In today’s rapidly evolving technology landscape, the creation of Artificial Intelligence (AI) agents and assistants is becoming increasingly accessible, thanks to the emergence of powerful tools that eliminate technical barriers. An impressive example of such solutions is LangFlow.
This NoCode tool lets you create AI agents with an intuitive interface without sacrificing customization flexibility. With the ability to add Python code to extend its functionality, LangFlow is an ideal solution for both beginners and experienced developers.
The Evolution of AI Agents in the NoCode Scenario
In recent years, the development of tools NoCode has revolutionized the way software and automations are created. NoCode allows individuals without a technical background to create functional applications, websites and complex automations.
Following this line, LangFlow emerges as a powerful tool for the development of Artificial Intelligence agents, allowing the creation of simple agents, agents with persistent memories and even multi-agent systems using native modules.
The term LangFlow refers to the famous LangChain framework, which has gained popularity for facilitating the development of AI applications. Thus, LangChain simplifies the interaction with large language models (LLMs), such as GPT, LLaMA, and Claude, integrating functionalities such as memories, RAG (Retrieval-Augmented Generation) functions, and API calls.
LangFlow adds a visual abstraction layer to this framework, allowing anyone, even without programming knowledge, to create robust agents.
What are AI Agents?
Before we delve into the features of LangFlow, it is important to understand what they are. AI agents. Basically, an AI agent is an application that uses a language model (such as GPT, LLaMA, or Claude) to perform a series of actions based on an initial prompt or instruction.
These agents can be integrated into systems and applications, automatically performing tasks, responding to users, or performing complex functions based on provided data.
Agents can be classified into different levels of complexity:
Level Zero: agents that only interact with an LLM, without persistent memory.
Level One: agents with memory, capable of remembering the context of previous interactions.
Level Two: agents using RAG, combining external data to increase their knowledge base.
Level Three: agents that make calls to APIs and can interact with other systems.
Advanced Levels: multi-agent agents and self-feedback agents, which can perform complex tasks in a hierarchical manner.
The Potential of LangFlow in Agent Creation
The great thing about LangFlow is its ability to create these different levels of agents easily, using a visual interface. Imagine dragging and dropping blocks that represent user inputs, language models, and outputs, all without having to write a line of code.
Thus, for those with technical knowledge, LangFlow still offers the possibility of customizing the code of the blocks, creating flows that are highly adaptable to the specific needs of each project.
A notable feature of LangFlow is its integration with AI models like OpenAI, Anthropic and even Azure, allowing users to choose which technology to use in each project.
Additionally, the tool also offers persistent memory functionality, allowing agents to remember the context of past interactions and improve their responses based on historical data.
The Flexibility of LangFlow
In addition to being a visual tool, LangFlow stands out for its flexibility. It allows you to select, for example, which AI model to use in each part of the process.
A clear example is the creation of agents that interact with an external knowledge base through Vector Store RAG, a process in which an agent can access vectorized data (such as PDFs or spreadsheets) to enrich its responses.
In addition, another interesting feature is the ability to create multi-agent systems. In these systems, different agents can be configured to work together, performing sequential or even hierarchical tasks. In this way, each agent can be responsible for a specific part of the flow, which makes it possible to create extremely sophisticated automation systems.
Creating Your First Agent in LangFlow
For those who want to get started with LangFlow, the visual interface makes it easy to get started. A basic usage example involves creating a simple agent that responds to interactions with a language model like GPT. This flow consists of three main components:
Chat Input: Where the user enters his message.
Base Prompt: Pre-configured instructions to guide the agent's behavior.
AI Model: Which processes user input and responds based on the prompt instructions.
In LangFlow, memories allow the agent to remember previous discussions, and RAG tools add external data to enrich its knowledge.
Exploring Predefined Templates
One of LangFlow's most attractive features is its predefined templates, which provide a ready-made base for creating different types of agents.
For example, one of the templates allows you to create an agent that interacts with documents, making it possible to query an entire PDF, or even create a multi-agent system that performs sequential or hierarchical tasks.
