Streaming Vs Single Mode ↗

anthropic-platform guide intermediate streaming agents

Editorial Notes

This guide addresses a fundamental architectural decision you will face early in any agent project. Single-turn mode is simpler to implement and debug because you get a complete response at once, making error handling straightforward and retry logic clean. Streaming mode provides real-time token-by-token output that is essential for user-facing applications where perceived latency matters, but it introduces complexity around partial results and connection management. Start with single-turn mode during development and prototyping, then migrate to streaming when you need production-quality user experience.

Original Documentation

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#

Attach images directly to messages for visual analysis and understanding Send multiple messages that process sequentially, with ability to interrupt Full access to all tools and custom MCP servers during the session Use lifecycle hooks to customize behavior at various points See responses as they’re generated, not just final results Maintain conversation context across multiple turns naturally

Implementation Example#



async function* generateMessages() {
  // First message
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: "Analyze this codebase for security issues"
    }
  };

  // Wait for conditions or user input
  await new Promise((resolve) => setTimeout(resolve, 2000));

  // Follow-up with image
  yield {
    type: "user" as const,
    message: {
      role: "user" as const,
      content: [
        {
          type: "text",
          text: "Review this architecture diagram"
        },
        {
          type: "image",
          source: {
            type: "base64",
            media_type: "image/png",
            data: await readFile("diagram.png", "base64")
          }
        }
      ]
    }
  };
}

// Process streaming responses
for await (const message of query({
  prompt: generateMessages(),
  options: {
    maxTurns: 10,
    allowedTools: ["Read", "Grep"]
  }
})) {
  if (message.type === "result") {
    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#

Single message input mode does not support:

Direct image attachments in messages
Dynamic message queueing
Real-time interruption
Hook integration
Natural multi-turn conversations

Implementation Example#


// 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") {
    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") {
    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())

Link last verified June 7, 2026. View original ↗

Source: Anthropic Platform Docs

Appears in Learning Paths

Agent Sdk Deep Dive

Link last verified: 2026-02-26