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Open-source workspace for AI agents and workflows
Orchestrating multi-agent workflows quickly becomes a mess when handled purely in code, especially when maintaining complex sequential, parallel, or loop communication patterns. Sim Studio solves this by providing a unified visual canvas that bridges the gap between low-code layout and actual deployment. The fact that it is fully open-source (Apache 2.0) and self-hostable while maintaining extensive out-of-the-box integration coverage makes it highly appealing for production-oriented teams who don't want to get locked into commercial enterprise platforms. As a relatively young project compared to long-standing workflow builders, its documentation and edge-case error handling are still maturing. The agent composition architecture is predominantly workflow-centric right now; introducing more native, role-based orchestration paradigms (similar to the explicit "role, goal, backstory" approach) would broaden its architectural flexibility. Additionally, adding a built-in, native evaluation and tracing matrix directly on the canvas would significantly simplify tracking execution state and debugging token drift during long-running agent cycles. I’ve evaluated several alternatives in the agentic ecosystem, including standard code-only frameworks like CrewAI and heavy proprietary hubs. While code frameworks offer high flexibility, they completely lack native visual introspection for team behaviors. On the other hand, most existing visual UI tools are either heavily restricted by restrictive licensing terms or tightly coupled to closed-source cloud ecosystems. I chose Sim Studio because it delivers a flexible, TypeScript-native visual workspace without enforcing vendor lock-in or imposing artificial commercial scaling penalties.
👋 Hey Product Hunt! I'm Emir, co-founder and CEO of Sim. Today Sim is open to everyone, and I couldn't be more excited to share it ❤️ Sim started with a mess of our own making. Waleed (my best friend and co-founder) and I were prompting Claude to build sophisticated automations in n8n, storing data in Supabase, and standing up infra for our APIs and MCPs, and we realized the stack we'd assembled just to build agents that automated our own work was a complete mess. Not to mention our token spend... So we set out to build the one platform we wished we had. The problem: building AI agents today means stitching together frameworks, one-off scripts, and brittle automations that break the moment anything changes. We wanted one place to build an agent, give it access to our data across 1,000+ integrations, build a brain for memory, deploy it, and actually manage it over time. So we built one. Sim is the open-source AI workspace for agents. Here's what makes it different: 🗣️ Build by chatting — describe what you want and Sim builds the agent and workflow for you. Or design it visually on a canvas. Or drop into code. Whatever fits the job. 🔌 1,000+ integrations + every major LLM — Slack, Notion, HubSpot, Salesforce, Gmail, and more, connected out of the box. Your agent says "message me on Slack when a deal closes" and it just works. 🧠 One workspace, shared context — Workflows, Tables, Knowledge Bases, and Files all live together, so your agents share memory and data instead of living in disconnected tools. 💸 Cost-efficient by design — Sim swaps token-hungry tool calls for deterministic steps and real code wherever it counts, so you're not burning tokens (and money) on work that never needed an LLM in the first place. 🚀 Built for real work — Slack bots, compliance agents, data pipelines, research assistants. Not demos, actual production agents. 🔓 Open source (Apache 2.0), SOC2, and already trusted by 100,000+ builders. Who it's for: teams who want to put AI agents to work (IT, ops, and technical teams who need governance and control), and individual builders who care about speed and open source. We're shipping fast and want to build this with you. Tell us what's missing, what's broken, and what would make Sim 10x more useful for you. Try it -> sim.ai I'll be here all day and will read and reply to every single comment 🙌 Show more
The "chat to build" vs canvas vs code approach is interesting, most tools force you to pick one. Which one do most of your users actually end up sticking with?
Congrats on the launch! 100k builders on an open source tool is genuinely impressive. BTW, what's the split between solo devs and actual teams using this in production?
who do you think gets the most value out of Sim today, developers, technical teams, or can non-technical users get productive quickly as well?
@emirkarabeg The mess of stitching frameworks and watching token spend go wild is incredibly relatable. Sim solving this by bringing workflows, tables, and knowledge bases into one shared context is huge for real production work. Can we seamlessly toggle between the no-code canvas and dropping into raw code for specific nodes, or do we have to pick one style for the workflow?
Love the emphasis on replacing unnecessary LLM calls with deterministic code instead of assuming everything needs an agent. Just Curious have you noticed users converging toward a small set of reusable agent patterns over time, or is every team's workflow still highly bespoke? Congrats on the launch! 🚀
@abod_rehman Thanks so much! Great question. Solo agent builders account for 70%+ of the users on the platform, while teams actually account for 90%+ of agent and workflow runs.
@boyuan_deng1 Originally, Sim users were only building on the canvas. Now, more than 90% of the platform's usage comes from the chat. Sims love prompting!
@aymi_malik Great question. Technical teams building agents and solo devs looking for automations get the most value out of Sim today. Non-technical users still get loads of value from the Sim chat.
@emirkarabeg Being able to interchange it node-by-node gives the perfect balance between speed and full control. Absolute game-changer for dev workflows. Thanks for clarifying.
How does Sim actually compare to n8n or Langflow for someone who already has a few agent flows running? Curious what the real differentiator is beyond the open-source angle.
@gkeaksoyakhabw Sim's biggest differentiator is the ability to build across your entire workspace with chat. You're able to monitor and debug production workflows with Sim and have it alert you for any errors or anomalies in production agents. Sim has agent skills, file storage, native tables and vector storage, and some more capabilities that are built natively alongside workflows. See a full list of comparisons here: https://www.sim.ai/comparison
@emirkarabeg Congratulations on the launch! "1,000 integrations" that's massive🙌🏽
@kutlwano_melamu thank you!
@tehreem_fatima5 you can decide which option you'd like to work with and interchange for each node in your workflow.
@tarqiya_forgah A pattern we're seeing often is when teams put a set of deterministic nodes first (fetching data, cleaning it, etc.) and then put the LLM-based nodes at the end to handle the last-mile and other nuances.
