feat: generic OpenAI Compatible LLM Provider setup (#9968) to release v3.1 (#9975)

Co-authored-by: Jamison Lahman <jamison@lahman.dev>

backend/onyx/llm/constants.py

@@ -26,6 +26,7 @@ class LlmProviderNames(str, Enum):
     MISTRAL = "mistral"
     LITELLM_PROXY = "litellm_proxy"
     BIFROST = "bifrost"
+    OPENAI_COMPATIBLE = "openai_compatible"
 
     def __str__(self) -> str:
         """Needed so things like:
@@ -46,6 +47,7 @@ WELL_KNOWN_PROVIDER_NAMES = [
     LlmProviderNames.LM_STUDIO,
     LlmProviderNames.LITELLM_PROXY,
     LlmProviderNames.BIFROST,
+    LlmProviderNames.OPENAI_COMPATIBLE,
 ]
 
 
@@ -64,6 +66,7 @@ PROVIDER_DISPLAY_NAMES: dict[str, str] = {
     LlmProviderNames.LM_STUDIO: "LM Studio",
     LlmProviderNames.LITELLM_PROXY: "LiteLLM Proxy",
     LlmProviderNames.BIFROST: "Bifrost",
+    LlmProviderNames.OPENAI_COMPATIBLE: "OpenAI Compatible",
     "groq": "Groq",
     "anyscale": "Anyscale",
     "deepseek": "DeepSeek",
@@ -116,6 +119,7 @@ AGGREGATOR_PROVIDERS: set[str] = {
     LlmProviderNames.AZURE,
     LlmProviderNames.LITELLM_PROXY,
     LlmProviderNames.BIFROST,
+    LlmProviderNames.OPENAI_COMPATIBLE,
 }
 
 # Model family name mappings for display name generation

backend/onyx/llm/multi_llm.py

@@ -305,12 +305,19 @@ class LitellmLLM(LLM):
         ):
             model_kwargs[VERTEX_LOCATION_KWARG] = "global"
 
-        # Bifrost: OpenAI-compatible proxy that expects model names in
-        # provider/model format (e.g. "anthropic/claude-sonnet-4-6").
-        # We route through LiteLLM's openai provider with the Bifrost base URL,
-        # and ensure /v1 is appended.
-        if model_provider == LlmProviderNames.BIFROST:
+        # Bifrost and OpenAI-compatible: OpenAI-compatible proxies that send
+        # model names directly to the endpoint. We route through LiteLLM's
+        # openai provider with the server's base URL, and ensure /v1 is appended.
+        if model_provider in (
+            LlmProviderNames.BIFROST,
+            LlmProviderNames.OPENAI_COMPATIBLE,
+        ):
             self._custom_llm_provider = "openai"
+            # LiteLLM's OpenAI client requires an api_key to be set.
+            # Many OpenAI-compatible servers don't need auth, so supply a
+            # placeholder to prevent LiteLLM from raising AuthenticationError.
+            if not self._api_key:
+                model_kwargs.setdefault("api_key", "not-needed")
             if self._api_base is not None:
                 base = self._api_base.rstrip("/")
                 self._api_base = base if base.endswith("/v1") else f"{base}/v1"
@@ -427,17 +434,20 @@ class LitellmLLM(LLM):
         optional_kwargs: dict[str, Any] = {}
 
         # Model name
-        is_bifrost = self._model_provider == LlmProviderNames.BIFROST
+        is_openai_compatible_proxy = self._model_provider in (
+            LlmProviderNames.BIFROST,
+            LlmProviderNames.OPENAI_COMPATIBLE,
+        )
         model_provider = (
             f"{self.config.model_provider}/responses"
             if is_openai_model  # Uses litellm's completions -> responses bridge
             else self.config.model_provider
         )
-        if is_bifrost:
-            # Bifrost expects model names in provider/model format
-            # (e.g. "anthropic/claude-sonnet-4-6") sent directly to its
-            # OpenAI-compatible endpoint. We use custom_llm_provider="openai"
-            # so LiteLLM doesn't try to route based on the provider prefix.
+        if is_openai_compatible_proxy:
+            # OpenAI-compatible proxies (Bifrost, generic OpenAI-compatible
+            # servers) expect model names sent directly to their endpoint.
+            # We use custom_llm_provider="openai" so LiteLLM doesn't try
+            # to route based on the provider prefix.
             model = self.config.deployment_name or self.config.model_name
         else:
             model = f"{model_provider}/{self.config.deployment_name or self.config.model_name}"
@@ -528,7 +538,10 @@ class LitellmLLM(LLM):
         if structured_response_format:
             optional_kwargs["response_format"] = structured_response_format
 
