Today marks a huge milestone for our HybridAI platform: we’ve fully integrated N8N – and it’s a game changer for anyone working with automation and intelligent agents.
What’s new?
🔗 Deep integration with N8N workflows Every HybridAI user now gets free access to our dedicated N8N server. Even better: from inside any N8N workflow, you can now send a Function Call directly to your chatbot or agent – with a single click.
Example: “Send a follow-up email to all leads from today.” → Your bot instantly triggers the corresponding N8N workflow.
Why does it matter?
Agentic AI means that your bot doesn’t just talk, it takes action. It can now handle complex workflows, launch services, update databases, and more – autonomously.
To achieve this, you need two things:
A smart control center → your HybridAI agent
A powerful action engine → N8N
Now you get both, perfectly connected.
What is N8N, anyway?
N8N is a no-code automation tool developed in Berlin. With it, you can:
Connect APIs and AI models
Read/write Google Docs
Send emails
Query or update databases
Build custom nodes for anything else
And now, your HybridAI chatbot can trigger it all seamlessly from any conversation.
How to get started?
If you have a HybridAI account, just go to your “AI Functions & Actions” section in the admin area and create a Function Call pointing to your N8N webhook. That’s it – your bot is ready to act.
🎯 Try it now and explore new levels of automation with HybridAI + N8N.
We’re excited to introduce a powerful new feature on our platform: the ability to stream IoT sensor data directly into your chatbot’s context window. This isn’t about triggering an external API tool call—it’s about augmenting the bot’s real-time understanding of the world.
How it works
IoT sensors—whether connected via MQTT, HTTP, or other protocols—can now send live data to our system. These values are not fetched on-demand via function calls. Instead, they’re continuously injected into the active context window of your agent, making the data instantly available for reasoning and conversation.
Real-World Use Cases
🏃♂️ Fitness and Weight Loss
A health coach bot can respond based on your real-time activity:
“You’ve already reached 82% of your 10,000 step goal—great job! Want to plan a short walk tonight?”
Or reflect weight trends from smart scales:
“Your weight dropped by 0.8 kg since last week—awesome progress! Should we review your meals today?”
⚡️ E-Mobility and Charging
A mobility assistant knows your car’s charging state:
“Your battery is at 23%. The nearest fast charger is 2.4 km away—shall I guide you there?”
Bots can also keep track of live station availability and recommend based on up-to-date infrastructure status.
🏗 Accessibility and Public Infrastructure
A public-facing city bot could say:
“The elevator at platform 5 is currently out of service. I recommend using platform 6 and taking the overpass. Need directions?”
Perfect for people in wheelchairs or with limited mobility.
🏭 Smart Manufacturing and Industry
A factory assistant can act on process data:
“Flow rate on line 2 is below target. Should I trigger the maintenance routine for the filter system?”
This allows for natural language monitoring, error detection, and escalation—all in real time.
What Makes This Different?
🔍 Contextual Awareness, Not Tool-Calling Sensor data is part of the active reasoning window—not fetched via a slow external call, but immediately available to the model during inference.
🤖 True Multimodal Awareness Bots now reason not just over language but also over live numerical signals—physical reality meets LLM intelligence.
🚀 Plug & Play Integration Bring your own sensors: from wearables to factory machines to public infrastructure. We help you connect them.
In Summary
This new feature unlocks unprecedented potential for intelligent agents—combining the power of conversational AI with a live, evolving understanding of the physical world. Whether you’re building a wellness coach, a mobility assistant, or an industrial controller, your agent can now think with real-world data in real time.
Yes, in the current AI hype discourse this statement almost feels like suicide, but I want to briefly explain why we at HybridAI came to the conclusion not to set up or use an MCP server for now.
MCP servers are a (currently still “desired”) standard developed and promoted by Anthropic, which is currently gaining a lot of traction in the AI community.
An MCP server is about standardizing the tool calls (or “function calls”) that are so important for today’s “agentic” AI applications – specifically, the interface from the LLM (tool call) to the external service or tool interface, usually some REST API.
