Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/anth-core-workflow-b/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill anth-core-workflow-b. Copyright stays with the author (MIT).

Anthropic Core Workflow B — Streaming & Batches

Overview

Two complementary patterns: real-time streaming for interactive UIs (SSE events via POST /v1/messages with stream: true) and the Message Batches API (POST /v1/messages/batches) for processing up to 100,000 requests asynchronously at 50% cost reduction.

Prerequisites

• Completed anth-install-auth setup
• Familiarity with anth-core-workflow-a (Messages API basics)
• For batches: understanding of async/polling patterns

Instructions

Streaming — Python SDK

import anthropic

client = anthropic.Anthropic()

# Method 1: High-level streaming (recommended)
with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    messages=[{"role": "user", "content": "Write a short story about a robot."}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

    # After stream completes, access full message
    final_message = stream.get_final_message()
    print(f"\nUsage: {final_message.usage.input_tokens}+{final_message.usage.output_tokens}")

# Method 2: Event-level streaming (for custom event handling)
with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=2048,
    messages=[{"role": "user", "content": "Explain REST APIs."}]
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if event.delta.type == "text_delta":
                print(event.delta.text, end="")
        elif event.type == "message_stop":
            print("\n[Stream complete]")

Streaming — TypeScript SDK

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic();

// High-level streaming
const stream = client.messages.stream({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 2048,
  messages: [{ role: 'user', content: 'Write a haiku about code.' }],
});

stream.on('text', (text) => process.stdout.write(text));
stream.on('finalMessage', (msg) => {
  console.log(`\nTokens: ${msg.usage.input_tokens}+${msg.usage.output_tokens}`);
});

await stream.finalMessage();

Streaming with Tool Use

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=tools,  # Same tools array from core-workflow-a
    messages=[{"role": "user", "content": "What's the weather?"}]
) as stream:
    for event in stream:
        if event.type == "content_block_start":
            if event.content_block.type == "tool_use":
                print(f"Tool call: {event.content_block.name}")
        elif event.type == "content_block_delta":
            if event.delta.type == "input_json_delta":
                print(event.delta.partial_json, end="")  # Tool input arrives incrementally

Message Batches API — Bulk Processing

# Create a batch of up to 100,000 requests (50% cost savings)
batch = client.messages.batches.create(
    requests=[
        {
            "custom_id": "req-001",
            "params": {
                "model": "claude-sonnet-4-20250514",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Summarize: ...article1..."}]
            }
        },
        {
            "custom_id": "req-002",
            "params": {
                "model": "claude-sonnet-4-20250514",
                "max_tokens": 1024,
                "messages": [{"role": "user", "content": "Summarize: ...article2..."}]
            }
        },
        # ... up to 100,000 requests
    ]
)

print(f"Batch ID: {batch.id}")          # msgbatch_01HBMt...
print(f"Status: {batch.processing_status}")  # in_progress
print(f"Counts: {batch.request_counts}")     # {processing: 2, succeeded: 0, ...}

Poll for Batch Completion

import time

while True:
    batch_status = client.messages.batches.retrieve(batch.id)
    if batch_status.processing_status == "ended":
        break
    print(f"Processing... {batch_status.request_counts}")
    time.sleep(30)

# Stream results (returns JSONL)
for result in client.messages.batches.results(batch.id):
    if result.result.type == "succeeded":
        text = result.result.message.content[0].text
        print(f"[{result.custom_id}]: {text[:100]}...")
    elif result.result.type == "errored":
        print(f"[{result.custom_id}] ERROR: {result.result.error}")

Output

The streaming workflow emits ordered text deltas for an interactive caller and ends with a final message containing the stop reason and token usage. The batch workflow returns a durable batch ID, a terminal processing status, and one result per custom_id; callers must retain that ID and reconcile both successful and errored records before marking the source workload complete.

Examples

Use streaming for a chat endpoint that should show a response as it is generated: forward text deltas to the browser, then store the final message and usage after message_stop. Use a batch for a nightly summarization job: assign each document a stable custom_id, submit the requests once, poll until ended, and write every returned result into a table keyed by that ID. A per-item error is a retry or triage item, not a reason to discard successful records from the same batch.

SSE Event Types Reference

Error Handling

Resources

• Streaming Messages
• Message Batches API
• Batch Results

Next Steps

For common errors, see anth-common-errors.