Anth webhooks events
'Implement event-driven patterns with Claude API: streaming SSE events,From its SKILL.md
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Anthropic Events & Async Processing
Overview
The Claude API does not use traditional webhooks. Instead it provides two event-driven patterns: Server-Sent Events (SSE) for real-time streaming and the Message Batches API for async bulk processing. This skill covers both.
SSE Streaming Events
import anthropic
client = anthropic.Anthropic()
# Process each SSE event type
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Explain microservices."}]
) as stream:
for event in stream:
match event.type:
case "message_start":
print(f"Started: {event.message.id}")
case "content_block_start":
if event.content_block.type == "tool_use":
print(f"Tool call: {event.content_block.name}")
case "content_block_delta":
if event.delta.type == "text_delta":
print(event.delta.text, end="", flush=True)
elif event.delta.type == "input_json_delta":
print(event.delta.partial_json, end="")
case "message_delta":
print(f"\nStop: {event.delta.stop_reason}")
print(f"Output tokens: {event.usage.output_tokens}")
case "message_stop":
print("[Complete]")
SSE Event Reference
| Event | When | Key Data |
|---|---|---|
message_start | Stream begins | message.id, message.model, message.usage.input_tokens |
content_block_start | New block begins | content_block.type (text or tool_use), index |
content_block_delta | Incremental content | delta.text or delta.partial_json |
content_block_stop | Block finishes | index |
message_delta | Message-level update | delta.stop_reason, usage.output_tokens |
message_stop | Stream complete | (empty) |
ping | Keepalive | (empty) |
Async Batch Processing
# Submit batch (up to 100K requests, 50% cheaper)
batch = client.messages.batches.create(
requests=[
{
"custom_id": f"doc-{i}",
"params": {
"model": "claude-sonnet-4-20250514",
"max_tokens": 1024,
"messages": [{"role": "user", "content": f"Summarize: {doc}"}]
}
}
for i, doc in enumerate(documents)
]
)
# Poll for completion
import time
while True:
status = client.messages.batches.retrieve(batch.id)
if status.processing_status == "ended":
break
counts = status.request_counts
print(f"Processing: {counts.processing} | Done: {counts.succeeded} | Errors: {counts.errored}")
time.sleep(30)
# Stream results
for result in client.messages.batches.results(batch.id):
if result.result.type == "succeeded":
print(f"[{result.custom_id}]: {result.result.message.content[0].text[:100]}")
else:
print(f"[{result.custom_id}] ERROR: {result.result.error}")
Event-Driven Architecture Pattern
# Use queues to decouple Claude requests from user-facing endpoints
from redis import Redis
from rq import Queue
redis = Redis()
queue = Queue(connection=redis)
def process_with_claude(prompt: str, callback_url: str):
"""Background job for async Claude processing."""
client = anthropic.Anthropic()
msg = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
# Notify your system via internal callback
import requests
requests.post(callback_url, json={
"text": msg.content[0].text,
"usage": {"input": msg.usage.input_tokens, "output": msg.usage.output_tokens}
})
# Enqueue from your API handler
job = queue.enqueue(process_with_claude, prompt="...", callback_url="https://internal/callback")
Error Handling
| Issue | Cause | Fix |
|---|---|---|
| Stream disconnects | Network timeout | Reconnect and re-request (responses are not resumable) |
Batch expired | Not processed in 24h | Resubmit the batch |
errored results | Individual request was invalid | Check result.error.message per request |
Resources
Next Steps
For performance optimization, see anth-performance-tuning.
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