With the current ChatGPT image engine generated – I love these trashy AI images a little and will miss them…
At HybridAI, we have long relied on a strong implementation of function calls. We can look back on a few dozen implemented and production-deployed function calls, used by over 450 AI agents. So, we have some experience in this field. We also use N8N for certain cases, which adds another relevant layer in practice. Our agents also expose APIs to the outside world, so we know the problem in both directions (i.e., we could both set up an MCP server for our agents and query other MCPs in our function calls).
So why don’t I think MCP servers are super cool?
Simple: they solve a problem that, in my opinion, barely exists and leave the two much more important problems of function calls and agentic setups unsolved.
First: Why does the problem of needing to standardize foreign tool APIs hardly exist? Two reasons. (1) Existing APIs and tools usually have REST APIs or similar, meaning they already use a standardized interface. These are quite stable, which you can tell from API URLs still using “/v1/…” or “/v2/…”. They remain stable and accessible for a long time. Older APIs are often still relevant – like those of the ISS, the European Patent Office, or some city’s Open Data API. These services won’t offer MCP interfaces anytime soon – so you’ll have to deal with those old APIs for a long time. (2) And this surprises me a bit given the MCP hype: LLMs are actually pretty good at querying old APIs – better than other systems I’ve seen. You just throw the API output into the LLM and let it respond. No parsing, no error handling, no deciphering XML syntax. The LLM handles it reliably and fault-tolerantly. So why abstract that with MCP?
In reality, MCP adds another tech layer to solve a problem that isn’t that big in daily tool-calling.
The bigger issues are:
–> Tool selection
–> Tool execution and code security
Tool selection: Agentic solutions work by allowing multiple tools, sometimes chained sequentially, with the LLM deciding which to use and how to combine them. This process can be influenced with tool descriptions – small mini-prompts describing functions and arguments. But this can get messy fast. For example, we have a tool call for Perplexity when current events are involved (“what’s the weather today…”), but the LLM calls it even when the topic is just a bit complex. Or it triggers the WordPress Search API, though we wanted GPT-4.1 web search. It’s messy and will get more complex with increased autonomy.
Tool execution: A huge issue for scaling and security is the actual execution of tool code. This happens locally on your system. Ideally, at HybridAI, we’d offer customers the ability to submit their own code, which would be executed as tool calls when the LLM triggers them. But in terms of code integrity, platform stability, and security, that’s a nightmare (anyone who submitted a WordPress plugin knows what I mean). This issue will grow with more use of “operator” or “computer use” tools – as those also run locally, not at OpenAI.
For these two issues, I’d like ideas – maybe a TOP (Tool Orchestration Protocol) or a TEE (Tool Execution Environment). But hey.
SaaS platforms have long included help widgets, onboarding tours, and support ticket systems. But what if your app had a conversational layer that not only explained features – but also triggered them?
With HybridAI, this is now possible. Our system enables you to create agentic chatbots that speak your domain language, understand user intent, and call backend functions directly via Function Calls and Website Actions.
From Support Widget to Smart Assistant
Traditional support widgets are passive: they answer FAQs or forward tickets. A HybridAI bot, however, can do things like:
Trigger onboarding steps (“Show me how to create a new project”)
Fetch user data (“What was my latest invoice?”)
Execute actions (“Cancel my subscription”)
All of this is powered by safe, declarative function calls that you define – so you stay in control.
How It Works
Define Actions: You provide a list of available operations (e.g. getUser, updateRecord, createInvoice) and their input parameters.
Connect via API or Function-Call Interface: HybridAI receives these as tools it can call from natural language.
Bot Instructs + Responds: The chatbot interprets the user prompt, selects a matching function, fills in parameters, and calls it.
Real-Time Feedback: The user receives immediate confirmation or result, without ever leaving the chat.
Integration Benefits
No coding required to get started – Just define what your functions do.
