feat: add ResponsesApiTransport + wire all Codex transport paths

Add ResponsesApiTransport wrapping codex_responses_adapter.py behind the
ProviderTransport ABC. Auto-registered via _discover_transports().

Wire ALL Codex transport methods to production paths in run_agent.py:
- build_kwargs: main _build_api_kwargs codex branch (50 lines extracted)
- normalize_response: main loop + flush + summary + retry (4 sites)
- convert_tools: memory flush tool override
- convert_messages: called internally via build_kwargs
- validate_response: response validation gate
- preflight_kwargs: request sanitization (2 sites)

Remove 7 dead legacy wrappers from AIAgent (_responses_tools,
_chat_messages_to_responses_input, _normalize_codex_response,
_preflight_codex_api_kwargs, _preflight_codex_input_items,
_extract_responses_message_text, _extract_responses_reasoning_text).
Keep 3 ID manipulation methods still used by _build_assistant_message.

Update 18 test call sites across 3 test files to call adapter functions
directly instead of through deleted AIAgent wrappers.

24 new tests. 343 codex/responses/transport tests pass (0 failures).

PR 4 of the provider transport refactor.

agent/transports/__init__.py

@@ -37,3 +37,7 @@ def _discover_transports() -> None:
         import agent.transports.anthropic  # noqa: F401
     except ImportError:
         pass
+    try:
+        import agent.transports.codex  # noqa: F401
+    except ImportError:
+        pass

agent/transports/codex.py

@@ -0,0 +1,217 @@
+"""OpenAI Responses API (Codex) transport.
+
+Delegates to the existing adapter functions in agent/codex_responses_adapter.py.
+This transport owns format conversion and normalization — NOT client lifecycle,
+streaming, or the _run_codex_stream() call path.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from agent.transports.base import ProviderTransport
+from agent.transports.types import NormalizedResponse, ToolCall, Usage
+
+
+class ResponsesApiTransport(ProviderTransport):
+    """Transport for api_mode='codex_responses'.
+
+    Wraps the functions extracted into codex_responses_adapter.py (PR 1).
+    """
+
+    @property
+    def api_mode(self) -> str:
+        return "codex_responses"
+
+    def convert_messages(self, messages: List[Dict[str, Any]], **kwargs) -> Any:
+        """Convert OpenAI chat messages to Responses API input items."""
+        from agent.codex_responses_adapter import _chat_messages_to_responses_input
+        return _chat_messages_to_responses_input(messages)
+
+    def convert_tools(self, tools: List[Dict[str, Any]]) -> Any:
+        """Convert OpenAI tool schemas to Responses API function definitions."""
+        from agent.codex_responses_adapter import _responses_tools
+        return _responses_tools(tools)
+
+    def build_kwargs(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        tools: Optional[List[Dict[str, Any]]] = None,
+        **params,
+    ) -> Dict[str, Any]:
+        """Build Responses API kwargs.
+
+        Calls convert_messages and convert_tools internally.
+
+        params:
+            instructions: str — system prompt (extracted from messages[0] if not given)
+            reasoning_config: dict | None — {effort, enabled}
+            session_id: str | None — used for prompt_cache_key + xAI conv header
+            max_tokens: int | None — max_output_tokens
+            request_overrides: dict | None — extra kwargs merged in
+            provider: str | None — provider name for backend-specific logic
+            base_url: str | None — endpoint URL
+            base_url_hostname: str | None — hostname for backend detection
+            is_github_responses: bool — Copilot/GitHub models backend
+            is_codex_backend: bool — chatgpt.com/backend-api/codex
+            is_xai_responses: bool — xAI/Grok backend
+            github_reasoning_extra: dict | None — Copilot reasoning params
+        """
+        from agent.codex_responses_adapter import (
+            _chat_messages_to_responses_input,
+            _responses_tools,
+        )
+
+        from run_agent import DEFAULT_AGENT_IDENTITY
+
+        instructions = params.get("instructions", "")
+        payload_messages = messages
+        if not instructions:
+            if messages and messages[0].get("role") == "system":
+                instructions = str(messages[0].get("content") or "").strip()
+                payload_messages = messages[1:]
+        if not instructions:
+            instructions = DEFAULT_AGENT_IDENTITY
+
+        is_github_responses = params.get("is_github_responses", False)
+        is_codex_backend = params.get("is_codex_backend", False)
+        is_xai_responses = params.get("is_xai_responses", False)
+
+        # Resolve reasoning effort
+        reasoning_effort = "medium"
+        reasoning_enabled = True
+        reasoning_config = params.get("reasoning_config")
+        if reasoning_config and isinstance(reasoning_config, dict):
+            if reasoning_config.get("enabled") is False:
+                reasoning_enabled = False
+            elif reasoning_config.get("effort"):
+                reasoning_effort = reasoning_config["effort"]
+
+        _effort_clamp = {"minimal": "low"}
+        reasoning_effort = _effort_clamp.get(reasoning_effort, reasoning_effort)
+
+        kwargs = {
+            "model": model,
+            "instructions": instructions,
