 you you you you you you you you you you you you you you Thank you. you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you you Thank you. you you you Also, everybody can take a seat and we'll start in a few minutes. Thank you. you you you you you you you you you you you you you you you you you you you you you you you you Teksting av Nicolai Winther Welcome everybody. I'm very happy to see so many faces here. Welcome to this AI Agents Hackathon today in San Francisco. We also have quite a few people joining us online. Right now they're watching live. So I'm very happy to see you all. My name is Emanoel Yarte. I will be your MC for tonight. I'm an AI architect at New Native and LabLab AI. So, first of all, I would like to welcome Bibu to give some welcoming words. Thank you. to give some welcoming words. Thank you. Hey, I'm Vibhu. I'm here with MindsDB. Welcome to our office. So just to kick things off, I want to thank all the sponsors, everyone putting this together. So we've got AgentOps, TogetherAI, Upstage, the official meta team is here, LabLab. It's been a lot of work to get this done. So it's nice, welcome to everyone online as well. So instead of giving you slides about MindsDB, we've got a quick start rolling around. We'll have a workshop. Our team is here, so we're gonna save time for everyone else. Just quick logistics and everything, restrooms are there. We'll have schedule and everything going on here. But yeah, we're really excited to host events like this. So welcome to our space. We host a lot of things. We host a lot of hackathons. This is part two of our agents hackathon. So with the agent ops crew here in April, we did the first one. We had the meta team. We had a lot of great hackathon energy. So we're excited to see what people build next. The theme of this hackathon is agents and part of that is what we're working on. So we actually have a big launch coming up on September 24th. Everyone here, you're all welcome. So we have a big celebration in the afternoon. So that'll kind of be our little launch party. We'll have a live stream and everything. We'll send out details for that. But yeah, come hang out. MindsDB, we have like a pretty big open source AI platform. So we help you connect different data sources to different AI like engines. And we help you build this real-time automation build AI application. So all around, you'll see our quick starts. Feel free to use our platform. It's fully open source. You need any help, we're around. But before kicking into too much of everyone's time, we just want to, you know, welcome everyone. We'll have a lot of mentors. We want to invite everyone to our space. We host other stuff. Yesterday, we had the OpenAI team here. They did a big Q&A of their O1 model. We might have them come back throughout the hackathon. So if anyone's interested in the new model, they might roll around. Some people from Google will be mentoring. And yeah, it'll be good energy. We're around here as mentors, sponsors, hosts, and everything. But before that, let's kick it off to some of the other speakers and talk. So bring it back. Thank you, Vibhu. Well, I think he described it very well. We have three very exciting days in front of us. So this is going to be the schedule for the next three days. Right now, of course, we're going to have very interesting speakers coming to stage. Then we'll do some networking. And tomorrow we open doors at 10 a.m. We'll be hacking all day until 9 p.m. And lastly, on Sunday, we'll open doors at 9 a.m. and we'll announce winners by 7.30. So I'm going to walk you through the hackathon challenge. Basically, you have to build an innovative......enhancing tasks and workflows. The idea is to demonstrate how your agent can transform standard processes, tackle complex tasks, and address pressing social issues. You can use any technology you like, but remember that our partners will offer special rewards if you use their technology in your applications. So the prices for this hackathon is $5,000 in cash for the first place, $3,000 for second place, and $2,000 for third place place and our special awards for the use of the technology from our partners. Best use of Composio will have two hundred fifty dollars and free Composio credits. Best use of DocumentParse and SolarPro will have three thousand in upstage API credits. Best use of Together AI will have 2500 in Together API credits and the best use of LAMA will have 3000 in cash. Also, well the success criteria for this hackathon is divided in four. The application of technology, how efficiently and effectively the technology that you chose is integrated into your solution. The business value, which is the potential impact and practical value of the solution. Originality, which is the uniqueness and creativity in addressing the challenge. And the presentation, basically the clarity and effectiveness of the project presentation. and effectiveness of the project presentation. So without further ado, I would like to give the stage to Simon Olson and Pavel Chek, founders of LabLab AI and Unate2. I'm going to stand like this. This is awesome. It's great to see so many people here. We came a long way for this, so I'm very excited that so many people showed up. I think I checked the numbers just prior to walking on here. I think we had like 800 people online or something like that. So it's quite a lot actually. But just to say a couple of words of what we do, what LabLab do, we started this business about two years ago. And we started as a full community business. We really tried to focus on helping developers get access to this new platform shift, these technologies that are coming out in the AI space. And by that time, it was quite hard to actually get your hands on the basic APIs, even for well-established models at that time. But we've been doing that now for two years. And really what we're trying to achieve is democratize the access to as many people as possible. We've been doing that now quite successfully all over the world, not only here in the Bay Area, but with a big focus on the European markets as well as the Asian markets. So we've had a lot of fun doing that, and we'd love to keep on doing it. So we're very happy to have such great partners with us. Thank you for the space as well, MindDB. It's great to have you take part of this as well as Meta, as well as Together AI, which we've done several events with in the past as well. So super happy that we're able to do this. And to say a couple of words about the challenge as well as Together AI, which we've done several events with in the past as well. So super happy that we're able to do this. And to say a couple of words about the challenge as well, I mean, this entire sort of agent shift, it happened quite recently. It was like a year ago, a year and a half ago. And I think that this is probably one of the biggest opportunities of a lifetime to fundamentally change the way that you build and operate software. And we have done anything really over the last one and a half year from simple, trivial media applications to quite advanced deep tech genome sequencing algorithms on our platform. And so I'm kind of excited to see what you guys are going to dazzle us with this weekend. I have high expectations. So with that, I'm super happy to be here, super happy to have you guys participate in this hackathon, and good luck. It's very nice to see so many people that are builders. That's okay. No, no, it's fine. It's fine. It's fine. Is it working? Test? Test, test? Yeah? Okay, guys, but the only thing I want to say is that it's so good to see people that are builders and builders at heart. And building really makes people progress. And especially building together makes us a lot stronger, right? And building together also allows us to fail. And that's also good. Not everything always is just one huge stream of success but I do believe that your products will be amazing. But it's very important that we're in a community here in San Francisco but also through Lablab you can find people in the community everywhere in the world. So there's people joining us from the US but also from Mexico and Indonesia and from Warsaw and from Sweden and everywhere else in the world. So you can find your tribe. You can find teams, you can find your tribe, you can find your colleagues and people that are as excited to build things together with you. And that's really the strength of builders and that's really strength of new technologies coming in that we get to start something, start something exciting. And it's not set in stone the future, the next big company might be in this room. I firmly believe that there's a shake in the foundation of business models. You can see it in Silicon Valley and people are building amazing new businesses and even the biggest, nobody's safe. There is nobody safe right now. Whether it's Google or anybody else, it doesn't matter. Not singling them out. So it's an amazing movement, amazing new idea, and it's the act of creation. So a lot of people don't understand that, but I just wanted to say that whether you're writing a poem or you're writing a song or you're writing a novel or you're creating code, it's an act of creation. So it's a very good proud moment And you can feel very good And Sunday