Frontend or backend integration via JS events or APIs
Custom styling + voice – the bot looks like part of your product
Multi-language and context-aware – excellent for international SaaS
Use Cases
CRM assistants that update leads or pull sales data
Analytics bots that explain dashboards or alerts
HR bots that automate time-off requests
Support bots that resolve issues without agents
Ready to Try?
You can test HybridAI’s function-calling capability today with our Quickstart Bot – no sign-up required.
And if you’re ready to bring this into production, reach out to us – we’ll help you integrate HybridAI into your stack in days, not months.
This week we tested HybridAI for the first time in a real school environment. The students of Stadt-Gymnasium Köln-Porz had the opportunity to spend a German lesson with us under the guidance of Sven Welbers – on the wonderful topic: Grammar!
What could possibly be better!
It was genuinely exciting, as we configured HybridAI according to the teacher’s specifications to present a detective story that could only be solved step by step by completing grammar exercises. Since the stories were generated by the AI, each student had a unique version, with delightful variations even when new stories were generated.
Throughout the lesson, the bot provided feedback on progress and occasionally injected humorous messages.
Conclusion: The students certainly had a lot of fun! Not always guaranteed with such topics. The teacher was impressed by the educational quality of this lesson. Despite the dry material, the students appeared engaged and focused.
In the near future, we will develop further examples for the educational sector. The next session with a bot on the topic “Konjunktiv I and II” is already being prepared!
The world of AI chatbots is evolving at a rapid pace, and 2025 will mark a new era in intelligent, interactive website assistants. Businesses and website owners can now integrate AI chatbots that go far beyond simple scripted responses. These AI-driven assistants are more powerful, engaging, and action-oriented than ever before. Here’s what you can expect from the latest AI chatbot technology—and why it might be time to upgrade your website’s chatbot.
Core Features: The Must-Haves for 2025
Function Calling: More Than Just Chat AI chatbots are no longer just answering questions—they are taking action. With function calling, chatbots can trigger automated processes, retrieve live data, and even control external applications. Imagine a chatbot that not only tells your customers their order status but also updates it in real-time. Or think of a system that can call several APIs in the background and integrate the results in the ongoing chat seamlessly.
Rich Media Display: Images & Videos Websites are visual, and chatbots should be too. In 2025, AI chatbots seamlessly integrate with media libraries, displaying images, GIFs, and even videos within the chat. This is ideal for product demonstrations, interactive customer support, or guided tutorials. Your Chatbot should offer an interface to upload and manage media-files in a way that the LLM can understand and use them, when the conversation would benefit from it.
Logging and Analytics: Know Your Users Keeping track of chatbot interactions helps businesses refine their strategy. AI chatbots now log conversations, analyze engagement trends, and provide deep insights into user behavior—all from a single dashboard. That is important as you are planning to offload one of the precioust things you have – the conversations with your customers – to the AI. The Chatbot should offer an easy interface to observe the conversations and maybe even refine them where necessary. A download of Logfiles is also something you should expect for further analysis, for instance if you want to compile some KPIs or dig deeper into the conversations.
File Upload & Sharing Chatbots now support file uploads from both users and website owners. Whether it’s customers submitting documents for verification or business owners providing deeper insight material for the AI, this feature enhances workflow automation. As everyone is using Chat-GPT from time to time these days users are expecting this functionality and therefore your ChatBot should offer it.
Live Streaming Responses Speed is key. AI chatbots now stream their responses in real-time, ensuring a more natural and engaging conversation flow. No more waiting for a full answer—users see it as it’s generated. And it underlines the feeling of magic when people interact with AI systems – a nice flowing streamed response creates the feeling to speak to something special and fascinates many users.
Multiple AI Models for Maximum Flexibility Why limit yourself to one AI model? Hybrid chatbots allow businesses to use multiple LLMs (Large Language Models) for different tasks, choosing the best tool for each interaction. This ensures higher accuracy and better responses. Sometimes it is because of certain functionality, sometimes it can be speed, but LLM-models also vary in other aspects like restrictions, openness or recency of the training material.