+            "input": _chat_messages_to_responses_input(payload_messages),
+            "tools": _responses_tools(tools),
+            "tool_choice": "auto",
+            "parallel_tool_calls": True,
+            "store": False,
+        }
+
+        session_id = params.get("session_id")
+        if not is_github_responses and session_id:
+            kwargs["prompt_cache_key"] = session_id
+
+        if reasoning_enabled and is_xai_responses:
+            kwargs["include"] = ["reasoning.encrypted_content"]
+        elif reasoning_enabled:
+            if is_github_responses:
+                github_reasoning = params.get("github_reasoning_extra")
+                if github_reasoning is not None:
+                    kwargs["reasoning"] = github_reasoning
+            else:
+                kwargs["reasoning"] = {"effort": reasoning_effort, "summary": "auto"}
+                kwargs["include"] = ["reasoning.encrypted_content"]
+        elif not is_github_responses and not is_xai_responses:
+            kwargs["include"] = []
+
+        request_overrides = params.get("request_overrides")
+        if request_overrides:
+            kwargs.update(request_overrides)
+
+        max_tokens = params.get("max_tokens")
+        if max_tokens is not None and not is_codex_backend:
+            kwargs["max_output_tokens"] = max_tokens
+
+        if is_xai_responses and session_id:
+            kwargs["extra_headers"] = {"x-grok-conv-id": session_id}
+
+        return kwargs
+
+    def normalize_response(self, response: Any, **kwargs) -> NormalizedResponse:
+        """Normalize Codex Responses API response to NormalizedResponse."""
+        from agent.codex_responses_adapter import (
+            _normalize_codex_response,
+            _extract_responses_message_text,
+            _extract_responses_reasoning_text,
+        )
+
+        # _normalize_codex_response returns (SimpleNamespace, finish_reason_str)
+        msg, finish_reason = _normalize_codex_response(response)
+
+        tool_calls = None
+        if msg and msg.tool_calls:
+            tool_calls = []
+            for tc in msg.tool_calls:
+                provider_data = {}
+                if hasattr(tc, "call_id") and tc.call_id:
+                    provider_data["call_id"] = tc.call_id
+                if hasattr(tc, "response_item_id") and tc.response_item_id:
+                    provider_data["response_item_id"] = tc.response_item_id
+                tool_calls.append(ToolCall(
+                    id=tc.id if hasattr(tc, "id") else (tc.function.name if hasattr(tc, "function") else None),
+                    name=tc.function.name if hasattr(tc, "function") else getattr(tc, "name", ""),
+                    arguments=tc.function.arguments if hasattr(tc, "function") else getattr(tc, "arguments", "{}"),
+                    provider_data=provider_data or None,
+                ))
+
+        # Extract reasoning items for provider_data
+        provider_data = {}
+        if msg and hasattr(msg, "codex_reasoning_items") and msg.codex_reasoning_items:
+            provider_data["codex_reasoning_items"] = msg.codex_reasoning_items
+        if msg and hasattr(msg, "reasoning_details") and msg.reasoning_details:
+            provider_data["reasoning_details"] = msg.reasoning_details
+
+        return NormalizedResponse(
+            content=msg.content if msg else None,
+            tool_calls=tool_calls,
+            finish_reason=finish_reason or "stop",
+            reasoning=msg.reasoning if msg and hasattr(msg, "reasoning") else None,
+            usage=None,  # Codex usage is extracted separately in normalize_usage()
+            provider_data=provider_data or None,
+        )
+
+    def validate_response(self, response: Any) -> bool:
+        """Check Codex Responses API response has valid output structure.
+
+        Returns True only if response.output is a non-empty list.
+        Does NOT check output_text fallback — the caller handles that
+        with diagnostic logging for stream backfill recovery.
+        """
+        if response is None:
+            return False
+        output = getattr(response, "output", None)
+        if not isinstance(output, list) or not output:
+            return False
+        return True
+
+    def preflight_kwargs(self, api_kwargs: Any, *, allow_stream: bool = False) -> dict:
+        """Validate and sanitize Codex API kwargs before the call.
+
+        Normalizes input items, strips unsupported fields, validates structure.
+        """
+        from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+        return _preflight_codex_api_kwargs(api_kwargs, allow_stream=allow_stream)
+
+    def map_finish_reason(self, raw_reason: str) -> str:
+        """Map Codex response.status to OpenAI finish_reason.
+
+        Codex uses response.status ('completed', 'incomplete') +
+        response.incomplete_details.reason for granular mapping.
+        This method handles the simple status string; the caller
+        should check incomplete_details separately for 'max_output_tokens'.
+        """
+        _MAP = {
+            "completed": "stop",
+            "incomplete": "length",
+            "failed": "stop",
+            "cancelled": "stop",
+        }
+        return _MAP.get(raw_reason, "stop")
+
+
+# Auto-register on import
+from agent.transports import register_transport  # noqa: E402
+
+register_transport("codex_responses", ResponsesApiTransport)

