MuleSoft Artificial Intelligence: Tutorial & Examples

This article explores practical ways to enhance MuleSoft workflows using artificial intelligence and other automation techniques, from using Anypoint Code Builder for faster development to automating testing with MUnit and simplifying API documentation.

Alongside a comprehensive list of MuleSoft’s native AI and automation tools, we highlight CurieTech AI, a third-party solution offering advanced AI-powered automation designed to complement the MuleSoft platform. It helps with tasks like DWL transformations, flow generation, and code enhancements, reducing manual effort.

Through real-world examples, this article provides hands-on insights for experienced MuleSoft users looking to work more efficiently.

Summary of key MuleSoft AI concepts

The table below covers the core AI topics associated with MuleSoft.

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Anypoint Code Builder

Anypoint Code Builder (ACB) is MuleSoft’s modern development environment that is designed to simplify integration projects. As automation and AI-driven tools reshape development workflows, ACB provides a seamless way to build, test, and deploy APIs with minimal friction.

It’s a cloud-based integrated development environment (IDE) for collaboration and a desktop version for offline development teams. Teams can work remotely, share projects easily, and develop integrations regardless of location. ACB provides a clean, familiar interface with tools for writing and managing APIs, ensuring developers don't need to juggle between platforms.

The screenshot below displays the options available in Anypoint Code Builder (ACB) on the MuleSoft Anypoint Platform. It allows us to choose between launching ACB on a cloud-based server (Anypoint Platform) or using the desktop version in Visual Studio Code.

Some important features of Anypoint Code Builder include syntax highlighting, autocompletion, debugging tools for testing and troubleshooting Mule applications, and code snippets and reusable components to streamline development. It also offers unified API design and implementation, providing seamless integration with the Anypoint platform, so developers can publish APIs directly to Anypoint Exchange and deploy applications to CloudHub or any other runtime environments.

The screenshot below depicts one feature of ACB in Visual Studio, allowing a user to get multiple options related to the MuleSoft automation task ACB can perform. To get this pop-up, the user only needs to type “>mule” in the command palette, as shown.

AI-powered code assistance

AI-powered code assistance for Mulesoft enhances the development experience by using artificial intelligence to simplify and accelerate the creation of API and integration flows. Integrated into tools like Anypoint Code Builder, it allows developers to leverage natural language prompts and intelligent recommendations to boost productivity and reduce errors.

Context-aware code suggestion means that the AI provides real-time contextual suggestions and autocompletion, minimizing manual efforts and ensuring that developers have cleaner, better-optimized code.

The screenshot below depicts a scenario where a RAML file is created in the API designer in ACB. Once we start typing, the tool automatically assists with appropriate methods, resources, parameters, and security components based on the cursor's current position.

Another example of a context-aware suggestion is getting an “Extract to Fragment” option, which means extracting a part of a RAML file into a fragment. By doing this, we move the selected part into a separate RAML fragment file that is outside the code and published to Exchange and can import it for other projects as required. This makes the code reusable as well as readable. Similarly, if we choose “Extract to Declaration,” we can simply move the selected part of RAML from an inline definition to a named declaration in the same file. This helps with reusability.

Here is another example that shows how the tool suggests possible commands based on the current stage of the implementation. In this case, the code implementation is completed and the tool shows the option to debug the application (which is one of the next stages after development).

As shown above, in addition to the AI assistance provided by MuleSoft, CurieTech AI simplifies MuleSoft integrations by helping developers manage and organize APIs across multiple projects. It helps developers streamline workflows and maintain code consistency, making MuleSoft projects more efficient and scalable.

CurieTech AI has similar capabilities to MuleSoft's AI but is more advanced: We can use this tool to detect and debug errors, find anti-patterns in code, and offer intelligent fixes. CurieTech AI’s Code Enhancer can also enhance existing code. If the flow has broken transformations or missing exception handling, the tool will highlight the issue and provide suggestions.

Here is an example of using the Code Enhancer, where we upload the project folder and a description explaining the required changes. Multiple options are provided to choose from, such as “Upgrade Connector Version” or “Update Logging.”

After uploading the project folder, we pass the description as “Change the datatype of employeeID from string to a number,” choose the description “Change DataWeave Logic,” and click Submit.

Once the script is completed, we see all the changes made by the tool to the project, showing that the field type has been changed to “Number” as requested.

Furthermore, we can export the generated project as a zip file, which can be imported into Anypoint Studio (IDE for MuleSoft applications).

To summarize, both approaches help developers by making coding easier and faster. As discussed in the section above, AI-powered code assistance suggests code, but it may not handle complex integrations well. CurieTech AI goes beyond by improving workflows and providing deeper insights for MuleSoft projects.

