RESOURCE_EXHAUSTED (429) errors when triggering ADK agents.

[enesdemirag]

RESOURCE_EXHAUSTED (429) errors when triggering ADK agents concurrently via Vertex AI Reasoning Engine

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Issue Description

Describe the Bug:
When triggering an ADK agent multiple times in quick succession, the request fails with a streaming error that ultimately resolves to a 429 RESOURCE_EXHAUSTED error from Vertex AI. The error is surfaced by ADK as a 500 during response streaming.

Observed error:

{
  "error": "500: An error occurred while streaming the response: 429 Too Many Requests." 
  "details": {
    "message": "Resource exhausted. Please try again later.",
    "status": "RESOURCE_EXHAUSTED"
  }
}

The error message points to ADK and Vertex AI 429 documentation but it’s unclear where the actual bottleneck is and how it should be handled when using ADK in production.

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Steps to Reproduce:

1. Deploy a backend service on Google Cloud Run.
2. Use Google ADK with Vertex AI Reasoning Engine (us-central1).
3. Trigger the same agent multiple times in a short time window (≈ concurrent or burst traffic).
4. Observe 429 RESOURCE_EXHAUSTED errors surfaced as streaming failures.

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Expected Behavior:
Requests may slow down or queue, but should not fail with a hard error during streaming. Ideally, retries or backoff would be handled gracefully.

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Observed Behavior:
Requests fail with 429 RESOURCE_EXHAUSTED, wrapped as a 500 streaming error by ADK.

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Environment Details:

• ADK Library Version: latest
• Python Version: 3.12

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Model Information:

• Are you using LiteLLM: No
• Models used: gemini-2.5-pro, gemini-2.5-flash
• Gemini tier: Tier 2
• Reasoning Engine location: us-central1

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❓ Questions / Clarification Needed

1. Is this error strictly caused by the Vertex AI quota below?

   Query Reasoning Engine requests per minute per region = 30
   

2. Will increasing this quota fully resolve the issue, or are there additional ADK-level or Reasoning Engine concurrency limits/bottlenecks?

3. Does ADK provide any built-in retry, backoff, or queueing mechanism for 429 RESOURCE_EXHAUSTED errors?

4. Are there recommended production patterns when using ADK + Reasoning Engine behind Cloud Run?

5. Is it possible to self host the reasoning engine locally inside my server and use ADK, so that I only need to worry about the Gemini LLM Request quotas?

I’m planning to launch this service soon and want to ensure the setup is production-safe under burst traffic.

[adk-bot]

Notification: The author has updated the issue description. Maintainers, please review.

[llalitkumarrr]

Hello @enesdemirag ,

While this issue appears to be related to quota limits, we need to investigate further to confirm the root cause. As a first step, please try adjusting the number of calls while keeping the workflow the same to see if that resolves the issue consistently. Additionally, if you are using a Gemini models we recommend using Gemini (from google.adk.models.google_llm import Gemini), as it allows you to configure retry_options.

Furthermore, if you could share minimal reproducible code, it would help analyze this issue better.

[adk-bot]

This issue has been automatically marked as stale because it has not had recent activity for 7 days after a maintainer requested clarification. It will be closed if no further activity occurs within 7 days.