> ## Documentation Index
> Fetch the complete documentation index at: https://ekacare-fix-ts-sdk-github-npm-links.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Streaming

> Real-time response streaming with typed events

Stream responses in real-time with typed events.

## StreamEventType

| Event             | Description          |
| ----------------- | -------------------- |
| `TEXT`            | Text content chunk   |
| `TOOL_CALL_START` | Agent calling a tool |
| `TOOL_CALL_END`   | Tool call completed  |
| `DONE`            | Stream completed     |
| `ERROR`           | Error occurred       |

## Basic Streaming

```python theme={null}
from echo import get_llm, LLMConfig, StreamEventType, ConversationContext

llm = get_llm(LLMConfig(provider="openai", model="gpt-4o-mini"))
context = ConversationContext()

# Add user message
context.add_message(Message(
    role=MessageRole.USER,
    content=[TextMessage(text="Explain diabetes briefly")],
))

# Stream response
async for event in llm.invoke_stream(context, system_prompt="You are helpful."):
    if event.type == StreamEventType.TEXT:
        print(event.text, end="", flush=True)
    elif event.type == StreamEventType.TOOL_CALL_START:
        print(f"\n[Tool: {event.json.get('tool_name')}]", end="")
    elif event.type == StreamEventType.TOOL_CALL_END:
        print(" ✓")
    elif event.type == StreamEventType.DONE:
        print("\n--- Done ---")
        final_response = event.llm_response
        context = event.context
    elif event.type == StreamEventType.ERROR:
        print(f"\n[Error: {event.error}]")
```

## Streaming with Tools

```python theme={null}
from echo_agents.tools.elicitation import SelectionElicitationTool

llm = get_llm(LLMConfig(provider="openai", model="gpt-4o-mini"))
tools = [SelectionElicitationTool()]

async for event in llm.invoke_stream(context, tools=tools, system_prompt=prompt):
    if event.type == StreamEventType.TEXT:
        print(event.text, end="", flush=True)
    elif event.type == StreamEventType.TOOL_CALL_START:
        print(f"\n🔧 {event.json.get('tool_name')}")
    elif event.type == StreamEventType.TOOL_CALL_END:
        print("   ✓ Done")
    elif event.type == StreamEventType.DONE:
        # Check for elicitations
        if event.llm_response.elicitations:
            elicit = event.llm_response.elicitations[0]
            print(f"\n📋 {elicit.details.input.get('text')}")
            for opt in elicit.details.input.get('options', []):
                print(f"   • {opt}")
```

## DONE Event

The `DONE` event contains:

```python theme={null}
elif event.type == StreamEventType.DONE:
    response = event.llm_response

    # Final text
    print(response.text)

    # Verbose tool calls
    for item in response.verbose:
        if item.type == "tool":
            print(f"Tool used: {item.tool_name}")

    # Elicitations (UI components)
    for elicit in response.elicitations:
        print(f"UI: {elicit.details.component}")

    # Updated context for next turn
    context = event.context
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Examples" icon="lightbulb" href="/ai-tools/agent-kit/examples">
    Complete streaming examples
  </Card>

  <Card title="MCP Integration" icon="plug" href="/ai-tools/agent-kit/mcp-integration">
    Stream with MCP tools
  </Card>
</CardGroup>