cxas-scrapi 1.0.0

A high level scripting API for CX Agent Studio developers.

CX Agent Studio Scripting API (CXAS SCRAPI)

A powerful Python API, CLI, and set of Agent Skills for CX Agent Studio to automate, evaluate, and scale your agents with ease.

Important Links: Docs, Examples, Agent Skills, Core SDK

CX Agent Studio Scripting API (CXAS SCRAPI) is an open-source Python scripting API, CLI, and set of Agent Skills for CX Agent Studio. It is designed to simplify building, deploying, and orchestrating agent workflows, from simple tasks to complex systems. It integrates seamlessly with Agentic IDEs like Gemini CLI, Claude Code, and Antigravity, exposing advanced tooling for deep evaluations, real-time latency metrics, offline linting, and conversation history.

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Built With

• Python 3.11+

Authentication

Authentication can vary depending on how and where you are interacting with SCRAPI.

Google Colab

If you're using CXAS SCRAPI with a Google Colab notebook, you can add the following to the top of your notebook for easy authentication:

project_id = '<YOUR_GCP_PROJECT_ID>'

# this will launch an interactive prompt that allows you to auth with GCP in a browser
!gcloud auth application-default login --no-launch-browser

# this will set your active project to the `project_id` above
!gcloud auth application-default set-quota-project $project_id

After running the above, Colab will pick up your credentials from the environment and pass them to CXAS SCRAPI directly. No need to use Service Account keys! You can then use CXAS SCRAPI simply like this:

from cxas_scrapi import Apps

project_id = '<YOUR_GCP_PROJECT_ID>'
location = 'us'

app_client = Apps(project_id=project_id, location=location) # <-- Creds will be automatically picked up from the environment
apps_map = app_client.get_apps_map()

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Cloud Functions / Cloud Run

If you're using CXAS SCRAPI with Cloud Functions or Cloud Run, CXAS SCRAPI can pick up on the default environment creds used by these services without any additional configuration!

1. Add cxas-scrapi to your requirements.txt file
2. Ensure the Cloud Function / Cloud Run service account has the appropriate Custom Agent / Conversational Agents IAM Role

Once you are setup with the above, your function code can be used easily like this:

from cxas_scrapi import Agents

app_name = '<YOUR_APP_NAME>'
a = Agents(project_id='<YOUR_GCP_PROJECT_ID>', location='global')
agents_map = a.get_agents_map(app_name)

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Local Python Environment

Similar to Cloud Functions / Cloud Run, CXAS SCRAPI can pick up on your local authentication creds if you are using the gcloud CLI.

1. Install gcloud CLI.
2. Run gcloud init.
3. Run gcloud auth login
4. Run gcloud auth application-default login
5. Run gcloud auth list to ensure your principal account is active.

This will authenticate your principal GCP account with the gcloud CLI, and SCRAPI can pick up the creds from here.

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Exceptions and Misc.

If you prefer to explicitly assign Service Account credentials programmatically instead of relying on the environmental application-default, you can pass the path to your JSON key using creds_path.

from cxas_scrapi import Tools

creds_path = '<PATH_TO_YOUR_SERVICE_ACCOUNT_JSON_FILE>'

t = Tools(project_id='<YOUR_GCP_PROJECT_ID>', location='global', creds_path=creds_path)
tools_map = t.get_tools_map('<YOUR_APP_NAME>')

Getting Started

Environment Setup

Set up Google Cloud Platform credentials and install dependencies.

gcloud auth login
gcloud auth application-default login
gcloud config set project <project name>

python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt

Usage

To run a simple bit of code you can do the following:

• Import a Class from cxas_scrapi
• Define your GCP Project and Location

from cxas_scrapi import Apps

# Instantiate your class object and pass in your credentials
app_client = Apps(project_id='<YOUR_GCP_PROJECT_ID>', location='global')

# Retrieve all Apps existing in your project
apps = app_client.list_apps()
for app in apps:
    print(app.display_name, app.name)

Library Composition

Here is a brief overview of the CXAS SCRAPI library's structure and the motivation behind that structure.

Core

The src/cxas_scrapi/core directory contains the high level building blocks of CXAS SCRAPI, mapped to core resource types in the CXAS environment (Apps, Agents, Tools, Guardrails, Deployments, Sessions, etc.)

Utils

The src/cxas_scrapi/utils directory contains helper functions and background logic for pagination, response flattening, proto conversions, and external integrations like Google Sheets.

Evals

The src/cxas_scrapi/evals directory provides tools for executing and analyzing agent performance evaluations, including Golden tests and simulation runs, and extracting metrics like latency.

CLI

The src/cxas_scrapi/cli directory implements the command line interface for SCRAPI, offering tools like cxas lint to automate development and validation workflows.

Migration

The src/cxas_scrapi/migration directory contains tools to facilitate transitioning legacy Dialogflow CX agents to CXAS, including agent generation from flows and artifact building.

Documentation

The full documentation site is built with MkDocs Material. To run it locally:

# Install docs dependencies (inside your virtualenv)
pip install -r requirements-docs.txt

# Install the package so API reference pages can render
pip install -e .

# Start the local dev server
mkdocs serve

Open http://127.0.0.1:8000 in your browser. Changes to files in docs/ will reload automatically.

To build the static site without serving:

mkdocs build          # output goes to site/
mkdocs build --strict # also fails on warnings (used in CI)

Contributing

We welcome any contributions or feature requests you would like to submit!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the Apache 2.0 License. See LICENSE for more information.

Contact

Patrick Marlow - [e-mail] - @kmaphoenix

Project Link: https://github.com/GoogleCloudPlatform/cxas-scrapi

Acknowledgements

Google Cloud Customer Engagement AI