run_agent.py

@@ -4308,10 +4308,6 @@ class AIAgent:
         if self._memory_store:
             self._memory_store.load_from_disk()
 
-    def _responses_tools(self, tools: Optional[List[Dict[str, Any]]] = None) -> Optional[List[Dict[str, Any]]]:
-        """Convert chat-completions tool schemas to Responses function-tool schemas."""
-        return _codex_responses_tools(tools if tools is not None else self.tools)
-
     @staticmethod
     def _deterministic_call_id(fn_name: str, arguments: str, index: int = 0) -> str:
         """Generate a deterministic call_id from tool call content.
@@ -4335,33 +4331,6 @@ class AIAgent:
         """Build a valid Responses `function_call.id` (must start with `fc_`)."""
         return _codex_derive_responses_function_call_id(call_id, response_item_id)
 
-    def _chat_messages_to_responses_input(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        """Convert internal chat-style messages to Responses input items."""
-        return _codex_chat_messages_to_responses_input(messages)
-
-    def _preflight_codex_input_items(self, raw_items: Any) -> List[Dict[str, Any]]:
-        return _codex_preflight_codex_input_items(raw_items)
-
-    def _preflight_codex_api_kwargs(
-        self,
-        api_kwargs: Any,
-        *,
-        allow_stream: bool = False,
-    ) -> Dict[str, Any]:
-        return _codex_preflight_codex_api_kwargs(api_kwargs, allow_stream=allow_stream)
-
-    def _extract_responses_message_text(self, item: Any) -> str:
-        """Extract assistant text from a Responses message output item."""
-        return _codex_extract_responses_message_text(item)
-
-    def _extract_responses_reasoning_text(self, item: Any) -> str:
-        """Extract a compact reasoning text from a Responses reasoning item."""
-        return _codex_extract_responses_reasoning_text(item)
-
-    def _normalize_codex_response(self, response: Any) -> tuple[Any, str]:
-        """Normalize a Responses API object to an assistant_message-like object."""
-        return _codex_normalize_codex_response(response)
-
     def _thread_identity(self) -> str:
         thread = threading.current_thread()
         return f"{thread.name}:{thread.ident}"
@@ -4854,7 +4823,7 @@ class AIAgent:
         active_client = client or self._ensure_primary_openai_client(reason="codex_create_stream_fallback")
         fallback_kwargs = dict(api_kwargs)
         fallback_kwargs["stream"] = True
-        fallback_kwargs = self._preflight_codex_api_kwargs(fallback_kwargs, allow_stream=True)
+        fallback_kwargs = self._get_codex_transport().preflight_kwargs(fallback_kwargs, allow_stream=True)
         stream_or_response = active_client.responses.create(**fallback_kwargs)
 
         # Compatibility shim for mocks or providers that still return a concrete response.
@@ -6596,6 +6565,15 @@ class AIAgent:
             self._anthropic_transport = t
         return t
 
+    def _get_codex_transport(self):
+        """Return the cached ResponsesApiTransport instance (lazy singleton)."""
+        t = getattr(self, "_codex_transport", None)
+        if t is None:
+            from agent.transports import get_transport
+            t = get_transport("codex_responses")
+            self._codex_transport = t
+        return t
+
     def _prepare_anthropic_messages_for_api(self, api_messages: list) -> list:
         if not any(
             isinstance(msg, dict) and self._content_has_image_parts(msg.get("content"))
@@ -6752,14 +6730,7 @@ class AIAgent:
             }
 
         if self.api_mode == "codex_responses":
-            instructions = ""
+            _ct = self._get_codex_transport()
-            payload_messages = api_messages
-            if api_messages and api_messages[0].get("role") == "system":
-                instructions = str(api_messages[0].get("content") or "").strip()
-                payload_messages = api_messages[1:]
-            if not instructions:
-                instructions = DEFAULT_AGENT_IDENTITY
-
             is_github_responses = (
                 base_url_host_matches(self.base_url, "models.github.ai")
                 or base_url_host_matches(self.base_url, "api.githubcopilot.com")
@@ -6771,64 +6742,20 @@ class AIAgent:
                     and "/backend-api/codex" in self._base_url_lower
                 )
             )
-
-            # Resolve reasoning effort: config > default (medium)
-            reasoning_effort = "medium"
-            reasoning_enabled = True
-            if self.reasoning_config and isinstance(self.reasoning_config, dict):
-                if self.reasoning_config.get("enabled") is False:
-                    reasoning_enabled = False
-                elif self.reasoning_config.get("effort"):
-                    reasoning_effort = self.reasoning_config["effort"]
-
-            # Clamp effort levels not supported by the Responses API model.
-            # GPT-5.4 supports none/low/medium/high/xhigh but not "minimal".
-            # "minimal" is valid on OpenRouter and GPT-5 but fails on 5.2/5.4.
-            _effort_clamp = {"minimal": "low"}
-            reasoning_effort = _effort_clamp.get(reasoning_effort, reasoning_effort)
-
-            kwargs = {
-                "model": self.model,
-                "instructions": instructions,
-                "input": self._chat_messages_to_responses_input(payload_messages),
-                "tools": self._responses_tools(),
-                "tool_choice": "auto",
-                "parallel_tool_calls": True,
-                "store": False,
-            }
-
-            if not is_github_responses:
-                kwargs["prompt_cache_key"] = self.session_id
-
             is_xai_responses = self.provider == "xai" or self._base_url_hostname == "api.x.ai"
-
+            return _ct.build_kwargs(
-            if reasoning_enabled and is_xai_responses:
+                model=self.model,
-                # xAI reasons automatically — no effort param, just include encrypted content
+                messages=api_messages,
-                kwargs["include"] = ["reasoning.encrypted_content"]
+                tools=self.tools,
-            elif reasoning_enabled:
+                reasoning_config=self.reasoning_config,
-                if is_github_responses:
+                session_id=getattr(self, "session_id", None),
-                    # Copilot's Responses route advertises reasoning-effort support,
+                max_tokens=self.max_tokens,
-                    # but not OpenAI-specific prompt cache or encrypted reasoning
+                request_overrides=self.request_overrides,
-                    # fields. Keep the payload to the documented subset.
+                is_github_responses=is_github_responses,
-                    github_reasoning = self._github_models_reasoning_extra_body()
+                is_codex_backend=is_codex_backend,
-                    if github_reasoning is not None:
+                is_xai_responses=is_xai_responses,
-                        kwargs["reasoning"] = github_reasoning
+                github_reasoning_extra=self._github_models_reasoning_extra_body() if is_github_responses else None,
-                else:
+            )
-                    kwargs["reasoning"] = {"effort": reasoning_effort, "summary": "auto"}
-                    kwargs["include"] = ["reasoning.encrypted_content"]
-            elif not is_github_responses and not is_xai_responses:
-                kwargs["include"] = []
-
-            if self.request_overrides:
-                kwargs.update(self.request_overrides)
-
-            if self.max_tokens is not None and not is_codex_backend:
-                kwargs["max_output_tokens"] = self.max_tokens
-
-            if is_xai_responses and getattr(self, "session_id", None):
-                kwargs["extra_headers"] = {"x-grok-conv-id": self.session_id}
-
-            return kwargs
 
