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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.

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:

Check each message's type to distinguish StreamEvent from other message types
For StreamEvent, extract the event field and check its type
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:

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())

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);
}
}
}
}

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:

@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

type SDKPartialAssistantMessage = {
type: "stream_event";
event: RawMessageStreamEvent; // From Anthropic SDK
parent_tool_use_id: string | null;
uuid: UUID;
session_id: string;
};

The event field contains the raw streaming event from the Claude API. 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:

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:

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())

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

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

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())

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;
}
}
}
}

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.

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())

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 ---");
}
}

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 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: choose between input modes for your use case
Structured outputs: get typed JSON responses from the agent
Permissions: control which tools the agent can use

agent-sdk/streaming-output.md 2026-04-09 21:14 UTC to 2026-04-10 21:09 UTC