Provider CRD

The Provider custom resource defines a reusable LLM provider configuration that can be referenced by multiple AgentRuntimes. This enables centralized credential management and consistent model configuration across agents.

API Version

apiVersion: omnia.altairalabs.ai/v1alpha1
kind: Provider

Spec Fields

`type`

The provider wire protocol (message/response format).

Value	Description	Requires Credentials
`claude`	Anthropic Claude wire protocol	Yes (unless hosted via `platform`)
`openai`	OpenAI chat completions wire protocol	Yes (unless hosted via `platform`)
`gemini`	Google Gemini wire protocol	Yes (unless hosted via `platform`)
`vllm`	vLLM-served OpenAI-compatible endpoint	No (auth via custom `headers`)
`voyageai`	Voyage AI embedding models	Yes (`VOYAGE_API_KEY`)
`ollama`	Local Ollama models (for development)	No
`mock`	Mock provider (for testing)	No

spec:
  type: claude

Hyperscaler hosting (AWS Bedrock, Azure AI Foundry, GCP Vertex AI) is expressed by setting spec.platform (and spec.auth) on a claude, openai, or gemini provider — not as a separate provider type. See platform below.

`model`

The model identifier to use. If not specified, the provider’s default model is used.

Provider	Example Models
Claude (direct)	`claude-sonnet-4-20250514`, `claude-opus-4-20250514`
Claude on Bedrock	`claude-sonnet-4-20250514` — auto-mapped to the Bedrock model ID
OpenAI (direct)	`gpt-4o`, `gpt-4-turbo`, `gpt-3.5-turbo`
OpenAI on Azure	Your Azure deployment name (e.g., `gpt-4o`)
Gemini (direct)	`gemini-pro`, `gemini-1.5-pro`
Gemini on Vertex	`gemini-1.5-pro`, `gemini-1.5-flash`
vLLM / Ollama	Model name served by the endpoint (e.g., `llama3:8b`)

spec:
  type: claude
  model: claude-sonnet-4-20250514

`credential`

Provider credential configuration. Exactly one sub-field must be specified (or omit credential entirely for providers that don’t need credentials, e.g. mock, ollama, vllm).

Field	Type	Description
`credential.secretRef.name`	string	Name of a Kubernetes Secret
`credential.secretRef.key`	string	Specific key within the Secret (auto-detected if omitted)
`credential.envVar`	string	Environment variable name containing the credential
`credential.filePath`	string	Path to a file containing the credential

Using a Kubernetes Secret

Reference to a Kubernetes Secret containing the credential value:

spec:
  credential:
    secretRef:
      name: anthropic-credentials
      key: ANTHROPIC_API_KEY  # optional

Using an environment variable

For CI/CD pipelines or environments where credentials are pre-injected as environment variables:

spec:
  credential:
    envVar: ANTHROPIC_API_KEY

The variable must be available in the runtime pod. The controller cannot validate its presence — a CredentialConfigured condition is set with reason EnvVar.

Using a mounted file

For credentials mounted as files (e.g., via a volume mount or CSI driver):

spec:
  credential:
    filePath: /var/secrets/api-key

The file must be mounted in the runtime pod. The controller cannot validate its presence — a CredentialConfigured condition is set with reason FilePath.

`platform`

Hyperscaler-specific configuration. Required for provider types bedrock, vertex, and azure-ai.

Field	Type	Required	Description
`platform.type`	string	Yes	Hyperscaler hosting platform: `bedrock`, `vertex`, or `azure`
`platform.region`	string	No	Cloud region (e.g., `us-east-1`, `us-central1`). Required for `bedrock` and `vertex`.
`platform.project`	string	No	GCP project ID — required when `platform.type` is `vertex`.
`platform.endpoint`	string	No	Override the default platform API endpoint — required when `platform.type` is `azure`.

Provider × platform combinations:

Six pairs of wire protocol (spec.type) and hosting platform (spec.platform.type) are supported — the CRD admits them and the PromptKit runtime (v1.4.6+) routes requests correctly:

provider type	supported platforms
`claude`	`bedrock`, `vertex`, `azure`
`openai`	`azure`, `bedrock`
`gemini`	`vertex`

Three pairs are rejected at admission because the hyperscaler does not host that model vendor as a partner endpoint:

openai × vertex — Vertex AI does not host OpenAI.
gemini × bedrock — AWS Bedrock does not host Gemini.
gemini × azure — Azure AI Foundry does not host Gemini.

