"""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_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_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 assert kw.get("extra_body", {}).get("prompt_cache_key") == "conv-xai-1" 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 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_xai_headers_preserve_request_override_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, request_overrides={"extra_headers": {"X-Test": "1", "X-Trace": "abc"}}, ) assert kw.get("extra_headers") == { "X-Test": "1", "X-Trace": "abc", "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" def test_xai_reasoning_effort_passed(self, transport): messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) # xAI Responses receives reasoning.effort on the allowlisted models. assert kw.get("reasoning") == {"effort": "high"} # As of May 2026 (post-revert of PR #26644) we DO request # reasoning.encrypted_content back from xAI so we can replay it # across turns for cross-turn coherence — xAI explicitly relies # on this for their partnership integration. See # tests/run_agent/test_codex_xai_oauth_recovery.py for the # full history. assert "reasoning.encrypted_content" in kw.get("include", []) def test_xai_reasoning_disabled_no_reasoning_key(self, transport): messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"enabled": False}, ) # When reasoning is disabled, do not send the reasoning key at all assert "reasoning" not in kw def test_xai_minimal_effort_clamped(self, transport): messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.3", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "minimal"}, ) # "minimal" should be clamped to "low" for xAI as well assert kw.get("reasoning", {}).get("effort") == "low" # --- 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_omits_reasoning_effort(self, transport): """grok-4 / grok-4-0709 reject reasoning.effort with HTTP 400.""" messages = [{"role": "user", "content": "Hi"}] for model in ("grok-4", "grok-4-0709"): 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 a reasoning key (xAI rejects it)" ) # Even without the effort dial we still ask xAI to echo back # encrypted reasoning content so it can be replayed next turn. assert "reasoning.encrypted_content" in kw.get("include", []) def test_xai_grok_4_fast_omits_reasoning_effort(self, transport): """grok-4-fast and grok-4-1-fast variants reject reasoning.effort.""" messages = [{"role": "user", "content": "Hi"}] for model in ( "grok-4-fast-reasoning", "grok-4-fast-non-reasoning", "grok-4-1-fast-reasoning", "grok-4-1-fast-non-reasoning", ): kw = transport.build_kwargs( model=model, messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "low"}, ) assert "reasoning" not in kw, ( f"{model} must not receive a reasoning key (xAI rejects it)" ) def test_xai_grok_3_non_mini_omits_reasoning_effort(self, transport): """Plain grok-3 rejects reasoning.effort — only grok-3-mini accepts it.""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-3", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "medium"}, ) assert "reasoning" not in kw def test_xai_grok_3_mini_keeps_reasoning_effort(self, transport): """grok-3-mini and -fast variants do accept the effort dial.""" messages = [{"role": "user", "content": "Hi"}] for model in ("grok-3-mini", "grok-3-mini-fast"): kw = transport.build_kwargs( model=model, messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) assert kw.get("reasoning") == {"effort": "high"} 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" def test_xai_grok_4_20_multi_agent_keeps_reasoning_effort(self, transport): """grok-4.20-multi-agent-0309 is the one grok-4.20 variant that accepts effort.""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-4.20-multi-agent-0309", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "low"}, ) assert kw.get("reasoning") == {"effort": "low"} def test_xai_grok_code_fast_omits_reasoning_effort(self, transport): """grok-code-fast-1 rejects reasoning.effort.""" messages = [{"role": "user", "content": "Hi"}] kw = transport.build_kwargs( model="grok-code-fast-1", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) assert "reasoning" not in kw def test_xai_aggregator_prefix_stripped(self, transport): """`x-ai/grok-3-mini` (OpenRouter-style slug) still resolves correctly.""" messages = [{"role": "user", "content": "Hi"}] # Effort-capable kw = transport.build_kwargs( model="x-ai/grok-3-mini", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) assert kw.get("reasoning") == {"effort": "high"} # Effort-incapable kw = transport.build_kwargs( model="x-ai/grok-4-0709", messages=messages, tools=[], is_xai_responses=True, reasoning_config={"effort": "high"}, ) assert "reasoning" not in kw 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_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_zero_timeout_dropped(self, transport): kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], timeout=0, ) assert "timeout" not in kw def test_none_timeout_omitted(self, transport): kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], timeout=None, ) assert "timeout" not in kw 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 def test_bool_timeout_dropped(self, transport): """``True`` is technically int but must not survive — caller bug guard.""" kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], timeout=True, ) assert "timeout" not in kw def test_request_overrides_can_supply_timeout(self, transport): """request_overrides["timeout"] is honored when no explicit kwarg passed.""" kw = transport.build_kwargs( model="gpt-5.5", messages=[{"role": "user", "content": "hi"}], tools=[], request_overrides={"timeout": 450.0}, ) assert kw.get("timeout") == 450.0 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_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" ]