add openrouter as a generator provider

openapi.yaml

@@ -9676,6 +9676,7 @@ components:
       oneOf:
         - $ref: '#/components/schemas/OllamaEmbedderConfig'
         - $ref: '#/components/schemas/OpenAIEmbedderConfig'
+        - $ref: '#/components/schemas/OpenRouterEmbedderConfig'
         - $ref: '#/components/schemas/BedrockEmbedderConfig'
         - $ref: '#/components/schemas/CohereEmbedderConfig'
         - $ref: '#/components/schemas/GoogleEmbedderConfig'
@@ -9686,6 +9687,7 @@ components:
         mapping:
           ollama: '#/components/schemas/OllamaEmbedderConfig'
           openai: '#/components/schemas/OpenAIEmbedderConfig'
+          openrouter: '#/components/schemas/OpenRouterEmbedderConfig'
           bedrock: '#/components/schemas/BedrockEmbedderConfig'
           cohere: '#/components/schemas/CohereEmbedderConfig'
           gemini: '#/components/schemas/GoogleEmbedderConfig'
@@ -19132,6 +19134,9 @@ components:
         API key via `api_key` field or `OPENROUTER_API_KEY` environment variable.
 
 
+        Antfly currently supports dense text embeddings through this provider.
+
+
         **Example Models:** openai/text-embedding-3-small (default), openai/text-embedding-3-large,
 
         google/gemini-embedding-001, qwen/qwen3-embedding-8b
@@ -19148,6 +19153,10 @@ components:
           description: The OpenRouter model identifier (e.g., 'openai/text-embedding-3-small', 'google/gemini-embedding-001').
           default: openai/text-embedding-3-small
           example: openai/text-embedding-3-small
+        url:
+          type: string
+          format: uri
+          description: The OpenRouter API base URL. Defaults to OPENROUTER_BASE_URL or https://openrouter.ai/api/v1.
         api_key:
           type: string
           writeOnly: true
@@ -20298,13 +20307,66 @@ components:
       required:
         - provider
         - model
+    OpenRouterGeneratorConfig:
+      type: object
+      description: >
+        Configuration for the OpenRouter generative AI provider.
+      properties:
+        provider:
+          type: string
+          enum:
+            - openrouter
+        model:
+          type: string
+          description: The OpenRouter model identifier to use.
+          example: openai/gpt-4.1
+        url:
+          type: string
+          format: uri
+          description: The URL of the OpenRouter API endpoint.
+          default: https://openrouter.ai/api/v1
+        api_key:
+          type: string
+          writeOnly: true
+          description: The OpenRouter API key.
+        temperature:
+          type: number
+          format: float
+          description: Controls randomness in generation (0.0-2.0).
+          minimum: 0.0
+          maximum: 2.0
+        max_tokens:
+          type: integer
+          description: Maximum number of tokens to generate in the response.
+        top_p:
+          type: number
+          format: float
+          description: Nucleus sampling parameter (0.0-1.0).
+          minimum: 0.0
+          maximum: 1.0
+        frequency_penalty:
+          type: number
+          format: float
+          description: Penalty for token frequency (-2.0 to 2.0).
+          minimum: -2.0
+          maximum: 2.0
+        presence_penalty:
+          type: number
+          format: float
+          description: Penalty for token presence (-2.0 to 2.0).
+          minimum: -2.0
+          maximum: 2.0
+      required:
+        - provider
+        - model
     GeneratorProvider:
       type: string
       enum:
         - gemini
         - vertex
         - ollama
         - openai
+        - openrouter
         - antfly
       description: Generator providers implemented by Antfly's generation runtime.
     GeneratorConfig:
@@ -20317,6 +20379,7 @@ components:
             - $ref: '#/components/schemas/OllamaGeneratorConfig'
             - $ref: '#/components/schemas/AntflyGeneratorConfig'
             - $ref: '#/components/schemas/OpenAIGeneratorConfig'
+            - $ref: '#/components/schemas/OpenRouterGeneratorConfig'
         - properties:
             rate_limit:
               x-go-type-skip-optional-pointer: false

py/packages/sdk/src/antfly/client_generated/models/__init__.py

@@ -791,6 +791,8 @@
 from .open_ai_generator_config_provider import OpenAIGeneratorConfigProvider
 from .open_router_embedder_config import OpenRouterEmbedderConfig
 from .open_router_embedder_config_provider import OpenRouterEmbedderConfigProvider
+from .open_router_generator_config import OpenRouterGeneratorConfig
+from .open_router_generator_config_provider import OpenRouterGeneratorConfigProvider
 from .package_artifact import PackageArtifact
 from .package_artifact_kind import PackageArtifactKind
 from .package_dependency import PackageDependency
@@ -1853,6 +1855,8 @@
     "OpenAIGeneratorConfigProvider",
     "OpenRouterEmbedderConfig",
     "OpenRouterEmbedderConfigProvider",
+    "OpenRouterGeneratorConfig",
+    "OpenRouterGeneratorConfigProvider",
     "PackageArtifact",
     "PackageArtifactKind",
     "PackageDependency",

py/packages/sdk/src/antfly/client_generated/models/create_embeddings_index_request.py