-        if not (is_claude_model or is_ollama or is_mistral) or is_bifrost:
+        if (
+            not (is_claude_model or is_ollama or is_mistral)
+            or is_openai_compatible_proxy
+        ):
             # Litellm bug: tool_choice is dropped silently if not specified here for OpenAI
             # However, this param breaks Anthropic and Mistral models,
             # so it must be conditionally included unless the request is

backend/onyx/llm/well_known_providers/constants.py

@@ -15,6 +15,8 @@ LITELLM_PROXY_PROVIDER_NAME = "litellm_proxy"
 
 BIFROST_PROVIDER_NAME = "bifrost"
 
+OPENAI_COMPATIBLE_PROVIDER_NAME = "openai_compatible"
+
 # Providers that use optional Bearer auth from custom_config
 PROVIDERS_WITH_SPECIAL_API_KEY_HANDLING: dict[str, str] = {
     LlmProviderNames.OLLAMA_CHAT: OLLAMA_API_KEY_CONFIG_KEY,

backend/onyx/llm/well_known_providers/llm_provider_options.py

@@ -19,6 +19,7 @@ from onyx.llm.well_known_providers.constants import BIFROST_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import LITELLM_PROXY_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import LM_STUDIO_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import OLLAMA_PROVIDER_NAME
+from onyx.llm.well_known_providers.constants import OPENAI_COMPATIBLE_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import OPENAI_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import OPENROUTER_PROVIDER_NAME
 from onyx.llm.well_known_providers.constants import VERTEXAI_PROVIDER_NAME
@@ -51,6 +52,7 @@ def _get_provider_to_models_map() -> dict[str, list[str]]:
         OPENROUTER_PROVIDER_NAME: [],  # Dynamic - fetched from OpenRouter API
         LITELLM_PROXY_PROVIDER_NAME: [],  # Dynamic - fetched from LiteLLM proxy API
         BIFROST_PROVIDER_NAME: [],  # Dynamic - fetched from Bifrost API
+        OPENAI_COMPATIBLE_PROVIDER_NAME: [],  # Dynamic - fetched from OpenAI-compatible API
     }
 
 
@@ -336,6 +338,7 @@ def get_provider_display_name(provider_name: str) -> str:
         VERTEXAI_PROVIDER_NAME: "Google Vertex AI",
         OPENROUTER_PROVIDER_NAME: "OpenRouter",
         LITELLM_PROXY_PROVIDER_NAME: "LiteLLM Proxy",
+        OPENAI_COMPATIBLE_PROVIDER_NAME: "OpenAI Compatible",
     }
 
     if provider_name in _ONYX_PROVIDER_DISPLAY_NAMES:

