Configure model providers for Engine on self-hosted

Running Engine on your own model providers requires LangSmith Helm chart 0.17.0 or later. Earlier chart versions run Engine only on LangSmith Intelligence.

On self-hosted LangSmith, an Organization Admin chooses where LangSmith Engine runs its model calls: on LangSmith Intelligence (LSI), on your own model providers, or both. Running Engine on your own providers keeps prompts and responses under your agreements with those providers. This page covers how to select providers, authenticate to them, and connect to them or to LSI over private networking. For a comparison of the two options, see Choose how Engine runs its models. Complete Install Engine first.

Use your own model providers

Engine calls your providers directly from your cluster. Prompts and responses go only to your provider, under your agreement with that provider. LangChain does not receive them. Sandbox usage by Engine is included in Engine’s LSU billing. The sandboxes Engine uses to diagnose issues, generate fixes, and write evaluators do not incur additional Sandboxes product charges. Engine supports Anthropic, OpenAI, Amazon Bedrock, Google Vertex AI, and Azure AI Foundry. It uses a mix of models from the providers you select, so for the best results, add credentials for every provider you have access to. Enable access to Anthropic’s Claude models and OpenAI’s GPT models in your provider accounts, where your provider offers them. On Azure, use an Azure AI Foundry resource. To confirm that Engine can reach the models it uses, run Test connection.

Choose model providers

Under Settings > Engine > Model providers, an Organization Admin selects LangSmith Intelligence, one or more of your own providers, or both. Credentials are saved as organization secrets and apply to every workspace in the organization. LangSmith Intelligence appears only when your installation’s engine.intelligenceBaseUrl serves Engine’s models. When LangSmith Intelligence is available and selected, it takes priority over your selected providers. Leave it unselected to keep inference on your own providers. Engine selects models from the providers you enable. Enable access to Claude 5.5-class models or GPT 5.6-class models in your provider accounts, depending on which models each provider offers. Model requirements follow the Engine image tag in images.engineInsightsAgentImage.tag, not the Helm chart version. Run Test connection to identify the required models and confirm access for your installed Engine image. To use your own providers:

Add credentials

Click Add credentials next to the provider and enter:

Anthropic
Amazon Bedrock
Google Vertex AI
Azure AI Foundry
OpenAI

An Anthropic API key.

An AWS access key ID and secret access key, with an optional session token, or a Bedrock API key (bearer token). Optionally, an AWS region.Engine uses the Bedrock Mantle API. Enable access to the required models in the selected AWS account and region. Grant the calling identity Mantle inference and model-subscription permissions. See AmazonBedrockMantleInferenceAccess for the required actions.If no region is saved, Engine uses us-east-1, including with workload identity. It does not inherit the EKS cluster’s region.To use the cloud identity of Engine’s pods instead, see Use cloud identity for your model providers.

A service account key in JSON format. Engine uses the JSON’s project_id as the inference project and calls the global location.Enable the required models through Model Garden in that project. Grant the service account roles/aiplatform.user in the same project.To use the cloud identity of Engine’s pods instead, see Use cloud identity for your model providers.

A provider with complete credentials shows as configured. To change them later, click the key icon next to the provider.

Test the provider

Click Test connection. Engine sends a small request to each model it uses on that provider, with the saved credentials or configured cloud identity. It shows whether each request succeeded and, if not, why.

Select providers and save

Select the providers Engine may use, then save.

If Engine runs fail after you save:

No provider selected: Engine does not start runs until at least one provider is selected and saved.
Rejected credentials: Test connection reports which provider rejected them. Update the credentials and test again.
Model not available: enable the model in your provider account, then test again.

Use cloud identity for your model providers

Instead of saving credentials, Amazon Bedrock, Google Vertex AI, and Azure AI Foundry can authenticate with the cloud identity of Engine’s pods instead of credentials saved in LangSmith. List those providers in engine.workloadIdentityProviders. Configure identity for both the API server and queue of the shared Engine and Insights deployment:

Amazon EKS
Google GKE
Azure AKS

Configure IAM roles for service accounts (IRSA). Trust the namespace and service account of both Engine workloads, and grant the role access to the Bedrock models Engine uses.

engine:
  workloadIdentityProviders:
    - bedrock

engineInsightsAgent:
  apiServer:
    serviceAccount:
      annotations:
        eks.amazonaws.com/role-arn: "arn:aws:iam::<account-id>:role/<engine-model-access-role>"
  queue:
    serviceAccount:
      annotations:
        eks.amazonaws.com/role-arn: "arn:aws:iam::<account-id>:role/<engine-model-access-role>"

