---
title: Workflow Agent
weight: 1
type: docs
---
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## Overview

A workflow style agent in Flink-Agents is an agent whose reasoning and behavior are organized as a directed workflow of modular steps, called actions, connected by events. This design is inspired by the need to orchestrate complex, multi-stage tasks in a transparent, extensible, and data-centric way, leveraging Apache Flink's streaming architecture.

In Flink-Agents, a workflow agent is defined as a class that inherits from the `Agent` base class. The agent's logic is expressed as a set of actions, each of which is a function decorated with `@action(EventType)` in python (or a method annotated with `@Action(listenEventTypes = {})` in java). Actions consume events, perform reasoning or tool calls, and emit new events, which may trigger downstream actions. This event-driven workflow forms a directed cyclic graph of computation, where each node is an action and each edge is an event type.

A workflow agent is well-suited for scenarios where the solution requires explicit orchestration, branching, or multi-step reasoning, such as data enrichment, multi-tool pipelines, or complex business logic.

{{< hint info >}}
For guidance on choosing Java or Python, see [Should I choose Java or Python?]({{< ref "docs/faq/faq#q3-should-i-choose-java-or-python" >}}).
{{< /hint >}}

## Workflow Agent Example

{{< tabs "Workflow Agent Example" >}}

{{< tab "Python" >}}
```python
class ReviewAnalysisAgent(Agent):
    """An agent that uses a large language model (LLM) to analyze product reviews
    and generate a satisfaction score and potential reasons for dissatisfaction.

    This agent receives a product review and produces a satisfaction score and a list
    of reasons for dissatisfaction. It handles prompt construction, LLM interaction,
    and output parsing.
    """

    @prompt
    @staticmethod
    def review_analysis_prompt() -> Prompt:
        """Prompt for review analysis."""
        return review_analysis_prompt

    @tool
    @staticmethod
    def notify_shipping_manager(id: str, review: str) -> None:
        """Notify the shipping manager when product received a negative review due to
        shipping damage.

        Parameters
        ----------
        id : str
            The id of the product that received a negative review due to shipping damage
        review: str
            The negative review content
        """
        # reuse the declared function, but for parsing the tool metadata, we write doc
        # string here again.
        notify_shipping_manager(id=id, review=review)

    @chat_model_setup
    @staticmethod
    def review_analysis_model() -> ResourceDescriptor:
        """ChatModel which focus on review analysis."""
        return ResourceDescriptor(
            clazz=ResourceName.ChatModel.OLLAMA_SETUP,
            connection="ollama_server",
            model="qwen3:8b",
            prompt="review_analysis_prompt",
            tools=["notify_shipping_manager"],
            extract_reasoning=True,
        )

    @action(InputEvent.EVENT_TYPE)
    @staticmethod
    def process_input(event: Event, ctx: RunnerContext) -> None:
        """Process input event and send chat request for review analysis."""
        input_event = InputEvent.from_event(event)
        input: ProductReview = input_event.input
        ctx.short_term_memory.set("id", input.id)

        content = f"""
            "id": {input.id},
            "review": {input.review}
        """
        msg = ChatMessage(role=MessageRole.USER)
        ctx.send_event(
            ChatRequestEvent(
                model="review_analysis_model",
                messages=[msg],
                prompt_args={"input": content},
            )
        )

    @action(ChatResponseEvent.EVENT_TYPE)
    @staticmethod
    def process_chat_response(event: Event, ctx: RunnerContext) -> None:
        """Process chat response event and send output event."""
        chat_response = ChatResponseEvent.from_event(event)
        try:
            json_content = json.loads(chat_response.response.content)
            ctx.send_event(
                OutputEvent(
                    output=ProductReviewAnalysisRes(
                        id=ctx.short_term_memory.get("id"),
                        score=json_content["score"],
                        reasons=json_content["reasons"],
                    )
                )
            )
        except Exception:
            logging.exception(
                f"Error processing chat response {chat_response.response.content}"
            )

            # To fail the agent, you can raise an exception here.
```
{{< /tab >}}