backend/onyx/server/manage/llm/api.py

@@ -74,6 +74,8 @@ from onyx.server.manage.llm.models import ModelConfigurationUpsertRequest
 from onyx.server.manage.llm.models import OllamaFinalModelResponse
 from onyx.server.manage.llm.models import OllamaModelDetails
 from onyx.server.manage.llm.models import OllamaModelsRequest
+from onyx.server.manage.llm.models import OpenAICompatibleFinalModelResponse
+from onyx.server.manage.llm.models import OpenAICompatibleModelsRequest
 from onyx.server.manage.llm.models import OpenRouterFinalModelResponse
 from onyx.server.manage.llm.models import OpenRouterModelDetails
 from onyx.server.manage.llm.models import OpenRouterModelsRequest
@@ -1575,3 +1577,95 @@ def _get_bifrost_models_response(api_base: str, api_key: str | None = None) -> d
         source_name="Bifrost",
         api_key=api_key,
     )
+
+
+@admin_router.post("/openai-compatible/available-models")
+def get_openai_compatible_server_available_models(
+    request: OpenAICompatibleModelsRequest,
+    _: User = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> list[OpenAICompatibleFinalModelResponse]:
+    """Fetch available models from a generic OpenAI-compatible /v1/models endpoint."""
+    response_json = _get_openai_compatible_server_response(
+        api_base=request.api_base, api_key=request.api_key
+    )
+
+    models = response_json.get("data", [])
+    if not isinstance(models, list) or len(models) == 0:
+        raise OnyxError(
+            OnyxErrorCode.VALIDATION_ERROR,
+            "No models found from your OpenAI-compatible endpoint",
+        )
+
+    results: list[OpenAICompatibleFinalModelResponse] = []
+    for model in models:
+        try:
+            model_id = model.get("id", "")
+            model_name = model.get("name", model_id)
+
+            if not model_id:
+                continue
+
+            # Skip embedding models
+            if is_embedding_model(model_id):
+                continue
+
+            results.append(
+                OpenAICompatibleFinalModelResponse(
+                    name=model_id,
+                    display_name=model_name,
+                    max_input_tokens=model.get("context_length"),
+                    supports_image_input=infer_vision_support(model_id),
+                    supports_reasoning=is_reasoning_model(model_id, model_name),
+                )
+            )
+        except Exception as e:
+            logger.warning(
+                "Failed to parse OpenAI-compatible model entry",
+                extra={"error": str(e), "item": str(model)[:1000]},
+            )
+
+    if not results:
+        raise OnyxError(
+            OnyxErrorCode.VALIDATION_ERROR,
+            "No compatible models found from OpenAI-compatible endpoint",
+        )
+
+    sorted_results = sorted(results, key=lambda m: m.name.lower())
+
+    # Sync new models to DB if provider_name is specified
+    if request.provider_name:
+        _sync_fetched_models(
+            db_session=db_session,
+            provider_name=request.provider_name,
+            models=[
+                SyncModelEntry(
+                    name=r.name,
+                    display_name=r.display_name,
+                    max_input_tokens=r.max_input_tokens,
+                    supports_image_input=r.supports_image_input,
+                )
+                for r in sorted_results
+            ],
+            source_label="OpenAI Compatible",
+        )
+
+    return sorted_results
+
+
+def _get_openai_compatible_server_response(
+    api_base: str, api_key: str | None = None
+) -> dict:
+    """Perform GET to an OpenAI-compatible /v1/models and return parsed JSON."""
+    cleaned_api_base = api_base.strip().rstrip("/")
+    # Ensure we hit /v1/models
+    if cleaned_api_base.endswith("/v1"):
+        url = f"{cleaned_api_base}/models"
+    else:
+        url = f"{cleaned_api_base}/v1/models"
+
+    return _get_openai_compatible_models_response(
+        url=url,
+        source_name="OpenAI Compatible",
+        api_key=api_key,
+    )

backend/onyx/server/manage/llm/models.py

@@ -464,3 +464,18 @@ class BifrostFinalModelResponse(BaseModel):
     max_input_tokens: int | None
     supports_image_input: bool
     supports_reasoning: bool
+
+
+# OpenAI Compatible dynamic models fetch
+class OpenAICompatibleModelsRequest(BaseModel):
+    api_base: str
+    api_key: str | None = None
+    provider_name: str | None = None  # Optional: to save models to existing provider
+
+
+class OpenAICompatibleFinalModelResponse(BaseModel):
+    name: str  # Model ID (e.g. "meta-llama/Llama-3-8B-Instruct")
+    display_name: str  # Human-readable name from API
+    max_input_tokens: int | None
+    supports_image_input: bool
+    supports_reasoning: bool