Enable Workload Identity Federation for GKE on the cluster and the node pools that run Engine. Grant each Engine Kubernetes service account roles/iam.workloadIdentityUser on the Google service account. Bind each member as serviceAccount:<cluster-project-id>.svc.id.goog[<namespace>/<kubernetes-service-account>].Grant the Google service account Vertex AI inference permissions, such as roles/aiplatform.user, in the project that hosts the models. Enable the required models in that project.

engine:
  workloadIdentityProviders:
    - vertex

engineInsightsAgent:
  apiServer:
    serviceAccount:
      annotations:
        iam.gke.io/gcp-service-account: "<service-account>@<project-id>.iam.gserviceaccount.com"
    deployment:
      extraEnv:
        - name: GOOGLE_CLOUD_PROJECT
          value: "<vertex-project-id>"
  queue:
    serviceAccount:
      annotations:
        iam.gke.io/gcp-service-account: "<service-account>@<project-id>.iam.gserviceaccount.com"
    deployment:
      extraEnv:
        - name: GOOGLE_CLOUD_PROJECT
          value: "<vertex-project-id>"

Engine uses Application Default Credentials (ADC) to resolve the identity and project. Set GOOGLE_CLOUD_PROJECT on both workloads to the intended Vertex AI project. Engine currently uses the global Vertex AI location, so the required models must be available there.

Enable the AKS OIDC issuer and Microsoft Entra Workload ID. Create a federated identity credential for each Engine Kubernetes service account on your managed identity. Use the cluster’s OIDC issuer, audience api://AzureADTokenExchange, and subject system:serviceaccount:<namespace>:<kubernetes-service-account> for each.Grant the identity the Cognitive Services User role, scoped to the Azure AI Foundry resource that hosts Engine’s models. An Azure OpenAI role alone does not establish access to Claude models.

engine:
  workloadIdentityProviders:
    - azure

engineInsightsAgent:
  apiServer:
    serviceAccount:
      annotations:
        azure.workload.identity/client-id: "<managed-identity-client-id>"
    deployment:
      labels:
        azure.workload.identity/use: "true"
  queue:
    serviceAccount:
      annotations:
        azure.workload.identity/client-id: "<managed-identity-client-id>"
    deployment:
      labels:
        azure.workload.identity/use: "true"

Both workloads need the service account annotation and pod label. Save the Foundry resource name under Settings > Engine > Model providers.

A provider listed in engine.workloadIdentityProviders counts as configured without saved credentials, so any Organization Admin can select it. Scope the identity to the models Engine uses.

Saved credentials take precedence over cloud identity. When switching a provider, apply the cloud identity configuration before removing its saved credentials. Then remove only that provider’s saved API keys or service account JSON under Settings > Engine > Model providers. For Bedrock, also remove any saved access key, secret access key, and session token. Keep the Azure AI Foundry resource name and any Bedrock region setting, then test the provider again.

Connect privately

Engine works over public egress. To keep its traffic on private networking, connect to LangSmith Intelligence with AWS PrivateLink, or connect to your own model providers through your cloud’s private endpoints.

Connect with AWS PrivateLink

The LangSmith Intelligence gateway, beacon.aws.langchain.com, routes requests to Amazon Bedrock in LangChain’s AWS environment. Before configuring PrivateLink, complete Install Engine, including its Helm and egress configuration. AWS PrivateLink routes Engine traffic from your VPC to LSI without exposing that traffic to the public internet. The LSI endpoint service is hosted in us-east-2, and AWS supports access from VPCs in other regions. Before you begin, collect your AWS account ID, VPC ID, private subnet IDs, and a security group for the interface endpoint. Configure that endpoint security group to allow inbound TCP traffic on port 443 only from the security group attached to the nodes or workloads that run Engine, or from the smallest private CIDR that contains them. Do not allow 0.0.0.0/0. To connect your VPC to LSI:

Request access

Contact your account representative or sales@langchain.dev with your AWS account ID. LangChain adds your account to the endpoint service’s allowed principals list.

Create the interface VPC endpoint

Configure the AWS provider for the region that contains your VPC. Keep service_region set to us-east-2, including when your VPC is in another region. Select one private subnet per availability zone.

