teradata-agentstack 20.0.0.1

Teradata SDK for OpenAPI-based dynamic client generation

teradata-agentstack

A Python SDK package that dynamically generates client classes from OpenAPI specifications for Teradata AI/ML platform services.

It provides a unified way to work with Teradata AgentStack services from Python.

For community support, visit Teradata Community.

For Teradata customer support, visit Teradata Support.

Copyright 2026 Teradata. All rights reserved.

Table of Contents

• Overview
• Release Notes
• Installation
• Authentication
• Quick Start
• Pydantic Model Support
• Polling Async Resources
• The blueprint() Function
• How Dynamic Generation Works
• Notes and Limitations
• License

---

Overview

teradata-agentstack provides a unified framework for interacting with Teradata platform REST APIs. Instead of hand-writing API wrappers, each SDK module reads the bundled OpenAPI JSON specification at import time and automatically creates Python classes and methods:

• OpenAPI Tags are converted to CamelCase Python classes.
• Operation IDs are converted to snake_case instance methods.
• A blueprint() helper lists all generated classes for a module.

Included SDKs

Module	Client Class	Generated Classes	Service
teradata_agentstack.agentops	AgentOpsClient	Definitions, Deployments, Secrets, Health	Teradata AgentOps
teradata_agentstack.rayops	RayClusterManagementClient	RayClusterManagement, ...	Ray Cluster Management
teradata_agentstack.agento11y	AgentO11yClient	Traces, Projects, Evaluations, ...	Agent Observability
teradata_agentstack.access_manager	AccessManagerClient	Keys, Admin, Proxy	API Access Manager
teradata_agentstack.inference_engine	InferenceEngineClient	Secrets, Pools, Endpoints, ...	Inference Engine
teradata_agentstack.mcp_management	MCPManagementClient	Servers, ...	MCP Management Service
teradata_agentstack.mcp_endpoint_management	MCPEndpointManagementClient	Endpoints, ...	MCP Endpoint Management

---

Release Notes

Version 20.00.00.01

• ELE-10086 : Fixed a startup issue that could stop RayOps from loading in some environments.
• Fixed Access Manager fallback models so API responses are recognized correctly and work reliably.

Version 20.00.00.00

• First release of teradata-agentstack.
• Supported SDK modules in this release:
  • agentops
  • rayops
  • agento11y
  • access_manager
  • inference_engine
  • mcp_management
  • mcp_endpoint_management

---

Installation

Version: 20.00.00.00 | Python: ≥ 3.9 (64-bit) | Platform: Windows, macOS, Linux

pip install teradata-agentstack

Note: A 64-bit Python environment is required. Installation will fail on 32-bit platforms.

Dependencies

Package	Version
teradataml	>=20.00.00.10
requests	≥ 2.33.0
oauthlib	≥ 3.2.2
requests-oauthlib	≥ 2.0.0
pydantic	≥ 2.10.6
PyYAML	≥ 6.0.2
pandas	≥ 0.22

---

Authentication

All SDK clients accept the same authentication objects, imported from the top-level package:

from teradata_agentstack import ClientCredentialsAuth, DeviceCodeAuth, BearerAuth
from teradata_agentstack._auth_modes import BasicAuth

ClientCredentialsAuth — OAuth2 Client Credentials

auth = ClientCredentialsAuth(
    auth_token_url="https://your-sso/token",
    auth_client_id="your_client_id",
    auth_client_secret="your_client_secret"
)

DeviceCodeAuth — OAuth2 Device Code Flow

auth = DeviceCodeAuth(
    auth_token_url="https://your-sso/token",
    auth_device_auth_url="https://your-sso/device_authorization",
    auth_client_id="your_client_id"
)

Tokens are cached at ~/.teradataml/sdk/.token and reused on subsequent calls.

