last day of consumer week

Hours

Minutes

Seconds

LangFlow | Your Next-Level AI Agents

September 10, 2024
5 min

Estimated reading time: 7 minutes

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:

LangFlow-01

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

LangFlow-02

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

LangFlow-03

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!

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.

Also visit our Youtube channel

Learn how to create AI Applications, Agents and Automations without having to code

More Articles from No-Code Start-Up:

Neto Camarano

AI for Text Summarization: How to Automate Articles and Documents

Do you still waste hours reading long articles, emails or documents? Artificial Intelligence (AI) can do it for you in seconds. AI tools AI to summarize texts are revolutionizing the way we study, work and produce content. With the advancement of technology, it has become possible to delegate the reading and synthesis of large volumes of information to intelligent systems, allowing you to focus on what really matters: making decisions based on accurate and relevant information.

In this article, you will discover how to summarize texts with AI, what are the best tools, practical examples and how to automate this task with no-code platforms, even if you don't have advanced technical knowledge.

Introduction to using AI to summarize texts

Recommended Reading

What is an AI for Text Summarization?

Imagine having to read a document that is dozens of pages long and needing to capture the essentials in just a few minutes. This is exactly where summary AIs come in. They are like those attentive coworkers who know exactly what to filter out in a sea of information. With each paragraph you read, these digital assistants separate the essential from the accessory, creating a summary that makes sense to you. And the best part is that they do this with the naturalness of someone who understands our way of thinking, highlighting precisely what is worth reading. This makes our routine easier, more productive and organized.

The use of AI allows for greater efficiency when dealing with long texts, providing a more dynamic and optimized reading experience. And the best part is that you can shape these tools to your liking: it’s as if you were teaching the AI to speak your language and that of your audience. Do you want a more direct summary for executives? Or something more detailed for students? With small adjustments to the settings, it’s as if you were giving personalized instructions to an assistant that understands exactly what you want.

What Types of Text Can AI Summarize Well?

Articles and blog posts
Corporate reports and emails
Meeting or video transcripts
PDFs and academic documents

These are just a few examples. AI can also work with texts in different languages, respecting cultural and linguistic contexts. This makes it a valuable tool in international corporate environments and academic institutions.

Top AI Tools for Text Summarization

ChatGPT

ChatGPT can be used with custom prompts or plugins to generate summaries with different styles and levels of detail. For example, you can configure a prompt to summarize academic articles into bullet points, adapt corporate reports to more objective language, or even generate executive summaries from meeting minutes. Additionally, with memory functionality or API integration, ChatGPT can be incorporated into automated workflows where it learns from feedback and adjustments, making each summary more aligned to your needs.

QuillBot

QuillBot offers a dedicated tool for summarizing sentences or paragraphs, ideal for academic texts and articles. It also allows you to adjust the level of detail in your summary, which is great for those who need a quick overview or a more in-depth summary. QuillBot also includes additional features such as paraphrasing and a grammar checker, which makes it an even more complete tool for those who work with large volumes of text.

SMMRY

SMMRY is a simple, online solution that focuses on condensing texts into a few user-adjustable sentences. What sets this tool apart is its minimalist approach, ideal for those who need a quick, straight-to-the-point summary. You can control the number of sentences you want in the final result and adapt the tool to remove specific sentences, such as those containing certain keywords or quotes.

Resoomer

Resoomer is designed for argumentative and academic texts, with support for multiple languages. The tool is especially useful for those who need to analyze long texts with a clear logical structure, such as essays, dissertations, and legal articles. Resoomer allows you to quickly identify the main arguments, central ideas, and conclusions, making dense texts easier to understand. In addition, it offers browser integration to summarize web content in real time, which is a benefit for researchers and students.

Scholarcy

Scholarcy makes it easy to summarize scientific articles and also generates keywords and study sheets. This tool is especially effective for those who work with academic publications, as it not only condenses the content but also highlights important sections such as objectives, methodology, results and conclusions. Scholarcy also allows the extraction of tables, figures and references, organizing this information in a quick-read format. Integration with reference managers such as EndNote and Zotero is a plus for researchers.