Another popular template allows you to create agents with RAG, where a vector database is used to store additional knowledge. This template is especially useful for companies that need agents that can answer questions based on large volumes of data.
Customization and Integrations
And despite being a NoCode tool, LangFlow allows for full customization through Python. This means that if you have programming knowledge, you can edit the code of the pre-built components or add new custom modules to expand the platform’s functionality.
For example, the agent can easily access a specific API to query external data through the tool's code interface.
Additionally, LangFlow offers a wide range of integrations with other platforms and systems. From APIs to databases, you can connect your agent to virtually any web service, further expanding its possibilities of use.
Conclusion
Thus, LangFlow is a powerful tool that brings the simplicity of NoCode to the world of Artificial Intelligence, allowing anyone, regardless of their level of technical knowledge, to create robust and flexible AI agents.
With its intuitive interface, the possibility of customization via Python and its advanced integrations, LangFlow positions itself as one of the most promising tools on the market.
So, if you are looking to explore the world of AI agents, whether for business automation, chatbot creation or multi-agent systems, LangFlow is a complete and affordable solution. Learn more by joining NoCodeIA Training!
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Neto specialized in Bubble due to the need to create technologies quickly and cheaply for his startup. Since then, he has been creating systems and automations with AI. At the Bubble Developer Summit 2023, he was listed as one of the greatest Bubble mentors in the world. In December, he was named the largest member of the global NoCode community at the NoCode Awards 2023 and first place in the best application competition organized by Bubble itself. Today, Neto focuses on creating AI Agent solutions and automations using N8N and Open AI.
A new Chinese platform that has attracted attention for its ability to perform complex tasks in a fully automated manner is Manus AI.
Compared to other market giants, such as OpenAI, Manus stands out for its use of intelligent agents. It can search for information, generate codes, create reports and even develop games — all without any human intervention after the initial command.
In this article, you will understand what Manus AI Agents is and how it works. You will also see real examples of what this powerful tool can create.
What is Manus AI Agents?
Manus AI is an impressive platform that goes far beyond the simple virtual assistants you’re used to. To understand the power of this tool, let’s explore a practical example of what it can create.
Perform complex tasks alone with Manus AI Agents
Imagine you need to organize a 7-day trip to Japan, with a specific budget. The itinerary also includes secret places and a special marriage proposal.
With Manus AI Agents, you simply need to inform this objective and the agent will do all the work on its own, completely automatically. With this guidance, it can deliver:
complete research on specialized websites;
selection of the best itineraries and exclusive places;
detailed planning for the 7 days, with suggestions for activities and destinations;
budget organization, respecting the available amount.
This means you just need to describe what you want. From there, Manus AI Agents takes care of the rest, delivering a complete, personalized, ready-to-use plan.
And the most incredible thing is that you don’t need to do anything other than give the initial command. Manus AI Agents accesses multiple sources, cross-references the information and delivers a personalized and detailed travel plan.
This is just one example of what the platform can create. With autonomous artificial intelligence, Manus facilitates complex tasks that would take hours — or even days — to be done manually.
What can Manus AI Agents create?
The trip to Japan is just one example of what Manus IA Agents can do automatically. The platform goes far beyond simple planning. It offers advanced solutions in several areas, such as productivity, data analysis, application development and even creating complete games.
Imagine that an HR department needs to analyze dozens of resumes to hire the best candidate. With Manus AI Agents, you just need to describe the objective, and the agent automatically:
Reads and interprets all resumes;
Ranks candidates based on criteria such as experience, education, and location;
Create an organized spreadsheet with the final ranking, ready for you to make your decision.
This process, which would take hours to do manually, Manus performs in minutes — with precision and efficiency.
It is worth mentioning that Manus Agentes IA impresses by generating complete stock analysis reports.