backend/onyx/server/manage/llm/utils.py

@@ -26,6 +26,7 @@ DYNAMIC_LLM_PROVIDERS = frozenset(
         LlmProviderNames.OLLAMA_CHAT,
         LlmProviderNames.LM_STUDIO,
         LlmProviderNames.BIFROST,
+        LlmProviderNames.OPENAI_COMPATIBLE,
     }
 )
 

web/src/app/admin/configuration/llm/utils.ts

@@ -32,8 +32,10 @@ import {
   OpenRouterFetchParams,
   LiteLLMProxyFetchParams,
   BifrostFetchParams,
+  OpenAICompatibleFetchParams,
+  OpenAICompatibleModelResponse,
 } from "@/interfaces/llm";
-import { SvgAws, SvgBifrost, SvgOpenrouter } from "@opal/icons";
+import { SvgAws, SvgBifrost, SvgOpenrouter, SvgPlug } from "@opal/icons";
 
 // Aggregator providers that host models from multiple vendors
 export const AGGREGATOR_PROVIDERS = new Set([
@@ -44,6 +46,7 @@ export const AGGREGATOR_PROVIDERS = new Set([
   "lm_studio",
   "litellm_proxy",
   "bifrost",
+  "openai_compatible",
   "vertex_ai",
 ]);
 
@@ -82,6 +85,7 @@ export const getProviderIcon = (
     openrouter: SvgOpenrouter,
     litellm_proxy: LiteLLMIcon,
     bifrost: SvgBifrost,
+    openai_compatible: SvgPlug,
     vertex_ai: GeminiIcon,
   };
 
@@ -411,6 +415,64 @@ export const fetchBifrostModels = async (
   }
 };
 
+/**
+ * Fetches models from a generic OpenAI-compatible server.
+ * Uses snake_case params to match API structure.
+ */
+export const fetchOpenAICompatibleModels = async (
+  params: OpenAICompatibleFetchParams
+): Promise<{ models: ModelConfiguration[]; error?: string }> => {
+  const apiBase = params.api_base;
+  if (!apiBase) {
+    return { models: [], error: "API Base is required" };
+  }
+
+  try {
+    const response = await fetch(
+      "/api/admin/llm/openai-compatible/available-models",
+      {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          api_base: apiBase,
+          api_key: params.api_key,
+          provider_name: params.provider_name,
+        }),
+        signal: params.signal,
+      }
+    );
+
+    if (!response.ok) {
+      let errorMessage = "Failed to fetch models";
+      try {
+        const errorData = await response.json();
+        errorMessage = errorData.detail || errorData.message || errorMessage;
+      } catch {
+        // ignore JSON parsing errors
+      }
+      return { models: [], error: errorMessage };
+    }
+
+    const data: OpenAICompatibleModelResponse[] = await response.json();
+    const models: ModelConfiguration[] = data.map((modelData) => ({
+      name: modelData.name,
+      display_name: modelData.display_name,
+      is_visible: true,
+      max_input_tokens: modelData.max_input_tokens,
+      supports_image_input: modelData.supports_image_input,
+      supports_reasoning: modelData.supports_reasoning,
+    }));
+
+    return { models };
+  } catch (error) {
+    const errorMessage =
+      error instanceof Error ? error.message : "Unknown error";
+    return { models: [], error: errorMessage };
+  }
+};
+
 /**
  * Fetches LiteLLM Proxy models directly without any form state dependencies.
  * Uses snake_case params to match API structure.
@@ -531,6 +593,13 @@ export const fetchModels = async (
         provider_name: formValues.name,
         signal,
       });
+    case LLMProviderName.OPENAI_COMPATIBLE:
+      return fetchOpenAICompatibleModels({
+        api_base: formValues.api_base,
+        api_key: formValues.api_key,
+        provider_name: formValues.name,
+        signal,
+      });
     default:
       return { models: [], error: `Unknown provider: ${providerName}` };
   }
@@ -545,6 +614,7 @@ export function canProviderFetchModels(providerName?: string) {
     case LLMProviderName.OPENROUTER:
     case LLMProviderName.LITELLM_PROXY:
     case LLMProviderName.BIFROST:
+    case LLMProviderName.OPENAI_COMPATIBLE:
       return true;
     default:
       return false;

web/src/interfaces/llm.ts

@@ -14,6 +14,7 @@ export enum LLMProviderName {
   BEDROCK = "bedrock",
   LITELLM_PROXY = "litellm_proxy",
   BIFROST = "bifrost",
+  OPENAI_COMPATIBLE = "openai_compatible",
   CUSTOM = "custom",
 }
 