is creating together in this space. Thank you for all the partners not repeating everybody. And yeah, thank you guys for joining us. And please make sure to get your accounts, find people here in this room, so build the teams, and also find people online and bring them over so you can do it together. Thank you very much, guys, for showing up. Thank you, Pavel and Simon. Yeah, I have to agree. I think AI agencies are one of the things that have the biggest potential to change how we do things every day. So there's amazing tools out there. There's amazing frameworks that we can use. I'm very excited to see what everybody here and the 800 people online will build in the next three days. So please, everybody, welcome to the stage Lucy Park, Chief Product Officer from Upstage. All right. Oh, I wasn't supposed to touch this. All right. All right. Oh, I wasn't supposed to touch this. Okay. Good to meet you, everyone. So it's really nice to be here. I hope everybody's up for some fun. I just want to ask a question before. Who here has heard of Upstage before? Oh, yeah. Oh, yeah. I see some hands. All right. Great. Okay. I see some hands. All right, great. OK. Do I have my slides here? Yes, yes. I sent the slides. Yes, I'm from Upstage. I'm Lucy Park. I'm the chief product officer. I'm here to introduce two products from Upstage that can power your applications in this hackathon. I'm calling them the Upstage Duo for you to memorize them quickly. I'm going to introduce some hackathon projects and enterprise use cases that we have seen people work on and that we have worked on before. About Upstage, we are an enterprise AI startup. We are based in Seoul and we're currently expanding to the U.S. And we are a Series B company and our flagship products are LLM and Document AI. Companies range from SMBs to enterprises. You can see Samsung, Intel, Hyundai down there. And we work particularly in finance, legal, and bio. And we have participated in a lot of hackathons and it's really an honor to be here at Lab Lab. We are working with a lot of enterprise customers to work on their use cases powered by AI. But there's a difference between hackathon projects and enterprise use cases. A lot of the enterprise use cases are very ordinary workflows. They're not crazy new ideas, whereas in hackathons, we play with very crazy new ideas. Sometimes enterprise use cases are must-have practices and rather not good to have. For instance, insurance claim processing is a very mundane task, but it is powered in enterprises and is very important for our customers. And so we experience very exciting hackathons to very important enterprise use cases, and these are powered by the Upstage Duo. So the first is the Solar LLM family. The second is Document Parse. The Solar LLM family is obviously an LLM. And we have three LLMs prepared for you. Document Parse is a tool to make your documents LLM ready. So let me just introduce them to you one by one. So the Solar LLM family consists of Solar Mini, Solar Pro, Solar DocVision. I will Solar Mini, Solar Pro, Solar DocVision. I will stress more on Solar Pro today, but let me go through the use cases before I introduce Solar Pro to you. We had an internal toy project several days ago using Solar Pro and using it to debate. Solar Pro and using it to debate. So Solar Pro is talking to another Solar Pro, and we're asking questions like, how do you make smart LLMs? And it debates within itself. And as you know, OpenAI released a model called O1, right? We made a Solar O1 today and launched it internally. And you can actually try it out yourself in this link. And it's actually fun. We replicated 01 with Solar Pro today in one day. And there was a hackathon project in healthcare where people used solar to avoid eating prohibited food. So they took in menus, parsed the text within it, and asked solar whether we can eat this food or not. So it was a quite interesting project. This is an enterprise project that was done with a company called Tanawa. It's e-commerce price comparison service with 10 million MAU. They wanted a service that, so they do price comparisons. So they wanted to extract attribute values from products, product descriptions. Say, for this laptop, what is the brand? What is the color? What is the price, and so on. And they worked on this price, this attribute value extraction use case with humans before. And they wanted to automate this process. So they came to us and we used Solr. We built a Solr e-commerce for them, which was a 30 billion private MOE model to automate these AVE tasks and to power more use cases within this company. And so there were very more, there were a lot of interesting projects that powered use cases. And I want to introduce Solar Pro Preview, which is a preview model of Solar Pro that will be released in November. And we think, in our perspective, we think this is the most intelligent LLM that can go on a single GPU. It fits snugly into a 80 gig GPU. And APIs are free until November 24. So feel free to try it out. It's currently ranked number one on the Hugging Face Open LLM leaderboard among models below 70 billion. So if you can't really trust our internal benchmarks, you can probably trust the benchmarks here. It's very easy to use. If you use link chain, Lama parse, probably the OpenAI SDK, they're all compatible with them. So feel free to use them out. And let me also introduce Document Parse. So Document Parse is out there in the world, because we know that many of the documents you want to process with LLMs are very complex. Sometimes there are multi-columns, sometimes there are multi-page, and they're visually rich, meaning that they have a lot of tables and figures inside. And that's the reason we built Document Parse. It converts any document so that it could be consumed by any text-based LLM. It inputs almost any document, PDF, JPEG. If you have DOGX, PPTX, that's fine. You can just input into the API, and it will output structured text like HTML, Markdown, or text. If you see the table on the right, you will see that it has a lot of merged cells. It doesn't have explicit lines between the cells, but still, Document Par parse is very good at extracting this content. We did an internal benchmark with single page document QA. If you ask a document if you ask document if you ask a question to this document say like in table, what's the performance of blah, blah, blah, it takes the answer out perfectly. So we did a internal benchmark within our company and comparing pipelines between HTML extraction with document parse and QA with GPT-4.0. We did multi-model QA with GPT-4.0. We did multi-model QA with GPT-4.0. We also compared HTML extraction with GPT-4.0 with QA with GPT-4.0. And the accuracy for document parse was the highest, which means it is very accurate in extracting all the contents within the documents. So why is there this difference? We kind of realized that with GPT-4.0, there were a lot of hallucinations. So when document has complex tables, when document has many letters, it's better to use document parse. And there are also other cases where you want to use document parse. Speed, accuracy. If you're interested in multi-page, There are also other cases where you want to use document parse. Speed, accuracy. If you're interested in multi-page, document parse is good at taking care of documents up to 700, 800 pages. We have a sync mode, so you would definitely be interested if you have very big documents. Interpretability, if you want to cache the documents, you want to be interested in document parse. There were quite a lot of use cases. Some student groups built a triage system for alcohol and other drugs by parsing forms using SolarLM to ask questions toward these questionnaires, and made a checklist, and finally built a triage system. Another team worked on legal use cases, where they had a contract, a really long contract, and then summarized the contents. We also worked on an enterprise use case, where we did something very similar, but this time, we didn't just summarize one document. We put in multi-documents and made a summarization for lawyers because it takes a long time for the lawyers to go through a lot of documents. So this one's also very easy to use. And we have good news for people that are on site. If you work on your project during this hackathon and you give feedback to us, we will give you a free T-shirt over there. Galena is RPM is ready with the T-shirts for you. And you can just take this QR code and send us feedback and we'd be happy to give you a t-shirt. Finally, if you scan this QR code, you can go to the Upstage console and have fun. We will have 200 credits for everyone and awards will be out there as well. Thank you for listening. Thank you. Thank you very much. I apologize for the small technical difficulty. So, yes, indeed, Upstage will be offering $200 in API credits for participants of the hackathon. You just need to use the promo code AIagentshack2024 to access the credits in the Upstage console. You can find more information about Upstage as well in LabLab. So, I'll leave that there for a second. Well, thank you very much. Now I would like to welcome our next speaker, which is Lorenz J. from Crew.ai. So please, everybody, a round of applause. How's it going? Can everyone