For example, when requesting an analysis of Tesla, Manus first accesses databases, then cross-references information. After that, it generates dynamic graphs and, finally, delivers a ready-to-use report — which, by the way, guarantees a level of quality worthy of great analysts.
Creating apps, games and simulators with just one command
Another surprising example is the platform's ability to create complete applications, such as:
Interactive educational games, as a collision simulator to explain the theory of conservation of momentum, all automatically generated in HTML, with visualization of realistic physical scenarios and reactions;
Teleprompter Apps, ready to be used in YouTube videos, with speed adjustments, font size and customizable interface, created from a simple command;
Data Dashboards: When you request an AWS store analysis, Manus generates a page with graphs, KPIs, and detailed store data, offering strategic insights in minutes.
Manus AI Agents is also capable of developing complete games from a single prompt. One interesting example was the creation of a functional, programmed airplane game, generated in a matter of minutes without any technical intervention after the initial request.
In addition to these examples, Manus AI Agents continues to surprise new users with its automation capability, by automating more than 50 simultaneous tasks, being considered by experts as one of the fastest and most efficient platforms on the market, with superior performance to other famous AIs, such as DeepSeek.
This level of autonomy and intelligence makes Manus AI Agents a promising tool for those who want to integrate AI into complex processes and save time, money and effort.
How to access Manus IA Agents?
Although Manus AI already demonstrates impressive capabilities, the platform remains in the early stages of public release for now and is therefore not yet widely available.
Currently, to access Manus, an invitation is required, as the number of users is limited due to high demand — a demand that, in fact, reflects the growing interest in advanced artificial intelligence solutions.
Furthermore, as with other popular tools such as ChatGPT In its first months, restricted access aims to guarantee stability and quality of service.
Manus Agentes AI has certainly already attracted attention in the artificial intelligence market. In fact, a report published by Hugging Face evaluated the platform's performance in different benchmarks, including:
Logical reasoning;
Multimodal processing;
Executing complex tasks with autonomous agents.
If you want to understand more about the potential of Manus AI Agents, it's worth checking out the full report published by Hugging Face, which details how the tool was launched and what makes this new AI platform different.
Conclusion
By now, you have already realized that Manus IA Agents emerges as one of the most promising platforms on the market, showing that the future of intelligent automation is closer than we imagine.
And of course, the big expectation now is to know when and how the platform will release access. via API, allowing creators of agents and automations, as we teach here at NoCode Startup, can integrate Manus into their own solutions and products.
So if you want to learn how to create, manage and implement AI Agents for real business and, in addition, understand how to apply them in a practical way, you need to know the AI Agent Manager Training at NoCode Startup.
In it, you will discover how to create automations and lead projects with intelligent agents, in addition to being able to prepare yourself to explore innovative tools such as Manus IA Agents.
Start your journey now by creating smart solutions that can transform your career and generate new income opportunities with NoCode Startup.
The Official WhatsApp API is a powerful solution for businesses that want to automate their interactions via WhatsApp. In this article, you will learn how to configure and integrate the Official WhatsApp API using N8N, one of the best NoCode tools for automation. Read on to find out:
How to create a number in Meta to use the Official WhatsApp API;
How to send your first automated message;
How to set up triggers and automations to interact with incoming messages;
How to connect WhatsApp API to N8N, creating intelligent automation flows.
If you are looking for an efficient and practical way to integrate WhatsApp into your automation routine, this guide is for you. Happy reading!
Official WhatsApp API: Learn the complete integration guide on N8N
The Official WhatsApp API allows you to connect WhatsApp to any system, whether it is your own or integrated with automation tools. However, how do you configure this API correctly? And, furthermore, how do you create intelligent automated communication flows? See below a step-by-step guide on how to put this into practice!
Step 1: Creating an application in Meta
To use the Official WhatsApp API, the first step is to create an application on Meta. This will ensure that your integration works correctly on N8N and other automation tools. So, how can you ensure that everything is set up correctly and also avoid integration errors? Follow the steps below:
Create a new application in the panel, selecting the Business option and filling in the necessary data;
Configure the Official WhatsApp API by accessing the WhatsApp section and clicking on Setup;
Associate a verified Business Manager, essential for the API to work;
Use a valid phone number that is not already registered with WhatsApp.
