"""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 @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_cache_key_is_content_addressed_not_session_id(self, transport): """prompt_cache_key is content-addressed from the static prefix (instructions + tools), not the session_id. This keeps recurring cron jobs — whose session_id carries a per-fire timestamp — on a stable warm cache key. The key is a 'pck_' hash and must NOT equal session_id.""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], session_id="cron_job42_20260624_143000", ) pck = kw.get("prompt_cache_key", "") assert pck.startswith("pck_") assert pck != "cron_job42_20260624_143000" def test_cache_key_stable_across_session_ids(self, transport): """Same static prefix + different session_id (e.g. two cron fires of the same job) must yield the same prompt_cache_key — the whole point of the fix: repeated fires reuse the warm prefix instead of going cold.""" messages = [{"role": "user", "content": "Hi"}] kw1 = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], session_id="cron_job42_20260624_143000", ) kw2 = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], session_id="cron_job42_20260624_143500", ) assert kw1["prompt_cache_key"] == kw2["prompt_cache_key"] def test_cache_key_differs_across_unrelated_sessions(self, transport): """#78941: two unrelated sessions (different users/conversations) sharing the same static prefix must NOT collapse onto the same prompt_cache_key — session_id scopes the hash unless it is a cron per-fire id, which is normalized to its stable job prefix instead.""" messages = [{"role": "user", "content": "Hi"}] kw1 = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], session_id="session_alice_1", ) kw2 = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], session_id="session_bob_1", ) assert kw1["prompt_cache_key"] != kw2["prompt_cache_key"] def test_github_responses_drops_message_item_id_end_to_end(self, transport): # #32716: Copilot binds codex_message_items ids to a backend # "connection" that doesn't survive credential rotation, a gateway # restart, or load-balancer churn — replaying a stale id gets HTTP # 401 "input item ID does not belong to this connection", even for # ids well under the #27038 64-char length cap. build_kwargs must # thread is_github_responses through to the input converter so the # id never reaches the request. messages = [ {"role": "system", "content": "You are Hermes."}, { "role": "assistant", "content": "pong", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "in_progress", "content": [{"type": "output_text", "text": "pong"}], "id": "msg_short_but_connection_scoped", "phase": "final_answer", } ], }, ] kw = transport.build_kwargs( model="gpt-5.5", messages=messages, tools=[], is_github_responses=True, ) message_item = next(item for item in kw["input"] if item.get("type") == "message") assert "id" not in message_item assert message_item["phase"] == "final_answer" assert message_item["status"] == "in_progress" assert message_item["content"] == [{"type": "output_text", "text": "pong"}] def test_non_github_responses_keeps_message_item_id_end_to_end(self, transport): messages = [ {"role": "system", "content": "You are Hermes."}, { "role": "assistant", "content": "pong", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "pong"}], "id": "msg_short_id", } ], }, ] kw = transport.build_kwargs( model="gpt-5.5", messages=messages, tools=[], is_codex_backend=True, ) message_item = next(item for item in kw["input"] if item.get("type") == "message") assert message_item["id"] == "msg_short_id" @pytest.mark.parametrize("model", [ "gpt-5.5", "gpt-5.5-pro", "gpt-5.4", "gpt-5.2", "gpt-5.1-codex-max", "gpt-5.1", "gpt-5.1-codex", "gpt-5.1-codex-mini", "gpt-5.1-chat-latest", "gpt-5", "gpt-5-codex", "gpt-4.1", "openai.gpt-5.5-pro", "openai/gpt-5.1-codex-2026-01-01", ]) def test_extended_cache_models_set_24h_prompt_cache_retention(self, transport, model): messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model=model, messages=messages, tools=[], session_id="test-session", base_url="https://bedrock-mantle.us-west-2.api.aws/v1", ) assert kw["prompt_cache_retention"] == "24h" @pytest.mark.parametrize("model", ["gpt-5.6", "gpt-4o", "o3"]) def test_prompt_cache_retention_omitted_for_other_model_families(self, transport, model): kw = transport.build_kwargs( model=model, messages=[{"role": "user", "content": "Hi"}], tools=[], session_id="test-session", base_url="https://bedrock-mantle.us-west-2.api.aws/v1", ) assert "prompt_cache_retention" not in kw @pytest.mark.parametrize("base_url", [ "https://api.openai.com/v1", "https://example.openai.azure.com/openai/v1", "https://api.x.ai/v1", "https://models.github.ai/inference", "https://api.githubcopilot.com", "https://chatgpt.com/backend-api/codex", "https://responses.example.com/v1", "https://bedrock-mantle.us-west-2.api.aws.example/v1", "https://example.com/bedrock-mantle.us-west-2.api.aws/v1", ]) def test_prompt_cache_retention_omitted_for_non_mantle_endpoints(self, transport, base_url): kw = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "Hi"}], tools=[], base_url=base_url, ) assert "prompt_cache_retention" not in kw def test_xai_responses_sends_cache_key_via_extra_body(self, transport): """xAI's Responses API documents ``prompt_cache_key`` as the body-level cache-routing key (the ``x-grok-conv-id`` header is Chat-Completions-only). Passing it via ``extra_body`` is robust against openai SDK builds whose ``Responses.stream()`` kwarg signature ever drops the field — the body field still serializes and reaches xAI either way. The ``x-grok-conv-id`` header is kept as a belt-and-braces fallback so cache routing survives even when the body field would be stripped by an intermediate proxy. Ref: https://docs.x.ai/developers/advanced-api-usage/prompt-caching/maximizing-cache-hits """ messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], session_id="conv-xai-1", is_xai_responses=True, ) assert "prompt_cache_key" not in kw # Body-level prompt_cache_key is content-addressed (pck_ hash), not the # raw session_id, so recurring cron fires stay on a stable warm key. eb_pck = kw.get("extra_body", {}).get("prompt_cache_key", "") assert eb_pck.startswith("pck_") assert eb_pck != "conv-xai-1" # x-grok-conv-id stays the session/transcript id, not the cache key. assert kw.get("extra_headers", {}).get("x-grok-conv-id") == "conv-xai-1" def test_xai_responses_extra_body_preserves_caller_fields(self, transport): """When the caller already supplies ``extra_body`` (e.g. via request_overrides), the xAI cache-key injection must merge into the existing dict instead of overwriting it. Caller-supplied ``prompt_cache_key`` wins (setdefault semantics) so user overrides aren't silently clobbered by the transport.""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], session_id="conv-xai-1", is_xai_responses=True, request_overrides={"extra_body": {"prompt_cache_key": "caller-override", "other_field": 42}}, ) eb = kw.get("extra_body", {}) assert eb.get("prompt_cache_key") == "caller-override" assert eb.get("other_field") == 42 # ── Azure Foundry post-tool reasoning suppression ────────────────── # # Foundry's Responses surface accepts the initial function-call request # and ordinary multi-turn continuity, but rejects the post-tool follow-up # payload when a replayed encrypted ``reasoning`` item sits alongside # ``function_call`` / ``function_call_output`` (HTTP 400 invalid_payload). # Suppression is scoped to that follow-up turn only. @staticmethod def _reasoning_item(): return {"type": "reasoning", "encrypted_content": "sealed", "summary": []} @classmethod def _post_tool_messages(cls): """user → assistant(tool_calls + reasoning) → tool result.""" return [ {"role": "user", "content": "Create a marker"}, { "role": "assistant", "content": "", "codex_reasoning_items": [cls._reasoning_item()], "tool_calls": [ { "id": "call_marker", "type": "function", "function": {"name": "write_marker", "arguments": "{}"}, } ], }, { "role": "tool", "tool_call_id": "call_marker", "content": "marker written", }, ] AZURE_FOUNDRY_BASE_URL = ( "https://placeholder.services.ai.azure.com/" "api/projects/placeholder/openai/v1" ) def test_post_tool_replay_preserves_reasoning_for_default_responses(self, transport): """Non-Azure Responses endpoints keep post-tool reasoning replay.""" kw = transport.build_kwargs( model="gpt-5.4", messages=self._post_tool_messages(), tools=[], replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" in item_types assert "function_call" in item_types assert "function_call_output" in item_types assert kw.get("include") == ["reasoning.encrypted_content"] def test_azure_foundry_post_tool_replay_suppresses_reasoning_items(self, transport): """The rejected payload shape drops reasoning, keeps tool continuity.""" kw = transport.build_kwargs( model="gpt-5.4", messages=self._post_tool_messages(), tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" not in item_types assert "function_call" in item_types assert "function_call_output" in item_types assert kw.get("include") == [] def test_azure_foundry_detected_by_host_without_provider(self, transport): """Foundry detection works on the endpoint host alone.""" kw = transport.build_kwargs( model="gpt-5.4", messages=self._post_tool_messages(), tools=[], base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" not in item_types @pytest.mark.parametrize( "base_url", [ "https://proxy.example.com/.services.ai.azure.com/openai/v1", "https://openrouter.ai/api/v1?upstream=.services.ai.azure.com", "https://services.ai.azure.com.evil.example/v1", ], ) def test_non_foundry_host_lookalikes_keep_reasoning(self, transport, base_url): """A Foundry domain in a path/query/suffix is not a Foundry endpoint. Guards against the substring-match false positive: these URLs all contain the Foundry domain but are served by someone else, and suppressing their reasoning replay would silently degrade cross-turn coherence on an unrelated provider. """ kw = transport.build_kwargs( model="gpt-5.4", messages=self._post_tool_messages(), tools=[], base_url=base_url, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" in item_types assert kw.get("include") == ["reasoning.encrypted_content"] def test_azure_foundry_non_tool_follow_up_preserves_reasoning_items(self, transport): """Ordinary (non-tool) Azure Foundry continuity is unchanged. A plain assistant reasoning turn followed by another user message has no tool continuity, so the encrypted reasoning item must still be replayed — Foundry only rejects the post-tool payload. """ messages = [ {"role": "user", "content": "Explain recursion"}, { "role": "assistant", "content": "Recursion is when a function calls itself.", "codex_reasoning_items": [self._reasoning_item()], }, {"role": "user", "content": "Give an example"}, ] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" in item_types assert "function_call" not in item_types assert "function_call_output" not in item_types assert kw.get("include") == ["reasoning.encrypted_content"] def test_azure_foundry_user_turn_after_completed_tool_call_keeps_reasoning( self, transport ): """Suppression must not stick for the rest of the conversation. The tool call completed and the assistant already answered; this turn is a plain user follow-up whose payload ends on a user message, which Foundry accepts. A predicate that scanned the whole history for any tool call plus any tool result would suppress reasoning here — and on every later turn — which is the all-turns behavior this scoping exists to avoid. """ messages = self._post_tool_messages() + [ { "role": "assistant", "content": "Marker created.", "codex_reasoning_items": [self._reasoning_item()], }, {"role": "user", "content": "Now explain recursion"}, ] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" in item_types assert "function_call" in item_types assert "function_call_output" in item_types assert kw.get("include") == ["reasoning.encrypted_content"] def test_azure_foundry_parallel_tool_results_suppress_reasoning(self, transport): """A trailing run of parallel tool results is still the rejected shape.""" messages = [ {"role": "user", "content": "Read both files"}, { "role": "assistant", "content": "", "codex_reasoning_items": [self._reasoning_item()], "tool_calls": [ { "id": "call_a", "type": "function", "function": {"name": "read_file", "arguments": "{}"}, }, { "id": "call_b", "type": "function", "function": {"name": "read_file", "arguments": "{}"}, }, ], }, {"role": "tool", "tool_call_id": "call_a", "content": "a"}, {"role": "tool", "tool_call_id": "call_b", "content": "b"}, ] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" not in item_types assert item_types.count("function_call_output") == 2 def test_azure_foundry_respects_caller_replay_disabled(self, transport): """An explicit replay_encrypted_reasoning=False is not re-enabled.""" kw = transport.build_kwargs( model="gpt-5.4", messages=self._post_tool_messages(), tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=False, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" not in item_types assert kw.get("include") == [] @pytest.mark.parametrize( "tool_call,tool_call_id", [ # Responses histories carry the function call id in call_id while # ``id`` holds the response item id. Resumed legacy sessions and # host-fed histories still use this shape. ({"id": "fc_item_a", "call_id": "call_a"}, "call_a"), # Plain chat-completions shape: id IS the call id. ({"id": "call_a"}, "call_a"), # Bare fc_ id with no call_id — the converter derives call_. ({"id": "fc_a"}, "call_a"), # Composite stored id, on either side of the pairing. ({"id": "call_a|fc_a"}, "call_a"), ({"id": "call_a"}, "call_a|fc_a"), # call_id present, no id at all. ({"call_id": "call_a"}, "call_a"), ], ) def test_azure_foundry_suppresses_across_tool_call_id_shapes( self, transport, tool_call, tool_call_id ): """Every id shape the converter can pair must be detected. The converter resolves a function call's identity as ``call_id`` -> embedded ``id`` -> derived from an ``fc_`` item id, and splits composite ``"call_x|fc_y"`` ids. A predicate that matched only ``tool_calls[*].id`` would