Fixed integration with OpenAI Codex (v0.114.0) with gpt-5.4

.ccproxy.codex.msaf.toml.example

@@ -0,0 +1,65 @@
+# Example ccproxy config for Microsoft Agent Framework clients over Codex.
+
+enable_plugins = true
+enabled_plugins = ["oauth_codex", "codex"]
+
+[server]
+bypass_mode = false
+
+[llm]
+# Keep OpenAI-compatible responses free from <thinking>...</thinking> blocks.
+openai_thinking_xml = false
+
+[plugins.codex]
+enabled = true
+name = "codex"
+base_url = "https://chatgpt.com/backend-api/codex"
+requires_auth = true
+auth_type = "oauth"
+supports_streaming = true
+preferred_upstream_mode = "streaming"
+buffer_non_streaming = true
+enable_format_registry = true
+
+
+# Microsoft Agent Framework sends its own instructions/reasoning payloads.
+# Do not prepend captured Codex CLI templates to generic OpenAI-compatible calls.
+inject_detection_payload = false
+
+supported_input_formats = [
+  "openai.responses",
+  "openai.chat_completions",
+  "anthropic.messages",
+]
+
+detection_home_mode = "temp"
+
+[[plugins.codex.models_endpoint]]
+id = "gpt-5.4"
+object = "model"
+created = 1735689600
+owned_by = "openai"
+root = "gpt-5.4"
+permission = []
+
+[plugins.codex.oauth]
+base_url = "https://auth.openai.com"
+client_id = "app_EMoamEEZ73f0CkXaXp7hrann"
+scopes = ["openid", "profile", "email", "offline_access"]
+
+[plugins.oauth_codex]
+enabled = true
+base_url = "https://auth.openai.com"
+authorize_url = "https://auth.openai.com/oauth/authorize"
+token_url = "https://auth.openai.com/oauth/token"
+profile_url = "https://api.openai.com/oauth/profile"
+client_id = "app_EMoamEEZ73f0CkXaXp7hrann"
+redirect_uri = "http://localhost:1455/auth/callback"
+callback_port = 1455
+scopes = ["openid", "profile", "email", "offline_access"]
+audience = "https://api.openai.com/v1"
+user_agent = "Codex-Code/1.0.43"
+headers = { User-Agent = "Codex-Code/1.0.43" }
+request_timeout = 30
+callback_timeout = 300
+use_pkce = true

ccproxy/core/plugins/factories.py

@@ -14,6 +14,7 @@
 from ccproxy.models.provider import ProviderConfig
 from ccproxy.services.adapters.base import BaseAdapter
 from ccproxy.services.adapters.http_adapter import BaseHTTPAdapter
+from ccproxy.services.adapters.mock_adapter import MockAdapter
 from ccproxy.services.interfaces import (
     IMetricsCollector,
     IRequestTracer,
@@ -215,6 +216,23 @@ async def create_adapter(self, context: PluginContext) -> BaseAdapter:
         Returns:
             Adapter instance
         """
+        settings = context.get("settings")
+        service_container = context.get("service_container")
+        if settings and getattr(settings.server, "bypass_mode", False):
+            if not service_container:
+                raise RuntimeError(
+                    f"Cannot initialize plugin '{self.plugin_name}' in bypass mode: "
+                    "service container is required to create mock adapter. "
+                    "This is likely a configuration issue."
+                )
+            logger.warning(
+                "plugin_bypass_mode_enabled",
+                plugin=self.plugin_name,
+                adapter=self.adapter_class.__name__,
+                category="lifecycle",
+            )
+            return MockAdapter(service_container.get_mock_handler())
+
         # Extract services from context (one-time extraction)
         http_pool_manager: HTTPPoolManager | None = cast(
             "HTTPPoolManager | None", context.get("http_pool_manager")
@@ -232,7 +250,6 @@ async def create_adapter(self, context: PluginContext) -> BaseAdapter:
         config = context.get("config")
 
         # Get all adapter dependencies from service container
-        service_container = context.get("service_container")
         if not service_container:
             raise RuntimeError("Service container is required for adapter services")
 

ccproxy/llms/formatters/context.py

@@ -11,6 +11,9 @@
     "formatter_instructions", default=None
 )
 _TOOLS_VAR: ContextVar[list[Any] | None] = ContextVar("formatter_tools", default=None)
+_OPENAI_THINKING_XML_VAR: ContextVar[bool | None] = ContextVar(
+    "formatter_openai_thinking_xml", default=None
+)
 
 
 def register_request(request: Any | None, instructions: str | None = None) -> None:
@@ -114,3 +117,24 @@ def get_last_request_tools() -> list[Any] | None:
 
     cached = _TOOLS_VAR.get()
     return list(cached) if cached else None
+
+
+def register_openai_thinking_xml(enabled: bool | None) -> None:
+    """Cache OpenAI thinking serialization preference for active conversions.
+
+    Args:
+        enabled: Whether thinking blocks should be serialized with XML wrappers.
+            ``None`` means downstream conversion logic should use its default.
+
+    Note:
+        The value is stored in a ``ContextVar``, so concurrent async requests
+        keep independent preferences without leaking into each other.
+    """
+
+    _OPENAI_THINKING_XML_VAR.set(enabled)
+
+
+def get_openai_thinking_xml() -> bool | None:
+    """Return the OpenAI thinking serialization preference for active conversions."""
+
+    return _OPENAI_THINKING_XML_VAR.get()

