> ## Documentation Index
> Fetch the complete documentation index at: https://code.claude.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Stream responses in real-time

> Get real-time responses from the Agent SDK as text and tool calls stream in

By default, the Agent SDK yields complete `AssistantMessage` objects after Claude finishes generating each response. To receive incremental updates as text and tool calls are generated, enable partial message streaming by setting `include_partial_messages` (Python) or `includePartialMessages` (TypeScript) to `true` in your options.

<Tip>
  This page covers output streaming (receiving tokens in real-time). For input modes (how you send messages), see [Send messages to agents](/en/agent-sdk/streaming-vs-single-mode). You can also [stream responses using the Agent SDK via the CLI](/en/headless).
</Tip>

## Enable streaming output

To enable streaming, set `include_partial_messages` (Python) or `includePartialMessages` (TypeScript) to `true` in your options. This causes the SDK to yield `StreamEvent` messages containing raw API events as they arrive, in addition to the usual `AssistantMessage` and `ResultMessage`.

Your code then needs to:

1. Check each message's type to distinguish `StreamEvent` from other message types
2. For `StreamEvent`, extract the `event` field and check its `type`
3. Look for `content_block_delta` events where `delta.type` is `text_delta`, which contain the actual text chunks

The example below enables streaming and prints text chunks as they arrive. Notice the nested type checks: first for `StreamEvent`, then for `content_block_delta`, then for `text_delta`:

<CodeGroup>
  ```python Python theme={null}
  from claude_agent_sdk import query, ClaudeAgentOptions
  from claude_agent_sdk.types import StreamEvent
  import asyncio


  async def stream_response():
      options = ClaudeAgentOptions(
          include_partial_messages=True,
          allowed_tools=["Bash", "Read"],
      )

      async for message in query(prompt="List the files in my project", options=options):
          if isinstance(message, StreamEvent):
              event = message.event
              if event.get("type") == "content_block_delta":
                  delta = event.get("delta", {})
                  if delta.get("type") == "text_delta":
                      print(delta.get("text", ""), end="", flush=True)


  asyncio.run(stream_response())
  ```

  ```typescript TypeScript theme={null}
  import { query } from "@anthropic-ai/claude-agent-sdk";

  for await (const message of query({
    prompt: "List the files in my project",
    options: {
      includePartialMessages: true,
      allowedTools: ["Bash", "Read"]
    }
  })) {
    if (message.type === "stream_event") {
      const event = message.event;
      if (event.type === "content_block_delta") {
        if (event.delta.type === "text_delta") {
          process.stdout.write(event.delta.text);
        }
      }
    }
  }
  ```
</CodeGroup>

## StreamEvent reference

When partial messages are enabled, you receive raw Claude API streaming events wrapped in an object. The type has different names in each SDK:

* **Python**: `StreamEvent` (import from `claude_agent_sdk.types`)
* **TypeScript**: `SDKPartialAssistantMessage` with `type: 'stream_event'`

Both contain raw Claude API events, not accumulated text. You need to extract and accumulate text deltas yourself. Here's the structure of each type:

<CodeGroup>
  ```python Python theme={null}
  @dataclass
  class StreamEvent:
      uuid: str  # Unique identifier for this event
      session_id: str  # Session identifier
      event: dict[str, Any]  # The raw Claude API stream event
      parent_tool_use_id: str | None  # Parent tool ID if from a subagent
  ```

  ```typescript TypeScript theme={null}
  type SDKPartialAssistantMessage = {
    type: "stream_event";
    event: BetaRawMessageStreamEvent; // From Anthropic SDK
    parent_tool_use_id: string | null;
    uuid: UUID;
    session_id: string;
  };
  ```
</CodeGroup>

The `event` field contains the raw streaming event from the [Claude API](https://platform.claude.com/docs/en/build-with-claude/streaming#event-types). Common event types include:

| Event Type            | Description                                     |
| :-------------------- | :---------------------------------------------- |
| `message_start`       | Start of a new message                          |
| `content_block_start` | Start of a new content block (text or tool use) |
| `content_block_delta` | Incremental update to content                   |
| `content_block_stop`  | End of a content block                          |
| `message_delta`       | Message-level updates (stop reason, usage)      |
| `message_stop`        | End of the message                              |