miss the id=fc_ / call_id=call_ shape: the converter still emits paired function_call / function_call_output, so the exact payload Foundry rejects would ship with the reasoning item intact. """ messages = [ {"role": "user", "content": "Create a marker"}, { "role": "assistant", "content": "", "codex_reasoning_items": [self._reasoning_item()], "tool_calls": [ {**tool_call, "type": "function", "function": {"name": "write_marker", "arguments": "{}"}} ], }, {"role": "tool", "tool_call_id": tool_call_id, "content": "marker written"}, ] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] # The converter paired them, so this is the rejected shape. assert "function_call" in item_types assert "function_call_output" in item_types assert "reasoning" not in item_types assert kw.get("include") == [] def test_azure_foundry_unpaired_tool_result_keeps_reasoning(self, transport): """A tool result that pairs with nothing is not the rejected shape.""" messages = [ {"role": "user", "content": "Create a marker"}, { "role": "assistant", "content": "", "codex_reasoning_items": [self._reasoning_item()], "tool_calls": [ { "id": "fc_item_x", "call_id": "call_x", "type": "function", "function": {"name": "write_marker", "arguments": "{}"}, } ], }, {"role": "tool", "tool_call_id": "call_unrelated", "content": "?"}, ] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[], provider="azure-foundry", base_url=self.AZURE_FOUNDRY_BASE_URL, replay_encrypted_reasoning=True, ) item_types = [item.get("type") for item in kw["input"] if isinstance(item, dict)] assert "reasoning" in item_types assert kw.get("include") == ["reasoning.encrypted_content"] def test_xai_top_level_override_also_governs_extra_body(self, transport): """A caller's top-level request_overrides={"prompt_cache_key": ...} must win in extra_body.prompt_cache_key too -- the field xAI actually reads -- instead of being silently outrun by the auto-derived content-hash cache_key (#78941).""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], session_id="conv-xai-1", is_xai_responses=True, request_overrides={"prompt_cache_key": "caller-top-level"}, ) assert kw["prompt_cache_key"] == "caller-top-level" assert kw["extra_body"]["prompt_cache_key"] == "caller-top-level" @pytest.mark.parametrize("length", [64, 65]) def test_codex_cache_scope_boundary(self, transport, length): session_id = "s" * length kw = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "Hi"}], tools=[], session_id=session_id, is_codex_backend=True, request_overrides={"extra_headers": {"x-test": "1"}}, ) headers = kw["extra_headers"] assert headers["x-test"] == "1" # session_id header carries the raw physical id untouched regardless # of length (#57012); x-client-request-id mirrors the body's # effective (already-bounded) prompt_cache_key. assert headers["session_id"] == session_id assert headers["x-client-request-id"] == kw["prompt_cache_key"] assert len(headers["x-client-request-id"]) <= 64 def test_codex_cache_scope_headers_normalize_cron_session_id(self, transport): """x-client-request-id shares a cache scope across cron re-fires of the same job (cron per-fire timestamp stripped, same as prompt_cache_key), while session_id stays the raw per-fire physical id (#57012).""" first_run = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "Hi"}], tools=[], session_id="cron_job42_20260801_090000", is_codex_backend=True, )["extra_headers"] second_run = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "Hi"}], tools=[], session_id="cron_job42_20260802_090000", is_codex_backend=True, )["extra_headers"] other_job = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "Hi"}], tools=[], session_id="cron_job99_20260801_090000", is_codex_backend=True, )["extra_headers"] assert first_run["session_id"] == "cron_job42_20260801_090000" assert second_run["session_id"] == "cron_job42_20260802_090000" assert first_run["x-client-request-id"].startswith("pck_") assert first_run["x-client-request-id"] == second_run["x-client-request-id"] assert first_run["x-client-request-id"] != other_job["x-client-request-id"] def test_xai_injects_native_web_search_when_client_web_search_present(self, transport, monkeypatch): """When the active/configured search backend is xAI, swap client ``web_search`` for Grok's native built-in so server-side search completes (otherwise the turn stalls as incomplete → 3 retries). Non-conflicting client tools are preserved. """ import agent.transports.codex as codex_mod monkeypatch.setattr(codex_mod, "_xai_prefers_native_web_search", lambda: True) messages = [{"role": "user", "content": "Find current prices."}] kw = transport.build_kwargs( model="grok-composer-2.5-fast", messages=messages, tools=[ {"type": "function", "function": { "name": "read_file", "description": "Read a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}}}}, {"type": "function", "function": { "name": "web_search", "description": "Search the web.", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}}}}, ], is_xai_responses=True, ) tool_types = [t.get("type") for t in kw.get("tools", [])] assert "web_search" in tool_types, kw.get("tools") # Non-conflicting client-side tools are preserved. names = [t.get("name") for t in kw.get("tools", []) if t.get("type") == "function"] assert "read_file" in names assert "web_search" not in names assert "hermes_web_search" not in names def test_xai_renames_client_web_search_when_firecrawl_configured(self, transport, monkeypatch): """Configured Firecrawl (or any non-xai backend) must keep Hermes dispatch — rename the wire tool so Grok cannot hijack ``web_search``. """ import agent.transports.codex as codex_mod monkeypatch.setattr(codex_mod, "_xai_prefers_native_web_search", lambda: False) messages = [{"role": "user", "content": "Find current prices."}] kw = transport.build_kwargs( model="grok-4.5", messages=messages, tools=[ {"type": "function", "function": { "name": "read_file", "description": "Read a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}}}}, {"type": "function", "function": { "name": "web_search", "description": "Search the web.", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}}}}, ], is_xai_responses=True, ) tools = kw.get("tools", []) assert not any(t.get("type") == "web_search" for t in tools), tools names = [t.get("name") for t in tools if t.get("type") == "function"] assert "read_file" in names assert "hermes_web_search" in names assert "web_search" not in names def test_xai_normalize_maps_client_web_search_alias_back(self, transport, monkeypatch): """Alias used on the wire must become ``web_search`` for Hermes dispatch.""" import agent.transports.codex as codex_mod msg = SimpleNamespace( content=None, reasoning=None, tool_calls=[ SimpleNamespace( id="call_1", call_id="call_1", response_item_id="fc_1", function=SimpleNamespace( name=codex_mod._XAI_CLIENT_WEB_SEARCH_ALIAS, arguments='{"query":"hermes"}', ), ) ], codex_reasoning_items=None, codex_message_items=None, reasoning_details=None, ) response = SimpleNamespace(output=[], status="completed") monkeypatch.setattr( "agent.codex_responses_adapter._normalize_codex_response", lambda resp, issuer_kind=None: (msg, "tool_calls"), ) normalized = transport.normalize_response(response) assert normalized.tool_calls is not None assert len(normalized.tool_calls) == 1 assert normalized.tool_calls[0].name == "web_search" def test_xai_does_not_inject_native_web_search_without_client_web_search(self, transport): """The native ``web_search`` built-in is a 1:1 swap for an already-requested client ``web_search`` — NOT an additive grant. A turn whose toolset has no ``web_search`` (user never enabled the web toolset) must not get Grok server-side search force-injected, which would silently bypass Hermes's web-provider config and tool-trace plumbing for every xai-oauth turn. """ messages = [{"role": "user", "content": "Read this file."}] kw = transport.build_kwargs( model="grok-composer-2.5-fast", messages=messages, tools=[{"type": "function", "function": { "name": "read_file", "description": "Read a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}}}}], is_xai_responses=True, ) tools = kw.get("tools", []) assert not any(t.get("type") == "web_search" for t in tools), tools names = [t.get("name") for t in tools if t.get("type") == "function"] assert "read_file" in names def test_non_xai_path_does_not_inject_native_web_search(self, transport): """Native web_search injection is scoped to xAI — Codex/GitHub paths keep the client-side web_search function untouched.""" messages = [{"role": "user", "content": "Search."}] kw = transport.build_kwargs( model="gpt-5.4", messages=messages, tools=[{"type": "function", "function": { "name": "web_search", "description": "Search the web.", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}}}}], is_xai_responses=False, ) tools = kw.get("tools", []) assert not any(t.get("type") == "web_search" for t in tools) assert any( t.get("type") == "function" and t.get("name") == "web_search" for t in tools ) # --- Grok reasoning-effort capability allowlist --- # api.x.ai 400s with "Model X does not support parameter reasoningEffort" # on grok-4 / grok-4-fast / grok-3 / grok-code-fast / grok-4.20-0309-*. # Those models reason natively but don't expose the dial. The transport # must omit the `reasoning` key for them. As of May 2026 we DO request # ``reasoning.encrypted_content`` back from xAI on every model — # see test_xai_reasoning_effort_passed for the