hear me? People in the back, yeah? Sweet. All right. Demo time. Oops. Sweet. Demo time. Alright, everyone, my name is Lorenz. I'm a software engineer at Kru AI and today I'm going to show it off. Kru AI is the leading open source multi-agent framework. We're a universal framework as well. What does universal mean? Universal means you can connect any LLM provider from Ollama to Grok to OpenAI, Cohere, Anthromic, and even your own LLM. We have abilities to connect to any AI agent tool from like browser base that lets you autonomously scroll the web to things like Compazio, which is a sponsor here today, to the access search tool. to things like Composio, which is a sponsor here today, to the Exit Search tool. We're at a AI agent hackathon, so I'm sure everybody knows what AI agents are. But to those who don't know, AI agents are entities that take a high-level goal, like analyze market trends of my competitors, find the best talent for my job postings, to research the latest news about AI, or even create a marketing campaign for my product. It takes that high- level goal and generates steps, tasks, plans, actions, and needs to execute to achieve its goals. At Kru AI, we have a lot of powers for our individual agents, they have the ability to execute code in a Docker container, they have memory to understand what it should do, and has the abilities to use certain types of tools, to using any LLM to even function calling. At Krew AI, we've made creating agents super easy. All you need is a role, a goal, and a backstory. And then optionally, you can pass it some tools. Then you attach an agent to a task. Like hey, find the top three trending AI news headlines, and then attach that agent to that task. But what happens when your task becomes really complex, right? You want to research top candidates on LinkedIn. You want to analyze all their profiles. You want to create outreach templates for each and one of them. And you want to generate a report for your stakeholders. Well, you may not want to just use one agent. Instead you want to use multiple agents. And that's what we do really well at Koo.ai. We handle cooperation, role playing, delegation of tasks from one agent to another to help each other achieve its goals. Like in a team. You have the ability for caching and memory so it knows what tools to use. You can train your agents. As well as have guardrails to ensure that your agents are performing the way that they're supposed to. And we do this through cooperation and role-playing. And you could put all of them together with a CRUDE class, attach all your agents, attach all your tasks, and just define a process. So in just a couple lines of code, you have a CRUD crew. And we have different processes. A common one is a sequential process that has one agent to one task. And we have a hierarchical agent that has a manager agent delegating tasks to appropriate agents. And then you could chain all these together to run crews in parallel and asynchronously run tasks. I think this shows the power of agentic automations. And what can you use agentic automations for? We see them happening and impacting things in customer service to marketing, to sales, and even communication-based tasks. So, all right, we're at a hackathon. Let me show you how to build some stuff. So we're gonna automate the boring stuff with some agents. So I'm a software engineer, but sometimes I have to write some content. I have to write an example of how do you use Kru AI to build a LinkedIn scraper, right? So today I'm gonna show off how we can do a technical content writer with Kru. We're gonna have four agents. The first one is a researcher. The second one is a blog chapter writer. Then we're gonna have a blog writer itself. And then we're gonna have four agents. The first one is a researcher. The second one is a blog chapter writer. Then we're gonna have a blog writer itself. And then we're gonna have a tweet writer. At the end of all of this, we're gonna expect a technical blog post that uses Krew AI to build something with Composio. So we have a couple agents, and you could define it with a YAML format. So if you're not technical, or if coding is something that's still new to you, you could easily define your agents a YAML format. So if you're not technical or if coding is something that's still new to you, you could easily define your agents with YAML. So we have a researcher, the blog chapter writer, the blog writer, and the tweet writer. An example for a researcher, they're a senior dev rel researcher, they're going to research our GitHub repo name and you can see those brackets, that interpolates a string that you pass as inputs. And then they're going to research, write the chapters, and then ultimately write a blog for us. And here are our tasks. We have a research task, a chapter writer task, a blog post task, and tweet writer task. All of these map to the individual agents that I defined before. And then in your crew, you could pass in tools, like the Surfer Dev tool that will go through Google and find relevant links for them. And then the GitHub search tool that will kind of do RAG over your code base. And that's something that you see over here with the GitHub repo name. And then we could define all of our other agents. All you have to do is just annotate with the at agent and then the at task, which is the same things that we've defined in our YAML format. So it's super easy to use. And again, we just attach them to our crew class, from agents, our tasks, and the process that we wanna use. And then here's our topic. I wanna create a blog that uses crew AI agents with Composio tools to create a SQL agent that can query a database. Here's our tech stack. It's crew AI, Python, and Composio. And I want it to output to these paths. And then here's my GitHub repo name, which is the Krew AI open source project. And we're going to kick it off. And this is something that we get. It's pretty good. It's like 80% of the way there. But I want to improve it. But I don't want to continue writing. So let's train our Krew to improve. So here are some of the examples. So how our train works is it uses reinforcement learning with human feedback. So after every task gets executed we have this please provide feedback option where we could give it like suggestions as to what it should do. So the first slide the the top half is pretty much the the chapters that were written. So I'm like, hey, why don't we include a section of how to train this crew too? Here's references to our docs. And then the second one is the blog post. After it's done, let's integrate Composio tools and I sent it the repo to take a look at. And this is kind of like the results we've seen. So on the left, these are runs before we've trained. We use LLM as a judge and these are kind of like the results we've seen. So on the left, these are runs before we've trained. We use LLM as a judge, and these are kind of like the scores. So you could see crew is like just 8.5 as an average score before training. Then after training, we got a score of 9.3 out of 10. And we can even see improvements on execution times. We're saying like around 21 seconds. And then we get something a lot cleaner and a lot better. And it has more details as to how you can actually import Composio into your crew compared to the original one. And here's the tweet. I'll unleash the power of AI, learn how to use crew AI agents with Composio tools to create an SQL agent that simplifies your database queries, transform complex tasks into effortless actions. Then read our latest blog post. So yeah, so some tips for the hackathon. Find things that you do repetitively every day. They're mundane things, you don't wanna do them. So why don't you try to automate them with AI agents. Tips for orchestrating your agents. Split tasks that every agent has a specialty for, and then we have a chatGPT doc, a chatGPT agent that you can use during this hackathon for troubleshooting or bouncing ideas, and then ultimately have fun. It's gonna be a cool weekend, and here are some of our examples, our docs, and our community discourse. And I'll be here throughout the hackathon so if you guys have any questions, feel free to reach out to me. But yeah, that's it. Thanks. Thank you. So yeah, I'm pretty sure a lot of people here are familiar with Cray-Eye. I myself use Cray-Eye relatively often to automate a lot of my repetitive tasks, so I can't wait to see all the projects that everybody will come up with. So now please, everybody, give a round of applause to our next speaker, Ching-Yung Wu, founder of AutoGen. All right, everyone. Hello, everyone. This is Qingwu Wu. I'm an assistant professor at Penn State University, and I'm one of the original creators of Auditor. And it's great to be here together with you, sharing with you how to build the best agents team with AutoGen. So as many of the speakers and many people have already acknowledged that the future is about AI agents, and this is probably the largest revolution in the history of computing. So that's, I guess, why we are here in the TEDx and why it's so exciting to be here. And Alderden helps you answer this question, how to build the best agents and teams of agents. So to answer this question, we appeal to the wisdom of Aristotle. So