Claude Fable 5.1

Anthropic · type: "anthropic"

Model ids: claude-fable-5-1, claude-fable-5

1. Store the credential

Add the provider's API key to the Secrets Vault in the Manager, or with PUT /secretstore/secrets/default/anthropic-key, under the name used below. Configurations reference it as ${vault:…}, so the key never appears in plain text.

${vault:anthropic-key}

2. Configure the LLM task

Use this as the langchain.json of the agent's workflow, or paste the parameters into the LLM editor in the Manager.

{
  "tasks": [
    {
      "actions": [
        "send_message"
      ],
      "id": "chat",
      "type": "anthropic",
      "parameters": {
        "apiKey": "${vault:anthropic-key}",
        "modelName": "claude-fable-5-1",
        "maxTokens": "16384",
        "systemMessage": "You are a helpful assistant",
        "addToOutput": "true"
      }
    }
  ]
}

Or create the whole agent in one call

setup_agent writes the rules, LLM configuration and workflow, then deploys the agent. Call it over MCP or POST /administration/agents/setup.

setup_agent(
  agentName: "My claude-fable-5-1 agent",
  systemPrompt: "You are a helpful assistant.",
  provider: "anthropic",
  model: "claude-fable-5-1",
  apiKey: "${vault:anthropic-key}"
)

Amazon Bedrock · type: "bedrock"

Model ids: anthropic.claude-fable-5-1

1. Store the credential

This provider does not use an API key: Amazon Bedrock authenticates through the AWS credential chain: environment variables, an IAM role, or ~/.aws/credentials.

2. Configure the LLM task

Use this as the langchain.json of the agent's workflow, or paste the parameters into the LLM editor in the Manager.

{
  "tasks": [
    {
      "actions": [
        "send_message"
      ],
      "id": "chat",
      "type": "bedrock",
      "parameters": {
        "modelId": "anthropic.claude-fable-5-1",
        "region": "us-east-1",
        "maxTokens": "16384",
        "systemMessage": "You are a helpful assistant",
        "addToOutput": "true"
      }
    }
  ]
}