rationale. def test_xai_grok_4_20_0309_variants_omit_reasoning_effort(self, transport): """grok-4.20-0309-(non-)reasoning reject the effort dial. Counterintuitively, only grok-4.20-multi-agent-0309 accepts it. """ messages = [{"role": "user", "content": "Hi"}] for model in ("grok-4.20-0309-reasoning", "grok-4.20-0309-non-reasoning"): kw = transport.build_kwargs( model=model, messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) assert "reasoning" not in kw, f"{model} must not receive reasoning" class TestOpencodeReservedToolAliases: """OpenCode /v1/responses reserves web_search / search_files as function names (HTTP 400 "custom function name 'X' is reserved", #85589). The transport aliases them on the wire and maps them back on dispatch.""" @pytest.fixture def transport(self): from agent.transports.codex import ResponsesApiTransport return ResponsesApiTransport() _TOOLS = [ {"type": "function", "function": { "name": "search_files", "description": "Search files.", "parameters": {"type": "object", "properties": {"pattern": {"type": "string"}}}}}, {"type": "function", "function": { "name": "web_search", "description": "Search the web.", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}}}}, {"type": "function", "function": { "name": "read_file", "description": "Read a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}}}}, ] def _names(self, kw): return [t.get("name") for t in kw.get("tools", []) if t.get("type") == "function"] def test_builtin_opencode_go_aliases_reserved_names(self, transport): kw = transport.build_kwargs( model="grok-4.5", messages=[{"role": "user", "content": "hi"}], tools=list(self._TOOLS), provider="opencode-go", base_url="https://opencode.ai/zen/go/v1", ) names = self._names(kw) assert "hermes_search_files" in names assert "hermes_web_search" in names assert "search_files" not in names assert "web_search" not in names assert "read_file" in names # non-reserved untouched def test_custom_opencode_family_provider_aliases_reserved_names(self, transport): """Custom opencode-go-* providers get the same aliasing (#85589).""" kw = transport.build_kwargs( model="grok-4.5", messages=[{"role": "user", "content": "hi"}], tools=list(self._TOOLS), provider="opencode-go-bridge", base_url="https://opencode.ai/zen/go/v1", ) names = self._names(kw) assert "hermes_search_files" in names assert "search_files" not in names def test_opencode_host_match_without_family_provider(self, transport): """An arbitrary custom provider pointing at opencode.ai still aliases.""" kw = transport.build_kwargs( model="gpt-5.6-luna", messages=[{"role": "user", "content": "hi"}], tools=list(self._TOOLS), provider="my-oc-proxy", base_url="https://opencode.ai/zen/go/v1", ) names = self._names(kw) assert "hermes_search_files" in names assert "hermes_web_search" in names def test_non_opencode_backend_keeps_original_names(self, transport): kw = transport.build_kwargs( model="gpt-5.4", messages=[{"role": "user", "content": "hi"}], tools=list(self._TOOLS), provider="openai-codex", base_url="https://api.openai.com/v1", ) names = self._names(kw) assert "search_files" in names assert "web_search" in names assert "hermes_search_files" not in names def test_normalize_maps_reserved_aliases_back(self, transport, monkeypatch): msg = SimpleNamespace( content=None, reasoning=None, tool_calls=[ SimpleNamespace( id="call_1", call_id="call_1", response_item_id="fc_1", function=SimpleNamespace( name="hermes_search_files", arguments='{"pattern":"README"}', ), ), SimpleNamespace( id="call_2", call_id="call_2", response_item_id="fc_2", function=SimpleNamespace( name="hermes_web_search", arguments='{"query":"hermes"}', ), ), ], codex_reasoning_items=None, codex_message_items=None, reasoning_details=None, ) response = SimpleNamespace(output=[], status="completed") monkeypatch.setattr( "agent.codex_responses_adapter._normalize_codex_response", lambda resp, issuer_kind=None: (msg, "tool_calls"), ) normalized = transport.normalize_response(response) names = [tc.name for tc in normalized.tool_calls] assert names == ["search_files", "web_search"] class TestXaiWebSearchBackendPreference: """``_xai_prefers_native_web_search`` must honor web backend config.""" def test_explicit_firecrawl_prefers_client(self, monkeypatch): import agent.transports.codex as codex_mod monkeypatch.setattr( "agent.web_search_registry.get_active_search_provider", lambda: SimpleNamespace(name="firecrawl"), ) assert codex_mod._xai_prefers_native_web_search() is False def test_explicit_search_backend_xai_prefers_native(self, monkeypatch): import agent.transports.codex as codex_mod monkeypatch.setattr( "agent.web_search_registry.get_active_search_provider", lambda: SimpleNamespace(name="xai"), ) assert