Aristotle said, the whole is greater than the sum of parts. This is a simple yet profound idea that is at the heart of many successful AI systems. And this leads us to the idea of multi-agent system. By combining different AI components, we can create solutions that are more powerful, more flexible, and more capable than any single AI model could be on its own. Imagine having a team of AI experts, each specialized in different areas, working together seamlessly to create, to solve a complex problem. That's the potential of multi-agent systems. So with this wisdom, let's try to answer those following questions. The first is, what are the parts? Because we want, we are trying to create a better whole with the parts. And obviously we have all these very capable models, and also we have all these amazing tools and APIs we people have created over the past decades. And of course we have human intelligence that we may want to leverage in our future AI systems. So the next question is how to make a good whole. So the next question is how to make a good hole. So, Autogen can provide the framework for you to kind of build a hole. So it is a programming framework for agentic AI in general. And let's see how we answer those questions in the framework of Autogen. So in the framework of Autogen, the first step, what are the parts? So in autogen, the parts are all about agents, and more specifically, they are conversable agents, which means that each of the agents in this framework is able to talk to any other agents in this framework. So that's why we call it conversable agent. And the second unique feature of O2Gen agent is custom availability. So you can equip an agent with a large long model or a tool or even human input depending on your needs. And you can also kind of specify any combination of them. So that's the first step. So the second question is how to make a good call with autogen. So in autogen, it is intuitively very simple, which is let them to talk, because all the agents are conversable able to converse with each other so that's what we call conversation programs so basically specifying how the agents should talk to each other and what are the conversation patterns so the commonly used conversation patterns including sequential chat, message chat, group chat, hierarchical chat, and even more. You can specify whatever multi-agent conversation pattern you need. Of course, you can always use a single agent if that's good enough. So let me quickly go through some concrete examples. So the first example is a very simple application in which we want to write a blog post. For example, we want to write a blog post about keeplearning.ai, and we want to kind of have an iterative process where there is a critic agent that can help provide criticism or feedback on the blog post and so that we can keep improving it. So in this simple example we can just simply create these two agents by importing this assistant agent abstraction from AutoGen and then initiate chat between these two agents. And then these two agents will talk to each other autonomously until the problem is solved or until we reach a satisfying blog post. And of course, you can always specify how much runs you want to have at most. So that's a place where you may want to include max turns. Yeah, the next application I want to quickly go through is this custom onboarding task, which is a representative task that may need sequential chat. So sequential chat is a type of conversation pattern that is good for tasks that can be decomposed into subtasks. For example, in applications where you have clear SOP, for example. In that case, you can specify a group of chat or a pair of agent chat for each of these subtask or substep and then chain them together. So this visualization shows how you do that high-leveling with Sautogen. For example, in this custom onboarding task, you may want to have a step where you collect customer's information, and the second step is to survey customer's topic interest, and the third step is to engage with this customer. So there is a clear SOP here. Then let's see how we can achieve that with Autogen. So as I mentioned, it's just two simple steps. The first step is to define what are the agents needed. So here I'm omitting the definition of agents here, but it's pretty intuitive. You just define the name of the agent and the corresponding prompt. And then you just change them together as a list here. As you can see, each element in this list is a pair of agent. In this case, it's just a two agent chat. So it's just a pair of agents chatting with each other. And we will carry the summary from the previous chat to the next chat so that we can carry the necessary information needed for the next subtask without carrying like the lengthy context from the previous task. So that is sequential chat. Next is an advanced conversation pattern called message chat. So message chat is good for the use cases where you want to have the agent to think a little bit more before it talk to the other. So we all know the new release of OpenAI, O1 model basically is a similar philosophy of similar paradigm. So the O1 model is trained on chain of thought data so that it can think before it responds to the user. And here we are doing similar thing on the agent level. Here we are using this nested chat to achieve that. So basically we are using the nested chat, register nested chat function, you can add a nested group of agent chat within on top of another agent. For example, on top of this critic agent, we want to have this nested chat as the kind of inner monologue of this critic because we know when we try to review a particular blog post we have several different aspects, several specific aspects we may want to investigate on. For example, we want the blog post to have good, to be good in terms of SEO because we want it to be ranked higher in search engine. And we may want to ensure the blog post is good with respect to legal and ethics. So with this message chat, you can include ICO agent, legal agent, and think agent as in a monologue and have a more sophisticated reflection on how to criticize, criticize a blog post and then only return a summary or aggregated, aggregated reflection and then respond back to the other agent. So this is another example and this example is again using message chat but here I want to mention that on top of any agent you can rapidly register any tool on top of it to enable more sophisticated execution. For example in this example we are adding this chess tool to make sure that we can ensure each move of AI agent is the legal chess move so that we make a legal game eventually. So to learn more about this how to design sophisticated multi-agent conversation pattern. You can quickly go through this course, short course on deeplearning.ai. We met together with Andrew N. And we also have a growing autogen ecosystem. Now autogen supports over 75 models, including a lot of non-open-air models. And also we have a growing ecosystem of all kinds of support, for example, observability, and we have the support of AgentOps, and thanks to the Maybing partners from the AgentOps team. And we also have ecosystem with other type of tools, like different databases and the different platforms, and tools for handling long context memories, and also tools for improving prompts. And finally, we want to let you know that we have this dedicated autogen-AI organization to host the repo and also the whole ecosystem. And within this organization, you can find this field with autogen repo. And within this repository, you can find a lot of sample applications and cookbooks and the tools you can readily leverage. So I believe this will be helpful for the hack zone. Yeah, that's about all I want to share and happy hacking. And if you want to use Autogen, you can just pip install Autogen. And we used to use the name PyAutoGen, but now we have this even better name. So yeah, thank you all for having me. Thank you. Thank you very much. So thank you very much. Yeah, with all these frameworks and technologies we're looking at, it might be hard to keep track so I wanted to let everybody know that you can access links to all of these repositories and documentations through the LabLab platform in the technology section. But yeah, now please let's give a warm welcome to Nicky Birkner, Product Manager at Together AI. Thank you. I have your presentation. Perfect. Thank you. Okay. Hello. I don't know if the microphone works. Oh, great. Hi, everyone. My name is Nikki. I'm a product manager at Together AI, and I am going to tell you a little bit more about our company and also give you a quick demo of how to use our platform, and we hope that everyone uses our platform for the demo. So Together is the cloud platform for open source and independently developed AI systems. We were founded in June 2022. And we're headquartered right here in San Francisco. We have a very strong team that has had over a decade of research experience. So folks that have led the AI lab at Stanford and invented Flash Attention 2. And so we're really built on this foundational research and everything we have done. A bit about us and what we do and our innovations. So we, our research team has been able to implement a lot of really cool