{{< tab "Java" >}}
```java
/**
 * An agent that uses a large language model (LLM) to analyze product reviews and generate a
 * satisfaction score and potential reasons for dissatisfaction.
 *
 * <p>This agent receives a product review and produces a satisfaction score and a list of reasons
 * for dissatisfaction. It handles prompt construction, LLM interaction, and output parsing.
 */
public class ReviewAnalysisAgent extends Agent {

    private static final ObjectMapper MAPPER = new ObjectMapper();

    @Prompt
    public static org.apache.flink.agents.api.prompt.Prompt reviewAnalysisPrompt() {
        return REVIEW_ANALYSIS_PROMPT;
    }

    @ChatModelSetup
    public static ResourceDescriptor reviewAnalysisModel() {
        return ResourceDescriptor.Builder.newBuilder(ResourceName.ChatModel.OLLAMA_SETUP)
                .addInitialArgument("connection", "ollamaChatModelConnection")
                .addInitialArgument("model", "qwen3:8b")
                .addInitialArgument("prompt", "reviewAnalysisPrompt")
                .addInitialArgument("tools", Collections.singletonList("notifyShippingManager"))
                .addInitialArgument("extract_reasoning", true)
                .build();
    }

    /**
     * Tool for notifying the shipping manager when product received a negative review due to
     * shipping damage.
     *
     * @param id The id of the product that received a negative review due to shipping damage
     * @param review The negative review content
     */
    @Tool(
            description =
                    "Notify the shipping manager when product received a negative review due to shipping damage.")
    public static void notifyShippingManager(
            @ToolParam(name = "id") String id, @ToolParam(name = "review") String review) {
        CustomTypesAndResources.notifyShippingManager(id, review);
    }

    /** Process input event and send chat request for review analysis. */
    @Action(listenEventTypes = {InputEvent.EVENT_TYPE})
    public static void processInput(Event event, RunnerContext ctx) throws Exception {
        InputEvent inputEvent = InputEvent.fromEvent(event);
        String input = (String) inputEvent.getInput();
        MAPPER.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
        CustomTypesAndResources.ProductReview inputObj =
                MAPPER.readValue(input, CustomTypesAndResources.ProductReview.class);

        ctx.getShortTermMemory().set("id", inputObj.getId());

        String content =
                String.format(
                        "{\n" + "\"id\": %s,\n" + "\"review\": \"%s\"\n" + "}",
                        inputObj.getId(), inputObj.getReview());
        ChatMessage msg = new ChatMessage(MessageRole.USER, "");

        ctx.sendEvent(
                new ChatRequestEvent(
                        "reviewAnalysisModel", List.of(msg), Map.of("input", content), null));
    }

    @Action(listenEventTypes = {ChatResponseEvent.EVENT_TYPE})
    public static void processChatResponse(Event event, RunnerContext ctx)
            throws Exception {
        ChatResponseEvent chatResponse = ChatResponseEvent.fromEvent(event);
        JsonNode jsonNode = MAPPER.readTree(chatResponse.getResponse().getContent());
        JsonNode scoreNode = jsonNode.findValue("score");
        JsonNode reasonsNode = jsonNode.findValue("reasons");
        if (scoreNode == null || reasonsNode == null) {
            throw new IllegalStateException(
                    "Invalid response from LLM: missing 'score' or 'reasons' field.");
        }
        List<String> result = new ArrayList<>();
        if (reasonsNode.isArray()) {
            for (JsonNode node : reasonsNode) {
                result.add(node.asText());
            }
        }

        ctx.sendEvent(
                new OutputEvent(
                        new CustomTypesAndResources.ProductReviewAnalysisRes(
                                ctx.getShortTermMemory().get("id").getValue().toString(),
                                scoreNode.asInt(),
                                result)));
    }
}
```
{{< /tab >}}

{{< /tabs >}}

## Action

An action is a piece of code that can be executed. Each action listens to at least one type of event. When an event of the listening type occurs, the action will be triggered. An action can also generate new events, to trigger other actions.


To declare an action in Agent, user can use `@action` to decorate a function of Agent class in python (or annotate a method of Agent class in java), and declare the listened event types as decorator/annotation parameters. 

The decorated/annotated function signature should be `(Event, RunnerContext) -> None`. In Python, actions can also be defined as `async def` when using async execution (see [Async Execution](#async-execution)).

