AI (Artificial Intelligence) is everywhere we go and definitely it is here to stay.
A great use of AI for us as Software Developers is the creation of Intelligent Agents that, with the help of Large Language Models (LLMs), can solve problems that would be complex or impossible to address through traditional programming.
A few weeks ago I learned about the existence of a new framework that Rod Johnson (creator of the Spring framework) and other people are working on called Embabel
Embabel is a framework for creating agent workflows in the JVM by mixing interactions with LLMs via prompts and code with domain models (Java/Kotlin classes). The framework is built on top of Spring AI
The framework is relatively new and still in development, there is no official documentation yet and it’s possible that some things I explain here will change in the future (although I don’t think it will change radically).
The code for this example can be found in my Github repository at: abadongutierrez/basic-embabel-agent
Use Case: Web content summarization
Almost all of us have used LLMs to summarize some text. In fact, summarization is one of the great uses of LLMs, and in today’s example we’ll use Embabel to create an Agent that extracts content from websites we indicate and summarizes their content.
In general, we’ll use Embabel to build an agent that:
- Receives free text input from the user (via Spring Shell).
- Extracts web links mentioned by the user (with the help of LLM’s)
- Visits each site, obtains its content in the form of free text without HTML tags (using Tools).
- Generates a summary of each site’s content (again, using a LLM).
To visit each link and extract the content from that website, we’ll use the JSoup library. With this library we can easily connect to a website and extract only the text without HTML tags as follows:
// Connect and get the HTML document
Document doc = Jsoup.connect("https://en.wikipedia.org/").get();
// get only the text (no HTML tags)
doc.text();
How the Agent is created?
To define an Agent we need to create a class annotated with @Agent. This is very similar to using @Component and the derived annotations that exist in the Spring Framework. In fact, @Agent also derives from @Component so it’s managed as a Bean and, therefore, we can take advantage of dependency injection.
@Agent(description = "Agent to summarize content of web pages")
public class SummarizingAgent {
@Action
public WebPageLinks extractWebPagesLinks(UserInput userInput) { ... }
@Action
public SummarizedPages summarizeWebPages(WebPageLinks webPageLinks, OperationContext operationContext) { ... }
@AchievesGoal(description = "Show summarized content of the web pages to the user")
@Action
public SummarizedPages showSummarization(SummarizedPages summarizedPages) { ... }
}
It’s important to assign a good description to the agent, since when interacting with them through Spring Shell, Embabel uses an LLM to select which agent will respond to the user’s request. This selection is based on an analysis of the user’s intention and correspondence with the most suitable agent to handle it.
Each method that represents a step in the agent’s flow must be annotated with @Action. The method that represents the agent’s final goal is also annotated with @AchievesGoal.
When we interact with Agents via the Spring Shell interface, generally the first step is the @Action method that receives a UserInput as an argument. I mention this because using Agents via Spring Shell is not the only way to interact with them - you can also use other mechanisms that I’ll try to explore in future posts.
Agent Flow
There is no way to specify the Agent flow programmatically. The framework, as stated on the homepage, tries to go beyond simply specifying a flow through a state machine and applies intelligent planning at the beginning of the flow and after the execution of each step. The framework detects the flow through the relationship between methods by inspecting the data types in “inputs” (method arguments) and “outputs” (return type).
SummarizingAgent
In this tutorial we create the SummarizingAgent which as a first step extracts URLs from user input. To achieve this we use an LLM because since user instructions are free text without format, LLMs are good at analyzing text and extracting information that we indicate. This is implemented in the extractWebPagesLinks method.
@Action
public WebPageLinks extractWebPagesLinks(UserInput userInput) {
String prompt = String.format("""
Extracts the urls from the provided user input.
<user-input>
%s
</user-input>
Extract only the links mentioned in the user input, dont add any other links.