@@ -23,6 +23,7 @@
     from ..models.index_execution_config import IndexExecutionConfig
     from ..models.ollama_embedder_config import OllamaEmbedderConfig
     from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+    from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
     from ..models.vertex_embedder_config import VertexEmbedderConfig
 
 
@@ -69,8 +70,8 @@ class CreateEmbeddingsIndexRequest:
             product similarity. Use "l2_squared" for models trained with Euclidean distance. The default is "l2_squared".
         mem_only (bool | Unset): Whether to use in-memory only storage (dense only)
         embedder (AntflyEmbedderConfig | BedrockEmbedderConfig | CohereEmbedderConfig | GoogleEmbedderConfig |
-            OllamaEmbedderConfig | OpenAIEmbedderConfig | Unset | VertexEmbedderConfig): Embedding provider configuration
-            accepted when Antfly creates and
+            OllamaEmbedderConfig | OpenAIEmbedderConfig | OpenRouterEmbedderConfig | Unset | VertexEmbedderConfig):
+            Embedding provider configuration accepted when Antfly creates and
             maintains an embeddings index. This purpose-specific subset reuses the
             canonical provider configurations; it does not define a second provider
             namespace.
@@ -106,6 +107,7 @@ class CreateEmbeddingsIndexRequest:
         | GoogleEmbedderConfig
         | OllamaEmbedderConfig
         | OpenAIEmbedderConfig
+        | OpenRouterEmbedderConfig
         | Unset
         | VertexEmbedderConfig
     ) = UNSET
@@ -122,6 +124,7 @@ def to_dict(self) -> dict[str, Any]:
         from ..models.google_embedder_config import GoogleEmbedderConfig
         from ..models.ollama_embedder_config import OllamaEmbedderConfig
         from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+        from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
         from ..models.vertex_embedder_config import VertexEmbedderConfig
 
         type_ = self.type_.value
@@ -179,6 +182,8 @@ def to_dict(self) -> dict[str, Any]:
             embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, OpenAIEmbedderConfig):
             embedder = self.embedder.to_dict()
+        elif isinstance(self.embedder, OpenRouterEmbedderConfig):
+            embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, BedrockEmbedderConfig):
             embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, CohereEmbedderConfig):
@@ -268,6 +273,7 @@ def from_dict(cls: type[T], src_dict: Mapping[str, Any]) -> T:
         from ..models.index_execution_config import IndexExecutionConfig
         from ..models.ollama_embedder_config import OllamaEmbedderConfig
         from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+        from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
         from ..models.vertex_embedder_config import VertexEmbedderConfig
 
         d = dict(src_dict)
@@ -341,6 +347,7 @@ def _parse_embedder(
             | GoogleEmbedderConfig
             | OllamaEmbedderConfig
             | OpenAIEmbedderConfig
+            | OpenRouterEmbedderConfig
             | Unset
             | VertexEmbedderConfig
         ):
@@ -365,40 +372,48 @@ def _parse_embedder(
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_2 = BedrockEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_2 = OpenRouterEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_2
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_3 = CohereEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_3 = BedrockEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_3
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_4 = GoogleEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_4 = CohereEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_4
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_5 = VertexEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_5 = GoogleEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_5
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
+            try:
+                if not isinstance(data, dict):
+                    raise TypeError()
+                componentsschemas_index_embedder_config_type_6 = VertexEmbedderConfig.from_dict(data)
+
+                return componentsschemas_index_embedder_config_type_6
+            except (TypeError, ValueError, AttributeError, KeyError):
+                pass
             if not isinstance(data, dict):
                 raise TypeError()
-            componentsschemas_index_embedder_config_type_6 = AntflyEmbedderConfig.from_dict(data)
+            componentsschemas_index_embedder_config_type_7 = AntflyEmbedderConfig.from_dict(data)
 
-            return componentsschemas_index_embedder_config_type_6
+            return componentsschemas_index_embedder_config_type_7
 
         embedder = _parse_embedder(d.pop("embedder", UNSET))
 

py/packages/sdk/src/antfly/client_generated/models/embeddings_index_config.py

@@ -21,6 +21,7 @@
     from ..models.index_execution_config import IndexExecutionConfig
     from ..models.ollama_embedder_config import OllamaEmbedderConfig
     from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+    from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
     from ..models.vertex_embedder_config import VertexEmbedderConfig
 