## Message flow

With partial messages enabled, you receive messages in this order:

```text theme={null}
StreamEvent (message_start)
StreamEvent (content_block_start) - text block
StreamEvent (content_block_delta) - text chunks...
StreamEvent (content_block_stop)
StreamEvent (content_block_start) - tool_use block
StreamEvent (content_block_delta) - tool input chunks...
StreamEvent (content_block_stop)
StreamEvent (message_delta)
StreamEvent (message_stop)
AssistantMessage - complete message with all content
... tool executes ...
... more streaming events for next turn ...
ResultMessage - final result
```

Without partial messages enabled (`include_partial_messages` in Python, `includePartialMessages` in TypeScript), you receive all message types except `StreamEvent`. Common types include `SystemMessage` (session initialization), `AssistantMessage` (complete responses), `ResultMessage` (final result), and a compact boundary message indicating when conversation history was compacted (`SDKCompactBoundaryMessage` in TypeScript; `SystemMessage` with subtype `"compact_boundary"` in Python).

## Stream text responses

To display text as it's generated, look for `content_block_delta` events where `delta.type` is `text_delta`. These contain the incremental text chunks. The example below prints each chunk as it arrives:

<CodeGroup>
  ```python Python theme={null}
  from claude_agent_sdk import query, ClaudeAgentOptions
  from claude_agent_sdk.types import StreamEvent
  import asyncio


  async def stream_text():
      options = ClaudeAgentOptions(include_partial_messages=True)

      async for message in query(prompt="Explain how databases work", options=options):
          if isinstance(message, StreamEvent):
              event = message.event
              if event.get("type") == "content_block_delta":
                  delta = event.get("delta", {})
                  if delta.get("type") == "text_delta":
                      # Print each text chunk as it arrives
                      print(delta.get("text", ""), end="", flush=True)

      print()  # Final newline


  asyncio.run(stream_text())
  ```

  ```typescript TypeScript theme={null}
  import { query } from "@anthropic-ai/claude-agent-sdk";

  for await (const message of query({
    prompt: "Explain how databases work",
    options: { includePartialMessages: true }
  })) {
    if (message.type === "stream_event") {
      const event = message.event;
      if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
        process.stdout.write(event.delta.text);
      }
    }
  }

  console.log(); // Final newline
  ```
</CodeGroup>

## Stream tool calls

Tool calls also stream incrementally. You can track when tools start, receive their input as it's generated, and see when they complete. The example below tracks the current tool being called and accumulates the JSON input as it streams in. It uses three event types:

* `content_block_start`: tool begins
* `content_block_delta` with `input_json_delta`: input chunks arrive
* `content_block_stop`: tool call complete

<CodeGroup>
  ```python Python theme={null}
  from claude_agent_sdk import query, ClaudeAgentOptions
  from claude_agent_sdk.types import StreamEvent
  import asyncio


  async def stream_tool_calls():
      options = ClaudeAgentOptions(
          include_partial_messages=True,
          allowed_tools=["Read", "Bash"],
      )

      # Track the current tool and accumulate its input JSON
      current_tool = None
      tool_input = ""

      async for message in query(prompt="Read the README.md file", options=options):
          if isinstance(message, StreamEvent):
              event = message.event
              event_type = event.get("type")

              if event_type == "content_block_start":
                  # New tool call is starting
                  content_block = event.get("content_block", {})
                  if content_block.get("type") == "tool_use":
                      current_tool = content_block.get("name")
                      tool_input = ""
                      print(f"Starting tool: {current_tool}")

              elif event_type == "content_block_delta":
                  delta = event.get("delta", {})
                  if delta.get("type") == "input_json_delta":
                      # Accumulate JSON input as it streams in
                      chunk = delta.get("partial_json", "")
                      tool_input += chunk
                      print(f"  Input chunk: {chunk}")

              elif event_type == "content_block_stop":
                  # Tool call complete - show final input
                  if current_tool:
                      print(f"Tool {current_tool} called with: {tool_input}")
                      current_tool = None


  asyncio.run(stream_tool_calls())
  ```

  ```typescript TypeScript theme={null}
  import { query } from "@anthropic-ai/claude-agent-sdk";

  // Track the current tool and accumulate its input JSON
  let currentTool: string | null = null;
  let toolInput = "";

  for await (const message of query({
    prompt: "Read the README.md file",
    options: {
      includePartialMessages: true,
      allowedTools: ["Read", "Bash"]
    }
  })) {
    if (message.type === "stream_event") {
      const event = message.event;

      if (event.type === "content_block_start") {
        // New tool call is starting
        if (event.content_block.type === "tool_use") {
          currentTool = event.content_block.name;
          toolInput = "";
          console.log(`Starting tool: ${currentTool}`);
        }
      } else if (event.type === "content_block_delta") {
        if (event.delta.type === "input_json_delta") {
          // Accumulate JSON input as it streams in
          const chunk = event.delta.partial_json;
          toolInput += chunk;
          console.log(`  Input chunk: ${chunk}`);
        }
      } else if (event.type === "content_block_stop") {
        // Tool call complete - show final input
        if (currentTool) {
          console.log(`Tool ${currentTool} called with: ${toolInput}`);
          currentTool = null;
        }
      }
    }
  }
  ```
</CodeGroup>