@@ -181,6 +182,21 @@ export interface BifrostModelResponse {
   supports_reasoning: boolean;
 }
 
+export interface OpenAICompatibleFetchParams {
+  api_base?: string;
+  api_key?: string;
+  provider_name?: string;
+  signal?: AbortSignal;
+}
+
+export interface OpenAICompatibleModelResponse {
+  name: string;
+  display_name: string;
+  max_input_tokens: number | null;
+  supports_image_input: boolean;
+  supports_reasoning: boolean;
+}
+
 export interface VertexAIFetchParams {
   model_configurations?: ModelConfiguration[];
 }
@@ -199,5 +215,6 @@ export type FetchModelsParams =
   | OpenRouterFetchParams
   | LiteLLMProxyFetchParams
   | BifrostFetchParams
+  | OpenAICompatibleFetchParams
   | VertexAIFetchParams
   | LMStudioFetchParams;

web/src/lib/llmConfig/providers.ts

@@ -8,6 +8,7 @@ import {
   SvgCloud,
   SvgAws,
   SvgOpenrouter,
+  SvgPlug,
   SvgServer,
   SvgAzure,
   SvgGemini,
@@ -28,6 +29,7 @@ const PROVIDER_ICONS: Record<string, IconFunctionComponent> = {
   [LLMProviderName.OPENROUTER]: SvgOpenrouter,
   [LLMProviderName.LM_STUDIO]: SvgLmStudio,
   [LLMProviderName.BIFROST]: SvgBifrost,
+  [LLMProviderName.OPENAI_COMPATIBLE]: SvgPlug,
 
   // fallback
   [LLMProviderName.CUSTOM]: SvgServer,
@@ -45,6 +47,7 @@ const PROVIDER_PRODUCT_NAMES: Record<string, string> = {
   [LLMProviderName.OPENROUTER]: "OpenRouter",
   [LLMProviderName.LM_STUDIO]: "LM Studio",
   [LLMProviderName.BIFROST]: "Bifrost",
+  [LLMProviderName.OPENAI_COMPATIBLE]: "OpenAI Compatible",
 
   // fallback
   [LLMProviderName.CUSTOM]: "Custom Models",
@@ -62,6 +65,7 @@ const PROVIDER_DISPLAY_NAMES: Record<string, string> = {
   [LLMProviderName.OPENROUTER]: "OpenRouter",
   [LLMProviderName.LM_STUDIO]: "LM Studio",
   [LLMProviderName.BIFROST]: "Bifrost",
+  [LLMProviderName.OPENAI_COMPATIBLE]: "OpenAI Compatible",
 
   // fallback
   [LLMProviderName.CUSTOM]: "Other providers or self-hosted",

web/src/refresh-pages/admin/LLMConfigurationPage.tsx

@@ -46,6 +46,7 @@ import CustomModal from "@/sections/modals/llmConfig/CustomModal";
 import LMStudioForm from "@/sections/modals/llmConfig/LMStudioForm";
 import LiteLLMProxyModal from "@/sections/modals/llmConfig/LiteLLMProxyModal";
 import BifrostModal from "@/sections/modals/llmConfig/BifrostModal";
+import OpenAICompatibleModal from "@/sections/modals/llmConfig/OpenAICompatibleModal";
 import { Section } from "@/layouts/general-layouts";
 
 const route = ADMIN_ROUTES.LLM_MODELS;
@@ -67,6 +68,7 @@ const PROVIDER_DISPLAY_ORDER: string[] = [
   "openrouter",
   "lm_studio",
   "bifrost",
+  "openai_compatible",
 ];
 
 const PROVIDER_MODAL_MAP: Record<
@@ -147,6 +149,13 @@ const PROVIDER_MODAL_MAP: Record<
       onOpenChange={onOpenChange}
     />
   ),
+  openai_compatible: (d, open, onOpenChange) => (
+    <OpenAICompatibleModal
+      shouldMarkAsDefault={d}
+      open={open}
+      onOpenChange={onOpenChange}
+    />
+  ),
 };
 