That's it! Now you've finished creating your app and can configure the API. From there, you can, for example, send your first automated message!
Step 2: Sending the first message on the Official WhatsApp API
Now that you have completed creating your application, it is time to test the Official WhatsApp API and send your first automated message. To do this, we follow some essential steps to configure the environment and ensure that communication occurs correctly.
Generate an access token in the Official WhatsApp API dashboard. You will need this token to authenticate the messages you send;
Select a test number that Meta provides or register a new one, ensuring that it is not already registered on WhatsApp;
Get the account ID of the WhatsApp Business and the phone number ID within the Meta dashboard;
Use cURL, Client URL, (or other HTTP request tool) to send the first test message;
Finally, check the delivery of the message on your WhatsApp. Typically, the initial template sent is usually the “Hello World” template, previously approved by Meta.
Please note that the Official WhatsApp API requires that the first message sent to a user be a Meta-approved template. Additionally, after the user replies, the conversation remains open for 24 hours, allowing you to send personalized messages.
Now that your first message has been sent successfully, we move on to the next step: configuring the integration of the Official WhatsApp API with N8N!
Step 3: Integrating Official WhatsApp API with N8N for automation
Now that we have sent the first message on the Official WhatsApp API, it is time to integrate it with N8N to create intelligent automation flows. N8N is one of the most efficient NoCode tools for connecting systems and automate processes in a practical way.
So, how can you ensure that this integration is done correctly? Don't worry, it's simpler than it seems! To do this, just follow the steps below:
Create a new flow and add a manual trigger to start the automation;
Click on “+” and select Trigger Manual to run the flow manually in the tests;
Add the WhatsApp node and select the “Send Message” option;
Go to Credentials and enter the Access Token generated in the WhatsApp API panel and the WhatsApp Business account ID obtained in Meta;
Choose the phone number that will be used to send automated messages;
Configure the message (text, image, button, etc.);
Run the test flow and verify that the message was sent correctly.
With this configuration, your WhatsApp can now send messages directly from N8N, automating interactions and optimizing your company's communication!
But how does this work behind the scenes? For the message to be sent properly, it is first important to understand the structure of the API.
Understand more about the API structure
All communication between systems via API follows a predefined structure. In contrast, specifically in the case of the Official WhatsApp API, this communication occurs via HTTP requests — usually of the POST type for sending messages. Furthermore, these requests contain three main parts:
API URL: the main address that receives requests;
Headers: information such as the content type (Content-Type: application/json) and the authentication method (Authorization: Bearer );
Body (request body): the JSON structure containing the data sent.
With this understanding, it becomes much easier to configure the API correctly within the N8N. Node done, it's time to move on to the next step to create automated responses with Webhooks in the Official WhatsApp API!
Step 4: Creating automated responses with Webhooks in the Official WhatsApp API
Once we configure the sending of automated messages via the Official WhatsApp API in N8N, the next step, in turn, will be to create automatic responses whenever a user interacts with the company's WhatsApp.
To do this, we will use Webhooks, which allow us to capture events and trigger automation flows in real time.
They work as automatic triggers, firing events whenever interactions occur on WhatsApp. In turn, in the case of the Official WhatsApp API, these Webhooks are triggered as soon as a message arrives and then send the data to N8N for processing.
With this functionality, therefore, you can automate interactions such as:
Quick responses for customers;
Integration with systems such as CRMs and ERPs;
Automated support ticket generation;
Sending personalized messages based on user interaction.
To capture messages received on WhatsApp and trigger automations on N8N, follow the step by step below:
Access N8N and create a new automation flow.
Click on “+” and add a WhatsApp trigger node.
Select the “onMessage” option to capture incoming messages.
Configure a new credential by entering:
Client ID and Client Secret, obtained from Meta for Developers.
