HelixML

Configure LLM providers

Connect Anthropic, OpenAI, Google, Groq, AWS Bedrock, NVIDIA NIM, Ollama, and other providers to Helix so agents have inference available.

Helix doesn't bundle its own models — it routes to providers you configure. Providers can be set at three levels:

  • User — personal providers, visible only to you (Account → AI Providers)
  • Organisation — shared across all org members (Organisation → Providers); see Manage your organisation
  • Installation — configured in Helm values for self-hosted deployments; available to all users on the instance

This guide covers UI configuration and Helm values. For org-level providers, see Manage your organisation: Configure org-level AI providers.

Adding a provider in the UI

Go to Account → AI Providers and click Add Provider. Select the provider type, paste your API key, and save.

Supported providers

ProviderNotes
AnthropicClaude models (Sonnet, Opus, Haiku). Recommended for coding agents.
OpenAIGPT-4o, o1, o3, and other OpenAI models
Google GeminiGemini models via the Gemini API
NVIDIA NIMGPU-accelerated inference; add the NIM base URL and API key
AWS BedrockClaude and other models via Amazon Bedrock; configure region and AWS credentials
GroqUltra-low latency open-source model inference (Llama, Mistral, Gemma)
CerebrasWafer-scale inference for fast open-source models
xAI GrokGrok models from xAI
Together AIWide catalogue of hosted open-source models
Fireworks AIFast inference for open-source models
OllamaLocal model server; run models on your own hardware
Custom (OpenAI-compatible)Any endpoint that speaks the OpenAI /v1/chat/completions API — vLLM, LM Studio, LocalAI, etc.

Once a provider is added, its models become available in the Model dropdown on any project, agent app, or agent.

Choosing a model for a coding project

Set model and provider on the agent in your project YAML:

spec:
  agent:
    name: "My Agent"
    runtime: claude_code
    model: claude-sonnet-4-6
    provider: anthropic

Claude Code is an exception — it manages its own model selection. Set runtime: claude_code and omit model/provider.

Helix Cloud: built-in providers

On Helix Cloud, built-in Helix inference providers are available by default — you can use these without adding your own API keys. Add your own keys if you want to use a specific model tier or route to your own accounts.

Self-hosted: configure providers via Helm

For Kubernetes deployments, configure providers under controlplane.providers in your values.yaml. Use existingSecret to keep API keys out of your values file and git history:

kubectl create secret generic anthropic-api-key \
  --from-literal=api-key="sk-ant-..."
controlplane:
  providers:
    anthropic:
      existingSecret: "anthropic-api-key"
      existingSecretApiKeyKey: "api-key"
    openai:
      existingSecret: "openai-api-key"
      existingSecretApiKeyKey: "api-key"
    groq:
      existingSecret: "groq-api-key"
      existingSecretApiKeyKey: "api-key"
    vllm:
      baseUrl: "http://my-vllm.vllm.svc.cluster.local:8000/v1"

See Linux & Kubernetes for the full Helm values reference.

Local models with Ollama (Mac App)

On the Mac App with 64 GB+ unified memory, run models locally via Ollama:

  1. Install Ollama
  2. Pull a model: ollama pull llama3.3
  3. In Helix, add an Ollama or Custom (OpenAI-compatible) provider with base URL http://localhost:11434/v1 and no API key
  4. The model appears in the model dropdown as llama3.3

Toggle wifi off and the agent still works — entirely local.

AWS Bedrock

Bedrock uses AWS IAM credentials rather than an API key:

  1. Add an AWS Bedrock provider and choose your region.
  2. Provide an IAM access key and secret (or use an instance role for EC2/EKS deployments).
  3. Enable the models you want in the AWS Bedrock console — models must be explicitly enabled per region.

For Helm-based deployments:

controlplane:
  providers:
    bedrock:
      region: "us-east-1"
      existingSecret: "aws-credentials"
      existingSecretAccessKeyKey: "access-key-id"
      existingSecretSecretKeyKey: "secret-access-key"

Anthropic via GCP Vertex AI

For organisations that prefer to route Anthropic inference through Google Cloud:

controlplane:
  providers:
    anthropic:
      vertexProjectID: "my-gcp-project"
      vertexRegion: "us-east5"
      vertexCredentialsSecret: "gcp-vertex-credentials"

See the chart's values-example.yaml for the full shape.

Model availability

The Reference: Supported models page lists model identifiers and which providers support them.