Stream responses in real-time
Dapatkan respons real-time dari Agent SDK saat teks dan tool calls streaming masuk
Secara default, Agent SDK menghasilkan objek AssistantMessage lengkap setelah Claude selesai menghasilkan setiap respons. Untuk menerima pembaruan inkremental saat teks dan tool calls dihasilkan, aktifkan partial message streaming dengan mengatur include_partial_messages (Python) atau includePartialMessages (TypeScript) ke true dalam opsi Anda.
Halaman ini mencakup output streaming (menerima token secara real-time). Untuk input modes (cara Anda mengirim pesan), lihat Kirim pesan ke agents. Anda juga dapat stream responses using the Agent SDK via the CLI.
Enable streaming output
Untuk mengaktifkan streaming, atur include_partial_messages (Python) atau includePartialMessages (TypeScript) ke true dalam opsi Anda. Ini menyebabkan SDK menghasilkan pesan StreamEvent yang berisi raw API events saat tiba, selain AssistantMessage dan ResultMessage yang biasa.
Kode Anda kemudian perlu:
- Memeriksa tipe setiap pesan untuk membedakan
StreamEventdari tipe pesan lainnya - Untuk
StreamEvent, ekstrak fieldeventdan periksatype-nya - Cari event
content_block_deltadi manadelta.typeadalahtext_delta, yang berisi chunk teks aktual
Contoh di bawah mengaktifkan streaming dan mencetak chunk teks saat tiba. Perhatikan pemeriksaan tipe bersarang: pertama untuk StreamEvent, kemudian untuk content_block_delta, kemudian untuk 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
Ketika partial messages diaktifkan, Anda menerima raw Claude API streaming events yang dibungkus dalam objek. Tipe memiliki nama berbeda di setiap SDK:
- Python:
StreamEvent(import dariclaude_agent_sdk.types) - TypeScript:
SDKPartialAssistantMessagedengantype: 'stream_event'
Keduanya berisi raw Claude API events, bukan teks terakumulasi. Anda perlu mengekstrak dan mengakumulasi text deltas sendiri. Berikut adalah struktur setiap tipe:
@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: BetaRawMessageStreamEvent; // From Anthropic SDK
parent_tool_use_id: string | null;
uuid: UUID;
session_id: string;
ttft_ms?: number; // Time to first token in ms, present only on message_start events
};
Field event berisi raw streaming event dari Claude API. Tipe event umum meliputi:
| Event Type | Description |
|---|---|
message_start |
Awal pesan baru |
content_block_start |
Awal blok konten baru (teks atau tool use) |
content_block_delta |
Pembaruan inkremental ke konten |
content_block_stop |
Akhir blok konten |
message_delta |
Pembaruan tingkat pesan (stop reason, usage) |
message_stop |
Akhir pesan |
Alur pesan
Dengan partial messages diaktifkan, Anda menerima pesan dalam urutan ini:
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
Tanpa partial messages diaktifkan (include_partial_messages di Python, includePartialMessages di TypeScript), Anda menerima semua tipe pesan kecuali StreamEvent. Tipe umum meliputi SystemMessage (inisialisasi sesi), AssistantMessage (respons lengkap), ResultMessage (hasil akhir), dan pesan batas kompak yang menunjukkan kapan riwayat percakapan dikompres (SDKCompactBoundaryMessage di TypeScript; SystemMessage dengan subtype "compact_boundary" di Python).
Stream text responses
Untuk menampilkan teks saat dihasilkan, cari event content_block_delta di mana delta.type adalah text_delta. Ini berisi chunk teks inkremental. Contoh di bawah mencetak setiap chunk saat tiba:
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 juga streaming secara inkremental. Anda dapat melacak kapan tools dimulai, menerima input mereka saat dihasilkan, dan melihat kapan mereka selesai. Contoh di bawah melacak tool yang sedang dipanggil dan mengakumulasi input JSON saat streaming masuk. Ini menggunakan tiga tipe event:
content_block_start: tool dimulaicontent_block_deltadenganinput_json_delta: chunk input tibacontent_block_stop: tool call selesai
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
Contoh ini menggabungkan text dan tool streaming menjadi UI yang kohesif. Ini melacak apakah agent saat ini mengeksekusi tool (menggunakan flag in_tool) untuk menampilkan indikator status seperti [Using Read...] saat tools berjalan. Teks streaming secara normal ketika tidak dalam tool, dan penyelesaian tool memicu pesan "done". Pola ini berguna untuk antarmuka chat yang perlu menampilkan progress selama tugas agent multi-langkah.
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 ---");
}
}
Keterbatasan yang Diketahui
- Structured output: hasil JSON muncul hanya di
ResultMessage.structured_outputakhir, bukan sebagai streaming deltas. Lihat structured outputs untuk detail.
Langkah selanjutnya
Sekarang bahwa Anda dapat stream teks dan tool calls secara real-time, jelajahi topik terkait ini:
- Interactive vs one-shot queries: pilih antara input modes untuk use case Anda
- Structured outputs: dapatkan respons JSON yang diketik dari agent
- Permissions: kontrol tools mana yang dapat digunakan agent