{{< tabs "Action Function" >}}

{{< tab "Python" >}}
```python
class ReviewAnalysisAgent(Agent):
    @action(InputEvent.EVENT_TYPE)
    @staticmethod
    def process_input(event: Event, ctx: RunnerContext) -> None:
        # the action logic
```
{{< /tab >}}

{{< tab "Java" >}}
```java
public class ReviewAnalysisAgent extends Agent {
    /** Process input event and send chat request for review analysis. */
    @Action(listenEventTypes = {InputEvent.EVENT_TYPE})
    public static void processInput(Event event, RunnerContext ctx) throws Exception {
        InputEvent inputEvent = InputEvent.fromEvent(event);
        // the action logic
    }
}
```
{{< /tab >}}

{{< /tabs >}}

In the function, user can also send new events, to trigger other actions, or output the data.

**Trigger another action** — send a built-in or custom event that another action listens to:

{{< tabs "Trigger Another Action" >}}

{{< tab "Python" >}}
```python
@action(InputEvent.EVENT_TYPE)
@staticmethod
def process_input(event: Event, ctx: RunnerContext) -> None:
    # send a ChatRequestEvent to trigger the built-in chat-model action
    ctx.send_event(ChatRequestEvent(model="my_model", messages=messages))
```
{{< /tab >}}

{{< tab "Java" >}}
```java
@Action(listenEventTypes = {InputEvent.EVENT_TYPE})
public static void processInput(Event event, RunnerContext ctx) {
    // send a ChatRequestEvent to trigger the built-in chat-model action
    ctx.sendEvent(new ChatRequestEvent("my_model", messages));
}
```
{{< /tab >}}

{{< /tabs >}}

**Emit downstream output** — send an `OutputEvent` to produce an output of the agent:

{{< tabs "Emit Output" >}}

{{< tab "Python" >}}
```python
@action(ChatResponseEvent.EVENT_TYPE)
@staticmethod
def emit_output(event: Event, ctx: RunnerContext) -> None:
    # output data to downstream
    ctx.send_event(OutputEvent(output=result))
```
{{< /tab >}}

{{< tab "Java" >}}
```java
@Action(listenEventTypes = {ChatResponseEvent.EVENT_TYPE})
public static void emitOutput(Event event, RunnerContext ctx) {
    // output data to downstream
    ctx.sendEvent(new OutputEvent(result));
}
```
{{< /tab >}}

{{< /tabs >}}

{{< hint info >}}
An `OutputEvent` is collected and emitted to the agent's downstream **immediately**, bypassing
action routing, while other events (such as `ChatRequestEvent`) are routed to the actions that
listen for them. Sending a `ChatRequestEvent` and an `OutputEvent` from the same action is valid
API usage, but it produces both an immediate output and, once the chat response is handled, a
later model-based output. For the normal chat request/response workflow, emit the `OutputEvent`
from the action that handles the `ChatResponseEvent`, as shown above.
{{< /hint >}}

### Durable Execution

Use durable execution when you wrap a time-consuming or side-effecting operation. The framework persists the result and replays it on recovery when the same call is encountered, so the function will not be called again and side effects are avoided. When recovery re-enters an action that has not been recorded as completed, code outside `durable_execute` / `durable_execute_async` will still be re-executed.

**Constraints:**
- The function must be deterministic and called in the same order on recovery.
- Access to Memory and `send_event` is prohibited inside the function/callable.
- Arguments and results must be serializable.

{{< hint info >}}
Durable execution requires an external action state store. See
[Exactly-Once Action Consistency]({{< ref "docs/operations/deployment#exactly-once-action-consistency" >}})
on how to setup and configure the external action state store.
{{< /hint >}}

**Best-effort replay:**
- Results may not be reused if call order or arguments change (non-deterministic actions), which clears subsequent cached results and re-executes.
- If a failure happens after a function starts but before it completes and its result is persisted, the call will be re-executed. See the "With a reconciler" section below.
- In Python async actions, if `ctx.durable_execute_async(...)` is not awaited, the result is not recorded and cannot be replayed.

**With a reconciler:**

Use a reconciler for durable calls when the original call may already have completed but its result or failure has not yet been persisted, so the framework cannot determine during recovery whether the call needs to be executed again. A reconciler provides custom logic that can return the result or raise the failure for the durable call instead of re-executing the original call.

- A durable call may optionally provide a reconciler that is used only during recovery, when the same durable call is revisited and no execution result has been persisted for it yet.
- If the reconciler logic returns a result, the runtime persists and replays that recovered result.
- If the reconciler logic raises an exception, the runtime persists and replays that recovered failure.

