feat: add LiteLLM as AI gateway vision-language model provider

dimos/models/vl/create.py

@@ -29,3 +29,7 @@ def create(name: VlModelName) -> VlModel:
             from dimos.models.vl.moondream import MoondreamVlModel
 
             return MoondreamVlModel()
+        case "litellm":
+            from dimos.models.vl.litellm import LiteLLMVlModel
+
+            return LiteLLMVlModel()

dimos/models/vl/litellm.py

@@ -0,0 +1,146 @@
+# Copyright 2026 Dimensional Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from dimos.models.vl.base import VlModel, VlModelConfig
+from dimos.msgs.sensor_msgs.Image import Image
+from dimos.utils.logging_config import setup_logger
+
+logger = setup_logger()
+
+
+class LiteLLMVlModelConfig(VlModelConfig):
+    """Configuration for LiteLLM-backed vision-language model.
+
+    LiteLLM provides a unified interface to 100+ LLM providers (OpenAI, Anthropic,
+    Google, Azure, Bedrock, etc.) through a single API.
+    """
+
+    model_name: str = "gpt-4o-mini"
+    api_key: str | None = None
+    api_base: str | None = None
+
+
+class LiteLLMVlModel(VlModel):
+    """Vision-language model backed by LiteLLM.
+
+    Supports any vision-capable model available through LiteLLM, including
+    OpenAI GPT-4o, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock,
+    and many more through a single unified interface.
+
+    See https://docs.litellm.ai/docs/providers for the full list of supported
+    providers and model identifiers.
+    """
+
+    config: LiteLLMVlModelConfig
+
+    def _completion(self, **kwargs: Any) -> Any:
+        try:
+            import litellm
+        except ImportError as e:
+            raise ImportError(
+                "litellm is required for LiteLLMVlModel. "
+                "Install it with: pip install 'dimos[litellm]'"
+            ) from e
+
+        if self.config.api_key:
+            kwargs["api_key"] = self.config.api_key
+
+        if self.config.api_base:
+            kwargs["api_base"] = self.config.api_base
+
+        return litellm.completion(drop_params=True, **kwargs)
+
+    def query(
+        self,
+        image: Image | np.ndarray,
+        query: str,
+        response_format: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> str:
+        if isinstance(image, np.ndarray):
+            import warnings
+
+            warnings.warn(
+                "LiteLLMVlModel.query should receive standard dimos Image type, not a numpy array",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            image = Image.from_numpy(image)
+
+        image, _ = self._prepare_image(image)
+        img_base64 = image.to_base64()
+
+        api_kwargs: dict[str, Any] = {
+            "model": self.config.model_name,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:image/jpeg;base64,{img_base64}"},
+                        },
+                        {"type": "text", "text": query},
+                    ],
+                }
+            ],
+        }
+
+        if response_format:
+            api_kwargs["response_format"] = response_format
+
+        response = self._completion(**api_kwargs)
+        return response.choices[0].message.content or ""
+
+    def query_batch(
+        self,
+        images: list[Image],
+        query: str,
+        response_format: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> list[str]:
+        """Query VLM with multiple images using a single API call."""
+        if not images:
+            return []
+
+        content: list[dict[str, Any]] = [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{self._prepare_image(img)[0].to_base64()}"
+                },
+            }
+            for img in images
+        ]
+        content.append({"type": "text", "text": query})
+
+        api_kwargs: dict[str, Any] = {
+            "model": self.config.model_name,
+            "messages": [{"role": "user", "content": content}],
+        }
+        if response_format:
+            api_kwargs["response_format"] = response_format
+
+        response = self._completion(**api_kwargs)
+        response_text = response.choices[0].message.content or ""
+        return [response_text] * len(images)
+
+    def stop(self) -> None:
+        pass