BearerAuth — Static Bearer Token

auth = BearerAuth(auth_bearer="your_bearer_token")

BasicAuth — Username / Password

auth = BasicAuth(username="user", password="pass")

Authentication Precedence Order

Each configuration value (base_url, auth, ssl_verify) is resolved in the following order. The first source that provides a value wins:

1. Constructor arguments — values passed directly to the client (e.g., base_url=..., auth=...).
2. Environment variables — values read from the process environment (e.g., BASE_URL, BASE_API_AUTH_MODE).
3. YAML config file — values loaded from the config file (explicit config_file path, or the default ~/.teradataml/sdk/config.yaml).

If none of the three sources supplies a required value, a TeradataMlException is raised.

Authentication via Environment Variables

You can configure authentication without passing an auth object by setting environment variables:

Variable	Description
BASE_URL	Base URL of the API endpoint
BASE_API_AUTH_MODE	client_credentials, device_code, bearer, or basic
BASE_SSL_VERIFY	true or false
BASE_API_AUTH_CLIENT_ID	OAuth2 client ID
BASE_API_AUTH_CLIENT_SECRET	OAuth2 client secret
BASE_API_AUTH_TOKEN_URL	OAuth2 token endpoint URL
BASE_API_AUTH_DEVICE_AUTH_URL	OAuth2 device authorization URL
BASE_API_AUTH_BEARER_TOKEN	Static bearer token

Authentication via YAML Config File

# config.yaml
base_url: https://your-server
ssl_verify: true
auth_mode: client_credentials
auth_client_id: your_client_id
auth_client_secret: your_client_secret
auth_token_url: https://your-sso/token

Pass the path using the config_file argument on any client.

---

Quick Start

AgentOps SDK

from teradata_agentstack import DeviceCodeAuth
from teradata_agentstack.agentops import AgentOpsClient
from teradata_agentstack.agentops import Definitions, Deployments, Secrets, Health
from teradata_agentstack.agentops.models import (
    AgentDefinitionCreateRequest,
    DeploymentCreateRequest,
)

auth = DeviceCodeAuth(
    auth_token_url="https://your-sso/token",
    auth_device_auth_url="https://your-sso/device_authorization",
    auth_client_id="your_client_id"
)

client = AgentOpsClient(
    base_url="https://your-agentops-server",
    auth=auth,
    workspace_id="your-workspace-id",
    namespace="your-k8s-namespace"
)

# Create API instances
agent_definitions = Definitions(client=client)
deployments = Deployments(client=client)
secrets = Secrets(client=client)

# Create an agent definition — pass Pydantic model directly to body=
created = agent_definitions.create(
    body=AgentDefinitionCreateRequest(
        name="my-agent",
        framework="LangGraph",
        artifacts={"storage_type": "zip"},
    ),
    file="/path/to/agent.zip",
)

# Poll until status reaches terminal state (published/failed)
agent_definitions.poll(id=created.id)

# Create a deployment
deployment = deployments.create(body=DeploymentCreateRequest(...))

# Poll until deployment is active/failed/stopped
deployments.poll(id=deployment.agent_id)

Note: The old class names (AgentDefinitions, AgentDeployments, AgentSecrets) still work as backward-compatible aliases.