Builder with Einstein

Einstein is a smart tool that helps users create APIs and integrations easily, even without coding skills. It uses AI to suggest connections, automate data mapping, and fix issues before they happen. With a simple drag-and-drop interface, it makes building integrations faster and more efficient inside Anypoint Code Builder.

Using Einstein AI, developers can describe their desired flow or logic in simple terms, and Einstein translates it into executing the MuleSoft code or configuration. This is particularly useful for repetitive tasks or teams looking to accelerate delivery.

Here is an example of using Einstein in which we prompt the tool with a certain input and it generates a flow as an output. We prompt the tool to create a specific flow, which is highlighted in the below screenshot.

As a result, flow is generated as shown below, where it queries Salesforce and the database and updates either of the systems based on the values retrieved.

Now, let’s look at CurieTech AI’s Integration Generator, making it easier to manage complex workflows. We prompt the tool with a certain input, and it generates a flow as an output.

We prompt the tool to create a specific flow, as highlighted in the screenshot below, and click Submit.

As a result, flow is generated, as shown below, logging the message securely in a property file.

To summarize, both tools enhance MuleSoft's development with AI but focus on different areas. While Builder with Einstein helps with code suggestions and API design, CurieTech AI goes further by creating flows based on the prompts. Using both together can fill gaps and make development smoother.

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Generative AI for API specification

Generative AI for API specification helps create API specifications quickly using AI in Anypoint Code Builder. Instead of writing them manually, we describe what the API should do, and AI generates the specification. This saves time, reduces effort, and ensures that best practices are followed. It makes API development easier for both technical and non-technical users.

Here is an example of API specification using generative AI: We prompt the tool with a certain input, and it generates an API specification as an output.

To start, select the “Use AI option” shown in the screenshot below while creating the API on the platform. We can describe what we want the API to do in simple terms; for example, a user might say “I need an API to get order details by order ID.” The AI tool generates the following:

• Endpoint: / orders/ Id
• Method: Get
• Parameters: Order ID as a path parameter
• Response: JSON structure with order details

The generated specification will be ready in RAML or open API format, which can be refined further if needed.

DataWeave using AI

DataWeave using AI simplifies working with data by leveraging third-party AI tools like CurieTech AI - DataWeave Generator to assist in writing and improving DataWeave scripts. Instead of manually coding complex data transformations, AI can suggest, generate, and even fix scripts for us. This saves time, reduces errors, and makes data mapping much faster. It’s a great way to simplify data integration, even for those with little coding experience.

Here is an example of DataWeave Generator in which we pass sample input data and desired output along with notes: additional instructions to the AI tool like “Do not use hardcoded values.”

We pass sample input data and the desired output data, as shown in the screenshot below.

Once the script is completed, the required DWL is generated.

AI-driven API testing

AI-driven API testing uses third-party AI tools like CurieTech AI - MUnit Test Generator, to generate test suites for APIs. Instead of manually writing test cases, AI can write test suites, find issues, and suggest fixes. This saves time and improves accuracy. It makes API testing easier, even for teams with less testing expertise available. The tool covers multiple positive, negative, and edge scenarios while generating test cases.

Here is an example of the Munit Test Generator. We upload the project folder and provide the flow name for which MUnit test cases need to be created, along with the VM args and notes (additional instructions). We then click Submit, as shown in the screenshot below.

Once the script is finished, the Munit Test Generator will generate different test scenarios for the flow. In addition to handling success scenarios, it creates error scenarios test suites, as shown below.

We can export the tool's output file,  test_suite_get_members_by_id_loyalty_mgmt_1739811.xml, and import it into Anypoint Studio, as shown below.

Automated documentation

In MuleSoft projects, documents like API catalogs, sequence diagrams, flow diagrams, mapping documents, etc., must be created throughout the project lifecycle. Automating the process of creating these documents can help save time, keep documentation up to date, and make it easier for developers and business users to understand how APIs work.

We can use third-party AI tools like CurieTech AI to generate clean and detailed API documentation. Instead of drafting manually, AI generates descriptions, examples, and usage details as we build our APIs.

Here is an example of a diagram generator. We prompt the tool with a certain input in the task description, and it generates a sequence diagram that helps us understand the process. We pass the project's flow names in the task description, as shown in the screenshot below.

Once the script is completed, the required sequence diagram is generated, as shown below.

Intelligent document processing

Intelligent document processing (IDP) is a way to extract, process, and organize data from documents like PDFs, invoices, and forms using AI. Instead of manually entering data, AI reads and understands the documents, saving time and reducing errors. This makes it easier to integrate document data into systems and workflows. It helps businesses work faster and more efficiently with less manual effort. With MuleSoft, we can automate these tasks within our integration workflows.