         sanitized_messages = api_messages
         needs_sanitization = False
@@ -7438,7 +7365,7 @@ class AIAgent:
             if not _aux_available and self.api_mode == "codex_responses":
                 # No auxiliary client -- use the Codex Responses path directly
                 codex_kwargs = self._build_api_kwargs(api_messages)
-                codex_kwargs["tools"] = self._responses_tools([memory_tool_def])
+                codex_kwargs["tools"] = self._get_codex_transport().convert_tools([memory_tool_def])
                 if _flush_temperature is not None:
                     codex_kwargs["temperature"] = _flush_temperature
                 else:
@@ -7473,9 +7400,15 @@ class AIAgent:
             # Extract tool calls from the response, handling all API formats
             tool_calls = []
             if self.api_mode == "codex_responses" and not _aux_available:
-                assistant_msg, _ = self._normalize_codex_response(response)
+                _ct_flush = self._get_codex_transport()
-                if assistant_msg and assistant_msg.tool_calls:
+                _cnr_flush = _ct_flush.normalize_response(response)
-                    tool_calls = assistant_msg.tool_calls
+                if _cnr_flush and _cnr_flush.tool_calls:
+                    tool_calls = [
+                        SimpleNamespace(
+                            id=tc.id, type="function",
+                            function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
+                        ) for tc in _cnr_flush.tool_calls
+                    ]
             elif self.api_mode == "anthropic_messages" and not _aux_available:
                 _tfn = self._get_anthropic_transport()
                 _flush_nr = _tfn.normalize_response(response, strip_tool_prefix=self._is_anthropic_oauth)
@@ -8519,8 +8452,9 @@ class AIAgent:
                 codex_kwargs = self._build_api_kwargs(api_messages)
                 codex_kwargs.pop("tools", None)
                 summary_response = self._run_codex_stream(codex_kwargs)
-                assistant_message, _ = self._normalize_codex_response(summary_response)
+                _ct_sum = self._get_codex_transport()
-                final_response = (assistant_message.content or "").strip() if assistant_message else ""
+                _cnr_sum = _ct_sum.normalize_response(summary_response)
+                final_response = (_cnr_sum.content or "").strip()
             else:
                 summary_kwargs = {
                     "model": self.model,
@@ -8577,8 +8511,9 @@ class AIAgent:
                     codex_kwargs = self._build_api_kwargs(api_messages)
                     codex_kwargs.pop("tools", None)
                     retry_response = self._run_codex_stream(codex_kwargs)
-                    retry_msg, _ = self._normalize_codex_response(retry_response)
+                    _ct_retry = self._get_codex_transport()
-                    final_response = (retry_msg.content or "").strip() if retry_msg else ""
+                    _cnr_retry = _ct_retry.normalize_response(retry_response)
+                    final_response = (_cnr_retry.content or "").strip()
                 elif self.api_mode == "anthropic_messages":
                     _tretry = self._get_anthropic_transport()
                     _ant_kw2 = _tretry.build_kwargs(model=self.model, messages=api_messages, tools=None,
@@ -9340,7 +9275,7 @@ class AIAgent:
                     if self._force_ascii_payload:
                         _sanitize_structure_non_ascii(api_kwargs)
                     if self.api_mode == "codex_responses":
-                        api_kwargs = self._preflight_codex_api_kwargs(api_kwargs, allow_stream=False)
+                        api_kwargs = self._get_codex_transport().preflight_kwargs(api_kwargs, allow_stream=False)
 
                     try:
                         from hermes_cli.plugins import invoke_hook as _invoke_hook
@@ -9428,38 +9363,34 @@ class AIAgent:
                     response_invalid = False
                     error_details = []
                     if self.api_mode == "codex_responses":
-                        output_items = getattr(response, "output", None) if response is not None else None
+                        _ct_v = self._get_codex_transport()
-                        if response is None:
+                        if not _ct_v.validate_response(response):
-                            response_invalid = True
+                            if response is None:
-                            error_details.append("response is None")
-                        elif not isinstance(output_items, list):
-                            response_invalid = True
-                            error_details.append("response.output is not a list")
-                        elif not output_items:
-                            # Stream backfill may have failed, but
-                            # _normalize_codex_response can still recover
-                            # from response.output_text. Only mark invalid
-                            # when that fallback is also absent.
-                            _out_text = getattr(response, "output_text", None)
-                            _out_text_stripped = _out_text.strip() if isinstance(_out_text, str) else ""
-                            if _out_text_stripped:
-                                logger.debug(
-                                    "Codex response.output is empty but output_text is present "
-                                    "(%d chars); deferring to normalization.",
-                                    len(_out_text_stripped),
-                                )
-                            else:
-                                _resp_status = getattr(response, "status", None)
-                                _resp_incomplete = getattr(response, "incomplete_details", None)
-                                logger.warning(
-                                    "Codex response.output is empty after stream backfill "
-                                    "(status=%s, incomplete_details=%s, model=%s). %s",
-                                    _resp_status, _resp_incomplete,
-                                    getattr(response, "model", None),
-                                    f"api_mode={self.api_mode} provider={self.provider}",
-                                )
                                 response_invalid = True
-                                error_details.append("response.output is empty")
+                                error_details.append("response is None")
+                            else:
+                                # output_text fallback: stream backfill may have failed
+                                # but normalize can still recover from output_text
+                                _out_text = getattr(response, "output_text", None)
+                                _out_text_stripped = _out_text.strip() if isinstance(_out_text, str) else ""
+                                if _out_text_stripped:
+                                    logger.debug(
+                                        "Codex response.output is empty but output_text is present "
+                                        "(%d chars); deferring to normalization.",
+                                        len(_out_text_stripped),
+                                    )
+                                else:
+                                    _resp_status = getattr(response, "status", None)
+                                    _resp_incomplete = getattr(response, "incomplete_details", None)
+                                    logger.warning(
+                                        "Codex response.output is empty after stream backfill "
+                                        "(status=%s, incomplete_details=%s, model=%s). %s",
+                                        _resp_status, _resp_incomplete,
+                                        getattr(response, "model", None),
+                                        f"api_mode={self.api_mode} provider={self.provider}",
+                                    )
+                                    response_invalid = True
+                                    error_details.append("response.output is empty")
                     elif self.api_mode == "anthropic_messages":
                         _tv = self._get_anthropic_transport()
                         if not _tv.validate_response(response):
@@ -10885,7 +10816,40 @@ class AIAgent:
 