 // ============================================================================

web/src/sections/modals/llmConfig/OpenAICompatibleModal.tsx

@@ -0,0 +1,267 @@
+"use client";
+
+import { useState, useEffect } from "react";
+import { markdown } from "@opal/utils";
+import { useSWRConfig } from "swr";
+import { Formik, FormikProps } from "formik";
+import InputTypeInField from "@/refresh-components/form/InputTypeInField";
+import PasswordInputTypeInField from "@/refresh-components/form/PasswordInputTypeInField";
+import * as InputLayouts from "@/layouts/input-layouts";
+import {
+  LLMProviderFormProps,
+  LLMProviderName,
+  LLMProviderView,
+  ModelConfiguration,
+} from "@/interfaces/llm";
+import { fetchOpenAICompatibleModels } from "@/app/admin/configuration/llm/utils";
+import * as Yup from "yup";
+import { useWellKnownLLMProvider } from "@/hooks/useLLMProviders";
+import {
+  buildDefaultInitialValues,
+  buildDefaultValidationSchema,
+  buildAvailableModelConfigurations,
+  buildOnboardingInitialValues,
+  BaseLLMFormValues,
+} from "@/sections/modals/llmConfig/utils";
+import {
+  submitLLMProvider,
+  submitOnboardingProvider,
+} from "@/sections/modals/llmConfig/svc";
+import {
+  ModelsField,
+  DisplayNameField,
+  ModelsAccessField,
+  FieldSeparator,
+  FieldWrapper,
+  LLMConfigurationModalWrapper,
+} from "@/sections/modals/llmConfig/shared";
+import { toast } from "@/hooks/useToast";
+
+const OPENAI_COMPATIBLE_PROVIDER = LLMProviderName.OPENAI_COMPATIBLE;
+const DEFAULT_API_BASE = "";
+
+interface OpenAICompatibleModalValues extends BaseLLMFormValues {
+  api_key: string;
+  api_base: string;
+}
+
+interface OpenAICompatibleModalInternalsProps {
+  formikProps: FormikProps<OpenAICompatibleModalValues>;
+  existingLlmProvider: LLMProviderView | undefined;
+  fetchedModels: ModelConfiguration[];
+  setFetchedModels: (models: ModelConfiguration[]) => void;
+  modelConfigurations: ModelConfiguration[];
+  isTesting: boolean;
+  onClose: () => void;
+  isOnboarding: boolean;
+}
+
+function OpenAICompatibleModalInternals({
+  formikProps,
+  existingLlmProvider,
+  fetchedModels,
+  setFetchedModels,
+  modelConfigurations,
+  isTesting,
+  onClose,
+  isOnboarding,
+}: OpenAICompatibleModalInternalsProps) {
+  const currentModels =
+    fetchedModels.length > 0
+      ? fetchedModels
+      : existingLlmProvider?.model_configurations || modelConfigurations;
+
+  const isFetchDisabled = !formikProps.values.api_base;
+
+  const handleFetchModels = async () => {
+    const { models, error } = await fetchOpenAICompatibleModels({
+      api_base: formikProps.values.api_base,
+      api_key: formikProps.values.api_key || undefined,
+      provider_name: existingLlmProvider?.name,
+    });
+    if (error) {
+      throw new Error(error);
+    }
+    setFetchedModels(models);
+  };
+
+  // Auto-fetch models on initial load when editing an existing provider
+  useEffect(() => {
+    if (existingLlmProvider && !isFetchDisabled) {
+      handleFetchModels().catch((err) => {
+        toast.error(
+          err instanceof Error ? err.message : "Failed to fetch models"
+        );
+      });
+    }
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  return (
+    <LLMConfigurationModalWrapper
+      providerEndpoint={LLMProviderName.OPENAI_COMPATIBLE}
+      existingProviderName={existingLlmProvider?.name}
+      onClose={onClose}
+      isFormValid={formikProps.isValid}
+      isDirty={formikProps.dirty}
+      isTesting={isTesting}
+      isSubmitting={formikProps.isSubmitting}
+    >
+      <FieldWrapper>
+        <InputLayouts.Vertical
+          name="api_base"
+          title="API Base URL"
+          subDescription="The base URL of your OpenAI-compatible server."
+        >
+          <InputTypeInField
+            name="api_base"
+            placeholder="http://localhost:8000/v1"
+          />
+        </InputLayouts.Vertical>
+      </FieldWrapper>
+
+      <FieldWrapper>
+        <InputLayouts.Vertical
+          name="api_key"
+          title="API Key"
+          optional
+          subDescription={markdown(