Zamzar Summarizer

Zamzar Summarizer allows you to convert and summarize files such as PDF and DOCX in a simple and straightforward way. The great advantage of Zamzar is its ability to handle a wide variety of file formats, offering not only text summarization but also conversion between formats such as TXT, HTML, and EPUB. This makes it ideal for professionals who deal with documents on different platforms and need to integrate them into a single digital workflow. In addition, the tool can be used without the need for installation, directly from the browser, which further speeds up the process.

Notion AI

Notion AI is ideal for Notion users, making it easy to summarize documents and take notes within the app itself. In addition to summarizing text, Notion AI also allows you to rewrite paragraphs, generate headings, and create lists from text content. Integrated directly with your pages and databases, Notion AI streamlines the workflow for teams using the platform to manage projects, documentation, and ideas.

Automating summaries with AI and no-code platforms

Automating Summaries with AI and No-Code

Use AI to summarize texts goes far beyond manual tools. By combining no-code platforms like Make with robust AI, you can completely automate the process. Imagine you receive an email with a long document, and the system automatically summarizes the content and saves it to your cloud. This is productivity with AI applied intelligently.

Practical Example

“How I Built an Automatic Email Summarizer with Make + OpenAI”

In this example, you will learn step by step how to set up this automation. First, we define a trigger in Make to detect new emails with attachments. Then, we connect the flow to the OpenAI API, which processes the text and generates a summary. Finally, the summary is sent to your email or saved in a Google Doc. This solution is ideal for those who receive a lot of reports or proposals and need to evaluate them quickly.

Another interesting approach is to create a custom agent using the N8N it's the ChatGPT. With N8N, you can create more complex and adaptable workflows. For example, you can set up a workflow where the system automatically processes, via API, the documents you send to a Google Drive folder, summarizes them and classifies them by topic in spreadsheets. The agent learns from the edits the user makes, identifies text types and adjusts the summary style according to the topic, becoming more efficient with each interaction.

For those who want to create robust automations with N8N, check out our N8N Course.

Practical tips for improving results with AI

Practical Tips to Improve Your Results with AI

If you are just starting to use AI to summarize texts, some good practices can make all the difference:

Adjust the prompts: If the AI doesn’t deliver exactly what you want, refine your instruction.
Test different tools: Not all AIs respond the same way. Explore and see which one works best for you.
Automate when possible: Use no-code tools to create flows that save you time.
Review summaries: Even with AI, reviewing ensures that the content is aligned with your needs.

Let's clear up your doubts?

If you still have questions about how to apply AI to summarize texts, here are some answers that may help:

What are the best AI options? to create summaries? Many users use tools like ChatGPT, QuillBot and SMMRY for their practicality and the efficient results they offer. Depending on the volume and type of text, you may want to opt for a more automated solution, such as integration with Make.

Can I automatically summarize long texts? Yes! Using AI to summarize text in conjunction with automation platforms, you can set up systems that process long documents automatically.

How to ensure that the summary is unique? Personalize prompts and always review content. While AI rewrites, human adjustments add value and avoid originality issues.
With AI, you can transform the way you deal with information on a daily basis. From automating email summaries to creating complete workflows, the possibilities are endless. Integrating these tools into your daily routine is the first step to saving time, increasing productivity, and standing out professionally.

Want to master AI automation? Get started with our Makeup Course it's the Agents Course with OpenAI.

April 24, 2025
6 min

Neto Camarano

Artificial Intelligence in Education: How It’s Changing Teaching

Artificial intelligence (AI) is causing profound transformations in several sectors, and education is one of the fields most impacted. From personalized classrooms to automated assessment systems, the use of AI artificial intelligence in education has been gaining increasing prominence. As a result, the impact of AI in schools is reshaping the way teachers teach and students learn, promoting a true digital transformation in education.