             try:
                 if self.api_mode == "codex_responses":
-                    assistant_message, finish_reason = self._normalize_codex_response(response)
+                    _ct = self._get_codex_transport()
+                    _cnr = _ct.normalize_response(response)
+                    # Back-compat shim: downstream expects SimpleNamespace with
+                    # codex-specific fields (.codex_reasoning_items, .reasoning_details,
+                    # and .call_id/.response_item_id on tool calls).
+                    _tc_list = None
+                    if _cnr.tool_calls:
+                        _tc_list = []
+                        for tc in _cnr.tool_calls:
+                            _tc_ns = SimpleNamespace(
+                                id=tc.id, type="function",
+                                function=SimpleNamespace(name=tc.name, arguments=tc.arguments),
+                            )
+                            if tc.provider_data:
+                                if tc.provider_data.get("call_id"):
+                                    _tc_ns.call_id = tc.provider_data["call_id"]
+                                if tc.provider_data.get("response_item_id"):
+                                    _tc_ns.response_item_id = tc.provider_data["response_item_id"]
+                            _tc_list.append(_tc_ns)
+                    assistant_message = SimpleNamespace(
+                        content=_cnr.content,
+                        tool_calls=_tc_list or None,
+                        reasoning=_cnr.reasoning,
+                        reasoning_content=None,
+                        codex_reasoning_items=(
+                            _cnr.provider_data.get("codex_reasoning_items")
+                            if _cnr.provider_data else None
+                        ),
+                        reasoning_details=(
+                            _cnr.provider_data.get("reasoning_details")
+                            if _cnr.provider_data else None
+                        ),
+                    )
+                    finish_reason = _cnr.finish_reason
                 elif self.api_mode == "anthropic_messages":
                     _transport = self._get_anthropic_transport()
                     _nr = _transport.normalize_response(

tests/agent/transports/test_codex_transport.py

@@ -0,0 +1,220 @@
+"""Tests for the ResponsesApiTransport (Codex)."""
+
+import json
+import pytest
+from types import SimpleNamespace
+
+from agent.transports import get_transport
+from agent.transports.types import NormalizedResponse, ToolCall
+
+
+@pytest.fixture
+def transport():
+    import agent.transports.codex  # noqa: F401
+    return get_transport("codex_responses")
+
+
+class TestCodexTransportBasic:
+
+    def test_api_mode(self, transport):
+        assert transport.api_mode == "codex_responses"
+
+    def test_registered_on_import(self, transport):
+        assert transport is not None
+
+    def test_convert_tools(self, transport):
+        tools = [{
+            "type": "function",
+            "function": {
+                "name": "terminal",
+                "description": "Run a command",
+                "parameters": {"type": "object", "properties": {"command": {"type": "string"}}},
+            }
+        }]
+        result = transport.convert_tools(tools)
+        assert len(result) == 1
+        assert result[0]["type"] == "function"
+        assert result[0]["name"] == "terminal"
+
+
+class TestCodexBuildKwargs:
+
+    def test_basic_kwargs(self, transport):
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hello"},
+        ]
+        kw = transport.build_kwargs(
+            model="gpt-5.4",
+            messages=messages,
+            tools=[],
+        )
+        assert kw["model"] == "gpt-5.4"
+        assert kw["instructions"] == "You are helpful."
+        assert "input" in kw
+        assert kw["store"] is False
+
+    def test_system_extracted_from_messages(self, transport):
+        messages = [
+            {"role": "system", "content": "Custom system prompt"},
+            {"role": "user", "content": "Hi"},
+        ]
+        kw = transport.build_kwargs(model="gpt-5.4", messages=messages, tools=[])
+        assert kw["instructions"] == "Custom system prompt"
+
+    def test_no_system_uses_default(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(model="gpt-5.4", messages=messages, tools=[])
+        assert kw["instructions"]  # should be non-empty default
+
+    def test_reasoning_config(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            reasoning_config={"effort": "high"},
+        )
+        assert kw.get("reasoning", {}).get("effort") == "high"
+
+    def test_reasoning_disabled(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            reasoning_config={"enabled": False},
+        )
+        assert "reasoning" not in kw or kw.get("include") == []
+
+    def test_session_id_sets_cache_key(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            session_id="test-session-123",
+        )
+        assert kw.get("prompt_cache_key") == "test-session-123"
+
+    def test_github_responses_no_cache_key(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            session_id="test-session",
+            is_github_responses=True,
+        )
+        assert "prompt_cache_key" not in kw
+
+    def test_max_tokens(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            max_tokens=4096,
+        )
+        assert kw.get("max_output_tokens") == 4096
+
+    def test_codex_backend_no_max_output_tokens(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            max_tokens=4096,
+            is_codex_backend=True,
+        )
+        assert "max_output_tokens" not in kw
+
+    def test_xai_headers(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="grok-3", messages=messages, tools=[],
+            session_id="conv-123",
+            is_xai_responses=True,
+        )
+        assert kw.get("extra_headers", {}).get("x-grok-conv-id") == "conv-123"
+
+    def test_minimal_effort_clamped(self, transport):
+        messages = [{"role": "user", "content": "Hi"}]
+        kw = transport.build_kwargs(
+            model="gpt-5.4", messages=messages, tools=[],
+            reasoning_config={"effort": "minimal"},
+        )
+        # "minimal" should be clamped to "low"
+        assert kw.get("reasoning", {}).get("effort") == "low"
+
+
+class TestCodexValidateResponse:
+
+    def test_none_response(self, transport):
+        assert transport.validate_response(None) is False
+
+    def test_empty_output(self, transport):
+        r = SimpleNamespace(output=[], output_text=None)
+        assert transport.validate_response(r) is False
+
+    def test_valid_output(self, transport):
+        r = SimpleNamespace(output=[{"type": "message", "content": []}])
+        assert transport.validate_response(r) is True
+
+    def test_output_text_fallback_not_valid(self, transport):
+        """validate_response is strict — output_text doesn't make it valid.
+        The caller handles output_text fallback with diagnostic logging."""
+        r = SimpleNamespace(output=None, output_text="Some text")
+        assert transport.validate_response(r) is False
+
+
+class TestCodexMapFinishReason:
+
+    def test_completed(self, transport):
+        assert transport.map_finish_reason("completed") == "stop"
+
+    def test_incomplete(self, transport):
+        assert transport.map_finish_reason("incomplete") == "length"
+
+    def test_failed(self, transport):
+        assert transport.map_finish_reason("failed") == "stop"
+
+    def test_unknown(self, transport):
+        assert transport.map_finish_reason("unknown_status") == "stop"
+
+
+class TestCodexNormalizeResponse:
+
+    def test_text_response(self, transport):
+        """Normalize a simple text Codex response."""
+        r = SimpleNamespace(
+            output=[
+                SimpleNamespace(
+                    type="message",
+                    role="assistant",
+                    content=[SimpleNamespace(type="output_text", text="Hello world")],
+                    status="completed",
+                ),
+            ],
+            status="completed",
+            incomplete_details=None,
+            usage=SimpleNamespace(input_tokens=10, output_tokens=5,
+                                  input_tokens_details=None, output_tokens_details=None),
+        )
+        nr = transport.normalize_response(r)
+        assert isinstance(nr, NormalizedResponse)
+        assert nr.content == "Hello world"
+        assert nr.finish_reason == "stop"
+
+    def test_tool_call_response(self, transport):
+        """Normalize a Codex response with tool calls."""
+        r = SimpleNamespace(
+            output=[
+                SimpleNamespace(
+                    type="function_call",
+                    call_id="call_abc123",
+                    name="terminal",
+                    arguments=json.dumps({"command": "ls"}),
+                    id="fc_abc123",
+                    status="completed",
+                ),
+            ],
+            status="completed",
+            incomplete_details=None,
+            usage=SimpleNamespace(input_tokens=10, output_tokens=20,
+                                  input_tokens_details=None, output_tokens_details=None),
+        )
+        nr = transport.normalize_response(r)
+        assert nr.finish_reason == "tool_calls"
+        assert len(nr.tool_calls) == 1
+        tc = nr.tool_calls[0]
+        assert tc.name == "terminal"
+        assert '"command"' in tc.arguments

