A Piece of Pi: Embedding The OpenClaw Coding Agent In Your Product — Matthias Luebken, Tavon

Added: 2026-05-11

[00:00:15]All right. I was introduced to Pi by uh um looking into openclaw. there was a conference uh a meetup and said like okay we're doing open claw and I wasn't so much interested into like all the craziness things that people are doing but I was more interested in understanding uh of how these things work so I was looking into pi and you know uh understand the the whole world of what pi is able to do um this is the one picture you need to take please feel free to take more pictures uh but all the slides and the examples are there.

[00:00:52]Uh so that's the one slide. All right.

[00:00:55]Very quick uh about myself. Uh we're creating a small company uh TAI. We're building agents for uh organizations small out of Europe uh but getting started. And uh what I really like um about sorry uh what I really like about um uh Mario's talk is this this quote. Uh you probably have seen uh this this morning.

[00:01:21]We are on the uh we are in the around and find our own phase for coding agents. Right? So everything that I'm going to show you is what I know today.

[00:01:31]Right? And u I'm going to do the talk again in a couple of weeks and it's going to be most likely be different. uh but um as as Mario was showing this morning um he has created this minimal set right this this coding agent that is available um uh for for you for you guys to to fool around with and that's what I'd like to encourage you so coding agents and why is it so exciting for us to build more products this is Ken Thompson um inventor of uh Unix and this is the famous quote by him one of the quotes, write programs that do one thing and uh one thing well and um I really like that because that's that kind of like works uh to our advantage with agents and um the best part where I show this is with co-work so this is co-work uh cla's desktop um and they're basically are bundling their coding agent into something where they feel is more applicable um and to be honest I've seen very good receptions around this and when you use it uh with financing tools with their finance tools. You always need to work with Excel, right? So they have this Excel skill down there um and it talks to Excel, right? Well, it doesn't. Uh instead it uses a a set of small tools, small CLIs, um uh pandas, open pixl stuff from Libra Office and package this into their own skill uh to make it uh up and running. And I think this is a great example to kind of get your going, get your thoughts going of what what is doable.

[00:03:12]Um, I haven't written a book and nobody can write a book about this, right?

[00:03:18]Because there are no patterns, right? We need to figure this out. We're seeing some emerging patterns in the coding space, right? There's obviously tons of different coding agents and we're seeing this, but there's no authorative resource around this, right? So get going. One thing uh when I was talking to Ivan yesterday uh we realized is like one architectural pattern that we're seeing is that make it easy for coding agents right now that is very broad but think about it right like like make not don't try to be you know very um complex and things but think about the the coding agent uh what is it good at and how do I build my system so that the um agent is easy make it accessible and I I have some examples. All right, this is the rough agenda uh for the next 10 minutes or so. Um I'm not going to talk too much about pi in openclaw. Uh I have a two slides, slides are online, so we'll take it from there. So again, very brief uh introduction of pi. Um Mario uh great work. Something he didn't mention is that he's joining Arendelle uh which I think is awesome. uh it seems like uh you know great great folks working together and uh yeah it's open source it's minimal so it's it's just perfect to get started and the other part that I do want to reemphi emphasize on is is give it a try right we're going to talk about a little bit different but open up pi and ask it to build what you want right it's amazing of what it what it actually is able to do by the system prompt uh that Mario has shown.

[00:05:01]All right, these are the extensions. Um, so again, all the extensions you can download, uh, build yourself or download and yeah, ton tons to explore. All right, so let's going this talk is not about the coding agent itself. So using it for your daily dev works, but what can we potentially do with this? And the starting point are actually not coding agents, right? The starting point is um and I encourage you to do the the same is looking at the core agent itself and there's other SDKs but you know we're talking about pi so let's let's let's use pi and what is an agent an agent is actually just an LM agent that runs tools in a loop right so you have some goals you have some context information agents MD uh in many cases and then you do do coke tool calls right and you get some results and you know you basically do do it in a loop, right? That's it, right? There's not not much more. The rest is magic trying to put it in your use case a little bit more in the other use case a little bit in that direction.