features, both on the platform. Like one of our greatest characteristics of the company is being able to bring things from research or the lab to production. So that's how we've been able to build our inference engine and inventions like Flash Attention 2, sub-quadratic model architectures, as well as open source data sets like red pajama and cocktail SGD. Why use Together? We are a platform where you can create fine tuned models that are private and they're models that you own once you fine tune them. So you can just download them and take them wherever you want. One of the reasons why people come to us is also because of our speed. We spend a lot of time optimizing our inference stack so that we can bring you next-level efficiencies in inference and be able to scale with your needs. We are also very well known for a really good developer experience and just ease of use, and I will show you real quick in a demo later how that looks like. And also because we're hosting a lot of open source models, we have really good costs. So it's like 6x lower cost to alternatives. And a few details about our products. We have four main product lines, together inference, together fine tuning, together GPU clusters and together custom models. Inference and fine tuning are probably the ones you're going to be using here because you can just go to our API, create an account and get started. But we also offer GPU clusters. So basically like notes of GPU clusters of A100s, H100s to people who want to do pre-training as well as help people build their own custom models. In terms of inference, you can experience a bunch of models in chat language, image code and embeddings on our fast inference stack. So you can see some of the models that we offer here. And you can run inference on any of these at a very low cost. We have a playground where you can go in and try the different models to see which one best suits your needs. And we also have really powerful features like JSON mode, function calling, and messages support, async support. And we're also fully OpenAI compatible. So if you have any queries that you are sending to OpenAI, it should be really plug and play to go into our stack. In terms of our embeddings endpoints, we basically offer you the ability to build a RAG system and we have tutorials with Langchain, Lama Index, and MongoDB. It's important to know that we have cheaper pricing, better quality, and longer context than most alternatives out there, so I really recommend you to look into our embeddings API and endpoints if you want to build a rag system. And fine-tuning. You can fine-tune any of our 35 plus leading open source models with your private data. The cool thing about this is that you own and fully control the end results of the model. And it's very fast and easy. I'll show you in just a second. It's as easy as three commands. And then you can run inference on your models. You can deploy a dedicated endpoint of that model directly from our playground. So here are a few QR codes for our docs and also for getting more credits. Everyone gets $5 of credits when you sign up, but we are also giving everyone more credits. So feel free to fill out the form. One thing I will mention is that there is a limit to the file size to get the additional credits. And just make sure you're using models that are less than 40 billion parameters or use LoRa to make sure that everyone can fine tune efficiently. So we're going to be keeping an eye out for very large fine tune jobs to make sure that everyone has fair access to the API. So that's it on the slides. I'm gonna try to change this to the quick demo. Okay, so this is our platform, well, our playground. We basically built this playground around our API to give you a very easy way to be able to try different models and learn how to use our platform. So first part of this demo, and I'm forgetting my sentences. I'm going to show you kind of like this is using MetaLlama 3.1.70b, so one of the most recent models that LAMA launched. It's a pretty incredible model, so I highly recommend everyone to look at it. With the playground, you can tweak many parameters, so things like system prompts, stop sequence, add in a safety model like meta's LAMA guard, as well as these different parameters of output length, temperature, top P, top K, repetition penalty. All these are also parameters you can tweak in our API. But if it's easier, you can just come here, tweak, tune your recipe, and then you can really easily switch to what the API code would look like and just copy and paste in whatever language you want to take this into our API. The next part of this demo is, I want to show everyone how to start a fine-tuned job. So let me pull my terminal. All right. So the first thing you want to do is you want to make sure that you prepare your data well. We have a lot of documentation around which file format you should use for your data, how to do loss masking, training and validation splits. And so at the end, you should go ahead and choose. Okay, so I'm gonna do a files check just to make sure that my data looks good. I'm gonna use this legal data that I already have preloaded in my computer just for the use of this demo. And if it is checked, passed as true, it means like your data looks good. You should be able to fine-tune. So make sure to run that check before you upload your data. I'm now going to upload my data. So it should take a hot sec. You can see the data uploading. This is basically putting the data into our platform so we can then pull this for the fine tuning job. You will get a once that is uploaded, obviously in demos, you know, get slow. Once that is uploaded, you should get a file ID, and it's important for you to save this or store it somewhere, because you're going to need to use it for actually fine-tuning. So I'm now going to do the fine-tuning job. So I'm going to pull my training file. And I'm going to choose a model. In this case, it's going to be MetaLama 3.8b. And I'm also going to do LoRa. Great. And I'm also going to do Laura. Great. So now I'm going to go back to the playground. And you can see that the job has now started to run. And it's queued. That means that you should be good to go. You'll see different statuses for when the job is actually starting to process. When it's completed, you can go cancel it if you want to. And you'll be able to see also the different checkpoints. So I can see an event log of this one I just fine tuned right before this demo. You can see the different checkpoints that you run through. You can do multiple epochs if you want as well. And then you'll get all the different checkpoints. Once the model is ready, you can deploy it for inference. And so the way you would do it, you can also download it, by the way. The way you would do that is you go to the job page, then try to go to deploy it. You can select your hardware. So say I want to deploy this on an RTX 6000. I click play. It will take a little bit of time. And then you should also get some templates for how to run inference on this model. And then once this is good to go, you can actually go ahead and try out your model. So that's the demo. And I'm going to be around if anyone has any questions. But yeah, we're really excited to be sponsoring this hackathon and, yeah, use our platform. So thank you very much. Also, I wanted to let you know if you had maybe a hard time following up or didn't have enough time to take notes, this video will be available in our Twitch and YouTube, lablab.ai. So yes. And yes, also to help you boost your development, everybody participating will have access to get $50 in API credits from Together AI. You just have to go to the form together.ai slash forms slash hackathon. So everybody take some pictures. And now it's time to introduce our next speaker. So everybody, please, a round of applause for Alex Rittman, co-founder and CEO of Agency. Thank you. Cool, cool, everyone. My name is Alex. I am the co-founder of a company called Agency. We build a developer tool called AgentOps. Has anyone heard of AgentOps before? All right. Awesome. Most of the room. So that's really exciting. So I'll give a quick rundown about the world of AI agents, how to get better observability, how to understand what your agents are doing, and some ideas on some great hackathon projects that you could be working on. So let's kick it off. All right, so building AI agents with AgentOps. So what exactly is AgentOps in the first place? So we are a two line of code integration pattern that we work with most of the major AI agent frameworks. Crew AI, Lorenz came up earlier. AutoGen, we saw the AutoGen presentation earlier. LAM index, Cohere, Multion, Play LLM. If you're using any of these frameworks or projects for your hack, we really recommend just adding agent ops. Two things, number one, you get cost tracking baked in, which is extremely valuable, and two, you get all these session analytics replays, gives you a nice waterfall diagram, and more importantly, instead of scrolling through your terminal, which spits out a lot of text text you get a nice little chat viewer right over here it makes it substantially easier to debug and just develop faster when you're running the hackathon the number one thing that you need to manage is your time