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Anthropic Claude Vertex AI

cookbook/90_models/vertexai/claude/README.md

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Anthropic Claude Vertex AI

Models overview

Note: Fork and clone this repository if needed

1. Create and activate a virtual environment

shell
python3 -m venv ~/.venvs/aienv
source ~/.venvs/aienv/bin/activate

2. Export your environment variables

shell
export GOOGLE_CLOUD_PROJECT=your-project-id
export CLOUD_ML_REGION=your-region

3. Authenticate your CLI

shell
gcloud auth application-default login

or

4. Install libraries

shell
uv pip install -U anthropic ddgs duckdb agno

5. Run basic Agent

  • Streaming on
shell
python cookbook/92_models/vertexai/claude/basic_stream.py
  • Streaming off
shell
python cookbook/92_models/vertexai/claude/basic.py

6. Run Agent with Tools

  • DuckDuckGo Search
shell
python cookbook/92_models/vertexai/claude/tool_use.py

7. Run Agent that returns structured output

shell
python cookbook/92_models/vertexai/claude/structured_output.py

8. Run Agent that uses storage

shell
python cookbook/92_models/vertexai/claude/db.py

9. Run Agent that uses knowledge

Take note that claude uses OpenAI embeddings under the hood, and you will need an OpenAI API Key

shell
export OPENAI_API_KEY=***
shell
python cookbook/92_models/vertexai/claude/knowledge.py

10. Run Agent that uses memory

shell
python cookbook/92_models/vertexai/claude/memory.py

11. Run Agent that analyzes an image

shell
python cookbook/92_models/vertexai/claude/image_input_url.py

12. Run Agent with Thinking enabled

  • Streaming on
shell
python cookbook/92_models/vertexai/claude/thinking.py
  • Streaming off
shell
python cookbook/92_models/vertexai/claude/thinking_stream.py

13. Adaptive Thinking with output_config

For Claude 4.6 VertexAI models that support adaptive thinking, use output_config to control thinking depth via the effort parameter:

shell
python cookbook/90_models/vertexai/claude/adaptive_thinking.py
python
from agno.models.vertexai import Claude

model = Claude(
    id="claude-sonnet-4-6@20250514",
    max_tokens=4096,
    thinking={"type": "adaptive"},
    output_config={"effort": "high"},
)

Valid effort values:

  • "low" - Most efficient, significant token savings
  • "medium" - Balanced approach with moderate savings
  • "high" - Default, high capability for complex reasoning
  • "max" - Absolute maximum capability (Opus 4.6 only)