Next-Level Features: The Competitive Edge
Payment Integration: Monetize AI Conversations AI chatbots are not just support agents—they can be sales tools. With payment integration (e.g., PayPal, Stripe), customers can complete purchases, subscriptions, or donations directly in the chat. The ChatBot should support some ways of offering paid messages to the users.
Emotion Detection: Smarter, More Human AI AI chatbots are becoming emotionally intelligent. By analyzing user sentiment, they can adjust their tone, prioritize urgent messages, and escalate issues when frustration is detected.
Human Takeover: The Perfect AI-Human Blend Sometimes, AI isn’t enough. The best chatbots now feature smooth human takeover, allowing human agents to jump into conversations when needed. This seamless transition ensures customers get the best of both AI automation and real human support.
Task Management: Keep the user in the loop As Chat-Bots are evolving more and more towards full-blown agents and personal assistants you should expect some sort of task-management built into your Chatbot so that a user can say “please remind me of this workout tomorrow morning”.
Final Thoughts
AI chatbots in 2025 will be more than just digital assistants—they’ll be action-oriented, multimedia-rich, and deeply integrated with business processes. Whether it’s automating workflows, displaying visual content, or handling transactions, the next generation of AI chatbots will redefine how businesses engage with their audience.
If you’re looking to integrate an advanced AI chatbot on your website, now is the time to explore the latest technology and get ahead of the competition!
Artificial intelligence has made huge leaps in recent years, especially with LLMs (Large Language Models) like ChatGPT, which have revolutionized the way we interact with computers. Many users are amazed at how intuitive and helpful these systems are.
Whether for customer service, research, or creative tasks, the usefulness of ChatGPT increases significantly after just a short learning curve. But what if you could make ChatGPT even more customized?
Beyond a Simple Chatbot: Adding Domain-Specific Knowledge
One of the most exciting possibilities is enhancing the bot with your own knowledge. OpenAI offers Custom GPTs, which allow users to upload specific information—such as PDFs, databases, or manuals. This enables a chatbot to be perfectly tailored to individual use cases.
✅ Customize the chatbot with simple instructions for specific tasks ✅ Upload PDF files easily to provide domain-specific knowledge
Here the persona of the ChatBot is defined – in normal language, no programming skills required!
Here you can see how customer specific knowledge can be added to the Chatbot – by simply uploading relevant files
But that’s not all—modern AI chatbots are no longer just question-answering machines.
From Chatbot to Action Bot: An AI Assistant That Actually Does Things
Beyond simple conversations, modern chatbots can now perform real actions, such as:
✅ Retrieving data – e.g., comparing prices or fetching the latest information ✅ Placing orders – integrating directly into e-commerce platforms ✅ Highlighting elements on websites – for interactive guidance ✅ Triggering API-based workflows – connecting to calendars, CRMs, or internal tools
This is an example of a ChatBot that uses the ChatGPT Engine but also can query specific info from IT-Systems to answer questions more specifically for the user
These features transform a chatbot into a real digital assistant that doesn’t just respond to users but actually helps them take action.
The Big Question: How Can You Integrate ChatGPT into Your Website?
Answer: You can’t.
OpenAI does not offer a direct way to integrate ChatGPT into a website. If you were hoping to simply embed a ChatGPT button, you’ll be disappointed.
Luckily, there are alternatives.
HybridAI: The Perfect Solution for Website Chatbots
A better alternative is HybridAI, which leverages OpenAI’s powerful engine (the same technology behind ChatGPT) but provides the exact features needed for seamless website integration.
With HybridAI, you can:
🔹 Use ChatGPT functionality directly on your website 🔹 Upload PDFs and documents to provide domain-specific knowledge 🔹 Enable function calls to execute real actions 🔹 Create a fully customizable chatbot experience
That means: The full power of ChatGPT – but as a fully integrated solution for your website! And even at a lower cost than OpenAI’s direct services!