and agent ops helps you save time when you're debugging but besides that uh we are more or less on the AI software development lifecycle track. So what AgentOps does is we help developers build applications from prototype to production, specifically AI agents. So everything from understanding what the use cases are to you have an agent that's solving a problem in the lab and you want to bring it to production, we help make sure that iteration loop where you're trying to evaluate and understand how the agent's performing, we want to make that loop as fast and concise as possible. So what are some AI agents? We've seen a lot of AI agents in our days. Happy if anyone wants to chat with me later, brainstorm some ideas. We've seen a lot of really cool AI agents. So there's four primary use cases that we've seen come along on the space. So number one is the co-pilots. So co-pilots, you can kind of think of it as chat with your data, right? So all the enterprises are, they want to get chat GPT, they want to upload their PDFs, they want to upload their emails. These are the co-pilots and they deliver enormous amounts of value to your work, especially when you have unique data sets. So we saw the folks at Upstage talk about how they're having document readers, right? The chat with your data is a really, really valuable use case. Definitely, if you have a unique data set, copilots are the way to go. Now, chatbots. Now, this is actually, we're seeing a lot of value being delivered in the market. Specifically, this company called Klarna, they replaced 90% of their human support agents with AI agents, and they're saving $40 million a year, which is an enormous amount of money, right? We've seen people building AI agents with Crew AI. One user that we have, they are a SaaS company. They normally only have paid subscription services, but they've now expanded to the free tier. Problem with the free tier was all the free users were asking dumb questions to the human support agents. So they whipped up a crew and now they have AI agents answering questions better, more effectively, and the customers are super happy with it. Developer tools. We see a lot of things come out these days like we've seen Bitlify having kind of AI search tools. We see things like Greptile. We see things like Cursor, right? I use Cursor almost every single day. There's a lot of code co-pilots, we see things like cursor. I use cursor almost every single day. There's a lot of code copilots that we see that make it way easier to develop software just from a prompt. So these are the kinds of agents that are actually writing other agents in some ways. And then lastly, the autonomous agents. And these are the ones that are most creative, the ones that will probably earn you the most brownie points during the hackathon, honestly. But it's when you take domain knowledge about a very specific problem, you outfit your agents with tools and you give them the memory and you give them the ability to solve problems on their own in unique ways that aren't exactly orchestrated. Alright, so anyway, enough yapping about agents. What exactly are the agents? So we saw them kind of pop off about a year ago, about a year and a half ago, with a project called AutoGPT. The idea behind AutoGPT was it was a chat GPT prompting itself to get better and teaching how to make money. That's actually the original prompt. AutoGPT please make me money and then it just built upon itself to the point where it became autonomous with the help of some developers. And we saw some very other cool projects like AgentGPT, BabyAGI and Jarvis emerge. And the way that we describe agents in general is we call them like self-driving programs. So we have the self-driving cars outside in San Francisco. If anyone has seen the Waymos or the Cruzes or the Zookses, you have the self-driving cars. You give it a destination, and the car figures out how to get there. But the agent is you give it a prompt. You give it an explanation. I want this done, and it figures out how to get there on its own. We see a lot of cool agents coming out of the research labs, so there's a very, very cool agent called Voyager that really kind of visualizes what's possible with these things. So Voyager was by a team at NVIDIA, Caltech, Stanford, UT Austin, where they essentially taught an agent to teach itself how to create tools and solve problems in a simulation world of Minecraft. So the idea here is you could actually have a skill library and then the agent would write its own code to save skills to the skill library and then with that it could navigate the world more effectively. So here's kind of a demo video. You could just give it a high level prompt like gather a cactus. And this again, this is an AI agent playing the game. There's no humans behind this. And it would actually teach itself how to solve the problem. So again, think of these as a self-driving program. What kind of self-driving programs could you be creating that deliver value in this short period of time? So this all kind of merges together with this idea of the LLMOS. So Andrej Karpathy published this really interesting diagram where kind of the future of agentic compute is gonna look something like this, where you have like this LLM processor that connects with all sorts of other things. You have multi-modality with devices like video and audio. You give it access to the internet. You can talk to other agents. And it all coalesces in this stack, which is what we call the AI agent anatomy stack. So underlying everything. Every agent has an LLM underneath it. But when you connect an agent to tools and function calling, that's actually when it becomes an agente program. And it's almost similar to how humans are, right? So what separates humans from other species? Well, we're intelligent, so we have the LLMs, but we also have mastery of tools. We know how to create tools. We know how to teach ourselves these things. And that's what makes us really actually intelligent creatures. So think of it similar to AI agents, right? You connect LLMs to tools. Now, what we do with agent ops is we help track all of these aspects, right? So everything from the metadata, including the model context, the pricing, to external memory, right? If you're connecting to a vector database, what documents are you loading into context? It helps you solve problems. And also multi-agent tendencies. So we see a lot of frameworks these days like autogen and crew. You actually have multiple agents working together to solve problems and they do so more effectively. They do it faster and they don't get confused, they don't get stuck in loops. So anyway, that's kind of some food for thought about what agents are and what they're doing. So we, prizes. We are not doing any official prizes, but we have a community spotlight program. We find really, really cool projects that are built using AutoGen, CREW, and also AgentOps, and we do little mail-in packs. So if you're interested in this, again, it's two lines of code, just add agentops.init. If you're using any LLM provider in Python, if you're using any of the major frameworks, again, we'd really just recommend giving it a shot, see the dashboard. It will help you out a lot. And if it doesn't, just let us know and we'll very quickly iterate. And you will still also get some swag out of it. So to get started, please, we have a QR code that goes straight to agentops.ai. For the duration of the hackathon, you will have unlimited credits. You will not get any crazy unwanted bills. And again, like the installation pattern, it's very no-brainer. Two lines of code, you will not get any crazy unwanted bills. And again, like the installation pattern, it's very no-brainer. Two lines of code, you pip install it, and then you just agent ops that in it with an API key. So that is it from me. I'm looking forward to the next speaker. If you have any questions, you have any ideas, what are agents, what can they do, what is possible, just come speak with me. We're very open. We have an enormous community on Discord. If you have any questions about that, leave a question on Discord. And yeah, great hacking, and good luck to everyone. So, yes, I think I liked a lot about all the partners that we have for this hackathon, is that you don't really have to choose one or the other to build something. You can build an agent using agent ops with Cray. You can power Cray using maybe Solar Pro or any of the Lama 3.1 models, which I think is pretty cool. And talking about Lama models, I would like to focus our attention to our next speaker and give a big round of applause for Katie Watson, Privacy Policy Manager at Meta. Hello, hello. Thank you all so much for having me. I know that we are getting late in the evening, so thanks in advance too for your attention. I appreciate it. All right, I'm going to load this up. Okay. Perfect. Well, we might all just have to bear with my slideshow not operating because I didn't log into the Wi-Fi. And that's okay. We'll do it like this. Okay. Lovely to meet you all. My name is Katie Watson Jordan and I am a Product Strategy and Governance Policy Manager at Meta. What that means is a variety of things, but what it means today for this audience is that I am the co-lead of our Lama Impact