""".trim(), userInput.getContent());
return PromptRunner.usingLlm().createObjectIfPossible(prompt, WebPageLinks.class);
}
The second step in the flow is to extract the text content from each website and here we again rely on an LLM to obtain a summary of that content. This is implemented in the summarizeWebPages method. This method has 2 ways of acting and this depends on the app.useOpenAI flag defined in application.properties. This small application is designed to use Ollama and local models llama3.2 and all-minilm but you can also use OpenAI by setting the app.useOpenAI flag to true, which means you need to specify the OPENAI_API_KEY environment variable for the application to work correctly.
I implemented the summarizeWebPages method in 2 ways because llama3.2 is not as powerful a model as OpenAI models and I had many issues using the same prompt. So when using llama3.2 I used a different prompt and also an alternative in case the first prompt failed.
...
@Value("${app.useOpenAI:false}") boolean useOpenAI
...
@Action
public SummarizedPages summarizeWebPages(WebPageLinks webPageLinks, OperationContext operationContext) {
if (this.useOpenAI) {
return getSummarizedPagesUsingOpenAI(webPageLinks);
}
return getSummarizedPagesUsingLocalModels(webPageLinks, operationContext);
}
The last step in the flow is simply to return the set of pages and their summary so that the framework prints it to the Spring Shell console. This is implemented in the showSummarization method which must also be marked with the @AchievesGoal annotation because once this method is executed, the agent’s goal will have been achieved.
@AchievesGoal(description = "Show summarized content of the web pages to the user")
@Action
public SummarizedPages showSummarization(SummarizedPages summarizedPages) {
return summarizedPages;
}
Interaction with LLMs
The framework has the concept of PromptRunners which, as their name indicates, execute a prompt to an LLM.
A PromptRunner has methods to execute a prompt and convert the prompt output to a domain object with methods like createObjectIfPossible or createObject. This gives the advantage of applying strong typing in our programs and thus being able to use refactoring techniques more easily.
The framework defines certain LLMs that programs will use by default. In our case we’re using local models with Ollama so in the application.properties file we can find the following properties that indicate which models will be used by default:
embabel.models.default-llm=llama3.2:latest
embabel.models.default-embedding-model=all-minilm:latest
embabel.models.embedding-services.best=all-minilm:latest
embabel.models.embedding-services.cheapest=all-minilm:latest
embabel.models.llms.best=llama3.2:latest
embabel.models.llms.cheapest=llama3.2:latest
embabel.agent-platform.ranking.llm=llama3.2:latest
When performing operations with LLMs, one of the main things that must be specified are the prompts, but PromptRunners also provide the facility to specify the “Tools” we want to use as part of executing a prompt. In this small application we are specifying and using JSoup as a “Tool” to extract text from a website.
“Tools” can be specified using the Spring AI annotation @Tool and this can be seen implemented in the JSoupTool class which is also a Spring Bean that we easily inject into the Agent.
@Component
public class JSoupTool {
...
@Tool(name = "jsoup", description = "A tool to extract text from web pages using JSoup")
public String getPageTextTool(String url) {
...
}
PromptRunners use the LLMs specified by default or using LlmOptions you can specify different models. In the case of this application we use this functionality to specify the OpenAI model to use when the app.useOpenAI flag is active.
String prompt = " ... ";
BuildableLlmOptions llmOptions = LlmOptions.fromCriteria(
ModelSelectionCriteria.byName("gpt-4.1-mini")
);
return PromptRunner
.usingLlm(llmOptions)
.withToolObject(jSoupTool)
.createObjectIfPossible(prompt, SummarizedPages.class);
Execution of the program
This application is configured to run using Spring Shell as an interface and we can notice this by the @EnableAgentShell annotation in the class where the main method is located.
@SpringBootApplication
@EnableAgents(
loggingTheme = LoggingThemes.STAR_WARS,
localModels = {LocalModels.OLLAMA}
)
@EnableAgentShell
public class BasicEmbabelAgentApplication {
public static void main(String[] args) {
SpringApplication.run(BasicEmbabelAgentApplication.class, args);
}
}
Also as you can notice it’s configured to search for and use local LLMs using Ollama.