Setting spec.platform on any other provider type (e.g., vllm, ollama, mock) is also rejected at admission. Setting spec.platform without spec.auth (or vice versa) is likewise rejected.

The authentication method is determined by the platform (see auth below):

platform	allowed auth methods
bedrock	`workloadIdentity`, `accessKey`
vertex	`workloadIdentity`, `serviceAccount`
azure	`workloadIdentity`, `servicePrincipal`

Claude on AWS Bedrock

spec:
  type: claude
  model: claude-sonnet-4-20250514   # auto-mapped to the Bedrock model ID
  platform:
    type: bedrock
    region: us-east-1

Gemini on GCP Vertex AI

spec:
  type: gemini
  model: gemini-1.5-pro
  platform:
    type: vertex
    region: us-central1
    project: my-gcp-project

OpenAI on Azure AI Foundry

spec:
  type: openai
  model: gpt-4o
  platform:
    type: azure
    endpoint: https://my-resource.openai.azure.com

`auth`

Authentication configuration for platform-hosted providers. Required when spec.platform is set; forbidden otherwise.

Field	Type	Required	Description
`auth.type`	string	Yes	Authentication method (see matrix below)
`auth.roleArn`	string	No	AWS IAM role ARN for IRSA (only when `platform.type` is `bedrock`)
`auth.serviceAccountEmail`	string	No	GCP service account email (only when `platform.type` is `vertex`)
`auth.credentialsSecretRef`	SecretKeyRef	No	Secret containing platform credentials (required for static auth)

Platform × auth matrix (enforced by CEL)

`platform.type`	allowed `auth.type`
`bedrock`	`workloadIdentity`, `accessKey`
`vertex`	`workloadIdentity`, `serviceAccount`
`azure`	`workloadIdentity`, `servicePrincipal`

Expected secret keys per static auth type:

`auth.type`	Required keys in `credentialsSecretRef`
`accessKey`	`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` (optional `AWS_SESSION_TOKEN`)
`serviceAccount`	`credentials.json` (or a custom key set via `credentialsSecretRef.key`)
`servicePrincipal`	`AZURE_TENANT_ID`, `AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`

CEL rules:

credentialsSecretRef is required for accessKey, serviceAccount, and servicePrincipal.
credentialsSecretRef is disallowed for workloadIdentity (workload identity relies on pod-level identity).

Workload identity (recommended)

spec:
  type: claude
  platform:
    type: bedrock
    region: us-east-1
  auth:
    type: workloadIdentity
    roleArn: arn:aws:iam::123456789012:role/omnia-bedrock-role

Access key

spec:
  type: claude
  platform:
    type: bedrock
    region: us-east-1
  auth:
    type: accessKey
    credentialsSecretRef:
      name: aws-credentials

`baseURL`

Override the provider’s default API endpoint. Useful for proxies, gateways (OpenRouter), or self-hosted models.

spec:
  type: openai
  baseURL: https://my-openai-proxy.internal/v1

`headers`

Custom HTTP headers included on every provider request. Typical use: gateway providers that require attribution headers (OpenRouter’s HTTP-Referer and X-Title), or shared vLLM deployments that use tenant routing headers.

spec:
  type: openai
  baseURL: https://openrouter.ai/api/v1
  headers:
    HTTP-Referer: https://example.com
    X-Title: omnia
  credential:
    secretRef:
      name: openrouter-credentials

Collisions with built-in provider headers are rejected by PromptKit at request time.

`capabilities`

Lists what modalities and features this provider supports. Used for capability-based filtering when binding Arena providers. The field is optional — existing providers continue to work without it.

Value	Description
`text`	Text generation
`streaming`	Streaming responses
`vision`	Image/visual input
`tools`	Tool/function calling
`json`	Structured JSON output
`audio`	Audio input/output
`video`	Video input
`documents`	Document (PDF) input
`duplex`	Full-duplex (bidirectional streaming)

spec:
  capabilities:
    - text
    - streaming
    - vision
    - tools
    - json

`defaults`

Tuning parameters applied to all requests using this provider.

Field	Type	Range	Description
`temperature`	string	0.0-2.0	Controls randomness (lower = more focused)
`topP`	string	0.0-1.0	Nucleus sampling threshold
`maxTokens`	integer	-	Maximum tokens in response
`contextWindow`	integer	-	Model’s maximum context size in tokens. When conversation history exceeds this budget, truncation is applied. If not specified, no automatic truncation is performed.
`truncationStrategy`	string	-	How to handle context overflow: `sliding` (default — remove oldest messages first), `summarize` (summarize old messages before removing), `custom` (delegate to custom runtime implementation)

spec:
  defaults:
    temperature: "0.7"
    topP: "0.9"
    maxTokens: 4096
    contextWindow: 200000
    truncationStrategy: sliding

`pricing`

Custom pricing for cost tracking. If not specified, PromptKit’s built-in pricing is used.