## Build a streaming UI

This example combines text and tool streaming into a cohesive UI. It tracks whether the agent is currently executing a tool (using an `in_tool` flag) to show status indicators like `[Using Read...]` while tools run. Text streams normally when not in a tool, and tool completion triggers a "done" message. This pattern is useful for chat interfaces that need to show progress during multi-step agent tasks.

<CodeGroup>
  ```python Python theme={null}
  from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage
  from claude_agent_sdk.types import StreamEvent
  import asyncio
  import sys


  async def streaming_ui():
      options = ClaudeAgentOptions(
          include_partial_messages=True,
          allowed_tools=["Read", "Bash", "Grep"],
      )

      # Track whether we're currently in a tool call
      in_tool = False

      async for message in query(
          prompt="Find all TODO comments in the codebase", options=options
      ):
          if isinstance(message, StreamEvent):
              event = message.event
              event_type = event.get("type")

              if event_type == "content_block_start":
                  content_block = event.get("content_block", {})
                  if content_block.get("type") == "tool_use":
                      # Tool call is starting - show status indicator
                      tool_name = content_block.get("name")
                      print(f"\n[Using {tool_name}...]", end="", flush=True)
                      in_tool = True

              elif event_type == "content_block_delta":
                  delta = event.get("delta", {})
                  # Only stream text when not executing a tool
                  if delta.get("type") == "text_delta" and not in_tool:
                      sys.stdout.write(delta.get("text", ""))
                      sys.stdout.flush()

              elif event_type == "content_block_stop":
                  if in_tool:
                      # Tool call finished
                      print(" done", flush=True)
                      in_tool = False

          elif isinstance(message, ResultMessage):
              # Agent finished all work
              print(f"\n\n--- Complete ---")


  asyncio.run(streaming_ui())
  ```

  ```typescript TypeScript theme={null}
  import { query } from "@anthropic-ai/claude-agent-sdk";

  // Track whether we're currently in a tool call
  let inTool = false;

  for await (const message of query({
    prompt: "Find all TODO comments in the codebase",
    options: {
      includePartialMessages: true,
      allowedTools: ["Read", "Bash", "Grep"]
    }
  })) {
    if (message.type === "stream_event") {
      const event = message.event;

      if (event.type === "content_block_start") {
        if (event.content_block.type === "tool_use") {
          // Tool call is starting - show status indicator
          process.stdout.write(`\n[Using ${event.content_block.name}...]`);
          inTool = true;
        }
      } else if (event.type === "content_block_delta") {
        // Only stream text when not executing a tool
        if (event.delta.type === "text_delta" && !inTool) {
          process.stdout.write(event.delta.text);
        }
      } else if (event.type === "content_block_stop") {
        if (inTool) {
          // Tool call finished
          console.log(" done");
          inTool = false;
        }
      }
    } else if (message.type === "result") {
      // Agent finished all work
      console.log("\n\n--- Complete ---");
    }
  }
  ```
</CodeGroup>

## Known limitations

Some SDK features are incompatible with streaming:

* **Extended thinking**: when you explicitly set `max_thinking_tokens` (Python) or `maxThinkingTokens` (TypeScript), `StreamEvent` messages are not emitted. You'll only receive complete messages after each turn. Note that thinking is disabled by default in the SDK, so streaming works unless you enable it.
* **Structured output**: the JSON result appears only in the final `ResultMessage.structured_output`, not as streaming deltas. See [structured outputs](/en/agent-sdk/structured-outputs) for details.

## Next steps

Now that you can stream text and tool calls in real-time, explore these related topics:

* [Interactive vs one-shot queries](/en/agent-sdk/streaming-vs-single-mode): choose between input modes for your use case
* [Structured outputs](/en/agent-sdk/structured-outputs): get typed JSON responses from the agent
* [Permissions](/en/agent-sdk/permissions): control which tools the agent can use