Grant Program. And I am super excited to talk to you guys about that grant program and specifically about the ways that hopefully some of you all will be interested in applying to the program. So by way of introduction, I'm sure this comes as no surprise, but AI has been a critical technology for Meta for over a decade. We've been super invested in AI in a variety of formats since 2006 when AI first started to power our feed services. And since then, a couple moments of real sort of dedicated resourcing and inspiration for us came in 2013 with the foundation of FAIR and then in 2016 with the evolution and resourcing into PyTorch. The thing that we are most excited about today and that I'm chatting with you all about though is the evolution of Llama. So back in 2022 we started with Opt 170, and that was just for the research community. But we had such a great response to it that we built on it, and we introduced LLAMA1 in 2023, in early 2023. That was also just for the research community. Since then, we have released LLAMA2, CodeLLAMA, LLAMA3, LLAMA3.1, and those have all been open to all bearing certain eligibility. So this is something that we are super excited about and we have been really, really pleased in the uptick of sort of interest in this model, the utility of this model. And as of earlier this quarter, it has been, LAMA3 has been downloaded, or excuse me, the various iterations of LAMA have been downloaded over 300 million times. And as of earlier this year, we found that there's already 16,000 plus derivative models across LLAMA. And that number actually came before we released LLAMA 3 and LLAMA 3.1. So we're optimistic that it is quite a bit higher and that my slide is out of date. So with that sort of interest and with that kind of background, you know, LLAMA is, I hope, doing a lot for all of you all in lots of different ways but we have a real interest in figuring out how can this tool be something that actually is is truly beneficial to society how can we use this in fields that really need attention and investment and so we launched with Lama to last year the inaugural Lama impact grants so this was a process by which we had an open call and we asked folks to apply under three tracks they were education environment and open innovation and of about 800 applications from people in over 90 countries we selected 20 finalists for this process across those three tracks and one of the things that we actually found was super interesting was that within open innovation the vast majority of applications were actually focused on healthcare. And that was super, super exciting to us because there's so much that can be done in that space. And it seems like a real indicator of the kinds of places where people are willing to sort of invest resources and time and research. So we took our 20 finalists around the world and we brought them together for a refinement workshop. Come back to these in a second. To a refinement workshop at our offices in New York. These folks got together, they spent time with our technologists, they spent time with our policy team, our comms team, our various groups that could help them really work on the holistic aspects of their application, and they spent time with each other, learning from each other and the way that they were approaching these similar problems in really different ways. So as a result of that refinement workshop, we got really excited about the kinds of things that can happen when we get people in a room together exactly like this. And I'll highlight our sort of finalists for a second because one of the things that was really exciting here is that several of these finalists ultimately ended up combining their applications and doing things together because they found that doing things as a team, doing things especially in an open source environment as a team is really beneficial and can help you to do way more really good work than if you were doing it by yourself. So that continued to get us really excited about that kind of opportunity. And so we went back through the applications and we found 70 what we were calling runners up for this program. And we invited those folks out back to Menlo Park and we had our runners up for this program. We invited those folks out back to Menlo Park and we had our first global Lama community summit. This was a space where we had our engineers come and talk to people. We had developers with Lama doing demos and we spent a lot of time learning from each other about the kinds of things that were being done with Lama. That led us to launch another much faster tracked program. This is the Llama Impact Innovation Awards. And these were just open a couple of months ago. Application was really short from June to July. And it was specifically focused on the global south to figure out, you know, a lot of investment is happening in this space in the U.S. and Canada and in Europe. But what is going on already in the global south to solve some of the problems that we think are really addressable with open source AI? This kind of prize was only available to organizations that were already building and had something that we could help them build off of. And we're very excited that both the winners of that grant and our large-scale initial inaugural grants are going to be announced in two weeks. And we are very excited to tell everybody about the organizations that we've been working with. So that brings me to why I am here today and what we are working on next. A few weeks ago we launched the LLAMA 3.1 impact grants and essentially what we did is we've taken all the things that we've learned about those last several grant program structures and tried to create a more holistic and more on-the-ground kind of package of programming. So anyone is still welcome to apply to our global call for proposals. There are some criteria in terms of what kinds of impactful programs we're looking for, but it's still up to $500,000 in grants, and the eligibility criteria are pretty broad, and they're on our website. There are some things that are going to receive special consideration, and that is things like you using LLAMA 3.1? Are you showcasing new capabilities of it? Are you lowering barriers for other developers to help use these tools? And I've heard from several speakers that you are already doing that in really cool ways. So I don't know, maybe take a look at the grant website. But and then also are you addressing some of the topics that we think are most ripe for sort of social impact economic development and things like that around the world with your programs. So that's very traditional to our first program. We're really excited. The thing that I think is important for this group is that we're also doing a series of global events. And so these are things exactly like this hackathon, which we're super, super excited to be a part of and just really appreciate you all inviting us to be here. It's really, really exciting for us. And if you, by nature of participating in this event, if you use LLAMA in your program, we're inviting you to apply to a specialized track that is just for folks who have participated in something like this and will have a $100,000 prize per region. So in this case, that'll be just the U.S. and Canada and it will only be participants of this kind of event. Right now we're planning events such as this one. We'll have another in San Francisco likely later this fall. We're doing something in Montreal and then potentially in Austin, Texas. So if you happen to be in any of those places, please also check the website and see if you can sign up there. Similarly to the larger kind of grant call, we are still focused on social impact or on economic development. So if you can use Lama in some way that you can show is beneficial for other people, it's beneficial for society, it's beneficial for the ways that other people can also build using Lama, we really want to hear from you. And so for that reason, we're very excited to sponsor a specific prize for a Lama winner tonight and hope that you all will use this as an opportunity to make it full screen yes I also I'm gonna come up to uh I know my slideshow buttons not working because I didn't log on to the Wi-Fi and I'm very sorry about that but I hope that this is better and I'll pull up a URL in a second too so yeah we want to use that as sort of an entryway into this grant program. So please do consider applying. Really quickly, here's the evaluation criteria that's going to be applied to all the applications to the grant program. This is also on the website, so no need to spend too much time on it now. But it's all things that are, I hope, fairly straightforward and that you'll be really well prepared to speak to by nature of participating in this event. If you want to learn more, especially about the events, if you want to participate in more than one, you can send an email to this email address. We also are going to have a calendar up with the links to register for all these various events within the next week, so feel free to just check the website. feel free to just check the website and then we have two webinars going on later in the month or in October where you can come and just ask questions especially if you already are developing your