Conclusion: AI Chatbots Are the Future of Websites
A smart chatbot can automate customer service, boost sales, and improve information access. While OpenAI does not offer a direct website integration, platforms like HybridAI provide a powerful solution for businesses and website owners looking for a custom AI assistant.
🚀 Try it now and transform your website with an AI chatbot!
How to Integrate HybridAI into Your Website – Step-by-Step
Integrating HybridAI into your website is incredibly easy and takes just a few minutes. You only need to add a small JavaScript snippet to your website’s HTML code.
1️⃣ Add the JavaScript Code
Insert the following script into your <head> or <body> section:
<script> window.chatbotConfig = { chatbotId: "YOUR_CHATBOT_ID", // Replace with your unique chatbot ID chatbotServer: "https://hybridai.one" }; </script> <script src="https://hybridai.one/hai_embed.js?chatbotId=YOUR_CHATBOT_ID"></script>
(You get your chatbot ID by creating a free HybridAI account—it only takes a few minutes!)
2️⃣ Save and Upload
Save the file and upload it to your web server.
3️⃣ Done! The Chatbot Appears Automatically
Once the page is refreshed, a chat icon will appear in the bottom-right corner – your HybridAI-powered chatbot is now live! 🚀
💡 Try it out here on the blog! You can test HybridAI live at the bottom of this page to see how easily it integrates into websites.
In the video, you can see how a HybridAI ChatBot begins to step out of its chat box and starts controlling elements on the embedding website.
While this is not yet “agentic” in the way many imagine, it is a very pragmatic step from an AI chatbot that only talks to one that can actually take action. The value of website chatbots in customer interactions increases significantly with such functionality.
(Why This Year Marks the Great Leap from Conversation to Execution)
Chatbot Evolution Timeline
For decades, chatbots have been defined by their ability to converse. In the earliest days—dating back to the 1960s with ELIZA—they served mostly as novelty acts, reflecting user input through simple, scripted replies. Then came rule-based systems in the 1980s, followed by the deep-learning chatbots we rely on today. But 2025 is shaping up to be a watershed moment for chatbots: they are no longer just talking; they’re starting to take action on our behalf.
A Shift Beyond Conversation
Until recently, even the most advanced chatbots focused on interpreting user queries and offering relevant responses. Ask a chatbot what the weather is, and it gives you the forecast. Ask it for a recipe, and it might provide step-by-step instructions. These interactions improved dramatically thanks to deep learning and transformers, making conversation feel more natural. But fundamentally, they were still just “answer machines.”
Now, we’re witnessing the next evolution. Instead of limiting themselves to text-based chats, new-generation chatbots have the potential to perform tasks. Rather than just telling you the weather, they might turn on your smart heater. Rather than just suggesting a recipe, they could order your groceries from a partnering store. These systems are sometimes referred to as “agentic chatbots,” because they have the autonomy to act as an agent on your behalf.
Enter: HybridAI and Other Action-Oriented Systems
One prime example leading this charge is HybridAI. It’s designed to do more than talk: it can call specific API-actions during a conversation and even manipulate elements on the hosting web page if a user requests it. Imagine you’re browsing a shopping site and you ask the chatbot to add a particular item to your cart or apply a promotional code. Instead of replying with a link or instructions, the chatbot can just do it for you. This is a substantial leap from a typical conversation-only assistant.
HybridAI’s capabilities highlight a crucial point: people want chatbots that actually solve problems, not just talk about them. We’re seeing the dawn of chatbots that can handle everyday tasks—everything from scheduling calendar events to navigating complex enterprise workflows—at the user’s command.
The Hype Around “Agentic Systems”
The term “agentic systems” is currently a hot topic. Experts, tech enthusiasts, and enterprise leaders alike are buzzing about how AI-driven assistants may soon become fully autonomous, capable of orchestrating multiple APIs, services, and even hardware devices in the background. While these discussions are exciting, the reality is that it will take time to refine and scale these capabilities. Questions around reliability, security, and ethics must be addressed before chatbots gain wide autonomy across critical domains.
