ReAct Agent

Overview

ReAct Agent is a general paradigm that combines reasoning and action capabilities to solve complex tasks. Leveraging this paradigm, the user only needs to specify the goal with prompt and provide available tools, and the LLM will decide how to achieve the goal and take actions autonomously

For guidance on choosing Java or Python, see Should I choose Java or Python?.

ReAct Agent Example

Python

my_react_agent = ReActAgent(
    chat_model=chat_model_descriptor,
    prompt=my_prompt,
    output_schema=MyBaseModelDataType, # or output_schema=my_row_type_info
)

Java

ReActAgent myReActAgent =
        new ReActAgent(
                chatModelDescriptor,
                myPrompt,
                MyBaseModelDataType.class
                // or myRowTypeInfo
        );

Initialize Arguments

Chat Model

User should specify the chat model used in the ReAct Agent.

We use ResourceDescriptor to describe the chat model, includes chat model type and chat model arguments. See Chat Model for more details.

Python

chat_model_descriptor = ResourceDescriptor(
    clazz=ResourceName.ChatModel.OLLAMA_SETUP,
    connection="my_ollama_connection",
    model="qwen3:8b",
    tools=["my_tool1", "my_tool2"],
)

Java

ResourceDescriptor chatModelDescriptor =
                ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_SETUP)
                        .addInitialArgument("connection", "myOllamaConnection")
                        .addInitialArgument("model", "qwen3:8b")
                        .addInitialArgument(
                                "tools", List.of("myTool1", "myTool2"))
                        .build();

Prompt

User can provide prompt to instruct agent.

The prompt should contain two messages. The first message tells the agent what to do, and gives the input and output example. The second message tells how to convert input element to text string.

Python

system_prompt_str = """
    Analyze
    ...

    Example input format:
    ...

    Ensure your response can be parsed by Python JSON, using this format as an example:
    ...
    """

# Prompt for review analysis react agent.
my_prompt = Prompt.from_messages(
    messages=[
        ChatMessage(
            role=MessageRole.SYSTEM,
            content=system_prompt_str,
        ),
        # For react agent, if the input element is not primitive types,
        # framework will deserialize input element to dict and fill the prompt.
        # Note, the input element should be primitive types, BaseModel or Row.
        ChatMessage(
            role=MessageRole.USER,
            content="""
            "id": {id},
            "review": {review}
            """,
        ),
    ],
)

Java

String systemPromptString =
        "Analyze ..."
                + "Example input format:\n"
                + "..."
                + "Ensure your response can be parsed by Java JSON, using this format as an example:\n"
                + "...";

// Prompt for review analysis react agent.
Prompt myPrompt = Prompt.fromMessages(
        Arrays.asList(
                new ChatMessage(MessageRole.SYSTEM, systemPromptString),
                new ChatMessage(
                        MessageRole.USER,
                        "{\"id\": \"{id}\",\n" + "\"review\": \"{review}\"}")));

If the input element is primitive types, like int, str and so on, the second message should be

Python

ChatMessage(
    role=MessageRole.USER,
    content="{input}"
)

Java

new ChatMessage(MessageRole.USER, "{input}")

See Prompt for more details.

Output Schema

User can set output schema to configure the ReAct Agent output type. If output schema is set, the ReAct Agent will deserialize the llm response to expected type.

The output schema should be BaseModel or RowTypeInfo.

Python

class MyBaseModelDataType(BaseModel):
    id: str
    score: int
    reasons: list[str]

# Currently, for RowTypeInfo, only support BasicType fields.
my_row_type_info = RowTypeInfo(
        [BasicTypeInfo.STRING_TYPE_INFO(), BasicTypeInfo.INT_TYPE_INFO()],
        ["id", "score"],
    )

Java

@JsonSerialize
@JsonDeserialize
public static class MyBaseModelDataType {
    private final String id;
    private final int score;
    private final List<String> reasons;

    @JsonCreator
    public MyBaseModelDataType(
            @JsonProperty("id") String id,
            @JsonProperty("score") int score,
            @JsonProperty("reasons") List<String> reasons) {
        this.id = id;
        this.score = score;
        this.reasons = reasons;
    }

    public MyBaseModelDataType() {
        id = null;
        score = 0;
        reasons = List.of();
    }

    public String getId() {
        return id;
    }

    public int getScore() {
        return score;
    }

    public List<String> getReasons() {
        return reasons;
    }

    @Override
    public String toString() {
        return String.format(
                "MyBaseModelDataType{id='%s', score=%d, reasons=%s}", id, score, reasons);
    }
}

// Currently, for RowTypeInfo, only support BasicType fields.
RowTypeInfo myRowTypeInfo =
        new RowTypeInfo(
                new TypeInformation[] {
                        BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.INT_TYPE_INFO
                },
                new String[] {"id", "score"});

Overview

ReAct Agent Example

Initialize Arguments

Chat Model

Prompt

Output Schema

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