{{< tabs "Durable Execution" >}}
{{< tab "Python" >}}
Python actions can call `ctx.durable_execute(...)` to run a synchronous durable code block.
```python
@action(InputEvent.EVENT_TYPE)
@staticmethod
def process_input(event: Event, ctx: RunnerContext) -> None:
    input_event = InputEvent.from_event(event)
    def slow_external_call(data: str) -> str:
        time.sleep(2)
        return f"Processed: {data}"

    # Synchronous durable execution
    result = ctx.durable_execute(slow_external_call, input_event.input)
    ctx.send_event(OutputEvent(output=result))
```

You can also pass an optional `reconciler` callable to recover an execution outcome during recovery.
```python
@action(InputEvent.EVENT_TYPE)
@staticmethod
def process_input(event: Event, ctx: RunnerContext) -> None:
    input_event = InputEvent.from_event(event)

    def submit_payment(order_id: str) -> str:
        return payment_client.submit(order_id)

    def payment_reconciler() -> str:
        status = payment_client.get_status(input_event.input)
        if status == "SUCCEEDED":
            return payment_client.lookup_completed_payment(input_event.input)
        raise payment_client.get_failure(input_event.input)

    result = ctx.durable_execute(
        submit_payment,
        input_event.input,
        reconciler=payment_reconciler,
    )
    ctx.send_event(OutputEvent(output=result))
```
{{< /tab >}}

{{< tab "Java" >}}
Java actions use `DurableCallable<T>` with `ctx.durableExecute(...)`, where `getId()` must be stable and `getResultClass()` supports recovery deserialization.
```java
@Action(listenEventTypes = {InputEvent.EVENT_TYPE})
public static void processInput(Event event, RunnerContext ctx) throws Exception {
    InputEvent inputEvent = InputEvent.fromEvent(event);
    DurableCallable<String> call = new DurableCallable<>() {
        @Override
        public String getId() {
            return "slow_external_call";
        }

        @Override
        public Class<String> getResultClass() {
            return String.class;
        }

        @Override
        public String call() throws Exception {
            Thread.sleep(2000);
            return "Processed: " + inputEvent.getInput();
        }
    };

    String result = ctx.durableExecute(call);
    ctx.sendEvent(new OutputEvent(result));
}
```

Java actions can also override `reconciler()` to recover an execution outcome during recovery.
```java
@Action(listenEventTypes = {InputEvent.EVENT_TYPE})
public static void processInput(Event event, RunnerContext ctx) throws Exception {
    InputEvent inputEvent = InputEvent.fromEvent(event);
    DurableCallable<String> call = new DurableCallable<>() {
        @Override
        public String getId() {
            return "submit_payment";
        }

        @Override
        public Class<String> getResultClass() {
            return String.class;
        }

        @Override
        public String call() {
            return paymentClient.submit(inputEvent.getInput());
        }

        @Override
        public Callable<String> reconciler() {
            return () -> {
                PaymentStatus status =
                    paymentClient.getStatus(inputEvent.getInput());
                if (status == PaymentStatus.SUCCEEDED) {
                    return paymentClient.lookupCompletedPayment(
                        inputEvent.getInput());
                }
                throw paymentClient.getFailure(inputEvent.getInput());
            };
        }
    };

    String result = ctx.durableExecute(call);
    ctx.sendEvent(new OutputEvent(result));
}
```
{{< /tab >}}
{{< /tabs >}}

### Async Execution

Async execution uses the same durable semantics but yields while waiting for a thread-pool task. This is useful for high-latency I/O.

{{< tabs "Async Execution" >}}
{{< tab "Python" >}}
Define an `async def` action and `await ctx.durable_execute_async(...)`. The same optional `reconciler=...` argument is available for recovery.
```python
@action(InputEvent.EVENT_TYPE)
@staticmethod
async def process_with_async(event: Event, ctx: RunnerContext) -> None:
    input_event = InputEvent.from_event(event)
    def slow_external_call(data: str) -> str:
        time.sleep(2)
        return f"Processed: {data}"

    result = await ctx.durable_execute_async(slow_external_call, input_event.input)
    ctx.send_event(OutputEvent(output=result))
```
{{< hint info >}}
Python async actions only support `await ctx.durable_execute_async(...)`. Standard asyncio
functions like `asyncio.gather`, `asyncio.wait`, `asyncio.create_task`, and
`asyncio.sleep` are **NOT** supported because there is no asyncio event loop.
{{< /hint >}}
{{< /tab >}}