Webhook URL automatically generated by N8N.
From your Meta dashboard, go to Settings > Webhooks and:
add the Webhook URL generated in N8N as Callback URL.
define the events to be monitored (e.g. received messages).
Click Verify and Save to activate the Webhook.
Run a test by sending a message to the linked WhatsApp.
In N8N, confirm that the message was captured and processed correctly.
Conclusion
By now, you should have learned how to integrate the Official WhatsApp API with N8N allows you to efficiently automate communication processes, connecting WhatsApp to internal systems, CRMs, ERPs and other platforms.
Now that you've learned how to integrate the Official WhatsApp API with N8N, how about taking advantage of this time to deepen your knowledge and explore the automation possibilities even further?
Therefore, in the NoCode Startup N8N course, you will have access to complete and practical training on automation and data integration, and thus be able to make your company even more efficient.
If you are looking for a Free AI Agent Course for Beginners, you have come to the right place! After all, we are in the era of automation and artificial intelligence. In this context, companies and professionals are looking, above all, for intelligent solutions to optimize processes. In addition, they are looking to reduce costs and, consequently, improve the user experience.
In this context, AI agents stand out for their ability to automate complex tasks, interact naturally with users and integrate multiple systems without the need for constant supervision.
Continue reading this article and discover how the Free AI Agent Course can transform the way you work with artificial intelligence. Understand why AI agents are so powerful and learn how to start creating your own agent from scratch, using accessible and efficient tools, without needing any programming experience. Enjoy your reading!
From Zero to AI Agent: Learn how it works Free AI Agent Course for Beginners
If you want to learn for free and create your own AI Agent, the first step is to learn about the structure of the Free AI Agent Course for Beginners from NoCode Startup. If you want to start from scratch and develop your own Artificial Intelligence Agent, this content was made for you, in a complete material you will learn:
fundamentals of Artificial Intelligence Agents to build a solid foundation;
a step-by-step guide to creating practical agents, even without prior experience;
integrations with platforms such as Telegram, enabling the creation of interactive and dynamic agents.
Remember that the AI Agent course was developed so that anyone, even without prior programming knowledge, can create intelligent and scalable solutions. In other words, even if you have never programmed before, you can start without fear!
Why are AI agents so powerful?
Before understanding how to create your own AI Agent, it is essential to understand why these tools have become indispensable in different sectors. Therefore, it is worth reflecting: why is the use of these solutions growing so much? How do they impact the efficiency of processes?
Furthermore, understanding these aspects can reveal new opportunities for optimization and growth.
1. Integration with custom data (RAG)
One of the main reasons for the power of AI agents lies in the technique known as RAG (Retrieval-Augmented Generation). This methodology allows the AI model to be combined with personalized user or company data. This means that the agent can be trained to access specific information from:
PDF files;
corporate websites;
spreadsheets and databases;
Through this customization, the agent becomes able to perform advanced queries, access specific documents and respond accurately based on the available information.
2. Ability to execute actions (Function Calling)
In addition to RAG, another distinguishing feature of AI agents is the function called Function Calling (or tools), which allows the agent to not only analyze data, but also perform actions on different platforms. For example, among the main functions, the following stand out:
access and edit the calendar (schedule meetings, check events);
send, read and reply to emails;
interact with spreadsheets and databases (consult and update information);
perform direct tasks via corporate applications.
This capability turns the agent into a true virtual assistant. Imagine being able to send a simple message on WhatsApp, and the agent automatically accesses different systems, queries databases and sends comprehensive reports, all without direct human interaction.
Learn how to create AI Agents for different businesses
AI agents aren’t limited to basic tasks or simple interactions. In the Free AI Agents for Beginners Course, you’ll master tools like RAG and Function Calling and learn how to create intelligent solutions for different industries, without needing technical experience and without paying anything!
