sim

control: forced, auto - Agent memory: conversation memory with sliding window support (by last n messages or tokens) - Trace spans: detailed logging and observability for nested workflows and tool calling - Native RAG: upload documents, we chunk, embed with pgvector, and expose vector search to agents - Workflow deployment versioning with rollbacks - MCP support, Human-in-the-loop block - Copilot to build workflows using natural language (just shipped a new version that also acts as a superagent and can call into any of your connected services directly, not just build workflows) Under the hood, the workflow is a DAG with concurrent execution by default. Nodes run as soon as their dependencies (upstream blocks) are satisfied. Loops (for, forEach, while, do-while) and parallel fan-out/join are also first-class primitives. Agent blocks are pass-through to the provider. You pick your model (OpenAI, Anthropic, Gemini, Ollama, vLLM), and and we pass through prompts, tools, and response format directly to the provider API. We normalize response shapes for block interoperability, but we're not adding layers that obscure what's happening. We're currently working on our own MCP server and the ability to deploy workflows as MCP servers. Would love to hear your thoughts and where we should take it next :) [1] https://news.ycombinator.com/item?id=43823096 [2] https://news.ycombinator.com/item?id=44052766

oduction use cases. Specifically, we wanted the ability to write custom functions, force tool calls, and run parallel execution. We’re taking a different approach. Our visual canvas lets you explicitly define how agents interact, use external tools, and handle complex logic like branching, loops, and conditional execution. You can seamlessly integrate with popular tools like Supabase, Pinecone, Mem0, and Exa without obscure abstractions. You can simulate workflow runs, deploy workflows as APIs or standalone chat interfaces, and trigger them via webhooks. We offer built-in observability, detailed trace spans, and logs that make debugging straightforward. Try searching yourself below: Example message for https://hn.simstudio.ai: - Search [person] from [company] - "Search Emir Karabeg from Sim Studio" Check out our Apache 2.0 licensed repo at https://github.com/simstudioai/sim, explore our docs at https://docs.simstudio.ai, or try out our deployed chat from the demo video here: https://hn.simstudio.ai. We’d love to hear your feedback! Do you think our visual solution is a good approach to building agents?

oss 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!