{{< tab "Java" >}}
Use `ctx.durableExecuteAsync(DurableCallable)`; on **JDK 21+** it yields using Continuation,
and on **JDK < 21** it falls back to synchronous execution. The same optional `reconciler()` hook can be used for recovery.
```java
@Action(listenEventTypes = {InputEvent.EVENT_TYPE})
public static void processInput(Event event, RunnerContext ctx) throws Exception {
    InputEvent inputEvent = InputEvent.fromEvent(event);
    DurableCallable<String> call = new DurableCallable<>() {
        @Override
        public String getId() {
            return "slow_external_call";
        }

        @Override
        public Class<String> getResultClass() {
            return String.class;
        }

        @Override
        public String call() throws Exception {
            Thread.sleep(2000);
            return "Processed: " + inputEvent.getInput();
        }
    };

    String result = ctx.durableExecuteAsync(call);
    ctx.sendEvent(new OutputEvent(result));
}
```

{{< hint info >}}
To use async execution on JDK 21+, user should append jvm option `--add-exports=java.base/jdk.internal.vm=ALL-UNNAMED` to [env.java.opts.all](https://nightlies.apache.org/flink/flink-docs-stable/docs/deployment/config/#env-java-opts-all) before start the flink cluster.
{{< /hint >}}
{{< /tab >}}
{{< /tabs >}}

### Cross-language Actions

An action declared in one language can dispatch its body to the other language by setting a `target` on the decorator/annotation. The decorated function or annotated method then acts as a stub — it should raise so direct calls outside the framework fail loud.

{{< tabs "Cross-language Actions" >}}

{{< tab "Python" >}}
```python
from flink_agents.api.function import JavaFunction

class MyAgent(Agent):
    @action(
        InputEvent.EVENT_TYPE,
        # Action signatures are fixed (Event, RunnerContext), so for_action
        # fills the Java parameter types for you — only the class and method.
        target=JavaFunction.for_action("com.example.MyHandlers", "handleInput"),
    )
    @staticmethod
    def handle_input(event: Event, ctx: RunnerContext) -> None:
        raise NotImplementedError("cross-language stub")
```
{{< /tab >}}

{{< tab "Java" >}}
```java
public class MyAgent extends Agent {
    @Action(
            listenEventTypes = {InputEvent.EVENT_TYPE},
            target = @PythonFunction(
                    module = "my_pkg.handlers",
                    qualname = "handle_input"))
    public static void handleInput(Event event, RunnerContext ctx) {
        throw new UnsupportedOperationException("cross-language stub");
    }
}
```
{{< /tab >}}

{{< /tabs >}}

{{< hint warning >}}
**Limitations:**

- Cross-language actions are currently supported only when [running in Flink]({{< ref "docs/operations/deployment#run-in-flink" >}}), not in local development mode
- Complex object serialization between languages may have limitations
{{< /hint >}}

## Event

Events are JSON-serializable messages passed between actions. Every event has a `type` string used for routing and an `attributes` map that carries the payload. A single event may trigger multiple actions if they are all listening to its type.

### Special Events

* `InputEvent`: Generated by the framework, carrying an input data record that arrives at the agent in its `input` attribute. Actions listening to `InputEvent` are the entry points of the agent.
* `OutputEvent`: The framework listens to `OutputEvent` and converts its `output` attribute into outputs of the agent.

### Unified Event

For simple cases, users can pass data between actions directly using `Event` with a custom `type` and `attributes`, without needing to define a subclass. For more structured events, see [Custom Event Subclasses](#custom-event-subclasses) below.