tests/run_agent/test_provider_parity.py

@@ -12,6 +12,7 @@ from types import SimpleNamespace
 from unittest.mock import patch, MagicMock
 
 import pytest
+from agent.codex_responses_adapter import _chat_messages_to_responses_input, _normalize_codex_response, _preflight_codex_input_items
 
 sys.modules.setdefault("fire", types.SimpleNamespace(Fire=lambda *a, **k: None))
 sys.modules.setdefault("firecrawl", types.SimpleNamespace(Firecrawl=object))
@@ -446,7 +447,7 @@ class TestChatMessagesToResponsesInput:
         agent = _make_agent(monkeypatch, "openai-codex", api_mode="codex_responses",
                             base_url="https://chatgpt.com/backend-api/codex")
         messages = [{"role": "user", "content": "hello"}]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         assert items == [{"role": "user", "content": "hello"}]
 
     def test_system_messages_filtered(self, monkeypatch):
@@ -456,7 +457,7 @@ class TestChatMessagesToResponsesInput:
             {"role": "system", "content": "be helpful"},
             {"role": "user", "content": "hello"},
         ]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         assert len(items) == 1
         assert items[0]["role"] == "user"
 
@@ -472,7 +473,7 @@ class TestChatMessagesToResponsesInput:
                 "function": {"name": "web_search", "arguments": '{"query": "test"}'},
             }],
         }]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         fc_items = [i for i in items if i.get("type") == "function_call"]
         assert len(fc_items) == 1
         assert fc_items[0]["name"] == "web_search"
@@ -482,7 +483,7 @@ class TestChatMessagesToResponsesInput:
         agent = _make_agent(monkeypatch, "openai-codex", api_mode="codex_responses",
                             base_url="https://chatgpt.com/backend-api/codex")
         messages = [{"role": "tool", "tool_call_id": "call_abc", "content": "result here"}]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         assert items[0]["type"] == "function_call_output"
         assert items[0]["call_id"] == "call_abc"
         assert items[0]["output"] == "result here"
@@ -502,7 +503,7 @@ class TestChatMessagesToResponsesInput:
             },
             {"role": "user", "content": "continue"},
         ]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         reasoning_items = [i for i in items if i.get("type") == "reasoning"]
         assert len(reasoning_items) == 1
         assert reasoning_items[0]["encrypted_content"] == "gAAAA_test_blob"
@@ -515,7 +516,7 @@ class TestChatMessagesToResponsesInput:
             {"role": "assistant", "content": "hi"},
             {"role": "user", "content": "hello"},
         ]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         reasoning_items = [i for i in items if i.get("type") == "reasoning"]
         assert len(reasoning_items) == 0
 
@@ -539,7 +540,7 @@ class TestNormalizeCodexResponse:
             ],
             status="completed",
         )
-        msg, reason = agent._normalize_codex_response(response)
+        msg, reason = _normalize_codex_response(response)
         assert msg.content == "Hello!"
         assert reason == "stop"
 