Field	Type	Description
`inputCostPer1K`	string	Cost per 1000 input tokens
`outputCostPer1K`	string	Cost per 1000 output tokens
`cachedCostPer1K`	string	Cost per 1000 cached tokens

spec:
  pricing:
    inputCostPer1K: "0.003"
    outputCostPer1K: "0.015"
    cachedCostPer1K: "0.0003"

Status Fields

`phase`

Value	Description
`Ready`	Provider is configured and credentials are valid
`Error`	Configuration error or invalid credentials

`conditions`

Type	Description
`Ready`	Overall readiness of the Provider
`SecretFound`	Referenced Secret exists and contains required key
`CredentialConfigured`	Credential source is configured (secretRef, envVar, or filePath)
`AuthConfigured`	Auth configuration is valid (hyperscaler providers only)

Complete Examples

API key provider

apiVersion: v1
kind: Secret
metadata:
  name: anthropic-credentials
  namespace: agents
stringData:
  ANTHROPIC_API_KEY: "sk-ant-api03-..."
---
apiVersion: omnia.altairalabs.ai/v1alpha1
kind: Provider
metadata:
  name: claude-production
  namespace: agents
spec:
  type: claude
  model: claude-sonnet-4-20250514

  credential:
    secretRef:
      name: anthropic-credentials

  capabilities:
    - text
    - streaming
    - vision
    - tools
    - json

  defaults:
    temperature: "0.7"
    maxTokens: 4096
    contextWindow: 200000
    truncationStrategy: sliding

  pricing:
    inputCostPer1K: "0.003"
    outputCostPer1K: "0.015"

Claude on AWS Bedrock with workload identity

apiVersion: omnia.altairalabs.ai/v1alpha1
kind: Provider
metadata:
  name: bedrock-production
  namespace: agents
spec:
  type: claude
  model: claude-sonnet-4-20250514   # auto-mapped to the corresponding Bedrock model ID

  platform:
    type: bedrock
    region: us-east-1

  auth:
    type: workloadIdentity
    roleArn: arn:aws:iam::123456789012:role/omnia-bedrock-role

  capabilities:
    - text
    - streaming
    - vision
    - tools

  defaults:
    temperature: "0.7"
    maxTokens: 4096

Using Provider in AgentRuntime

Reference a Provider from an AgentRuntime using providerRef:

apiVersion: omnia.altairalabs.ai/v1alpha1
kind: AgentRuntime
metadata:
  name: my-agent
spec:
  promptPackRef:
    name: my-prompts

  providerRef:
    name: claude-production
    namespace: agents  # Optional, defaults to same namespace

  facade:
    type: websocket
    port: 8080

Multiple Providers

You can create multiple Provider resources for different use cases:

# Production provider with Claude Sonnet
apiVersion: omnia.altairalabs.ai/v1alpha1
kind: Provider
metadata:
  name: claude-production
spec:
  type: claude
  model: claude-sonnet-4-20250514
  credential:
    secretRef:
      name: prod-credentials
  defaults:
    temperature: "0.3"  # More deterministic
---
apiVersion: omnia.altairalabs.ai/v1alpha1
kind: Provider
metadata:
  name: claude-development
spec:
  type: claude
  model: claude-haiku-20250514
  credential:
    secretRef:
      name: dev-credentials
  defaults:
    temperature: "0.7"

Cross-Namespace References

Providers can be referenced across namespaces:

apiVersion: omnia.altairalabs.ai/v1alpha1
kind: AgentRuntime
metadata:
  name: my-agent
  namespace: app-team
spec:
  providerRef:
    name: shared-claude-provider
    namespace: shared-providers  # Provider in different namespace

Note: Ensure appropriate RBAC permissions are configured for cross-namespace access.

Embedding providers and memory

A Provider with spec.role: embedding backs the memory store’s semantic recall. The embedding vector dimension is taken from the configured model (declared via spec.embedding.dimensions); memory-api sizes its storage to match at startup, so any embedding model can back memory by configuration. Changing the dimension on a store that already holds embeddings is destructive (all vectors are discarded and re-embedded) and requires explicit one-shot consent — see Change the memory embedding model.