codex_mod._xai_prefers_native_web_search() is True def test_resolved_non_xai_provider_prefers_client(self, monkeypatch): import agent.transports.codex as codex_mod monkeypatch.setattr( "agent.web_search_registry.get_active_search_provider", lambda: SimpleNamespace(name="firecrawl"), ) assert codex_mod._xai_prefers_native_web_search() is False def test_no_provider_legacy_fallback_xai(self, monkeypatch): """When no provider is registered, fall back to _get_search_backend.""" import agent.transports.codex as codex_mod monkeypatch.setattr( "agent.web_search_registry.get_active_search_provider", lambda: None, ) monkeypatch.setattr( "tools.web_tools._get_search_backend", lambda: "xai", ) assert codex_mod._xai_prefers_native_web_search() is True def test_no_provider_legacy_fallback_non_xai(self, monkeypatch): """When no provider is registered and backend isn't xai, keep client.""" import agent.transports.codex as codex_mod monkeypatch.setattr( "agent.web_search_registry.get_active_search_provider", lambda: None, ) monkeypatch.setattr( "tools.web_tools._get_search_backend", lambda: "firecrawl", ) assert codex_mod._xai_prefers_native_web_search() is False class TestCodexValidateResponse: def test_none_response(self, transport): assert transport.validate_response(None) is False def test_valid_output(self, transport): r = SimpleNamespace(output=[{"type": "message", "content": []}]) assert transport.validate_response(r) is True @pytest.mark.parametrize("reason", ["max_output_tokens", "length", "", None]) def test_empty_output_other_incomplete_reasons_remain_invalid(self, transport, reason): r = SimpleNamespace( status="incomplete", incomplete_details=SimpleNamespace(reason=reason), output=[], output_text="", ) assert transport.validate_response(r) is False class TestCodexMapFinishReason: def test_completed(self, transport): assert transport.map_finish_reason("completed") == "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_message_items_preserved_in_provider_data(self, transport): """Codex assistant message item ids/phases must survive transport normalization.""" r = SimpleNamespace( output=[ SimpleNamespace( type="message", role="assistant", id="msg_abc", phase="final_answer", 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 nr.codex_message_items == [ { "type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Hello world"}], "id": "msg_abc", "phase": "final_answer", } ] 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 class TestCodexTransportTimeout: """Forward per-request timeout from build_kwargs to the SDK kwargs.""" def test_positive_timeout_preserved(self, transport): kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], timeout=600.0, ) assert kw.get("timeout") == 600.0 def test_inf_timeout_dropped(self, transport): kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], timeout=float("inf"), ) assert "timeout" not in kw class TestCodexTransportXaiReasoningEffort: @pytest.fixture def transport(self): from agent.transports.codex import ResponsesApiTransport return ResponsesApiTransport() def test_grok_46_preserves_xhigh(self, transport): kw = transport.build_kwargs( model="grok-4.6", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, reasoning_config={"effort": "xhigh"}, ) assert kw["reasoning"]["effort"] == "xhigh" @pytest.mark.parametrize("effort", ["max", "ultra"]) def test_grok_46_clamps_hermes_aliases_to_model_ceiling(self, transport, effort): """Hermes ladder aliases mean "this model's ceiling" — on grok-4.6 that is xhigh, not one rung below it (#87279).""" kw = transport.build_kwargs( model="x-ai/grok-4.6-latest", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, reasoning_config={"effort": effort}, ) assert kw["reasoning"]["effort"] == "xhigh" @pytest.mark.parametrize("effort", ["max", "ultra"]) def test_older_grok_clamps_aliases_to_high(self, transport, effort): """Older Grok tops out at high; above-ceiling aliases land there.""" kw = transport.build_kwargs( model="grok-4.5", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, reasoning_config={"effort": effort}, ) assert kw["reasoning"]["effort"] == "high" def test_older_grok_clamps_xhigh_to_high(self, transport): kw = transport.build_kwargs( model="grok-4.5", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, reasoning_config={"effort": "xhigh"}, ) assert kw["reasoning"]["effort"] == "high" class TestCodexTransportXaiServiceTierStrip: """xAI Responses API rejects ``service_tier`` (#28490). ``resolve_fast_mode_overrides`` only returns ``service_tier`` for OpenAI fast-eligible models, so on paper the field should never reach a Grok request. But ``self.service_tier`` lingers across model switches and can also be set directly via ``agent.service_tier`` in