[SKV-3992] Add OPENAI_COMPATIBLE for githubcopilot.com (#3993)

skyvern/config.py

@@ -216,6 +216,7 @@ class Settings(BaseSettings):
     OPENAI_COMPATIBLE_ADD_ASSISTANT_PREFIX: bool = False
     OPENAI_COMPATIBLE_MODEL_KEY: str = "OPENAI_COMPATIBLE"
     OPENAI_COMPATIBLE_REASONING_EFFORT: str | None = None
+    OPENAI_COMPATIBLE_GITHUB_COPILOT_DOMAIN: str = "githubcopilot.com"
 
     # AZURE
     AZURE_DEPLOYMENT: str | None = None

skyvern/forge/sdk/api/llm/api_handler_factory.py

@@ -35,7 +35,12 @@ from skyvern.forge.sdk.api.llm.models import (
     LLMRouterConfig,
 )
 from skyvern.forge.sdk.api.llm.ui_tars_response import UITarsResponse
-from skyvern.forge.sdk.api.llm.utils import llm_messages_builder, llm_messages_builder_with_history, parse_api_response
+from skyvern.forge.sdk.api.llm.utils import (
+    is_image_message,
+    llm_messages_builder,
+    llm_messages_builder_with_history,
+    parse_api_response,
+)
 from skyvern.forge.sdk.artifact.manager import BulkArtifactCreationRequest
 from skyvern.forge.sdk.artifact.models import ArtifactType
 from skyvern.forge.sdk.core import skyvern_context
@@ -223,21 +228,42 @@ class LLMAPIHandlerFactory:
 
         return _normalize(left) == _normalize(right)
 
+    @staticmethod
+    def _extract_token_counts(response: ModelResponse | CustomStreamWrapper) -> tuple[int, int, int, int]:
+        """Extract token counts from response usage information."""
+        input_tokens = 0
+        output_tokens = 0
+        reasoning_tokens = 0
+        cached_tokens = 0
+
+        if hasattr(response, "usage") and response.usage:
+            input_tokens = getattr(response.usage, "prompt_tokens", 0)
+            output_tokens = getattr(response.usage, "completion_tokens", 0)
+
+            # Extract reasoning tokens from completion_tokens_details
+            completion_token_detail = getattr(response.usage, "completion_tokens_details", None)
+            if completion_token_detail:
+                reasoning_tokens = getattr(completion_token_detail, "reasoning_tokens", 0) or 0
+
+            # Extract cached tokens from prompt_tokens_details
+            cached_token_detail = getattr(response.usage, "prompt_tokens_details", None)
+            if cached_token_detail:
+                cached_tokens = getattr(cached_token_detail, "cached_tokens", 0) or 0
+
+            # Fallback: Some providers expose cache_read_input_tokens directly on usage
+            if cached_tokens == 0:
+                cached_tokens = getattr(response.usage, "cache_read_input_tokens", 0) or 0
+
+        return input_tokens, output_tokens, reasoning_tokens, cached_tokens
+
     @staticmethod
     def _apply_thinking_budget_optimization(
         parameters: dict[str, Any], new_budget: int, llm_config: LLMConfig | LLMRouterConfig, prompt_name: str
     ) -> None:
         """Apply thinking budget optimization based on model type and LiteLLM reasoning support."""
         # Compute a safe model label and a representative model for capability checks
-        model_label = getattr(llm_config, "model_name", None)
-        if model_label is None and isinstance(llm_config, LLMRouterConfig):
-            model_label = getattr(llm_config, "main_model_group", "router")
-        check_model = model_label
-        if isinstance(llm_config, LLMRouterConfig) and getattr(llm_config, "model_list", None):
-            try:
-                check_model = llm_config.model_list[0].model_name or model_label  # type: ignore[attr-defined]
-            except Exception:
-                check_model = model_label
+        model_label = LLMAPIHandlerFactory._get_model_label(llm_config)
+        check_model = LLMAPIHandlerFactory._get_check_model(llm_config, model_label)
 