In this article, you will understand how AI is being applied in education, what the main tools are, the benefits (and challenges) of this revolution and how to prepare for this new scenario.

Recommended readings for further study:

Application of artificial intelligence in the classroom

What is Artificial Intelligence and how does it connect to education?

Artificial Intelligence is a field of computing that seeks to develop machines capable of learning, reasoning and making decisions. In the educational context, it is applied to personalize learning, automate administrative processes and offer more dynamic and inclusive teaching experiences.

The applications of artificial intelligence in education They range from smart tutors to platforms that automatically correct tests or create lesson plans based on each student's needs.

Practical applications of AI in the classroom

Artificial intelligence is no longer a distant promise and has become an integral part of contemporary educational reality. Instead of waiting for an uncertain future, teachers and students are already using AI-based technologies in a wide range of school activities. This transformation is happening quietly but with great impact — and best of all, with accessible tools that do not require advanced technical knowledge.

Next, we will explore how this presence of AI is realized in everyday education. More than presenting platform names, the goal is to reveal practical possibilities, demonstrate how each resource can be inserted into pedagogical dynamics and, most importantly, encourage educators to begin their journey with AI — even if it is with small steps. After all, each tool presented here can be the starting point for a profound transformation in the way we teach and learn.

1. Adaptive learning platforms

Solutions like Khan Academy with AI from OpenAI offer personalized learning paths based on student performance, adjusting content in real time.

2. Auto-correction and smart feedback

Platforms like Gradescope and Socrative use AI to correct objective tests and offer detailed performance analysis, saving educators time.

Additionally, you can create automated correction flows using Make integrated with Google Docs or Notion, allowing you to deliver activities with personalized feedback.

3. Virtual Assistants and AI Tutors

Tools like ChatGPT, Google Bard and Khanmigo are used by students to clarify doubts and reinforce content based on natural language.

Educators can also use Dify to create custom educational interfaces and subject- or skill-specific tutor bots.

4. Content creation with AI

Educators can use AI to generate quizzes, presentations, and even lesson plans with tools like AI-powered Canva, Notion AI, and ChatGPT, accelerating the preparation of teaching materials.

If you want to go a step further, you can use the Agents Course with OpenAI to create interactive educational experiences with full personalization of content and the student journey.

Further reading: The best Artificial Intelligence tools

Practical examples of the use of AI in education

Benefits of AI in Education

In addition to its growing presence, artificial intelligence in education offers significant advantages, such as:

Personalizing learning: each student learns at their own pace and style. As a result, it is possible to better meet individual needs.
Automation of repetitive tasks: more time for the teacher to focus on mediation and relationships. In addition, it reduces the wear and tear on operational processes.
Inclusion: Students with special needs benefit from features like auto-reading, captions, and real-time translation. On the other hand, it’s important to make sure these tools are accessible to everyone.
Engagement: More interactive and adaptive content increases student interest. This makes learning more engaging and effective.

Challenges and precautions in the use of AI

Despite the benefits, the use of AI in education also requires attention to points such as:

Student Data Privacy
Over-reliance on technology
Inequality of access to digital tools
Digital literacy for teachers

Therefore, it is essential that educators prepare themselves not only to use these tools, but also to deal with the impacts they can generate in the school environment.

How Educators Can Start Using AI

To start using the artificial intelligence in education, even without technical knowledge, teachers can explore AI in their daily lives with simple tools. Here are some initial ideas:

Try ChatGPT to generate questions, summaries or lesson plans.
Use Canva to create visuals with automatic suggestions.
Explore platforms like Khan Academy with AI to enrich teaching.

In addition to these options, it is possible to implement more robust solutions with no-code tools:

Agents Course with OpenAI to create virtual tutors, study assistants or automatic correction systems.
Make-up Course (Integromat) to build automated pedagogical flows, such as sending activities and personalized feedback.
Dify Course to develop personalized educational interfaces with artificial intelligence.