config.yaml — both leak paths plumb through ``request_overrides`` and would 400 against xAI's ``/v1/responses``. Strip defensively when targeting xAI. """ @pytest.fixture def transport(self): from agent.transports.codex import ResponsesApiTransport return ResponsesApiTransport() def test_xai_strips_service_tier_from_request_overrides(self, transport): """Headline #28490 case: service_tier=priority leaks through request_overrides, must not reach the xAI request body.""" kw = transport.build_kwargs( model="grok-4.3", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, request_overrides={"service_tier": "priority"}, ) assert "service_tier" not in kw, ( f"service_tier must be stripped on xAI requests, " f"got {kw.get('service_tier')!r}" ) def test_grok_46_preserves_priority_service_tier(self, transport): kw = transport.build_kwargs( model="x-ai/grok-4.6-latest", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, request_overrides={"service_tier": "priority"}, ) assert kw.get("service_tier") == "priority" def test_grok_46_strips_non_priority_service_tier(self, transport): kw = transport.build_kwargs( model="grok-4.6", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=True, request_overrides={"service_tier": "unsupported"}, ) assert "service_tier" not in kw def test_non_xai_codex_preserves_service_tier(self, transport): """The strip is xAI-only — native Codex DOES accept service_tier=priority (OpenAI Priority Processing). Stripping it elsewhere would silently disable the user's fast-mode opt-in. """ kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], is_xai_responses=False, is_codex_backend=True, request_overrides={"service_tier": "priority"}, ) assert kw.get("service_tier") == "priority", ( "non-xAI codex_responses providers must keep service_tier" ) def test_github_responses_preserves_service_tier(self, transport): """GitHub Models (Copilot) is another codex_responses surface that should not be affected by the xAI strip.""" kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], is_github_responses=True, request_overrides={"service_tier": "priority"}, ) assert kw.get("service_tier") == "priority" class TestPreflightSlashEnumStrip: """xAI Responses safety-net: strip slash-containing enum values when the model name indicates a Grok target (#28490). Native Codex accepts ``/``-containing enums; xAI rejects them with HTTP 400 "Invalid arguments passed to the model". The main agent loop and the auxiliary client already sanitize at request-build time; this preflight catches any future code path that bypasses those — gated on model name so we don't unnecessarily strip on non-xAI providers. """ def _make_kwargs(self, model: str, enum_values: list[str]) -> dict: return { "model": model, "instructions": "test", "input": [{"role": "user", "content": "hi"}], "tools": [ { "type": "function", "name": "pick_model", "description": "pick a model", "parameters": { "type": "object", "properties": { "model_id": { "type": "string", "enum": enum_values, }, }, }, }, ], } def test_grok_model_strips_slash_enum_values(self): """When the model name is Grok-family, slash-containing enum values are stripped so xAI doesn't 400 on the tool schema.""" from agent.codex_responses_adapter import _preflight_codex_api_kwargs kwargs = self._make_kwargs( "grok-4.3", ["Qwen/Qwen3.5-0.8B", "openai/gpt-oss-20b", "plain-id"], ) result = _preflight_codex_api_kwargs(kwargs) # The enum keyword itself is stripped (per strip_slash_enum's # semantics — it removes the constraint entirely when any value # contains /). params = result["tools"][0]["parameters"] assert "enum" not in params["properties"]["model_id"], ( "slash-containing enum must be stripped on Grok" ) def test_aggregator_prefixed_grok_also_strips(self): """Aggregator-prefixed (x-ai/grok-*) names hit the same path.""" from agent.codex_responses_adapter import _preflight_codex_api_kwargs kwargs = self._make_kwargs( "x-ai/grok-4.3", ["Qwen/Qwen3.5-0.8B"], ) result = _preflight_codex_api_kwargs(kwargs) assert "enum" not in result["tools"][0]["parameters"]["properties"]["model_id"] def test_non_grok_model_preserves_slash_enum_values(self): """Native Codex / GitHub Models DO accept slash-containing enums. The safety-net must NOT strip there or we silently degrade tool-schema constraints on every codex_responses provider that isn't xAI.""" from agent.codex_responses_adapter import _preflight_codex_api_kwargs kwargs = self._make_kwargs( "gpt-5.5", ["Qwen/Qwen3.5-0.8B", "plain-id"], ) result = _preflight_codex_api_kwargs(kwargs) params = result["tools"][0]["parameters"] # The enum must survive on non-xAI providers. assert params["properties"]["model_id"].get("enum") == [ "Qwen/Qwen3.5-0.8B", "plain-id" ]