Scheduling agents, for example, can automate appointment scheduling, eliminating the need for direct human interaction. Key application examples include:
medical consultations: the agent checks available times, schedules the appointment and sends confirmation to the patient;
barbershops and beauty salons: the agent manages the professionals' schedule and allows clients to choose times directly via WhatsApp or Instagram;
classes and events: Want to schedule an adventure class or special event? The agent automates the process and confirms details with participants;
restaurants and snack bars: the agent acts as an intermediary between the customer and the establishment, optimizing orders and integrating with the restaurant system;
e-commerces: manage orders, inventory and customer service in an automated way, using agents integrated with the main sales platforms;
veterinary clinics: allow appointment scheduling, vaccination control and automatic notifications for customers;
gyms and studios: the agent manages class reservations, waitlists and sends automatic reminders to students.
Understand the Architecture of an AI Agent
Creating an AI Agent is more than just programming a chatbot. It’s about developing an intelligent, autonomous solution that transforms processes!
To do this, it is essential to understand the architecture that supports these agents, ensuring that they are capable of performing complex tasks, interacting with different platforms and delivering accurate and contextualized responses.
Below, learn more about this framework and how each component contributes to the advanced performance of AI agents.
Input Layer: where the agent receives information from the user through different channels (WhatsApp, Instagram, email or website), whether in text, voice or specific commands;
natural language processing (NLP): responsible for interpreting messages, understanding intentions and extracting relevant information, such as dates, times and user preferences;
connectors and APIs (Function Calling): allow the agent to perform real actions, such as checking available times, consulting menus or accessing internal systems, through external integrations;
RAG (Retrieval-Augmented Generation): combines natural language generation with external data retrieval, allowing the agent to search for information in databases or on the internet in real time before responding;
decision making and automation: After processing the information, the agent performs actions such as scheduling appointments, forwarding orders or sending notifications;
real-time feedback: keeps the user informed about the status of the service, sending automatic updates at each stage of the process.
N8N: The most complete tool for creating AI agents
n8n complete tool to create AI agents
Creating AI agents goes far beyond just setting up simple bots. There are robust tools on the market that allow you to build complex, interactive, and fully automated agents. Choosing the right tool makes all the difference in the performance and possibilities of your project.
In this way, the N8N stands out for integrating two essential worlds: advanced automation and creation of AI agents.
Originally designed for complex automation, the platform has evolved and today offers a powerful framework for creating intelligent and scalable agents. Among the main differentiators of N8N are:
creation of complex automations and integrations on a single platform;
integration with multiple AI models such as GPT, Llama, Claude and Gemini;
ability to host the system on your own servers, reducing costs;
intuitive interface with support for the “No-Code” concept, ideal for beginners'
integration with external tools such as calendars, spreadsheets, emails and databases.
Additionally, N8N offers a visual interface for creating automation flows, making the job easier even for those with no prior programming experience. And best of all, you can take a 14-day free trial with credits included to use OpenAI's resources.
OpenAI: Simplicity and Scalability
OpenAI offers one of the most robust solutions on the market, enabling the creation of powerful AI agents through the use of GPT models (such as GPT-4).
With a simple-to-use API and excellent documentation, OpenAI has become a reference for developers who want to create scalable agents with high processing capacity. Among its main advantages are:
pre-trained models with high natural language understanding capacity;
easy integration with platforms like N8N;
scalability for projects of all sizes;
support for techniques such as RAG and Function Calling;
Dify: Open source and total flexibility
Dify stands out for being 100% open source, allowing developers to have complete freedom to adapt the agent according to their needs. Dify's main features are:
open source, allowing complete customizations;
possibility of hosting on own servers, reducing expenses;
broad integrations with databases, APIs and external tools;
But how do you choose the ideal tool? Choosing the ideal tool will depend on your goals and the level of complexity of your project:
If you are looking for something practical and scalable, OpenAI may be the best choice;
for those who need advanced automations and complex integrations, N8N stands out;
If the focus is total freedom of customization and an open source solution, Dify is perfect.