Once the application is executed, the Spring Shell prompt appears where we can use the x (execute) command to indicate the “user input” and make the Embabel agent platform search for and select the appropriate agent to handle the user’s request:
...
21:05:55.957 [main] INFO DelegatingAgentScanningBeanPostProcessor - All deferred beans were post-processed.
21:05:55.958 [main] INFO BasicEmbabelAgentApplication - Started BasicEmbabelAgentApplication in 1.834 seconds (process running for 2.074)
Fear is the path to the dark side.
starwars> x "summarize the content of the following page https://en.wikipedia.org/wiki/Alan_Turing"
output:
You asked: UserInput(content=summarize the content of the following page https://en.wikipedia.org/wiki/Alan_Turing, timestamp=2025-07-08T03:15:04.089557Z)
{
"summarizedPages" : [ {
"url" : "https://en.wikipedia.org/wiki/Alan_Turing",
"summary" : "Alan Turing (1912-1954) was a British mathematician, computer scientist, logician, philosopher, and cryptographer who made significant contributions to the development of computer science, artificial intelligence, and cryptography.\n\n**Early Life and Education**\n\nTuring was born on June 23, 1912, in London, England. He studied mathematics at King's College, Cambridge, where he graduated with a First Class Honours degree in Mathematics. During World War II, Turing worked at the Government Code and Cypher School (GC&CS) at Bletchley Park, where he played a crucial role in cracking the German Enigma code.\n\n**Contributions to Computing**\n\nTuring is considered one of the founders of computer science. He proposed the theoretical foundations of modern computer science, including:\n\n1. **The Turing Machine**: a mathematical model for a computer's central processing unit (CPU).\n2. **The Universal Turing Machine**: a machine that could simulate any other machine.\n3. **Computability Theory**: the study of what can be computed by a machine.\n\n**Codebreaking and Cryptography**\n\nAt Bletchley Park, Turing worked with a team to crack the Enigma code, which was used by the German military during World War II. His work significantly contributed to the Allied victory.\n\n**Personal Life and Later Years**\n\nTuring's personal life was marked by tragedy. In 1952, he was convicted of gross indecency for his relationship with a man, which led to his chemical castration and eventual death in 1954 at the age of 41.\n\n**Legacy**\n\nTuring's legacy is profound:\n\n1. **Computer Science**: Turing's work laid the foundation for modern computer science.\n2. **Artificial Intelligence**: His ideas on machine intelligence and computation have influenced AI research.\n3. **Cryptography**: Turing's contributions to codebreaking and cryptography have had a lasting impact on national security.\n\n**Recognition**\n\nIn 2009, the British government officially apologized for Turing's treatment and posthumously pardoned him. In 2017, he was featured on the £50 note, making him the first openly gay person to be featured on a British banknote.\n\nTuring's life and work serve as a testament to his innovative spirit and contributions to science and society. His legacy continues to inspire new generations of computer scientists, mathematicians, and thinkers."
} ]
}
If you carefully look at the “logs” that the application prints when executing, you’ll notice the steps that Embabel takes to select the Agent to execute, the actions/goals that the Agent contains, and the planning it does for executing the “actions” after the execution of each step.
Conclusion
Although Embabel is still in a development stage, it already demonstrates being a promising proposal for developers working on the JVM. Embabel is developed in Kotlin but, as Rod Johnson has mentioned in interviews, it should be able to be used naturally in Java as can be seen in the code of this example.
Its declarative approach allows creating intelligent agents using annotations, without defining flows explicitly. Instead, an AI algorithm (without using LLMs) infers the execution plan according to the agent’s context and after executing each step. Additionally, it integrates natively with known technologies like Spring and Spring AI, which facilitates its adoption. It also includes support for unit and integration testing, making it suitable for serious projects from the start.
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