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Reduce friction and latency for long-running jobs with Webhooks in Gemini API

Google AI Archived May 09, 2026 ✓ Full text saved

Event-Driven Webhooks are a push-based notification system that eliminates the need for inefficient polling.

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✦ AI Summary · Claude Sonnet


    Reduce friction and latency for long-running jobs with Webhooks in Gemini API May 04, 2026 We're making it easier and more efficient to build complex, long-running agentic applications with the Gemini API Webhooks. L Lucia Loher Product Manager, Gemini API H Hussein Hassan Harrirou Engineering, Gemini API Share Listen to article 2:26 minutes Today, we're making it easier and more efficient to build complex, long-running agentic applications with the Gemini API. We are introducing event-driven Webhooks, a push-based notification system that eliminates the need for inefficient polling. As Gemini shifts toward agentic workflows and high-volume processing — like Deep Research, long video generation, or processing thousands of prompts via the Batch API — operations can take minutes or even hours. Until now, developers had to rely on continuous polling (e.g., repeatedly callingGEToperations) to check if a job was completed. Now, the Gemini API can simply push a real-time HTTP POST payload to your server the instant a task finishes. We’ve built this with reliability and security in mind. Our implementation strictly adheres to the Standard Webhooks specification. Every request is signed using webhook-signature, webhook-id, and webhook-timestamp headers, ensuring idempotency and preventing replay attacks. We also guarantee "at-least-once" delivery with automatic retries for up to 24 hours. How it works You can configure webhooks globally at the project level (secured via HMAC), or override them dynamically on a per-request basis to route specific jobs (secured via JWKS). Here's a quick example of how you can dynamically configure a webhook for a batch task using the Python SDK: from google import genai from google.genai import types client = genai.Client() file_batch_job = client.batches.create( model="gemini-3-flash-preview", src=inline_requests, config={ "display_name": "My Setup", "webhook_config": { "uris": ["https://my-api.com/gemini-webhook-dynamic"], "user_metadata": {"job_group": "nightly-eval", "priority": "high"}, }, }, ) print(f"Created batch job: {file_batch_job.name}") Get started today This feature is available now for all developers using the Gemini API: Read the guide: Check out the Webhooks documentation to explore the full event catalog and learn how to secure your endpoints. Hands-on practice: We've prepared a comprehensive Cookbook to help you build an end-to-end integration with webhooks. POSTED IN:
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    Source
    Google AI
    Category
    ◬ AI & Machine Learning
    Published
    May 09, 2026
    Archived
    May 09, 2026
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