Furthermore, a practical way to advance is to train yourself with specific training. If you are an educator and want to master these technologies, also see the AI Agent and Automation Manager Training, where we teach how to use AI in practice, with a focus on productivity and automation in teaching.

Future of education with AI teacher interacting with dashboard

The Future of Education with AI

THE artificial intelligence in education It will not replace teachers, but it will be a powerful ally. The combination of the educator’s human perspective and the analytical capacity of AI can take teaching to a new level of personalization, scale and impact.

This movement represents a true digital transformation in schools, where technologies such as educational chatbots with AI, personalized tutors and pedagogical automation become part of everyday school life.

As more institutions adopt these tools, the difference will lie in how to use them ethically, creatively and strategically. To achieve this, ongoing training of education professionals will be essential.

Further reading:

Want to transform your educational practice with AI? Get started now with practical training, even if you've never programmed before: Discover our AI and NoCode course

April 16, 2025
5 min

Neto Camarano

Unofficial WhatsApp API: Learn How to Integrate It in N8N

Did you know that integrating WhatsApp with N8N using an Unofficial WhatsApp API can transform your business automation?

WhatsApp's unofficial APIs are an interesting alternative for those looking for more flexibility and savings, without compromising legality.

Although Meta does not directly link these APIs, they do, however, make it possible to create robust automations, send messages, configure chatbots, and integrate systems with WhatsApp in a practical and efficient way. In this article, you will learn how to:

Create your account on the unofficial API;
Configure the integration with N8N;
Automatically send messages via WhatsApp;
Trigger smart automations with chatbots and AI;
Use webhooks to receive and process messages.

All this using accessible tools, such as ZAPI and N8N, to transform your communication on WhatsApp into an automated and efficient process! Enjoy reading!

Unofficial WhatsApp API: What are they?

unofficial whatsapp api what are they

Unofficial WhatsApp APIs are developed by companies with no direct connection to Meta (the company responsible for WhatsApp). This does not mean that they are illegal, but rather that third parties offer solutions to integrate WhatsApp with automated systems.

While the official API is directly linked to Meta and has strict usage rules, unofficial APIs offer more flexibility and cost-effectiveness. Many companies use them extensively to create automations, chatbots, and send bulk messages.

If you want to understand more about the differences between the Unofficial WhatsApp API and Official WhatsApp Business API, check out the full article on the NoCode Startup blog.

Among the most popular Unofficial WhatsApp API options are:

Z-API: tool widely used for its simplicity and advanced features;
MegaAPI: known for its stability and technical support;
Evolution API: stands out for its easy integration with other systems.

Why use Unofficial APIs on N8N?

Using an Unofficial WhatsApp API in N8N allows you to create robust and customized integrations, ensuring flexibility and savings in customer communication. Using unofficial WhatsApp APIs in N8N, you gain:

Flexibility: customize message sending, create automated flows and integrate with other systems;
Economy: use affordable alternatives without compromising functionality
Speed of implementation: unofficial APIs are easy to configure and have detailed documentation;
Full automation: send messages, automatic responses and create chatbots in a practical way.

In other words, with the Unofficial WhatsApp API, you can set up automated flows, send messages automatically, and even create smart chatbots.

Additionally, you can integrate these features with other tools to further expand their capabilities.

Next, you will understand how to configure your Z-API account and start integrating with N8N to optimize sending messages via WhatsApp.

How does the integration with the unofficial WhatsApp API work? Check out the step-by-step guide

Integrate WhatsApp with N8N Using an Unofficial WhatsApp API is a simple and straightforward process. The main advantage of this method is flexibility, as it allows create custom automations for sending and receiving messages.

This in addition to enabling the creation of chatbots and artificial intelligence (AI) agents. Let's understand how it works?

