murb

hey! we'll build the same thing but just open source it and make it free :)

if you go to gumloop.com/mcp all these servers are hosted for you with auth handled via your free gumloop account.

Definitely at some point in the near future! started with python for now just because we had to pick a lane but we'll be porting them all over one day

actually made me laugh. will be using this in the future

nice breakdown

AgentHub.dev | Vancouver Canada or SF | Full-time | ONSITE | Founding Frontend/UX Engineer

AgentHub's a platform for automating complex tasks with AI via a drag and drop interface.

We're a team of 2 founders currently and just finished the YC W24 batch. We've grown from 0 to 10s of thousands in monthly revenue in the past 4 months and want someone to join us and help lead frontend dev/help improve overall platform UX.

Job posting: https://www.ycombinator.com/companies/agenthub/jobs/iIpDrPG-...

We think we can raise a series A and seriously scale with a small handful of people, just looking for the right ones!

Techstack for frontend: React, Typescript, Tailwindcss, Redux

Email us at careers@agenthub.dev or apply via that job posting link.

This is one of the few products I can say I actually love. Congrats on the launch guys!!

Great tip, we'll definitely try and use this as a fail safe. Thanks!

Thanks! They look great as well, I've never heard of them before so thanks for the link. We definitely have a vision where where we want to go but unsure where that will diverge from competitors. Focused on just building things people want at the moment :)

The starter plan is generally for experimentation purposes and for those who only need the less powerful models (GPT 3.5, gemini pro, perplexity 70b). Everyone on the pro plan can add their own API keys and unlock unlimited AI calls.

oh dang. Dalle prompt restrictions changing make older templates like that break. We'll adjust the default example to avoid that. Thanks for the callout

Thanks! You can click on nodes to add them to the canvas or drag. Both work

Thanks! We've been building based on user requests since we started. This is the direction our users pulled us in and we're pretty happy with it. The overall goal from the start was to lower the barrier of entry for someone interested in build useful tools with AI.

The people who seem to most enjoy and use the platforms are those that really benefit from the non-technical interface. There are other approaches to this that might be more efficient but this was the flavor we landed on. Subject to change though based on user needs!

Thanks, glad you like it! Totally agree with your points overall. Excited to keep building and compete if need be.

Appreciate the encouragement. I worked at Microsoft before all of this. Totally agree with your points. I love the culture and the people but I think we can compete with speed.

Thanks! I don't have the best answer to this question honestly. Our plan is to move faster and build a better product. That's pretty much it :)

We're two devs and built all of this for almost no money. We think if we can make something even remotely comparable alone then we can definitely compete in the future if we keep going. This is probably just me being extremely naive but I think we need to be a little bit to try. Very valid point though.

Yea I think all of the wonder of AI being able to critically think is wasted if it can't take meaningful action. This seems like a logical next step. We've been toying with the idea of AgentHub automations being tools for autonomous agents. We even shipped 'agents' on agenthub which are basically chatbots with access to your automations.

We thought that was the best feature we'd ever put out but our core users mainly stuck to running automations the standard way.

Fair point! There are a few key differentiators but the AI first approach is definitely a main one. Some of our most used features are nodes that use LLMs but provide a layer of abstraction on top of the LLM calls like our categorizing, scoring, data extraction and summarizer nodes. We support Google's gemini models and Perplexity's too :)

One thing we're struggling with is how to show the value of the platform in a generalized way that still shows the flexibility/power of it all.

All the examples we put out are pretty surface level and approachable because we don't want to alienate with something crazy complex or specific. The real value is in the fact that automations can get extremely complex and it's totally flexible. Our power users have at time 60+ nodes on a canvas with several automations nested in their builds layers deep.

Not sure how to convey that value without people seeing their hyper specific use case and wondering how it's at all relevant to them. If you had any thoughts on how to approach marketing that aspect we'd actually love external input.

Thanks!

They look great. https://flowiseai.com/ does something similar for building AI apps specifically. Less workflow centric but worth checking out regardless.

AgentHub was open source when I first started it. I was really excited about the idea of people building their own integrations and fostering some sort of community around cool automations.

We noticed a few things though. 1. People who were most excited about no code did not want to contribute code to the project. 2. We were 95% open source because we were dealing with credentials and sensitive info on our hosted servers. This 5% of obfuscation was enough to make contributing annoying since you didn't totally understand how some pieces fit together. 3. We were adding new features and redesigning aspects of the system so often that it felt simpler to close it and accelerate. Features like node versioning and secure credential storage made it all quite difficult to maintain in an open way.

I do still love the idea though. Having people contribute their own integrations would be an absolute dream.

Changed the setting! Thanks for pointing that out

These types of threads are why I love HN. Will check this out today.

Woah never heard of this repo but looks spot on. Checking it out now, thanks for the share

Ah yea, I've noticed that lag in a few places. Really got to address that. Thanks for the heads up.

I tried to be fancy and render automations completely dynamically on that templates preview page. Upside is when i change a template automation everything auto updates. Downside is it's rendering a pretty complex object for a simple preview. Gotta address some of that to reduce lag. Sorry about that!

This is a wonderful idea.

We've slowly been baking more and more logic into the AI nodes to make them easier to use. Adding categorizer and scorers instead of forcing people to define their own functions was a game changer. Definitely the direction we want to head in, thanks for the suggestion.

Oh! fixing now (I've never scrolled like this but come back in 2 hours and it'll be fixed, thanks for calling that out)

Great question. That's something we struggle with. Originally that was the goal, to automate anything and everything. However, the more we build for our users the more we realize we have to pick a lane and focus on it because we only have finite working hours in a day. Complex automations are our strong suit, simple workflows like that don't do a ton to separate us from Zapier although I know we could build it quite easily.

Since day 1 our approach has been to build exclusively based on what our users ask for however that user profile has changed with our pricing. Since our prices have recently been set kind of high, the types of users making requests are more aimed at business use cases and scaled builds rather than streamlining of work. We see requests for use cases like that less often as a result.

Each node in our backend has a cost associated with it that we defined on a 0-100 scale. Our plan is to aggregate those at the end of a run to determine it's cost. Tying that cost to some actual metric like CPU usage could be a great idea though.

Runs are basically dynamically generated scripts. Our backend parses the automation definition from the DAG on the frontend, fetches the definitions of each node and stitches it all together to run in a sandbox env. It started off quite simple in the early days but it's become quite an monstrous system with dynamic variables, nesting, looping, credential access and error handling.