@@ -557,7 +558,7 @@ class TestNormalizeCodexResponse:
             ],
             status="completed",
         )
-        msg, reason = agent._normalize_codex_response(response)
+        msg, reason = _normalize_codex_response(response)
         assert msg.content == "42"
         assert "math" in msg.reasoning
         assert reason == "stop"
@@ -576,7 +577,7 @@ class TestNormalizeCodexResponse:
             ],
             status="completed",
         )
-        msg, reason = agent._normalize_codex_response(response)
+        msg, reason = _normalize_codex_response(response)
         assert msg.codex_reasoning_items is not None
         assert len(msg.codex_reasoning_items) == 1
         assert msg.codex_reasoning_items[0]["encrypted_content"] == "gAAAA_secret_blob_123"
@@ -592,7 +593,7 @@ class TestNormalizeCodexResponse:
             ],
             status="completed",
         )
-        msg, reason = agent._normalize_codex_response(response)
+        msg, reason = _normalize_codex_response(response)
         assert msg.codex_reasoning_items is None
 
     def test_tool_calls_extracted(self, monkeypatch):
@@ -605,7 +606,7 @@ class TestNormalizeCodexResponse:
             ],
             status="completed",
         )
-        msg, reason = agent._normalize_codex_response(response)
+        msg, reason = _normalize_codex_response(response)
         assert reason == "tool_calls"
         assert len(msg.tool_calls) == 1
         assert msg.tool_calls[0].function.name == "web_search"
@@ -821,7 +822,7 @@ class TestCodexReasoningPreflight:
              "summary": [{"type": "summary_text", "text": "Thinking about it"}]},
             {"role": "assistant", "content": "hi there"},
         ]
-        normalized = agent._preflight_codex_input_items(raw_input)
+        normalized = _preflight_codex_input_items(raw_input)
         reasoning_items = [i for i in normalized if i.get("type") == "reasoning"]
         assert len(reasoning_items) == 1
         assert reasoning_items[0]["encrypted_content"] == "abc123encrypted"
@@ -837,7 +838,7 @@ class TestCodexReasoningPreflight:
         raw_input = [
             {"type": "reasoning", "encrypted_content": "abc123"},
         ]
-        normalized = agent._preflight_codex_input_items(raw_input)
+        normalized = _preflight_codex_input_items(raw_input)
         assert len(normalized) == 1
         assert "id" not in normalized[0]
         assert normalized[0]["summary"] == []  # default empty summary
@@ -849,7 +850,7 @@ class TestCodexReasoningPreflight:
             {"type": "reasoning", "encrypted_content": ""},
             {"role": "user", "content": "hello"},
         ]
-        normalized = agent._preflight_codex_input_items(raw_input)
+        normalized = _preflight_codex_input_items(raw_input)
         reasoning_items = [i for i in normalized if i.get("type") == "reasoning"]
         assert len(reasoning_items) == 0
 
@@ -868,7 +869,7 @@ class TestCodexReasoningPreflight:
             },
             {"role": "user", "content": "follow up"},
         ]
-        items = agent._chat_messages_to_responses_input(messages)
+        items = _chat_messages_to_responses_input(messages)
         reasoning_items = [i for i in items if isinstance(i, dict) and i.get("type") == "reasoning"]
         assert len(reasoning_items) == 1
         assert reasoning_items[0]["encrypted_content"] == "enc123"

tests/run_agent/test_run_agent.py

@@ -16,6 +16,7 @@ from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
+from agent.codex_responses_adapter import _chat_messages_to_responses_input, _normalize_codex_response, _preflight_codex_input_items
 
 import run_agent
 from run_agent import AIAgent
@@ -4248,7 +4249,7 @@ class TestNormalizeCodexDictArguments:
         json.dumps, not str(), so downstream json.loads() succeeds."""
         args_dict = {"query": "weather in NYC", "units": "celsius"}
         response = self._make_codex_response("function_call", args_dict)
-        msg, _ = agent._normalize_codex_response(response)
+        msg, _ = _normalize_codex_response(response)
         tc = msg.tool_calls[0]
         parsed = json.loads(tc.function.arguments)
         assert parsed == args_dict
@@ -4257,7 +4258,7 @@ class TestNormalizeCodexDictArguments:
         """dict arguments from custom_tool_call must also use json.dumps."""
         args_dict = {"path": "/tmp/test.txt", "content": "hello"}
         response = self._make_codex_response("custom_tool_call", args_dict)
-        msg, _ = agent._normalize_codex_response(response)
+        msg, _ = _normalize_codex_response(response)
         tc = msg.tool_calls[0]
         parsed = json.loads(tc.function.arguments)
         assert parsed == args_dict
@@ -4266,7 +4267,7 @@ class TestNormalizeCodexDictArguments:
         """String arguments must pass through without modification."""
         args_str = '{"query": "test"}'
         response = self._make_codex_response("function_call", args_str)
-        msg, _ = agent._normalize_codex_response(response)
+        msg, _ = _normalize_codex_response(response)
         tc = msg.tool_calls[0]
         assert tc.function.arguments == args_str
 

tests/run_agent/test_run_agent_codex_responses.py

@@ -640,7 +640,8 @@ def test_run_conversation_codex_tool_round_trip(monkeypatch):
 
 def test_chat_messages_to_responses_input_uses_call_id_for_function_call(monkeypatch):
     agent = _build_agent(monkeypatch)
-    items = agent._chat_messages_to_responses_input(
+    from agent.codex_responses_adapter import _chat_messages_to_responses_input
+    items = _chat_messages_to_responses_input(
         [
             {"role": "user", "content": "Run terminal"},
             {
@@ -668,7 +669,8 @@ def test_chat_messages_to_responses_input_uses_call_id_for_function_call(monkeyp
 