[00:06:06]So, that's really it, right? So, pretty please uh uh don't like open the curtain uh play around with it. Now, with agent um uh uh agent core, this looks a little bit something like this. You have an agent class. This is all Typescript. Um you can uh you know address all all sorts of information information you can prompt it um uh with different information uh and um also you you have an event system so you know a lot of things that that that are going on. So um small example uh this is a CRM lead qualifier. I don't know I've started the CRM use case for my personally and it it just sticks around. So um terminal interface obviously uh small uh TypeScript application three uh uh three files really easy and you can see this right you have a couple of commands that you can execute and you know show me all leads and score them right so that's what we do uh show all leads and score them and here you see all these you know things that are going on under the hood right you see that that the assistant is calling uh tools that you get some results and eventually you know, you get some input. Now, obviously, there's tons of things to do, but you know, I've just vibe coded this away uh uh uh and it's a good again good uh learning exercise.

[00:07:27]The system prompt um uh you know um as you could imagine, right? You know, calling out the different tools that what you do, right? So, all pretty straightforward if you are building an agent. This is an example of how you inject here, right? So um we said we want we we do call tool calling right we reach out to this uh and call a specific tool but for the agent for for steering it more right you know a typical hook would be before the tool call do something right and in this case we don't want to update a contact uh without you know checking something or I don't know you can imagine any types of authorative uh role based access whatever enterprise feature in here but basically you know uh just before the tool call. There's another one events.

[00:08:16]So, we've seen these, you know, uh uh the stream and you might have seen a little check mark there. Okay, the tool call was was fine and returned some result. So, again, we're subscribing to events. All pretty straightforward and again, please give it a try. All right, so this is simple agents others agents SDK uh are are available. Um and now we're moving through the coding agent.

[00:08:41]Now what's what's a coding agent? At the end of the day, it's really the same thing as we've seen before. It's a you know normal agent, right? It runs tools in the loop. But now we have a runtime and some type of shell, right? Bash is seems to be the uh the shell that that everyone is using. But we have a shell and a runtime to to start executing.

[00:09:04]And now things are getting interesting.

[00:09:06]And now the the magic of of what you've seen with OpenClaw uh suddenly shines.

[00:09:13]Uh um Peter uh shared this this example uh on some presentation where uh he sent a message to his open claw and sent a voice message. Now at that time openclaw um and I still don't know if there's any like special plugin but at that time openclaw didn't know anything about voice about voice messages. So what what it did is it it uh created and used different tools uh in the end one of the tools was uh ffmpeg right on the local local machine and it started this and this was one of the tools right so from the outside it it looks like learning but in the inside it's actually just another tool call that is available to the agent and that's why these things make it so interesting. So um again uh the example here um now this is a little bit more sophisticated but the uh important part and and this is the extension API and you know please look it up online. We we're going to do two things or the the things that I'm most mostly interested is in in session events and UI interaction and yeah uh uh look it up online. But here's here's the the actual extension. Now again this is what you would in a coding agent you probably just generate by asking it but here if if we have a look um this is a CRM uh TypeScript a small snippet of it and basically what we're now doing is we're doing the same example as before right and we have a new command called pipeline right so if you have the slash commands and you have a new command called pipeline and now we are able to we're loading all the context um and uh you see this little in um u don't have the lines just below step one uh you can see a context UI select right so all of the sudden we're not only interacting with the backend systems and and sessions and and of those sorts but we're also interacting with the UI and we're able to select right and that's that's got got me thinking um so right so you have this this command and again this is now just the coding agent Right?

[00:11:29]We're not talking about the core agent class, but but this is how you would load up Pi if you just don't download the the coding agent. And now with this new extension, we have Pi, right? And we can start selecting things, right? So this is a simple simple select here. Um and you know, you you even have dropdowns. Now the important part here is these are extensions and the framework uh that currently pi um has included is catered towards the use cases of a coding agent right so we you know there's lots of work and other things to do to make this ready for others for other types of applications but I hope you can see and understand the vision where where this is heading and um yeah you know this is all terminal Right? So you wonder how would this look like in the web? Um it currently is not possible if you ask Pi to build something. So I ask Pi to build something. Right? And this is the web UI. It would be a web UI. Same command, same selection, all based on the same extension mechanism. Now um there's a refactoring going on to make this better accessible and make it more clean. But I hope again it shows you a little bit of of where the where the things are going.

[00:12:49]All right. Now, um PI and OpenClaw um is um is a special special setup, right?