+            "Provide an API key if your server requires authentication."
+          )}
+        >
+          <PasswordInputTypeInField name="api_key" placeholder="API Key" />
+        </InputLayouts.Vertical>
+      </FieldWrapper>
+
+      {!isOnboarding && (
+        <>
+          <FieldSeparator />
+          <DisplayNameField disabled={!!existingLlmProvider} />
+        </>
+      )}
+
+      <FieldSeparator />
+
+      <ModelsField
+        modelConfigurations={currentModels}
+        formikProps={formikProps}
+        recommendedDefaultModel={null}
+        shouldShowAutoUpdateToggle={false}
+        onRefetch={isFetchDisabled ? undefined : handleFetchModels}
+      />
+
+      {!isOnboarding && (
+        <>
+          <FieldSeparator />
+          <ModelsAccessField formikProps={formikProps} />
+        </>
+      )}
+    </LLMConfigurationModalWrapper>
+  );
+}
+
+export default function OpenAICompatibleModal({
+  variant = "llm-configuration",
+  existingLlmProvider,
+  shouldMarkAsDefault,
+  open,
+  onOpenChange,
+  defaultModelName,
+  onboardingState,
+  onboardingActions,
+  llmDescriptor,
+}: LLMProviderFormProps) {
+  const [fetchedModels, setFetchedModels] = useState<ModelConfiguration[]>([]);
+  const [isTesting, setIsTesting] = useState(false);
+  const isOnboarding = variant === "onboarding";
+  const { mutate } = useSWRConfig();
+  const { wellKnownLLMProvider } = useWellKnownLLMProvider(
+    OPENAI_COMPATIBLE_PROVIDER
+  );
+
+  if (open === false) return null;
+
+  const onClose = () => onOpenChange?.(false);
+
+  const modelConfigurations = buildAvailableModelConfigurations(
+    existingLlmProvider,
+    wellKnownLLMProvider ?? llmDescriptor
+  );
+
+  const initialValues: OpenAICompatibleModalValues = isOnboarding
+    ? ({
+        ...buildOnboardingInitialValues(),
+        name: OPENAI_COMPATIBLE_PROVIDER,
+        provider: OPENAI_COMPATIBLE_PROVIDER,
+        api_key: "",
+        api_base: DEFAULT_API_BASE,
+        default_model_name: "",
+      } as OpenAICompatibleModalValues)
+    : {
+        ...buildDefaultInitialValues(
+          existingLlmProvider,
+          modelConfigurations,
+          defaultModelName
+        ),
+        api_key: existingLlmProvider?.api_key ?? "",
+        api_base: existingLlmProvider?.api_base ?? DEFAULT_API_BASE,
+      };
+
+  const validationSchema = buildDefaultValidationSchema().shape({
+    api_base: Yup.string().required("API Base URL is required"),
+  });
+
+  return (
+    <Formik
+      initialValues={initialValues}
+      validationSchema={validationSchema}
+      validateOnMount={true}
+      onSubmit={async (values, { setSubmitting }) => {
+        if (isOnboarding && onboardingState && onboardingActions) {
+          const modelConfigsToUse =
+            fetchedModels.length > 0 ? fetchedModels : [];
+
+          await submitOnboardingProvider({
+            providerName: OPENAI_COMPATIBLE_PROVIDER,
+            payload: {
+              ...values,
+              model_configurations: modelConfigsToUse,
+            },
+            onboardingState,
+            onboardingActions,
+            isCustomProvider: false,
+            onClose,
+            setIsSubmitting: setSubmitting,
+          });
+        } else {
+          await submitLLMProvider({
+            providerName: OPENAI_COMPATIBLE_PROVIDER,
+            values,
+            initialValues,
+            modelConfigurations:
+              fetchedModels.length > 0 ? fetchedModels : modelConfigurations,
+            existingLlmProvider,
+            shouldMarkAsDefault,
+            setIsTesting,
+            mutate,
+            onClose,
+            setSubmitting,
+          });
+        }
+      }}
+    >
+      {(formikProps) => (
+        <OpenAICompatibleModalInternals
+          formikProps={formikProps}
+          existingLlmProvider={existingLlmProvider}
+          fetchedModels={fetchedModels}
+          setFetchedModels={setFetchedModels}
+          modelConfigurations={modelConfigurations}
+          isTesting={isTesting}
+          onClose={onClose}
+          isOnboarding={isOnboarding}
+        />
+      )}
+    </Formik>
+  );
+}