And if your goal is to create complex automations with multiple integration points, N8N is the best choice. Its ability to combine automations with AI and the possibility of self-hosting make it one of the most powerful tools on the market.
Time to get your hands dirty: learn how to create your first AI Agent
If you've followed the Free AI Agent Course for Beginners | From Zero to AI Agent, it's time to put everything you've learned into practice! In this step, I'll guide you through the process of creating your first AI Agent, using accessible and efficient tools, such as N8N, OpenAI and Dify. Ready? Let's go!
1. Step 1: Defining your AI Agent front-end
The front-end is the interface of your project, the point of contact where the user interacts with your agent. In this content, we will use Telegram for its simplicity and versatility. Although it is possible integrate WhatsApp, this platform's API demands more complex processes.
So, for beginners, Telegram is the best choice. Later, you can explore the integration with WhatsApp.
2. Creating the Agent in N8n
N8N will be the main automation tool in your AI Agent. With it, you can create complex workflows without the need for advanced programming. Follow the steps below to get started:
create your free account on N8N with a 14-day free trial and credits to use the OpenAI API;
access the N8N panel and configure your credentials;
create a new workflow by clicking on “Start from scratch”;
choose your first trigger (e.g.: message received on Telegram);
add the “AI Agent” node and connect to the OpenAI GPT model.
3. Expanding the functionalities
Now that your basic AI Agent is up and running, it's the perfect time to enhance its capabilities, making it even more efficient and versatile!
Learn how to add advanced functionality that allows your agent to interact with different types of data, integrate new platforms, and provide a richer user experience.
1. Adding memory layer (WindowBufferMemory
For your AI Agent to have the ability to remember information during a conversation and maintain context between messages, it is essential to add a memory layer.
The implementation of WindowBufferMemory in N8N allows the agent to store recent interactions, ensuring more accurate responses aligned with the context of the dialogue. To implement, follow the steps below:
In N8N, add the WindowBufferMemory node to your agent flow.
configure the following parameters:
Window Size: define the number of messages the agent should remember (e.g.: 5 previous interactions);
storage method: For temporary storage, use N8N's default storage. For long-term storage, integrate with databases like Redis or Supabase;
Connect the WindowBufferMemory node to your AI Agent node so that the agent uses the history when generating responses.
To make the implementation clearer, imagine the following scenario: the user asks “What’s my appointment tomorrow?” and then simply writes “What about Friday?”.
Even without repeating the full question, the agent understands that the context is still about commitments and provides the correct answer.
Now that the agent is prepared to store contextual information, you can explore additional integrations and enhance its functionality, creating a more robust and efficient flow.
2. Integration with multiple tools (Function Calling)
To take your AI Agent to the next level, allow it to interact directly with other platforms and perform complex tasks. With Function Calling, the agent not only answers questions, but also performs practical actions across different systems. Key features you can integrate include:
Google Calendar: automatically schedule and list events;
Spreadsheets (Google Sheets/Excel): add, remove or search data in real time;
External APIs: perform queries on third-party services, such as weather forecasts, currency quotes or traffic information.
To set up these integrations, follow the steps below:
in N8N, add the node corresponding to the service you want to integrate (e.g. Google Sheets or Google Calendar);
In AI Agent, use the Function Calling function to enable the execution of automatic actions when certain commands are detected;
Create specific prompts to activate each tool, ensuring that the agent understands the user's requests. Practical examples:
“schedule a meeting for tomorrow at 2pm.”
“add the client João Silva to the contact spreadsheet.”
“send a confirmation email to [email@example.com].”
In this way, the agent becomes not only an intelligent assistant, but also an executor of complex tasks, expanding its functionalities and delivering a much richer and more dynamic experience to the user.
3. Implementing sentiment analysis
You can also enhance your AI Agent’s communication by empowering it to interpret the emotional tone of user messages and adjust its responses accordingly. This ability creates a more humanized, empathetic, and contextualized interaction.