ccproxy/llms/formatters/openai_to_openai/responses.py

@@ -15,6 +15,7 @@
     convert_openai_responses_usage_to_completion_usage,
     merge_thinking_segments,
 )
+from ccproxy.llms.formatters.context import get_openai_thinking_xml
 from ccproxy.llms.models import openai as openai_models
 
 from ._helpers import (
@@ -333,6 +334,10 @@ def convert__openai_responses_to_openai_chat__response(
     response: openai_models.ResponseObject,
 ) -> openai_models.ChatCompletionResponse:
     """Convert an OpenAI ResponseObject to a ChatCompletionResponse."""
+    include_thinking = get_openai_thinking_xml()
+    if include_thinking is None:
+        include_thinking = True
+
     text_segments: list[str] = []
     added_reasoning: set[tuple[str, str]] = set()
     tool_calls: list[openai_models.ToolCall] = []
@@ -353,7 +358,7 @@ def convert__openai_responses_to_openai_chat__response(
                     if thinking_text and len(thinking_text) > 30
                     else thinking_text,
                 )
-                if thinking_text:
+                if include_thinking and thinking_text:
                     key = (signature or "", thinking_text)
                     if key not in added_reasoning:
                         text_segments.append(_wrap_thinking(signature, thinking_text))

ccproxy/llms/formatters/openai_to_openai/streams.py

@@ -27,16 +27,14 @@
     get_last_instructions,
     get_last_request,
     get_last_request_tools,
+    get_openai_thinking_xml,
     register_request,
     register_request_tools,
 )
 from ccproxy.llms.models import openai as openai_models
 from ccproxy.llms.streaming.accumulators import OpenAIAccumulator
 
-from ._helpers import (
-    _convert_tools_chat_to_responses,
-    _get_attr,
-)
+from ._helpers import _convert_tools_chat_to_responses, _get_attr
 from .requests import _build_responses_payload_from_chat_request
 from .responses import (
     _collect_reasoning_segments,
@@ -61,6 +59,10 @@ def run(
         async def generator() -> AsyncGenerator[
             openai_models.ChatCompletionChunk, None
         ]:
+            include_thinking = get_openai_thinking_xml()
+            if include_thinking is None:
+                include_thinking = True
+
             model_id = ""
             role_sent = False
 
@@ -537,7 +539,7 @@ def create_text_chunk(
                             for entry in summary_list:
                                 text = _get_attr(entry, "text")
                                 signature = _get_attr(entry, "signature")
-                                if isinstance(text, str) and text:
+                                if include_thinking and isinstance(text, str) and text:
                                     chunk_text = _wrap_thinking(signature, text)
                                     sequence_counter += 1
                                     yield openai_models.ChatCompletionChunk(

ccproxy/llms/models/anthropic.py

@@ -352,8 +352,16 @@ class ThinkingConfigDisabled(ThinkingConfigBase):
     type: Literal["disabled"] = Field(default="disabled", alias="type")
 
 
+class ThinkingConfigAdaptive(ThinkingConfigBase):
+    """Configuration for adaptive thinking (Claude 4-6+)."""
+
+    type: Literal["adaptive"] = Field(default="adaptive", alias="type")
+    display: Literal["summarized", "omitted"] | None = None
+
+
 ThinkingConfig = Annotated[
-    ThinkingConfigEnabled | ThinkingConfigDisabled, Field(discriminator="type")
+    ThinkingConfigEnabled | ThinkingConfigDisabled | ThinkingConfigAdaptive,
+    Field(discriminator="type"),
 ]
 
 

ccproxy/plugins/claude_api/adapter.py

@@ -85,15 +85,14 @@ async def prepare_provider_request(
         # Always set Authorization from OAuth-managed access token
         filtered_headers["authorization"] = f"Bearer {token_value}"
 
-        # PATCH: Add Computer Use beta headers for Anthropic API
-        # These are required for browser automation tools to work
+        # Minimal beta tags required for OAuth-based Claude Code auth
         filtered_headers["anthropic-version"] = "2023-06-01"
-        filtered_headers["anthropic-beta"] = "computer-use-2025-01-24"
+        filtered_headers["anthropic-beta"] = "claude-code-20250219,oauth-2025-04-20"
 
-        # Add CLI headers if available, but never allow overriding auth
+        # Add CLI headers if available, but never allow overriding auth or beta
         cli_headers = self._collect_cli_headers()
         if cli_headers:
-            blocked_overrides = {"authorization", "x-api-key"}
+            blocked_overrides = {"authorization", "x-api-key", "anthropic-beta"}
             for key, value in cli_headers.items():
                 lk = key.lower()
                 if lk in blocked_overrides: