'http://host.docker.internal:11434'
(BTW I did NOT run `--profile local-cpu` because I didn't want to run ollama in a docker container, as it's already running on the host.)
waleedlatif1
i think it could be one of a few things: - first, even though your ollama is running on the host, you still need to use the local profile flag to enable ollama models in the UI. you can do this by running the docker compose command with `local-cpu` - also, make sure your host ollama is actually running and responding (curl http://localhost:11434/api/tags should show your models) - if neither of the above work, you may need to restart the app container after changing the OLLAMA_HOST value
artem_zin
Youtube demo looks intriguing, I'm self-hosting n8n for exact this purpose with a home run LLM machine in a local k8s cluster (lol) but out of the box I can tell your tool surpasses AI integrations and workflow in n8n.
Quick glance at GitHub suggests that GitHub package for the Docker image is missing, let me know if you need help with that — happy to contribute!
vseplet
It's funny, but I solved a similar problem myself, but instead of n8n I came to write my own solution. I even noticed this post thanks to automation and llm. Likewise, I'll be glad to help!
waleedlatif1
thanks! that would be awesome to have, always welcome contributors :)
gavmor
I'm sure that the complex logic and state management were not trivial to implement, but the link said GUI so I wanted to see some screenshots, but all I saw were two very dim simple forms.
This space is REALLY struggling to graduate from Gradio-like design sensibilities.
That being said, I'm looking forward to playing with this, congrats on the launch!
nico
The video linked in the post description shows everything pretty clearly, here: https://www.youtube.com/watch?v=JlCktXTY8sE
rancar2
The screenshot on the website are helpful, and it would be good to add them into the GitHub documentation as the OP mentioned.
waleedlatif1
thanks for the feedback! adding some screenshots to the docs showcasing more complex workflows we've created so far
joshcsimmons
Congrats on the launch the tool looks phenomenal.
I’m conflicted because n8n does feel like the right level of abstraction but the UI and dated JS runtime environment are horrible. I don’t really want to write my own memory functionality for my AI agents but wondering if it’s worth it just to have a nicer UI and more modern JS env.
waleedlatif1
for ease of use, we are exploring a way to add in short-term and medium-term memory out of the box in a way that doesn't require us to inject anything into the agent's context unless the user explicitly wants to. for longer-term memory, we support popular vector DB's like pinecone and integrations with mem0
dr_kiszonka
It looks very nice. What happens when a flow that takes 1 minute to complete is triggered by three different Slack messages from different users one second apart. Are flow executions queued or executed in parallel? Is it configurable?
waleedlatif1
thanks! this would actually trigger three separate workflow executions, and the messages would be processed in parallel by each execution. by default, within the workflow all the blocks execute once their dependencies are resolved, and for the workflow itself they are also executed in parallel. if three messages are sent by the same user, it is also executed as separate workflow executions. curious to hear about your usecase though, are you looking to process the messages in batches?
dr_kiszonka
Thanks for replying so quickly and in detail. I wanted to understand issues around scalability and integration into a larger system.
I have been looking for a good solution in this increasingly crowded space and if I could offer a word of unsolicited advice it would be to ensure documentation is top notch, truthful (some competitors mention non-existent features in their docs), and includes a relatively detailed roadmap.
Good luck with Sim Studio. I may try it out in a few weeks!
waleedlatif1
of course! we will be sure to keep the most up-to-date & accurate documentation and document all of the intricacies like this one, and create a roadmap as well. excited for you to try it out and let us know what you think
brene
I checked it out and it’s quite polished for a workflow builder. But I struggled for it to handle lists of content well. But I saw that’s already an ongoing feature request.
ekarabeg
Thank you! Yes - we are adding variable resolution to the for each input. Let us know if there's anything else you'd like to see!
null
badmonster
How can I add a custom tool or integration to Sim Studio and have it appear in the workflow builder UI?
waleedlatif1
If you want to add a custom tool for your agent to use, you can use the “add custom tool” in the agent block and just define the JSON schema and the code for the tool call.
If you want a custom integration, you can either request it, or if you are running locally we have really thorough instructions on how you can add a tool/block in the contributing guide for the repo. You can use this to extend the platform for yourself or add integrations to the main repo. Hope that helps!
Hi HN! We're Emir and Waleed, and we're building Sim Studio (https://simstudio.ai), an open-source drag and drop UI for building and managing multi-agent workflows as a directed graph. You can define how agents interact with each other, use tools, and handle complex logic like branching, loops, transformations, and conditional execution.
Our repo is https://github.com/simstudioai/sim, docs are at https://docs.simstudio.ai/introduction, and we have a demo here: https://youtu.be/JlCktXTY8sE?si=uBAf0x-EKxZmT9w4
Building reliable, multi-step agent systems with current frameworks often gets complicated fast. In OpenAI's 'practical guide to building agents', they claim that the non-declarative approach and single multi-step agents are the best path forward, but from experience and experimentation, we disagree. Debugging these implicit flows across multiple agent calls and tool uses is painful, and iterating on the logic or prompts becomes slow.
We built Sim Studio because we believe defining the workflow explicitly and visually is the key to building more reliable and maintainable agentic applications. In Sim Studio, you design the entire architecture, comprising of agent blocks that have system prompts, a variety of models (hosted and local via ollama), tools with granular tool use control, and structured output.
We have plenty of pre-built integrations that you can use as standalone blocks or as tools for your agents. The nodes are all connected with if/else conditional blocks, llm-based routing, loops, and branching logic for specialized agents.
Also, the visual graph isn't just for prototyping and is actually executable. You can run simulations of the workflows 1, 10, 100 times to see how modifying any small system prompt change, underlying model, or tool call change change impacts the overall performance of the workflow.
You can trigger the workflows manually, deploy as an API and interact via HTTP, or schedule the workflows to run periodically. They can also be set up to trigger on incoming webhooks and deployed as standalone chat instances that can be password or domain-protected.
We have granular trace spans, logs, and observability built-in so you can easily compare and contrast performance across different model providers and tools. All of these things enable a tighter feedback loop and significantly faster iteration.
So far, users have built deep research agents to detect application fraud, chatbots to interface with their internal HR documentation, and agents to automate communication between manufacturing facilities.
Sim Studio is Apache 2.0 licensed, and fully open source.
We're excited about bringing a visual, workflow-centric approach to agent development. We think it makes building robust, complex agentic workflows far more accessible and reliable. We'd love to hear the HN community's thoughts!