To do this, follow the steps to implement sentiment analysis:
in N8N, add the Text Analytics node or use external APIs like Google Natural Language or IBM Watson;
connect the node to the main flow of the agent, right after receiving the user's message;
configure the node to identify emotions such as happiness, anger, sadness, or neutrality;
In the AI Agent node, create branches in the flow to adapt the agent's responses based on the identified sentiment.
If the user types, “I’m very frustrated with the service,” the agent might respond with more empathy: “I’m sorry to hear that! I’ll do my best to help you resolve the issue as quickly as possible.”
This way, the agent becomes more attentive, improving the user experience and strengthening the bond of trust.
4. Transforming audio into text (Speech-to-Text)
You can also expand your AI Agent’s accessibility by enabling it to understand voice messages. Speech-to-Text functionality allows the agent to transcribe audio into text and interact normally with the user.
To enable audio transcription in N8N, follow these steps:
add Telegram Get File node to capture the audio file sent by the user;
connect the node to OpenAI's Whisper API or Google Speech-to-Text to perform audio-to-text transcription;
send the transcribed text to the AI Agent node so that the agent can process and respond to the command normally.
With voice message understanding enabled, the user can send an audio message saying: “Schedule a meeting with Pedro tomorrow at 10 am.” The agent transcribes the audio and executes the action on the calendar, ensuring a fluid and efficient interaction.
This functionality expands the agent's possibilities of use and creates a more dynamic service experience.
5. Automatic notifications and real-time alerts
How about taking your AI Agent to a new level of efficiency with RAG (Retrieval-Augmented Generation), allowing it to search for data from external sources before generating responses? With this technique, the agent provides updated information and contextualized responses. To do this, follow these steps to configure RAG:
in N8N, add the integration node with databases, external documents (PDFs) or public APIs;
In the AI Agent prompt, instruct the agent to query external sources before generating a response to the user;
test the agent with questions that require consultation in external databases.
By adding this automation, your AI Agent gains the ability to send personalized reminders like “You have a meeting scheduled for tomorrow at 9am.”, important announcements like “There’s been a change to Friday’s event.”, and strategic promotional messages like “Unmissable offer! Up to 30% off today.”
With RAG, the agent stops being just a text generator and becomes an intelligent, real-time query tool, ideal for corporate, educational and financial sectors.
Finally, you can take your AI Agent to the next level of efficiency by implementing RAG. To set up RAG on N8N, follow these steps:
add the integration node with databases, external documents (such as PDFs) or public APIs;
configure the AI Agent prompt to instruct it to perform external queries before formulating the response to the user;
take practical tests with questions that require searching for data in real time, such as:
“What was the revenue from the last quarter?” (consulting a database);
“What is the dollar rate today?” (using financial APIs).
This feature is especially useful in corporate, educational, and financial environments where decision-making depends on accurate, timely data.
4. Testing and adjustments
Now that your agent is up and running, it’s time to test it and tweak any details to improve its performance. You can use a testing checklist to check if your agent is working properly:
Is the agent receiving messages correctly?
does it respond based on the prompt instructions?
Can you create and list events in the calendar?
Are the answers clear and accurate for the user?
If the agent is returning incorrect information, adjust the prompt to better guide responses. You can also use N8N's execution history to identify failures and test the agent with different commands to validate its flexibility.
Conclusion
By now, you’ve probably realized that creating AI Agents isn’t just a technological trend, right? Quite the opposite, it’s a real opportunity to explore new markets, automate processes and, above all, boost business in a strategic and efficient way.
Whether to improve the customer service, optimize internal flows or create scalable SaaS solutions, agents offer versatility and scalability for professionals and companies.
The best thing of all is that with No Code tools Like N8N, anyone can start this journey, even without prior programming experience. The combination of techniques such as RAG and Function Calling allows you to create powerful agents, capable of acting in different sectors and solving complex problems.
Now is the time to learn for free and get your hands dirty! In the Free AI Agent Course for Beginners, you start from scratch and create your own intelligent agent, ready to automate tasks and generate business opportunities.
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