### Ray Cluster Management SDK

```python
from teradata_agentstack import ClientCredentialsAuth
from teradata_agentstack.rayops import RayClusterManagementClient

auth = ClientCredentialsAuth(
    auth_token_url="https://your-sso/token",
    auth_client_id="your_client_id",
    auth_client_secret="your_client_secret"
)

client = RayClusterManagementClient(
    base_url="https://your-ray-server",
    auth_data=auth
)

Agent Observability SDK

from teradata_agentstack._auth_modes import BearerAuth
from teradata_agentstack.agento11y import AgentO11yClient

auth = BearerAuth(auth_bearer="your_bearer_token")

client = AgentO11yClient(
    base_url="https://your-agento11y-server",
    auth=auth,
    project_id="your-project-id"
)

Access Manager SDK

from teradata_agentstack._auth_modes import BearerAuth
from teradata_agentstack.access_manager import AccessManagerClient

auth = BearerAuth(auth_bearer="your_bearer_token")

client = AccessManagerClient(
    base_url="https://your-server:9876",
    auth=auth
)

Inference Engine SDK

from teradata_agentstack import ClientCredentialsAuth
from teradata_agentstack.inference_engine import InferenceEngineClient

auth = ClientCredentialsAuth(
    auth_token_url="https://your-sso/token",
    auth_client_id="your_client_id",
    auth_client_secret="your_client_secret"
)

client = InferenceEngineClient(
    base_url="https://your-inference-server",
    auth=auth,
    workspace="your-workspace"
)

MCP Management SDK

from teradata_agentstack import ClientCredentialsAuth
from teradata_agentstack.mcp_management import MCPManagementClient

auth = ClientCredentialsAuth(
    auth_token_url="https://your-sso/token",
    auth_client_id="your_client_id",
    auth_client_secret="your_client_secret"
)

client = MCPManagementClient(
    base_url="https://your-mcp-server",
    auth=auth
)

MCP Endpoint Management SDK

from teradata_agentstack._auth_modes import BasicAuth
from teradata_agentstack.mcp_endpoint_management import MCPEndpointManagementClient

auth = BasicAuth(username="user", password="pass")

client = MCPEndpointManagementClient(
    base_url="https://your-mcp-server:8001",
    auth=auth
)

---

Pydantic Model Support

All SDK modules support passing Pydantic models directly to request body parameters. The SDK automatically serializes models before sending:

from teradata_agentstack.agentops.models import AgentDefinitionCreateRequest

# Pass Pydantic model directly — no need to call .model_dump()
result = agent_definitions.create(
    body=AgentDefinitionCreateRequest(name="my-agent", framework="LangGraph", ...),
    file="/path/to/agent.zip",
)

# Equivalent to manually serializing (still supported)
result = agent_definitions.create(
    body=AgentDefinitionCreateRequest(...).model_dump_json(by_alias=True, exclude_unset=True),
    file="/path/to/agent.zip",
)

---

Polling Async Resources

Some SDK classes support a .poll() method for resources that are processed asynchronously:

Module	Class	Terminal States	Success States
agentops	Definitions	published, failed	published
agentops	Deployments	active, failed, stopped	active

# Poll until agent definition is published (or failed)
result = agent_definitions.poll(id=agent_def_id, interval=5, max_attempts=20)

# Poll until deployment is active (or failed/stopped)
result = deployments.poll(id=deployment_id, interval=10, max_attempts=30)

Parameters:

• id (required): Resource ID to poll
• interval (optional): Seconds between polls (default varies by resource)
• max_attempts (optional): Maximum attempts before timeout

---

The blueprint() Function

Every SDK module exposes a blueprint() function that prints the dynamically generated classes available for that module:

from teradata_agentstack.agentops import blueprint
blueprint()
# ----------------------------------------------------------------
# Available classes for AgentOps SDK:
#     * teradata_agentstack.agentops.Definitions
#     * teradata_agentstack.agentops.Deployments
#     ...
# ----------------------------------------------------------------

---

How Dynamic Generation Works

When you import an SDK module (e.g., from teradata_agentstack.agentops import AgentOpsClient), the module:

1. Reads the bundled OpenAPI JSON specification file.
2. Converts each tag into a CamelCase Python class (unrecognized tags fall into DefaultApi).
3. Converts each operationId into a snake_case method on the corresponding class.
4. Validates parameters at call time — checking for required args, correct types, and permitted enum values.

All method arguments must be passed as keyword arguments (positional arguments are not supported).

---

Notes and Limitations

• Only keyword arguments are accepted in generated API methods; positional arguments are not supported.
• Validations are performed for string, integer, number, boolean, array, and object parameter types.
• Pydantic models can be passed directly to body parameters — the SDK auto-serializes them.
• TLS certificate verification is enabled by default. Disable only in trusted development environments (ssl_verify=False).

---

License

Copyright 2026 Teradata. All rights reserved. Teradata Confidential and Trade Secret.