web/src/sections/modals/llmConfig/getModal.tsx

@@ -10,6 +10,7 @@ import BedrockModal from "@/sections/modals/llmConfig/BedrockModal";
 import LMStudioForm from "@/sections/modals/llmConfig/LMStudioForm";
 import LiteLLMProxyModal from "@/sections/modals/llmConfig/LiteLLMProxyModal";
 import BifrostModal from "@/sections/modals/llmConfig/BifrostModal";
+import OpenAICompatibleModal from "@/sections/modals/llmConfig/OpenAICompatibleModal";
 
 function detectIfRealOpenAIProvider(provider: LLMProviderView) {
   return (
@@ -59,6 +60,8 @@ export function getModalForExistingProvider(
       return <LiteLLMProxyModal {...props} />;
     case LLMProviderName.BIFROST:
       return <BifrostModal {...props} />;
+    case LLMProviderName.OPENAI_COMPATIBLE:
+      return <OpenAICompatibleModal {...props} />;
     default:
       return <CustomModal {...props} />;
   }

web/src/sections/onboarding/forms/getOnboardingForm.tsx

@@ -14,6 +14,7 @@ import OpenRouterModal from "@/sections/modals/llmConfig/OpenRouterModal";
 import CustomModal from "@/sections/modals/llmConfig/CustomModal";
 import LMStudioForm from "@/sections/modals/llmConfig/LMStudioForm";
 import LiteLLMProxyModal from "@/sections/modals/llmConfig/LiteLLMProxyModal";
+import OpenAICompatibleModal from "@/sections/modals/llmConfig/OpenAICompatibleModal";
 
 // Display info for LLM provider cards - title is the product name, displayName is the company/platform
 const PROVIDER_DISPLAY_INFO: Record<
@@ -47,6 +48,10 @@ const PROVIDER_DISPLAY_INFO: Record<
     title: "LiteLLM Proxy",
     displayName: "LiteLLM Proxy",
   },
+  [LLMProviderName.OPENAI_COMPATIBLE]: {
+    title: "OpenAI Compatible",
+    displayName: "OpenAI Compatible",
+  },
 };
 
 export function getProviderDisplayInfo(providerName: string): {
@@ -124,6 +129,9 @@ export function getOnboardingForm({
     case LLMProviderName.LITELLM_PROXY:
       return <LiteLLMProxyModal {...providerProps} />;
 
+    case LLMProviderName.OPENAI_COMPATIBLE:
+      return <OpenAICompatibleModal {...providerProps} />;
+
     default:
       return <CustomModal {...sharedProps} />;
   }