Using IDP in MuleSoft is a simple process that helps automate document handling. First, the user must upload documents through APIs, email attachments, or other channels. Then, AI-powered optical character recognition (OCR) extracts text and images from scanned or digital documents, while natural language processing (NLP) interprets the content and structure. Next, MuleSoft uses DataWeave scripts to format the extracted data according to the user’s needs. Finally, the processed data is sent to other systems like Salesforce, SAP, or databases for further use, making document management faster and more efficient.

Here is an example of intelligent document processing in MuleSoft’s Anypoint Platform.

MuleSoft AI Chain Connector

MuleSoft AI Chain Connector is a prebuilt connector available within the MuleSoft Anypoint Platform that helps connect different AI models and services into a single workflow. It allows businesses to easily integrate AI-powered tools, like chatbots or machine learning models, with their existing systems. This simplifies task automation, data processing, and decision-making. With AI Chain Connector, companies can use AI more efficiently without complex coding.

Here is a screenshot depicting a few operations provided by the connector.

This connector is useful in many scenarios to make applications smarter. It can connect Mule flows to AI services for tasks like chatbots that handle user questions automatically. It helps analyze customer messages to find positive or negative feedback. In finance, it can detect unusual activities to prevent fraud. The connector also automates reading data from documents like invoices and contracts. It can provide product recommendations based on user behavior in e-commerce. Additionally, it can recognize images or text from scanned files and predict when machines might need maintenance by analyzing data from devices. It allows MuleSoft to work with AI easily, adding automation and smart features to different processes.

CurieTech AI’s role in complementing MuleSoft AI

MuleSoft AI lays a strong foundation for intelligent integrations, making complex workflows more seamless. CurieTech AI enhances further with specialized AI agents that can generate integration flows, DataWeave transformations, MUnit testing, and documentation, helping developers delegate tedious tasks to the agents. It’s like adding precision tools to an already powerful machine, fine-tuning the experience, and making MuleSoft implementations even more efficient. Together, they create a smooth and optimized integration journey.

CurieTech has categorized its AI-powered tools into different areas to support various aspects of MuleSoft development.

Coding agents

These tools generate code for seamless workflow integration. The tools in this category are the Integration Generator, DataWeave Generator, and Code Enhancer. We have already discussed these in earlier sections of the article.

Testing agents

These agents generate test cases and sample data to ensure code functionality and accuracy. The tools covered under this category are the MUnit Test Generator and the Sample Data Generator. We have already discussed the Munit Test Generator in the AI-driven API testing section of the article.

Here’s a look at the Sample Data Generator. Below, we pass a project folder and a DWL script to generate sample data, including variables, attributes, input payload, and DataWeave Output. We uploaded the project folder and DWL script and clicked submit, as shown in the screenshot below.

Once the script is completed, it generates sample data for the input provided.

In addition to sample data, it generates input payload, variables, attributes, and DataWeave output.

Code insight agents

These tools, which include the Single Repo Code Lens and Multi Repo Code Lens, provide in-depth analysis and answers across single or multiple code repositories.

Let’s examine the Single Repo Code Lens. Here is an example of prompting the tool with a question related to the project in the specified repository; it will provide detailed information about it.

We prompt the tool with “Give me all APIs in the repository,” as highlighted in the screenshot below.

Once the script is completed, it provides detailed information about all the project APIs, as shown in the screenshot below.

Here is an example of a Multi Repo Code Lens in which we prompt the tool with a question related to the project under the specified repository. First, we must choose one category: General, Troubleshoot, Security, Best Practices, or Code Review.

As shown in the screenshot, we select the repository and category. Then, we prompt the tool with any questions related to the project.

Once completed, the script provides detailed project information across all repositories based on the selected category.

Documentation agents

These agents create clear, structured documentation and diagrams for APIs, flows, and repositories. They include the Document Generator, Diagram Generator, Domain Diagram Generator, and Mapping Table Generator.

We already discussed the Diagram Generator. Here is an example of the Document Generator in which we pass a project folder along with the flow names to generate documentation. We upload the project folder and flow names and click submit as shown in the screenshot below.

Once the script completes, it generates a document file that contains a flow diagram, sequence diagram, mapping tables, and test scenarios for the given input.

Like the Diagram Generator AI tool discussed earlier in this article, the Domain Diagram Generator generates diagrams. The only difference is that it generates diagrams for multiple APIs.

Finally, here is an example of the Mapping Table Generator. In this example, we pass a project folder and a DataWeave script to generate a mapping document. We upload the project folder and DataWeave script and click submit, as shown in the screenshot below.

When the script finishes, it generates the mapping table, which explains the input and output data in tabular format.

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Conclusion

In this article, we discussed how MuleSoft helps businesses connect different systems easily and makes processes smarter and faster through AI. Tools like CurieTech AI complement MuleSoft by adding AI solutions that automate tasks, analyze data, and improve decision-making. Together, they help companies work more efficiently, reduce costs, and stay competitive in a digital world. This boosts MuleSoft’s integration power, creating smart and seamless business operations.