         # Check reasoning support (safe call - log but don't fail if litellm errors)
         supports_reasoning = False
@@ -273,7 +299,6 @@ class LLMAPIHandlerFactory:
                     model=model_label,
                 )
                 return
-
             # Apply optimization based on model type
             model_label_lower = (model_label or "").lower()
             if "gemini" in model_label_lower:
@@ -296,7 +321,6 @@ class LLMAPIHandlerFactory:
                     reasoning_effort="low",
                     model=model_label,
                 )
-
         except (AttributeError, KeyError, TypeError) as e:
             LOG.warning(
                 "Failed to apply thinking budget optimization",
@@ -312,6 +336,8 @@ class LLMAPIHandlerFactory:
         parameters: dict[str, Any], new_budget: int, llm_config: LLMConfig | LLMRouterConfig, prompt_name: str
     ) -> None:
         """Apply thinking optimization for Anthropic/Claude models."""
+        model_label = LLMAPIHandlerFactory._get_model_label(llm_config)
+
         if llm_config.reasoning_effort is not None:
             # Use reasoning_effort if configured in LLM config - always use "low" per LiteLLM constants
             parameters["reasoning_effort"] = "low"
@@ -350,8 +376,7 @@ class LLMAPIHandlerFactory:
         parameters: dict[str, Any], new_budget: int, llm_config: LLMConfig | LLMRouterConfig, prompt_name: str
     ) -> None:
         """Apply thinking optimization for Gemini models using exact integer budget value."""
-        # Get model label for logging — prefer main_model_group for router configs
-        model_label = llm_config.main_model_group if isinstance(llm_config, LLMRouterConfig) else llm_config.model_name
+        model_label = LLMAPIHandlerFactory._get_model_label(llm_config)
 
         # Models that use thinking_level (e.g. Gemini 3 Pro/Flash) don't support budget_tokens.
         # Their reasoning is already bounded by the thinking_level set in their config, so skip.
@@ -373,6 +398,34 @@ class LLMAPIHandlerFactory:
             model=model_label,
         )
 
+    @staticmethod
+    def _get_model_label(llm_config: LLMConfig | LLMRouterConfig) -> str | None:
+        """Extract a safe model label from LLMConfig or LLMRouterConfig for logging and capability checks."""
+        model_label = getattr(llm_config, "model_name", None)
+        # Compute a safe model label and a representative model for capability checks
+        if model_label is None and isinstance(llm_config, LLMRouterConfig):
+            model_label = getattr(llm_config, "main_model_group", "router")
+        return model_label
+
+    @staticmethod
+    def _get_check_model(llm_config: LLMConfig | LLMRouterConfig, model_label: str | None) -> str | None:
+        """Get a representative model name for capability checks from LLMRouterConfig or use the model label."""
+        check_model = model_label
+        if isinstance(llm_config, LLMRouterConfig) and getattr(llm_config, "model_list", None):
+            try:
+                check_model = llm_config.model_list[0].model_name or model_label  # type: ignore[attr-defined]
+            except Exception:
+                check_model = model_label
+        return check_model
+
+    @staticmethod
+    def is_github_copilot_endpoint() -> bool:
+        """Check if the OPENAI_COMPATIBLE endpoint is GitHub Copilot."""
+        return (
+            settings.OPENAI_COMPATIBLE_API_BASE is not None
+            and settings.OPENAI_COMPATIBLE_GITHUB_COPILOT_DOMAIN in settings.OPENAI_COMPATIBLE_API_BASE
+        )
+
     @staticmethod
     def get_override_llm_api_handler(override_llm_key: str | None, *, default: LLMAPIHandler) -> LLMAPIHandler:
         if not override_llm_key:
@@ -856,6 +909,11 @@ class LLMAPIHandlerFactory:
             llm_caller = LLMCaller(llm_key=llm_key, base_parameters=base_parameters)
             return llm_caller.call
 
+        # For GitHub Copilot via OPENAI_COMPATIBLE, use LLMCaller for a custom header
+        if llm_key == "OPENAI_COMPATIBLE" and LLMAPIHandlerFactory.is_github_copilot_endpoint():
+            llm_caller = LLMCaller(llm_key=llm_key, base_parameters=base_parameters)
+            return llm_caller.call
+
         assert isinstance(llm_config, LLMConfig)
 
