Streaming Input
Understanding the two input modes for Claude Agent SDK and when to use each
Overview
The Claude Agent SDK supports two distinct input modes for interacting with agents:
- Streaming Input Mode (Default & Recommended) - A persistent, interactive session
- Single Message Input - One-shot queries that use session state and resuming
This guide explains the differences, benefits, and use cases for each mode to help you choose the right approach for your application.
Streaming Input Mode (Recommended)
Streaming input mode is the preferred way to use the Claude Agent SDK. It provides full access to the agent's capabilities and enables rich, interactive experiences.
It allows the agent to operate as a long lived process that takes in user input, handles interruptions, surfaces permission requests, and handles session management.
How It Works
sequenceDiagram
participant App as Your Application
participant Agent as Claude Agent
participant Tools as Tools/Hooks
participant FS as Environment/<br/>File System
App->>Agent: Initialize with AsyncGenerator
activate Agent
App->>Agent: Yield Message 1
Agent->>Tools: Execute tools
Tools->>FS: Read files
FS-->>Tools: File contents
Tools->>FS: Write/Edit files
FS-->>Tools: Success/Error
Agent-->>App: Stream partial response
Agent-->>App: Stream more content...
Agent->>App: Complete Message 1
App->>Agent: Yield Message 2 + Image
Agent->>Tools: Process image & execute
Tools->>FS: Access filesystem
FS-->>Tools: Operation results
Agent-->>App: Stream response 2
App->>Agent: Queue Message 3
App->>Agent: Interrupt/Cancel
Agent->>App: Handle interruption
Note over App,Agent: Session stays alive
Note over Tools,FS: Persistent file system<br/>state maintained
deactivate Agent
Benefits
Image Uploads
Attach images directly to messages for visual analysis and understanding
Queued Messages
Send multiple messages that process sequentially, with ability to interrupt
Tool Integration
Full access to all tools and custom MCP servers during the session
Hooks Support
Use lifecycle hooks to customize behavior at various points
Real-time Feedback
See responses as they're generated, not just final results
Context Persistence
Maintain conversation context across multiple turns naturally
Implementation Example
import { query, type SDKUserMessage } from "@anthropic-ai/claude-agent-sdk";
import { readFile } from "fs/promises";
async function* generateMessages(): AsyncGenerator<SDKUserMessage> {
// First message
yield {
type: "user",
message: {
role: "user",
content: "Analyze this codebase for security issues"
},
parent_tool_use_id: null
};
// Wait for conditions or user input
await new Promise((resolve) => setTimeout(resolve, 2000));
// Follow-up with image
yield {
type: "user",
message: {
role: "user",
content: [
{
type: "text",
text: "Review this architecture diagram"
},
{
type: "image",
source: {
type: "base64",
media_type: "image/png",
data: await readFile("diagram.png", "base64")
}
}
]
},
parent_tool_use_id: null
};
}
// Process streaming responses
for await (const message of query({
prompt: generateMessages(),
options: {
maxTurns: 10,
allowedTools: ["Read", "Grep"]
}
})) {
if (message.type === "result" && message.subtype === "success") {
console.log(message.result);
}
}
from claude_agent_sdk import (
ClaudeSDKClient,
ClaudeAgentOptions,
AssistantMessage,
TextBlock,
)
import asyncio
import base64
async def streaming_analysis():
async def message_generator():
# First message
yield {
"type": "user",
"message": {
"role": "user",
"content": "Analyze this codebase for security issues",
},
}
# Wait for conditions
await asyncio.sleep(2)
# Follow-up with image
with open("diagram.png", "rb") as f:
image_data = base64.b64encode(f.read()).decode()
yield {
"type": "user",
"message": {
"role": "user",
"content": [
{"type": "text", "text": "Review this architecture diagram"},
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/png",
"data": image_data,
},
},
],
},
}
# Use ClaudeSDKClient for streaming input
options = ClaudeAgentOptions(max_turns=10, allowed_tools=["Read", "Grep"])
async with ClaudeSDKClient(options) as client:
# Send streaming input
await client.query(message_generator())
# Process responses
async for message in client.receive_response():
if isinstance(message, AssistantMessage):
for block in message.content:
if isinstance(block, TextBlock):
print(block.text)
asyncio.run(streaming_analysis())
Single Message Input
Single message input is simpler but more limited.
When to Use Single Message Input
Use single message input when:
- You need a one-shot response
- You do not need image attachments, hooks, etc.
- You need to operate in a stateless environment, such as a lambda function
Limitations
Implementation Example
import { query } from "@anthropic-ai/claude-agent-sdk";
// Simple one-shot query
for await (const message of query({
prompt: "Explain the authentication flow",
options: {
maxTurns: 1,
allowedTools: ["Read", "Grep"]
}
})) {
if (message.type === "result" && message.subtype === "success") {
console.log(message.result);
}
}
// Continue conversation with session management
for await (const message of query({
prompt: "Now explain the authorization process",
options: {
continue: true,
maxTurns: 1
}
})) {
if (message.type === "result" && message.subtype === "success") {
console.log(message.result);
}
}
from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage
import asyncio
async def single_message_example():
# Simple one-shot query using query() function
async for message in query(
prompt="Explain the authentication flow",
options=ClaudeAgentOptions(max_turns=1, allowed_tools=["Read", "Grep"]),
):
if isinstance(message, ResultMessage):
print(message.result)
# Continue conversation with session management
async for message in query(
prompt="Now explain the authorization process",
options=ClaudeAgentOptions(continue_conversation=True, max_turns=1),
):
if isinstance(message, ResultMessage):
print(message.result)
asyncio.run(single_message_example())