[00:12:57]So, PI and Open Claw what we have there um is that that now we're not only talking about like like um a single agent in a single session in a coding environment. Uh but now we have a multi-channel uh environment where uh we have um you know multiple threads going on, multiple agents going on. So there's a little bit more to it. Um this is um and and the interesting part right that's that's where where I got started is is like if you look into um you know the the packages um uh the core packages of of of pi all of them are used in openclaw right so open claw has this uh uh this function run embed uh uh pi agent and it creates a session right so sessions um uh pi itself has a great session support and It creates a session agent and streams all the information back. We have um the coding agent which we just talked about. We have agent core as um uh the other part that we talked about. And there's two other uh minor u or major packages pi for the unified lm abstraction and a terminal ui interface.

[00:14:15]Um there's um open claw has built its own plug-in mechanism and that's because um uh you know it's a different use case right and has different requirements. So you have plug-in support for multi- channelannel routing, different or uh u provider orchestration, sub agents, gateway support, yada yada yada all the things that you know by openclaw but it's based around the core mechanics of of pi and and and leverages it. Cool.

[00:14:46]But uh one thing and that's that's that's the like the the major gist I would like to bring across is like okay what do we do now with this? what are other options for us to do? And this is one of the applications we've been building um for um for a client. Um and basically um uh the the the use case is a sales process um they get um uh requests for proposals um of of an ordering another um another system, right? Um parts parts being sold by that company. And we're taking all this coding agent all all of that we're taking away right where we we're new fresh new thinking right and look at the process from the get-go. So um an email comes in right we we we monitor basically that inbox then we have some gateway because what we want to do is we want to forward this to different agents right so here I have um multiple agents right uh the way it's structured is we have one agent per customer and that agent has a general harness right agent MD uh um agents MDE as an example but you can obviously also use different ones and that helps um understanding the role of that agent. In the specific case it's it tells of how to use the system and how to react to certain you know inputs outputs etc. Now um the other one is customer MD where we basically explain the agent like you know the specific customer might have you know specific twerks right specific um uh access specific um um discounts and all of that sort and then right and that's what I said like earlier I I like using sessions then for each case right we're we're creating and reusing existing sessions so we can back and forth um um know what what was previously talked about. All right, so email comes in, we're looking at the gate um inbox and we route this to the different agents and now we have tools, right? So we have these different tools uh to talk to the CRM to talk to the ERP um and get the right information out of the system for this agent to look on like like behave like you know maybe it has you know new contact information or of that sorts and again we make this available we make it easy for the agent to access right and our way currently is doing this with CLIs right so CLIs our agents are really good at using CLI so we make it available as a CLI I we put we make sure that the data is secure. uh we have our own sandbox and then we're creating the drafts again right so that's the system and I hope by this point you basically understand like logically where these things uh uh fit together but how would this look like um oh one uh a final thing right there's always the question around okay sandboxing etc and and to be honest we're on the uh just on the on the steps of of getting there but if you've seen um Nvidia's announcement uh around um open claw, their policy, their open shell is really really interesting and um um it's it's it's a way of it's one ways of securing an an agent. We're looking into this. Please do as well.

[00:18:11]All right. So, how does this look like um to to to kind of like get you an understanding of of how these things, right? So, here's the dashboard. Um rather uh boring, but here's the in uh the email the inbox, right? So again we see the the email coming in and yeah we um it's one of one of many emails most of them ignored but this one is like the the DLM call said okay I'm I'm interested in this and it is associated to a case right we see the case up there now this case is again is an agent session right uh so we find the session and associated to it um we then create a draft uh so there's tons of calls which I'm going to show you in a But basically the output of all that is a draft email that the user will be able to use. Right? So our thinking is uh let them user stay in in email, let them stay in the the inbox and drafts and they don't even you know need to do a lot. So this is more like an admin interface. They can stay in email but basically the output would be a draft generated. And how does that look uh behind right? We we had the the different sessions before uh the threads and this is the same thing right the assistant says uh well apologies to German but uh now I'm looking at the articles it does different tool calls right it gets gets results and does this in a loop to result right the end effect for for the user is I'm looking at my inbox there's a new email it's associated to a case and I get a new draft which they can freely edit but um under under the hood we have all these um uh agents working. All right, that's that it is for me. Um again um here here you find the slides. Um key takeaways please. Coding agents are and will be a core building block uh for your software systems. I'm I'm betting on it. A lot of people are betting on it. So please give it a try. Pi is perfect for tinkering whether you like it or not. It's minimal. You can rip things apart and put things together. It's perfect. So, please go tinker. All right. Thank you.