application and want a little bit of guidance so please apply by November 22nd in order to be eligible for this hundred thousand dollar prize and when you get on to the application it'll just say like did you participate in a regional event just put the name of this event in there and that will make you eligible for entry into that prize and so with that I'm happy to take questions afterward I know there's more speakers but just want to say a huge thanks to everybody for all the work that you're doing with these models and in this ecosystem and we're very very excited to see all the things that you all build. Thanks. Thank you so much. Appreciate it. Okay. Thank you very much. And yes, so to reiterate what was already said, participating in this hackathon makes you eligible to unlock up to $100,000 through LAMA Impact grants. I really encourage everybody to read more about LAMA Impact and all their different events. But yeah, now to our next speaker, coming directly from Composio, Soham Ganatra, co-founder and CEO. Please, everybody, a big round of applause. Soham Ganatra, Co-founder and CEO. Please, everybody, a big round of applause. Thank you. Check. Hi, folks. I'm coming directly from India to present Composio. Composio is a very simple way to integrate tools with agents. Like so many folks before me spoke about what agents are and how they are going to change the world. The idea is that agents still need tools to change the world, right? They need tools to interact with the world. And what Composio does is a simple repository or a library to manage tools for all your agents. We do integration, we do authentication, we do runtime execution of every single tool and allow you to, in a very simple sort of one line of code, integrate more than 100 different sort of tools like Salesforce, HubSpot, System Manager, File Manager, directly with your agents. We are optimized for function calling. Let me quickly start. So what are we building? Right? So the idea is like Zapier existed before to connect different softwares and pull all of them together. Then needs a sort of similar kind of framework or a layer to do the same for agents or LLMs. And currently creating those tools is a very time consuming job and that requires a bunch of optimization to be done again and again. What we do is we provide a very simple SDK that allows you to sort of authenticate your agents. So, you know, when you are sending an email, you know you are sending the right email from the right account. We do function calling optimization. So we are increasing the reliability of the agents. Because again, agents are not really in production. Everyone here knows that. And so improving reliability helps a lot. And then we do secure execution. Because again, you can't really execute the code anywhere. And that could lead to a bunch of sort of security issues happening. So we provide a runtime environment for all the tools. We are compatible across the chain. So think about any single LLM. Think about any major agentic framework. We support all of them. We support Langchain, Lama Index, Krui, Autogen. We support Gemini, Cloud, OpenAI, Lama, right? We support every single LLM with function calling. We support close to 100 different tools. So we support HubSpot, Salesforce, any CRMs, any file manager. We also support browsers, so you can literally build your own browser controlling agent using us. You can build a Mac controlling agent. We support a bunch of system tools. And we are also extendable, so you can create your own tool and add it and basically publish it for anyone to use in Composio. We are open on GitHub. So you can literally just contribute your tools there. We have a lot of different cool examples folks have built using Composio. So we have an example of scheduling assistant out there. Newsletter summarizer out there. Feedback collection mechanism, wherein you can collect your feedback and like paste it on Slack or create an issue on Linear. All of these are open, by the way, the code is out there. We have a PR agent, wherein like every time you create a PR, you wanna review it, like you can do that using Autogen, right? We have a software engineering agent, which is literally like you just create an issue and the agent will automatically spawn and essentially end up committing some amount of sort of code and like creating a PR. We have all these examples out there. You can literally just go to the links, compose your hyphen py examples, git.new, compose your hyphen gs examples. Yeah, another thing, we also support Python and JS. So it doesn't matter which language makes you more comfortable. You can use all the tools in both the languages. Here's a very simple sort of code snippet showcasing how Composer could be used. So in a single line of code, you can literally give any action that you want. So here is a line of code giving OpenAI Assistant a tool saying, hey, I want to sort of allow this agent to start a GitHub repo. And that's it. Like, once you do that, you can literally pass that on to your sort of tools in any agentic framework that you are using. Give your sort of natural language execution task. And that's it. The tool sort of starts executing. We also do triggers, by the way. So triggers are one of the most important things that you want in production, which is like, when do I want my agent to execute? So let's say you want to build an agent that executes every time you get a new email. Easy. You literally just create a listener. You create a callback around that listener. You mention the trigger, saying, hey, I want to execute this agent every time a new email comes. It is like Gmail new email, Gmail messages. And that's it. The callback function is called. Your agent gets executed. And voila, like agent works with listener in the sort of play. We also do runtime environments. So we do local. We support Docker. We support E2B. We support Fly.io. So we support a bunch of different sort of runtime environments currently. In summary, we do a bunch of these things. So we do authentication management. We have a lot of tools. We do optimizations on tools. So you can clearly see 30% increase in reliability when you create your own tool versus directly using Compose.io. We have RPA tools. We have API tools. We have a bunch of different sort of tools. And we have SDK along Python and TypeScript. This is basically the summary of it. AI Agents built with Composio and AI Agents built without Composio. It's free to use. Check out git.news slash Composio. Check out docs.composio.dev, at.composio.dev, and there's a $250 price for the best thing that is built using Composio. Thank you. Thank you. So, thank you, everyone. Just want to add to that that another benefit of being part of the hackathon is that everybody will get access, full access to Composio. This access will be granted using the emails that you use to register for the event. And one last benefit for being part of this hackathon is that AIML API is giving away a thousand keys with $20 worth of API credits so you guys can use different models. The promo code is sfagentslab. So yeah, I mean, that's all in terms of speakers. Now I would like to do some final remarks about the platform itself and how does it work. And also I would like to bring your attention to the fact that we at Lablab have prepared some tutorials and some materials for you to use all the technologies with our partners. So I can show you that real quick. We have tutorials from LAMA, Composio, AIML, Together AI, how to use solar, building agents with CREAI, and there's many, many other tutorials that you can find in the platform. Some of them include notebooks or boilerplates so everybody can jump start their projects right away. You can find that in lablab.ai.t. Now in here there's a hackathon guide. If you scan the QR code, you're going to see the different steps that you will need to follow to participate in the hackathon. Tomorrow morning we will be hosting a small overview of the platform, how to create a team in the platform, how to create a team in the platform, how to register, and everything you need to know. Right now, if you want to take a look beforehand, please scan that QR code. There's also some content in additional tips on how to be successful in a hackathon. So if anybody wants to scan that. And yes, in here, there's a quick start on MindsDB. You can also find these in the screens outside. And also, follow our social media, lablabai everywhere. So thank you, everybody, for your attention, for being here. Thank you to all of our speakers. Everybody, a round of applause to all of our speakers, please. So right now we are hosting a workshop, Crew AI and Composio. It's upstairs, you can access it via Zoom link. You should have the information on Discord. And we will be doing team matchmaking and networking until 9 p.m. So thank you, everybody. Thank you. Thank you. Gracias.
AI Agents Hackathon with Lablab and MindsDB - Kickoff Stream
Join us today for our AI Agents Hackathon! 
We are excited to welcome speakers from Together.ai, Upstage, AgentOps. CrewAI, Composio, Autogen and Meta.
Each will bring unique insights and expertise to help you learn, collaborate and create innovative AI-driven solutions. Don't miss out on this incredible opportunity!