To perform the integration, we will use Z-API, one of the most popular unofficial APIs. ZAPI allows you to send messages and configure them automatically via WhatsApp. Here are the essential steps to configure it:

Step 1 – Registration on the Z-API API:

The first step is to register on the platform. To do this, follow the steps below:

Go to the official Z-API website and click on the “Take a free trial” option. If you do not have an account yet, click on the link provided to start the registration process;
Fill in the required fields with your information and, when finished, you will be directed to the Z-API control panel;
In the Z-API dashboard, you will see a dashboard that displays your instances. Each instance corresponds to a linked WhatsApp number. To connect your number to the Z-API dashboard, click on the instance you want to use;
On the instance screen, you will find a QR Code. To connect your number, open WhatsApp on your device, go to settings, click on “Connected Devices” and then “Add a device”.
Point the camera at the QR Code displayed on the Z-API, in the same way you do to use WhatsApp Web;
Once the number is successfully linked, the Z-API dashboard will indicate that the instance is “Connected”.

Now you are ready to start integrate WhatsApp with N8N and use the features of the unofficial API.

Step 2 – Configuring the API on N8N

How does unofficial WhatsApp API integration work? Check out the step by step

So the next step is configure API on N8N for the integration with WhatsApp to work correctly. To do this, follow the steps below:

Access your N8N account and click on the option to create a new workflow;
Add a manual trigger by clicking the add button and selecting “manual trigger” from the list of options;
Within the workflow, add a node of type “HTTP Request” to make the WhatsApp API call;
Set the method to “POST” and enter the URL provided by Z-API, which contains the endpoint to send messages (e.g., “api.z-api.io/instances/{your-instance}/sendText“);
In the “Headers” field, add the authentication information, including the client token and the account security token, as indicated in the Z-API documentation;
In the “Body” field, enter the mandatory parameters, such as the recipient’s number and the message to be sent.
After configuring the necessary fields, click “Run” to test sending the message. N8N will display a success confirmation.

Once you successfully send the message, the N8N interface will display the response, confirming that the integration is working correctly.

Step 3 – Testing Message Sending:

Now that you have configured the API in N8N, it is time to test sending the message to ensure that the integration is working correctly. Follow the steps below:

In N8N, click on the “Run” option to run the workflow you just configured;
Check the N8N interface to see if the message “Node executed successfully” appears. This indicates that the flow was executed correctly;
Now, open WhatsApp on the device you linked to the Z-API and check if the message sent through the workflow reached the recipient;
If the message was not sent, therefore, review the API settings in N8N, especially the URL, Headers and Body fields;
If the message arrived correctly, congratulations! The integration between WhatsApp and N8N using the unofficial API was successful.

Step 4 – Automating Message Receiving:

So far your integration is working correctly, the next step is to automate the receipt of messages in N8N:

In N8N, open the workflow you created earlier or, if you prefer, create a new workflow.
Add a trigger node of type “Webhook” to receive messages automatically;
Set the webhook method to “POST” and choose a name for the path, such as “webhook-api-whatsapp”;
Copy the webhook address that will be generated automatically;
In the Z-API dashboard, access the linked instance and go to the Webhook settings;
Paste the copied webhook address into the field indicated for receiving messages;
Save your changes and return to N8N to verify that the webhook is active and ready to receive messages.

Then, whenever a new message is received on the linked WhatsApp, N8N will automatically capture it. In addition, you can configure additional actions, such as automatic replies or processing of the received data.

Conclusion

step by step how to integrate whatsapp with unofficial api n8n conclusion 1

Integrating WhatsApp with N8N using an Unofficial WhatsApp API like Z-API is a practical and efficient solution to automate processes and improve communication with your customers.

By following the step-by-step instructions in this article, you are ready to transform your business's communication management with advanced and accessible resources.

So, how about starting now, becoming an automation expert with N8N? Take advantage now and access the NoCode Startup N8N course. A free 100% course that will teach you how to create incredible integrations and automate processes efficiently!

Don't miss this opportunity to learn from experts and enhance your automation skills.

April 8, 2025
6 min

EN