 def test_chat_messages_to_responses_input_accepts_call_pipe_fc_ids(monkeypatch):
     agent = _build_agent(monkeypatch)
-    items = agent._chat_messages_to_responses_input(
+    from agent.codex_responses_adapter import _chat_messages_to_responses_input
+    items = _chat_messages_to_responses_input(
         [
             {"role": "user", "content": "Run terminal"},
             {
@@ -696,7 +698,8 @@ def test_chat_messages_to_responses_input_accepts_call_pipe_fc_ids(monkeypatch):
 
 def test_preflight_codex_api_kwargs_strips_optional_function_call_id(monkeypatch):
     agent = _build_agent(monkeypatch)
-    preflight = agent._preflight_codex_api_kwargs(
+    from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+    preflight = _preflight_codex_api_kwargs(
         {
             "model": "gpt-5-codex",
             "instructions": "You are Hermes.",
@@ -724,7 +727,8 @@ def test_preflight_codex_api_kwargs_rejects_function_call_output_without_call_id
     agent = _build_agent(monkeypatch)
 
     with pytest.raises(ValueError, match="function_call_output is missing call_id"):
-        agent._preflight_codex_api_kwargs(
+        from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+        _preflight_codex_api_kwargs(
             {
                 "model": "gpt-5-codex",
                 "instructions": "You are Hermes.",
@@ -741,7 +745,8 @@ def test_preflight_codex_api_kwargs_rejects_unsupported_request_fields(monkeypat
     kwargs["some_unknown_field"] = "value"
 
     with pytest.raises(ValueError, match="unsupported field"):
-        agent._preflight_codex_api_kwargs(kwargs)
+        from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+        _preflight_codex_api_kwargs(kwargs)
 
 
 def test_preflight_codex_api_kwargs_allows_reasoning_and_temperature(monkeypatch):
@@ -752,7 +757,8 @@ def test_preflight_codex_api_kwargs_allows_reasoning_and_temperature(monkeypatch
     kwargs["temperature"] = 0.7
     kwargs["max_output_tokens"] = 4096
 
-    result = agent._preflight_codex_api_kwargs(kwargs)
+    from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+    result = _preflight_codex_api_kwargs(kwargs)
     assert result["reasoning"] == {"effort": "high", "summary": "auto"}
     assert result["include"] == ["reasoning.encrypted_content"]
     assert result["temperature"] == 0.7
@@ -764,7 +770,8 @@ def test_preflight_codex_api_kwargs_allows_service_tier(monkeypatch):
     kwargs = _codex_request_kwargs()
     kwargs["service_tier"] = "priority"
 
-    result = agent._preflight_codex_api_kwargs(kwargs)
+    from agent.codex_responses_adapter import _preflight_codex_api_kwargs
+    result = _preflight_codex_api_kwargs(kwargs)
     assert result["service_tier"] == "priority"
 
 
@@ -841,7 +848,8 @@ def test_run_conversation_codex_continues_after_incomplete_interim_message(monke
 
 def test_normalize_codex_response_marks_commentary_only_message_as_incomplete(monkeypatch):
     agent = _build_agent(monkeypatch)
-    assistant_message, finish_reason = agent._normalize_codex_response(
+    from agent.codex_responses_adapter import _normalize_codex_response
+    assistant_message, finish_reason = _normalize_codex_response(
         _codex_commentary_message_response("I'll inspect the repository first.")
     )
 
@@ -1068,7 +1076,8 @@ def test_normalize_codex_response_marks_reasoning_only_as_incomplete(monkeypatch
     sends them into the empty-content retry loop (3 retries then failure).
     """
     agent = _build_agent(monkeypatch)
-    assistant_message, finish_reason = agent._normalize_codex_response(
+    from agent.codex_responses_adapter import _normalize_codex_response
+    assistant_message, finish_reason = _normalize_codex_response(
         _codex_reasoning_only_response()
     )
 
@@ -1101,7 +1110,8 @@ def test_normalize_codex_response_reasoning_with_content_is_stop(monkeypatch):
         status="completed",
         model="gpt-5-codex",
     )
-    assistant_message, finish_reason = agent._normalize_codex_response(response)
+    from agent.codex_responses_adapter import _normalize_codex_response
+    assistant_message, finish_reason = _normalize_codex_response(response)
 
     assert finish_reason == "stop"
     assert "Here is the answer" in assistant_message.content
@@ -1186,7 +1196,8 @@ def test_chat_messages_to_responses_input_reasoning_only_has_following_item(monk
             ],
         },
     ]
-    items = agent._chat_messages_to_responses_input(messages)
+    from agent.codex_responses_adapter import _chat_messages_to_responses_input
+    items = _chat_messages_to_responses_input(messages)
 
     # Find the reasoning item
     reasoning_indices = [i for i, it in enumerate(items) if it.get("type") == "reasoning"]
@@ -1273,7 +1284,8 @@ def test_chat_messages_to_responses_input_deduplicates_reasoning_ids(monkeypatch
             ],
         },
     ]
-    items = agent._chat_messages_to_responses_input(messages)
+    from agent.codex_responses_adapter import _chat_messages_to_responses_input
+    items = _chat_messages_to_responses_input(messages)
 
     reasoning_items = [it for it in items if it.get("type") == "reasoning"]
     # Dedup: rs_aaa appears in both turns but should only be emitted once.
@@ -1299,7 +1311,8 @@ def test_preflight_codex_input_deduplicates_reasoning_ids(monkeypatch):
         {"type": "reasoning", "id": "rs_zzz", "encrypted_content": "enc_b"},
         {"role": "assistant", "content": "done"},
     ]
-    normalized = agent._preflight_codex_input_items(raw_input)
+    from agent.codex_responses_adapter import _preflight_codex_input_items
+    normalized = _preflight_codex_input_items(raw_input)
 
     reasoning_items = [it for it in normalized if it.get("type") == "reasoning"]
     # rs_xyz duplicate should be collapsed to one item; rs_zzz kept.