The service_region argument requires HashiCorp AWS provider 5.82.0 or later.

resource "aws_vpc_endpoint" "langsmith_intelligence" {
  vpc_id              = var.vpc_id
  service_name        = "com.amazonaws.vpce.us-east-2.vpce-svc-054f37092752bff6b"
  service_region      = "us-east-2"
  vpc_endpoint_type   = "Interface"
  subnet_ids          = var.private_subnet_ids
  security_group_ids  = [var.security_group_id]
  private_dns_enabled = false
}

Wait for LangChain to accept the connection

The endpoint status changes from pendingAcceptance to available after LangChain accepts the connection. Allow a few minutes for the change to propagate before testing connectivity.

Route the LSI hostname to the endpoint

Enable DNS resolution and DNS hostnames for your VPC. Then, create a Route 53 private hosted zone and alias record so beacon.aws.langchain.com resolves to the VPC endpoint inside your VPC. Keep this hostname unchanged so TLS certificate validation succeeds. The private hosted zone also prevents fallback to public DNS when the endpoint is unavailable.

resource "aws_route53_zone" "langsmith_intelligence" {
  name = "beacon.aws.langchain.com"

  vpc {
    vpc_id = var.vpc_id
  }
}

resource "aws_route53_record" "langsmith_intelligence" {
  zone_id = aws_route53_zone.langsmith_intelligence.zone_id
  name    = "beacon.aws.langchain.com"
  type    = "A"

  alias {
    name                   = aws_vpc_endpoint.langsmith_intelligence.dns_entry[0].dns_name
    zone_id                = aws_vpc_endpoint.langsmith_intelligence.dns_entry[0].hosted_zone_id
    evaluate_target_health = true
  }
}

If workloads use a corporate DNS resolver instead of the Amazon-provided resolver, configure conditional forwarding to Route 53 Resolver or create an equivalent private DNS override for beacon.aws.langchain.com that points to the endpoint DNS name.

Verify private connectivity

From a node or container that runs Engine, resolve the gateway hostname:

getent ahostsv4 beacon.aws.langchain.com

Confirm that the result contains the private IP addresses assigned to the endpoint network interfaces. Then start an analysis and confirm that it completes successfully. If the analysis does not complete, review the Engine installation and egress configuration.

Connect to your providers privately

Engine calls each provider’s standard API hostname from the standalone-insights pods. To keep that traffic off the public internet, set up your cloud’s private connection to the provider so the same hostname resolves to private addresses inside your network:

Provider	Hostname Engine calls	Private connection
Amazon Bedrock	`bedrock-mantle.<region>.api.aws`	An interface VPC endpoint for `com.amazonaws.<region>.bedrock-mantle`, with private DNS enabled or a Route 53 private hosted zone for the hostname
Google Vertex AI	`<region>-aiplatform.googleapis.com`, or `aiplatform.googleapis.com` for the global region	Private Service Connect for Google APIs, with private DNS for `googleapis.com`
Azure AI Foundry	`<resource>.openai.azure.com` and `<resource>.services.ai.azure.com`	A private endpoint for the Foundry resource, with its private DNS zones
Anthropic, OpenAI	`api.anthropic.com`, `api.openai.com`	Not available. These providers need public egress.

Engine does not accept custom endpoint URLs, so private connectivity works through DNS: the standard hostname must resolve to your private endpoint from the Engine pods. If Engine authenticates with cloud identity, the pods also need to reach your cloud’s token service, such as an interface VPC endpoint for AWS STS on AWS. With saved Vertex AI service account JSON, also allow HTTPS to oauth2.googleapis.com for token exchange, alongside aiplatform.googleapis.com for inference. Usage reporting to LangSmith Intelligence is a separate connection. On AWS, https://beacon.aws.langchain.com/intelligence records usage and supports AWS PrivateLink, so an installation can run Engine on Bedrock and report usage without public egress. The default https://beacon.langchain.com/intelligence needs public egress.

Self-hosted

Configure model providers for Engine on self-hosted

Use your own model providers

Choose model providers

Use cloud identity for your model providers

Connect privately

Connect with AWS PrivateLink

Connect to your providers privately

See also

​Use your own model providers

​Choose model providers

​Use cloud identity for your model providers

​Connect privately

​Connect with AWS PrivateLink

​Connect to your providers privately

​See also

Use your own model providers

Choose model providers

Use cloud identity for your model providers

Connect privately

Connect with AWS PrivateLink

Connect to your providers privately

See also