         @traced(tags=[llm_key])
@@ -1313,6 +1371,14 @@ class LLMCaller:
                 base_url=settings.OPENROUTER_API_BASE,
                 http_client=ForgeAsyncHttpxClientWrapper(),
             )
+        elif self.llm_key == "OPENAI_COMPATIBLE" and LLMAPIHandlerFactory.is_github_copilot_endpoint():
+            # For GitHub Copilot, use the actual model name from OPENAI_COMPATIBLE_MODEL_NAME
+            self.llm_key = settings.OPENAI_COMPATIBLE_MODEL_NAME or self.llm_key
+            self.openai_client = AsyncOpenAI(
+                api_key=settings.OPENAI_COMPATIBLE_API_KEY,
+                base_url=settings.OPENAI_COMPATIBLE_API_BASE,
+                http_client=ForgeAsyncHttpxClientWrapper(),
+            )
 
     def add_tool_result(self, tool_result: dict[str, Any]) -> None:
         self.current_tool_results.append(tool_result)
@@ -1598,12 +1664,22 @@ class LLMCaller:
         **active_parameters: dict[str, Any],
     ) -> ModelResponse | CustomStreamWrapper | AnthropicMessage | UITarsResponse:
         if self.openai_client:
-            # Extract OpenRouter-specific parameters
+            # Extract OpenRouter-specific and GitHub Copilot-specific parameters
             extra_headers = {}
             if settings.SKYVERN_APP_URL:
                 extra_headers["HTTP-Referer"] = settings.SKYVERN_APP_URL
                 extra_headers["X-Title"] = "Skyvern"
 
+            # Add required headers for GitHub Copilot API
+            if LLMAPIHandlerFactory.is_github_copilot_endpoint():
+                extra_headers["Copilot-Integration-Id"] = "copilot-chat"
+
+                # Add vision header when there are images in the request
+                has_images = any(is_image_message(msg) for msg in messages)
+                # Only set the header when there are actual images in the request
+                if has_images:
+                    extra_headers["Copilot-Vision-Request"] = "true"
+
             # Filter out parameters that OpenAI client doesn't support
             openai_params = {}
             if "max_completion_tokens" in active_parameters:
@@ -1740,6 +1816,19 @@ class LLMCaller:
         if self.original_llm_key.startswith("openrouter/"):
             return empty_call_stats
 
+        # Handle OPENAI_COMPATIBLE provider GitHub Copilot
+        if self.original_llm_key == "OPENAI_COMPATIBLE" and isinstance(response, (ModelResponse, CustomStreamWrapper)):
+            input_tokens, output_tokens, reasoning_tokens, cached_tokens = LLMAPIHandlerFactory._extract_token_counts(
+                response
+            )
+            return LLMCallStats(
+                llm_cost=0,  # TODO: calculate the cost according to the price: https://github.com/features/copilot/plans
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                cached_tokens=cached_tokens,
+                reasoning_tokens=reasoning_tokens,
+            )
+
         # Handle UI-TARS response (UITarsResponse object from _call_ui_tars)
         if isinstance(response, UITarsResponse):
             ui_tars_usage = response.usage
@@ -1771,28 +1860,9 @@ class LLMCaller:
             except Exception as e:
                 LOG.debug("Failed to calculate LLM cost", error=str(e), exc_info=True)
                 llm_cost = 0
-            input_tokens = 0
-            output_tokens = 0
-            reasoning_tokens = 0
-            cached_tokens = 0
-
-            if hasattr(response, "usage") and response.usage:
-                input_tokens = getattr(response.usage, "prompt_tokens", 0)
-                output_tokens = getattr(response.usage, "completion_tokens", 0)
-
-                # Extract reasoning tokens from completion_tokens_details
-                completion_token_detail = getattr(response.usage, "completion_tokens_details", None)
-                if completion_token_detail:
-                    reasoning_tokens = getattr(completion_token_detail, "reasoning_tokens", 0) or 0
-
-                # Extract cached tokens from prompt_tokens_details
-                cached_token_detail = getattr(response.usage, "prompt_tokens_details", None)
-                if cached_token_detail:
-                    cached_tokens = getattr(cached_token_detail, "cached_tokens", 0) or 0
-
-                # Fallback for Vertex/Gemini: LiteLLM exposes cache_read_input_tokens on usage
-                if cached_tokens == 0:
-                    cached_tokens = getattr(response.usage, "cache_read_input_tokens", 0) or 0
+            input_tokens, output_tokens, reasoning_tokens, cached_tokens = LLMAPIHandlerFactory._extract_token_counts(
+                response
+            )
             return LLMCallStats(
                 llm_cost=llm_cost,
                 input_tokens=input_tokens,