{{< tabs "Unified Event" >}}

{{< tab "Python" >}}
```python
# Send a unified event from one action
@action(InputEvent.EVENT_TYPE)
@staticmethod
def create_my_event(event: Event, ctx: RunnerContext) -> None:
    ctx.send_event(
        Event(type="my_event", attributes={"field1": "test", "field2": 42})
    )

# Consume it in another action
@action("my_event")
@staticmethod
def handle_my_event(event: Event, ctx: RunnerContext) -> None:
    field1: str = event.get_attr("field1")
    field2: int = event.get_attr("field2")
```
{{< /tab >}}

{{< tab "Java" >}}
```java
// Send a unified event from one action
@Action(listenEventTypes = {InputEvent.EVENT_TYPE})
public static void createMyEvent(Event event, RunnerContext ctx) {
    ctx.sendEvent(new Event("my_event", Map.of("field1", "test", "field2", 42)));
}

// Consume it in another action
@Action(listenEventTypes = {"my_event"})
public static void handleMyEvent(Event event, RunnerContext ctx) {
    String field1 = (String) event.getAttr("field1");
    int field2 = (int) event.getAttr("field2");
}
```
{{< /tab >}}

{{< /tabs >}}

### JSON Serialization

Events are serialized as JSON when passed between Python actions or across the Java-Python boundary. This means attribute values of non-trivial types (such as Pydantic models) lose their type information and arrive as plain `dict` objects. Users must manually reconstruct the typed object:

```python
input_event = InputEvent.from_event(event)
input_data = ItemData.model_validate(input_event.input)
```

### Custom Event Subclasses

Users can also define custom event subclasses for reusable, structured events. Data should be stored in the `attributes` map, and the subclass must implement a `from_event` / `fromEvent` factory method that validates required attributes and reconstructs typed objects from the deserialized data.

{{< tabs "Custom Event" >}}

{{< tab "Python" >}}
```python
class MyEvent(Event):
    EVENT_TYPE: ClassVar[str] = "my_event"

    def __init__(self, value: str) -> None:
        super().__init__(type=MyEvent.EVENT_TYPE, attributes={"value": value})

    @classmethod
    @override
    def from_event(cls, event: Event) -> "MyEvent":
        assert "value" in event.attributes
        result = MyEvent(value=event.attributes["value"])
        # Preserve the base event id. Assign it last: the content-based id is
        # regenerated whenever another field changes.
        result.id = event.id
        return result

    @property
    def value(self) -> str:
        return self.get_attr("value")
```
{{< /tab >}}

{{< tab "Java" >}}
```java
public class MyEvent extends Event {
    public static final String EVENT_TYPE = "my_event";

    public MyEvent(String value) {
        super(EVENT_TYPE);
        setAttr("value", value);
    }

    @JsonCreator
    public MyEvent(
            @JsonProperty("id") UUID id,
            @JsonProperty("attributes") Map<String, Object> attributes) {
        super(id, EVENT_TYPE, attributes);
    }

    public static MyEvent fromEvent(Event event) {
        // Preserve the base event id (and sourceTimestamp) so event logs, listeners,
        // correlation, deduplication, and timestamp propagation stay consistent.
        MyEvent result = new MyEvent(event.getId(), new HashMap<>(event.getAttributes()));
        if (event.hasSourceTimestamp()) {
            result.setSourceTimestamp(event.getSourceTimestamp());
        }
        return result;
    }

    public String getValue() {
        return (String) getAttr("value");
    }
}
```
{{< /tab >}}

{{< /tabs >}}

{{< hint info >}}
When reconstructing a typed event, preserve the base `Event` metadata so that event logs,
listeners, correlation, deduplication, and downstream timestamp propagation stay consistent with
built-in events:

- **`id`**: copy the source event's `id` onto the reconstructed event, as all built-in events do
  in both languages. In Python, assign `result.id = event.id` **last**, because the content-based
  `id` is regenerated whenever any other field changes.
- **`sourceTimestamp`** (Java only): carry it over with `setSourceTimestamp(...)` when
  `hasSourceTimestamp()` is true, matching built-in Java events. This field is runtime-internal and
  used for timestamp propagation; the Python `Event` has no equivalent.
{{< /hint >}}

{{< hint info >}}
All attribute values must be JSON-serializable. In Python, this means `BaseModel`-serializable or primitive types. In Java, values must be Jackson-serializable.
{{< /hint >}}

## Built-in Events and Actions

There are several built-in `Event` and `Action` in Flink-Agents:
* See [Chat Models]({{< ref "docs/development/chat_models#built-in-events-and-actions" >}}) for how to chat with a LLM leveraging built-in action and events.
* See [Tool Use]({{< ref "docs/development/tool_use#built-in-events-and-actions" >}}) for how to programmatically use a tool leveraging built-in action and events.
* See [Vector Stores]({{< ref "docs/development/vector_stores#built-in-events-and-actions" >}}) for how to retrieve context from vector stores leveraging built-in action and events.