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!
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.
Do you mind elaborating on what differentiates Sim Studio from n8n, Flowise, RAGFlow and other open source flow based AI automation platforms?
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.
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!
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.
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!