skyvern/forge/sdk/api/llm/ui_tars_llm_caller.py

@@ -15,13 +15,13 @@ UI-TARS LLM Caller that follows the standard LLMCaller pattern.
 
 import base64
 from io import BytesIO
-from typing import Any, Dict
 
 import structlog
 from PIL import Image
 
 from skyvern.forge.prompts import prompt_engine
 from skyvern.forge.sdk.api.llm.api_handler_factory import LLMCaller
+from skyvern.forge.sdk.api.llm.utils import is_image_message
 from skyvern.forge.sdk.models import Step
 from skyvern.forge.sdk.schemas.tasks import Task
 
@@ -33,15 +33,6 @@ def _build_system_prompt(instruction: str, language: str = "English") -> str:
     return prompt_engine.load_prompt("ui-tars-system-prompt", language=language, instruction=instruction)
 
 
-def _is_image_message(message: Dict[str, Any]) -> bool:
-    """Check if message contains an image."""
-    return (
-        message.get("role") == "user"
-        and isinstance(message.get("content"), list)
-        and any(item.get("type") == "image_url" for item in message["content"])
-    )
-
-
 class UITarsLLMCaller(LLMCaller):
     """
     UI-TARS specific LLM caller that manages conversation history.
@@ -114,7 +105,7 @@ class UITarsLLMCaller(LLMCaller):
         i = 1  # Start after system prompt (index 0)
         while i < len(self.message_history) and removed_count < images_to_remove:
             message = self.message_history[i]
-            if _is_image_message(message):
+            if is_image_message(message):
                 # Remove only the screenshot message, keep all assistant responses
                 self.message_history.pop(i)
                 removed_count += 1
@@ -131,7 +122,7 @@ class UITarsLLMCaller(LLMCaller):
         """Count existing image messages in the conversation history."""
         count = 0
         for message in self.message_history:
-            if _is_image_message(message):
+            if is_image_message(message):
                 count += 1
         return count
 

skyvern/forge/sdk/api/llm/utils.py

@@ -15,6 +15,15 @@ from skyvern.forge.sdk.api.llm.exceptions import EmptyLLMResponseError, InvalidL
 LOG = structlog.get_logger()
 
 
+def is_image_message(message: dict[str, Any]) -> bool:
+    """Check if message contains an image."""
+    return (
+        message.get("role") == "user"
+        and isinstance(message.get("content"), list)
+        and any(item.get("type") == "image_url" for item in message["content"])
+    )
+
+
 async def llm_messages_builder(
     prompt: str,
     screenshots: list[bytes] | None = None,

skyvern/services/task_v2_service.py

@@ -694,9 +694,6 @@ async def run_task_v2_helper(
             return workflow, workflow_run, task_v2
 
         LOG.info(f"Task v2 iteration i={i}", workflow_run_id=workflow_run_id, url=url)
-        task_type = ""
-        plan = ""
-        block: BlockTypeVar | None = None
         task_history_record: dict[str, Any] = {}
         context = skyvern_context.ensure_context()
 

skyvern/utils/env_paths.py

@@ -10,10 +10,7 @@ def resolve_backend_env_path(
     basename: str = BACKEND_ENV_DEFAULT,
 ) -> Path:
     """Return the backend env file path inside the current working directory."""
-
-    env_path = Path.cwd() / basename
-
-    return env_path
+    return Path.cwd() / basename
 
 
 def resolve_frontend_env_path() -> Optional[Path]: