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Agentic completed but raise error ClientError(status_code, response_json, response) google.genai.errors.ClientError: 404 NOT_FOUND. {'error': {'code': 404, 'message': 'Not Found. However, the operation was successful.', 'status': 'NOT_FOUND'}}

When you're calling your agentic app, you ran into this error  raise ClientError(status_code, response_json, response) google.genai.errors.ClientError: 404 NOT_FOUND. {'error': {'code': 404, 'message': 'Not Found. However, the operation was successful.', 'status': 'NOT_FOUND'}} And then you try to see what is wrong. This can lead into a whole rabbit whole. Took me 2 hours to resolve this.  This happens to me because I enabled gateway agent on my agent. So an operation that will generally be fine, has raise this error.  I had to disabled that be redeploy my runtime and removing gateway from it.
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google agentic calling your agent

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So what happens after you have deployed your agent to agentic platform, you should have code to call it. How can we do that? import asyncio import agentplatform client = agentplatform .Client(     project = "project-your-project-id" ,     location = "us-central1" , ) async def main ():     runtime = client .runtimes.get(         name = "projects/your-project-id/locations/us-central1/ reasoningEngines/2026277059566764032"     )     print ( runtime )     runtime .operation_schemas()     session = await runtime .async_create_session( user_id = "user_123" )     async for event in runtime .async_stream_query(         user_id = "user_123" ,         message = "What is my account balance including pending charges for user_123?" ,     ):         print ( event ) asyncio . run ( main ()) It is even documente...
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gcp agentic agent - how to use serverless model

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GCP serverless model are model like gemini-2.5-flash, gemini-3.5-flash that are generally avaiable. This include Claude Sonnet or Haiku 5.5.  If you ever wanted  your agent to use these model, all you need to do is the following code.  banking_agent = Agent (     name = "account_assistant" ,     model = "gemini-2.5-flash" ,     instruction = "You are a helpful customer support agent. Answer questions concisely." ,     tools = [ get_user_account_balance ],     before_model_callback = inspect_llm_input ) And that's is all. Can you believe it. Deploy it and then go to the playground to test it out.  The full code base is here :- import asyncio from urllib import response from google . adk . agents import Agent from google . adk . runners import Runner from google . adk . sessions import InMemorySessionService from google . genai import types from google . adk . agents . callback_context import Callba...
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google agentic deploying with gateway agent

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We can use the following python to perform deployment. Also notice that this is more configurable compare to glcoud command line.  So we will be deploying an agent to google agent runtime and it will use an gateway. This gateway is an ingress (client-to-agent)  and it is called my-first-gateway. If you have another setup client-to-everywhere else (egress) setup would be slightly different. import asyncio from urllib import response from google . adk . agents import Agent from google . adk . runners import Runner from google . adk . sessions import InMemorySessionService from google . genai import types from google . adk . agents . callback_context import CallbackContext from google . adk . models . llm_request import LlmRequest from vertexai import types as vertex_types from google . genai import types import vertexai # Ensure this matches your actual GCP Project ID, not a placeholder vertexai . init ( project = "project-015f2405-16e7-4b36-996" ,...
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local llm image understanding using GLM OCR

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GLM OCR is pretty lightweight and fit for purpose when i tried it out on invoice images.  Here is the code for setting up and running it locally.  """ Extract line items from an invoice image using a local model served at http://localhost:8888/v1, called through LiteLLM. Setup:     pip install litellm pydantic     export LOCAL_API_KEY="your-local-server-key" Usage:     python extract_invoice_local.py invoice.jpg """ import base64 import json import mimetypes import os import re import sys from typing import Optional import litellm from pydantic import BaseModel # Same settings as your LiteLlm config --------------------------------------- MODEL = "openai/ggml-org/GLM-OCR-GGUF" API_BASE = "http://localhost:8888/v1" API_KEY = "sk-unsloth-4d0a1b198bd177a2a72ee1954585342a"   # keep the key out of source code TEMPERATURE = 0.0 # Ask the server to constrain output to the schema (llama.cpp-based server...
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ai platform using llama-4-maverick 17b for image understanding

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We have been dealing with alot of text completion and it is time that we try to use model with image. This is a sample code that we can use to extract contents from an image (invoice) and return line item in json format - so we can proceed it or fill up a form.  I assume you have enable llama-4-maverick serveless instance.  """ Extract line items from an invoice image using Llama 4 Maverick on Vertex AI, through the OpenAI-compatible Chat Completions endpoint. Setup:     pip install openai pydantic google-auth requests     gcloud auth application-default login      # or use a service account Usage:     python extract_invoice_vertex.py invoice.jpg """ import base64 import json import mimetypes import re import sys from typing import Optional import google . auth import google . auth . transport . requests from openai import OpenAI from pydantic import BaseModel PROJECT_ID = "YOUR-PROJECT-ID" LOCATION = "us-ea...
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google adk using model from AI platform via endpoint

Sometimes we would like to make use of a deployed model in AI platform and this is what post intent to achieve. We wrap our class in BaseLLM and then implement the relevant code in this case override the based method generate_content_async. And plug it into our root agent and use runner to execute this code. This make it really easy to use. import asyncio from typing import AsyncGenerator from google . cloud import aiplatform from google . adk . agents import Agent from google . adk . models import BaseLlm , LlmRequest , LlmResponse from google . genai import types from google . adk . runners import Runner from google . adk . sessions import InMemorySessionService from google . genai import types class VertexEndpointLlm ( BaseLlm ):     project : str     location : str     endpoint_id : str     async def generate_content_async (         self , llm_request : LlmRequest , stream : bool = False  ...
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gcp agentic platform consuming llama4 serverless

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You don't necessary have to deploy llama-4 to use it, all we need to do is 'enable' and then we will get an endpoint where we can consume it.  We can use curl command :- export ENDPOINT=us-east5-aiplatform.googleapis.com exort REGION=us-east5 export PROJECT_ID="project-id"  curl \   - X POST \   - H "Authorization: Bearer $(gcloud auth print-access-token)" \   - H "Content-Type: application/json" https : // $ { ENDPOINT } / v1 / projects / $ { PROJECT_ID } / locations / $ { REGION } / endpoints / openapi / chat / completions \   - d '{"model":"meta/llama-4-maverick-17b-128e-instruct-maas", "stream":true, "messages":[{"role": "user", "content": "Summer travel plan to Paris"}]}'  Or we can just use to consume it via python OpenAI endpoint :- from openai import OpenAI   import google . auth , google . auth . transport . requests creds , _ = ...
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