Nonetheless, 2025 is shaping up to be the Year of Chatbot Action, the tipping point where the first wave of agentic systems begins to enter mainstream use. We’ll see more prototypes and pilot programs adopting these features, proving the concept and building trust with end-users. Like every transformative technology, it won’t happen overnight. But it’s closer than many realize—and it’s sure to reshape how we interact with both the digital and physical worlds.
Why This Matters
The impact of action-capable chatbots is enormous. Businesses will gain efficiency by reducing repetitive workflows; end-users will enjoy seamless convenience in everyday tasks. If you think about it, the shift from just “talking” to “doing” echoes the broader trend in AI: we want collaborative, proactive, and truly helpful systems.
We might still be a few years out from fully autonomous agentic systems, but the seeds are planted. Tools like HybridAI show us the immediate possibilities—chatbots can learn your needs, integrate with apps you use, and execute tasks in real time. In short, the future is already making its way into the present. And if 2025 is indeed the “Year of Chatbot Action,” imagine how much further they’ll go by the end of this decade.
In the digital world of 2025, a website chatbot is no longer just a nice-to-have feature but an essential tool to improve customer experiences and streamline business processes. But what makes a truly great chatbot? Here are seven things a modern website chatbot must be able to do in 2025:
1. Provide Deeplinks to the Website
A chatbot should be able to independently crawl the website and extract relevant links. This allows it to respond directly to queries like “Where can I find the return policy?” or “Show me the latest offers” with appropriate deeplinks. This saves users time and simplifies website navigation significantly.
The ChatBot has generated this deeplink by crawling the Website automatically
2. Utilize Website Functions with Function Calls
Modern chatbots must seamlessly interact with website features. For example, users should be able to check the status of an order or initiate a return directly within the chat. This is enabled by function calls, allowing the chatbot to access APIs and other technical interfaces of the website.
3. Address Users in Their Language Automatically
A good chatbot recognizes the user’s preferred language and adapts accordingly. Whether the user speaks German, English, or another language, the chatbot should effortlessly start the conversation in the correct language. This function significantly improves the user experience and makes the chatbot globally applicable.
The ChatBot detects the users language from the Browser without asking and responds accordingly
4. Allow for Human Takeover at Any Time
Even the best chatbot sometimes reaches its limits. In such situations, it is essential that users can easily switch to speaking with a human agent. Even better, the chatbot should facilitate this transition smoothly by passing on all relevant information to the agent. AI-powered human takeover options can further optimize this process.
Sometimes it is important that a human agent takes over from the AI – this system detects a situation like that automatically and can call a human…
…which then smartly can pick it up and calm down the situation
5. Provide Information Based on Uploaded Materials
A truly versatile chatbot should be able to analyze uploaded materials such as product PDFs, price lists, or presentations and derive accurate information from them. This enables it to answer questions about technical specifications, pricing, or other details directly. This function is especially valuable in complex B2B scenarios.
Here you can see an example how deep the response of a Website ChatBot can be, if he has been trained with enough specific material (PDFs, other Websites etc.)
6. Multichannel Availability
Communication should take place where the user feels most comfortable. A modern chatbot is not only available on the website but also on channels like WhatsApp, Instagram, or Telegram – with the same functionality. This flexibility ensures that users can use the chatbot on their preferred platform without compromising performance.
In 2025 Users expect that they can communicate in the Channel/Tool that they like most.
7. Configuration with Different LLM Models
As AI models continuously evolve, a chatbot should be configurable with various large language models (LLMs). This allows businesses to benefit from advancements in AI technology or, if needed, use European AI models to meet data protection requirements and regional regulations.
Different LLM Models for Choice.
Conclusion
The demands on chatbots in 2025 are higher than ever. From smart navigation via deeplinks to multichannel functionality and the use of the latest AI technologies – a high-performing chatbot offers far more than just simple answers to standard questions. Companies that focus on these seven features can ensure they not only meet user expectations but are also future-proof.