 
@@ -65,8 +66,8 @@ class EmbeddingsIndexConfig:
                 product similarity. Use "l2_squared" for models trained with Euclidean distance. The default is "l2_squared".
             mem_only (bool | Unset): Whether to use in-memory only storage (dense only)
             embedder (AntflyEmbedderConfig | BedrockEmbedderConfig | CohereEmbedderConfig | GoogleEmbedderConfig |
-                OllamaEmbedderConfig | OpenAIEmbedderConfig | Unset | VertexEmbedderConfig): Embedding provider configuration
-                accepted when Antfly creates and
+                OllamaEmbedderConfig | OpenAIEmbedderConfig | OpenRouterEmbedderConfig | Unset | VertexEmbedderConfig):
+                Embedding provider configuration accepted when Antfly creates and
                 maintains an embeddings index. This purpose-specific subset reuses the
                 canonical provider configurations; it does not define a second provider
                 namespace.
@@ -98,6 +99,7 @@ class EmbeddingsIndexConfig:
         | GoogleEmbedderConfig
         | OllamaEmbedderConfig
         | OpenAIEmbedderConfig
+        | OpenRouterEmbedderConfig
         | Unset
         | VertexEmbedderConfig
     ) = UNSET
@@ -114,6 +116,7 @@ def to_dict(self) -> dict[str, Any]:
         from ..models.google_embedder_config import GoogleEmbedderConfig
         from ..models.ollama_embedder_config import OllamaEmbedderConfig
         from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+        from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
         from ..models.vertex_embedder_config import VertexEmbedderConfig
 
         publication_policy: str | Unset = UNSET
@@ -158,6 +161,8 @@ def to_dict(self) -> dict[str, Any]:
             embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, OpenAIEmbedderConfig):
             embedder = self.embedder.to_dict()
+        elif isinstance(self.embedder, OpenRouterEmbedderConfig):
+            embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, BedrockEmbedderConfig):
             embedder = self.embedder.to_dict()
         elif isinstance(self.embedder, CohereEmbedderConfig):
@@ -236,6 +241,7 @@ def from_dict(cls: type[T], src_dict: Mapping[str, Any]) -> T:
         from ..models.index_execution_config import IndexExecutionConfig
         from ..models.ollama_embedder_config import OllamaEmbedderConfig
         from ..models.open_ai_embedder_config import OpenAIEmbedderConfig
+        from ..models.open_router_embedder_config import OpenRouterEmbedderConfig
         from ..models.vertex_embedder_config import VertexEmbedderConfig
 
         d = dict(src_dict)
@@ -294,6 +300,7 @@ def _parse_embedder(
             | GoogleEmbedderConfig
             | OllamaEmbedderConfig
             | OpenAIEmbedderConfig
+            | OpenRouterEmbedderConfig
             | Unset
             | VertexEmbedderConfig
         ):
@@ -318,40 +325,48 @@ def _parse_embedder(
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_2 = BedrockEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_2 = OpenRouterEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_2
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_3 = CohereEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_3 = BedrockEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_3
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_4 = GoogleEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_4 = CohereEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_4
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
             try:
                 if not isinstance(data, dict):
                     raise TypeError()
-                componentsschemas_index_embedder_config_type_5 = VertexEmbedderConfig.from_dict(data)
+                componentsschemas_index_embedder_config_type_5 = GoogleEmbedderConfig.from_dict(data)
 
                 return componentsschemas_index_embedder_config_type_5
             except (TypeError, ValueError, AttributeError, KeyError):
                 pass
+            try:
+                if not isinstance(data, dict):
+                    raise TypeError()
+                componentsschemas_index_embedder_config_type_6 = VertexEmbedderConfig.from_dict(data)
+
+                return componentsschemas_index_embedder_config_type_6
+            except (TypeError, ValueError, AttributeError, KeyError):
+                pass
             if not isinstance(data, dict):
                 raise TypeError()
-            componentsschemas_index_embedder_config_type_6 = AntflyEmbedderConfig.from_dict(data)
+            componentsschemas_index_embedder_config_type_7 = AntflyEmbedderConfig.from_dict(data)
 
-            return componentsschemas_index_embedder_config_type_6
+            return componentsschemas_index_embedder_config_type_7
 
         embedder = _parse_embedder(d.pop("embedder", UNSET))
 

py/packages/sdk/src/antfly/client_generated/models/generator_provider.py

@@ -6,6 +6,7 @@ class GeneratorProvider(StrEnum):
     GEMINI = "gemini"
     OLLAMA = "ollama"
     OPENAI = "openai"
+    OPENROUTER = "openrouter"
     VERTEX = "vertex"
 
     def __str__(self) -> str: