import sys
import types
from types import SimpleNamespace
import pytest
sys.modules.setdefault("fire", types.SimpleNamespace(Fire=lambda *a, **k: None))
sys.modules.setdefault("firecrawl", types.SimpleNamespace(Firecrawl=object))
sys.modules.setdefault("fal_client", types.SimpleNamespace())
import run_agent
@pytest.fixture(autouse=True)
def _no_codex_backoff(monkeypatch):
"""Short-circuit retry backoff so Codex retry tests don't block on real
wall-clock waits (5s jittered_backoff base delay + tight time.sleep loop)."""
import time as _time
monkeypatch.setattr(run_agent, "jittered_backoff", lambda *a, **k: 0.0)
monkeypatch.setattr(_time, "sleep", lambda *_a, **_k: None)
def _patch_agent_bootstrap(monkeypatch):
monkeypatch.setattr(
run_agent,
"get_tool_definitions",
lambda **kwargs: [
{
"type": "function",
"function": {
"name": "terminal",
"description": "Run shell commands.",
"parameters": {"type": "object", "properties": {}},
},
}
],
)
monkeypatch.setattr(run_agent, "check_toolset_requirements", lambda: {})
def _build_agent(monkeypatch):
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model="gpt-5-codex",
base_url="https://chatgpt.com/backend-api/codex",
api_key="codex-token",
quiet_mode=True,
max_iterations=4,
skip_context_files=True,
skip_memory=True,
)
agent._cleanup_task_resources = lambda task_id: None
agent._persist_session = lambda messages, history=None: None
agent._save_trajectory = lambda messages, user_message, completed: None
return agent
def _build_copilot_agent(monkeypatch, *, model="gpt-5.4"):
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model=model,
provider="copilot",
api_mode="codex_responses",
base_url="https://api.githubcopilot.com",
api_key="gh-token",
quiet_mode=True,
max_iterations=4,
skip_context_files=True,
skip_memory=True,
)
agent._cleanup_task_resources = lambda task_id: None
agent._persist_session = lambda messages, history=None: None
agent._save_trajectory = lambda messages, user_message, completed: None
return agent
def _codex_message_response(text: str):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
content=[SimpleNamespace(type="output_text", text=text)],
)
],
usage=SimpleNamespace(input_tokens=5, output_tokens=3, total_tokens=8),
status="completed",
model="gpt-5-codex",
)
def _codex_tool_call_response():
return SimpleNamespace(
output=[
SimpleNamespace(
type="function_call",
id="fc_1",
call_id="call_1",
name="terminal",
arguments="{}",
)
],
usage=SimpleNamespace(input_tokens=12, output_tokens=4, total_tokens=16),
status="completed",
model="gpt-5-codex",
)
def _codex_incomplete_message_response(text: str):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
status="in_progress",
content=[SimpleNamespace(type="output_text", text=text)],
)
],
usage=SimpleNamespace(input_tokens=4, output_tokens=2, total_tokens=6),
status="in_progress",
model="gpt-5-codex",
)
def _codex_max_output_incomplete_response(text: str = ""):
content = []
if text:
content.append(SimpleNamespace(type="output_text", text=text))
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
status="incomplete",
content=content,
)
],
usage=SimpleNamespace(input_tokens=270_000, output_tokens=1, total_tokens=270_001),
status="incomplete",
incomplete_details=SimpleNamespace(reason="max_output_tokens"),
model="gpt-5-codex",
)
def _codex_commentary_message_response(text: str):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
phase="commentary",
status="completed",
content=[SimpleNamespace(type="output_text", text=text)],
)
],
usage=SimpleNamespace(input_tokens=4, output_tokens=2, total_tokens=6),
status="completed",
model="gpt-5-codex",
)
def _codex_commentary_final_tool_response(commentary: str, final_answer: str = "Done."):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
phase="commentary",
status="completed",
content=[SimpleNamespace(type="output_text", text=commentary)],
),
SimpleNamespace(
type="message",
phase="final_answer",
status="completed",
content=[SimpleNamespace(type="output_text", text=final_answer)],
),
SimpleNamespace(
type="function_call",
id="fc_1",
call_id="call_1",
name="terminal",
arguments="{}",
),
],
usage=SimpleNamespace(input_tokens=8, output_tokens=5, total_tokens=13),
status="completed",
model="gpt-5-codex",
)
def _codex_ack_message_response(text: str):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
status="completed",
content=[SimpleNamespace(type="output_text", text=text)],
)
],
usage=SimpleNamespace(input_tokens=4, output_tokens=2, total_tokens=6),
status="completed",
model="gpt-5-codex",
)
def _codex_final_answer_with_top_level_incomplete_response(text: str):
return SimpleNamespace(
output=[
SimpleNamespace(
type="message",
phase="final_answer",
status="completed",
content=[SimpleNamespace(type="output_text", text=text)],
)
],
usage=SimpleNamespace(input_tokens=4, output_tokens=2, total_tokens=6),
status="incomplete",
model="gpt-5.4",
)
class _FakeCreateStream:
"""Iterable-only fake for ``responses.create(stream=True)`` outputs.
The event-driven Codex path expects an iterable that yields SSE events;
tests use this to drive it through the same code paths the wire does.
"""
def __init__(self, events):
self._events = list(events)
self.closed = False
def __iter__(self):
return iter(self._events)
def close(self):
self.closed = True
def _codex_request_kwargs():
return {
"model": "gpt-5-codex",
"instructions": "You are Hermes.",
"input": [{"role": "user", "content": "Ping"}],
"tools": None,
"store": False,
}
def test_api_mode_uses_explicit_provider_when_codex(monkeypatch):
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model="gpt-5-codex",
base_url="https://openrouter.ai/api/v1",
provider="openai-codex",
api_key="codex-token",
quiet_mode=True,
max_iterations=1,
skip_context_files=True,
skip_memory=True,
)
assert agent.api_mode == "codex_responses"
assert agent.provider == "openai-codex"
def test_build_api_kwargs_codex(monkeypatch):
agent = _build_agent(monkeypatch)
kwargs = agent._build_api_kwargs(
[
{"role": "system", "content": "You are Hermes."},
{"role": "user", "content": "Ping"},
]
)
assert kwargs["model"] == "gpt-5-codex"
assert kwargs["instructions"] == "You are Hermes."
assert kwargs["store"] is False
assert isinstance(kwargs["input"], list)
assert kwargs["input"][0]["role"] == "user"
assert kwargs["tools"][0]["type"] == "function"
assert kwargs["tools"][0]["name"] == "terminal"
assert kwargs["tools"][0]["strict"] is False
assert "function" not in kwargs["tools"][0]
assert kwargs["store"] is False
assert kwargs["tool_choice"] == "auto"
assert kwargs["parallel_tool_calls"] is True
assert isinstance(kwargs["prompt_cache_key"], str)
assert len(kwargs["prompt_cache_key"]) > 0
# ``timeout`` is now wired from ``_resolved_api_call_timeout`` (default 1800s)
# so per-provider ``request_timeout_seconds`` actually reaches the SDK.
assert isinstance(kwargs.get("timeout"), float)
assert kwargs["timeout"] > 0
assert "max_tokens" not in kwargs
assert "extra_body" not in kwargs
def test_build_api_kwargs_mantle_sets_extended_prompt_cache_retention(monkeypatch):
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model="openai.gpt-5.5",
provider="custom",
api_mode="codex_responses",
base_url="https://bedrock-mantle.us-west-2.api.aws/v1",
api_key="test-token",
quiet_mode=True,
max_iterations=1,
skip_context_files=True,
skip_memory=True,
)
kwargs = agent._build_api_kwargs([{"role": "user", "content": "Ping"}])
assert kwargs["prompt_cache_retention"] == "24h"
# ---------------------------------------------------------------------------
# #27907: xAI tool-schema sanitization must NOT mutate ``agent.tools`` in place
#
# ``strip_slash_enum`` and ``strip_pattern_and_format`` are documented to
# mutate their input in place ("Callers that need to preserve the original
# should deep-copy first" — see ``tools/schema_sanitizer.py``). Until this
# fix, ``chat_completion_helpers.build_api_kwargs`` and ``auxiliary_client``
# passed ``agent.tools`` straight through to the sanitizers. The first xAI
# request would permanently strip slash-containing enum constraints and the
# ``pattern``/``format`` keywords from the per-agent tool registry — any
# subsequent non-xAI call from the same agent (auxiliary task routed to
# Anthropic, OpenRouter fallback, mid-session model switch) saw the
# already-stripped schema.
#
# Fix: deepcopy ``tools_for_api`` before handing it to the sanitizers.
# ---------------------------------------------------------------------------
def _build_xai_agent_with_slash_enum_tool(monkeypatch):
"""Build an xAI agent whose tool registry has a slash-containing enum.
Mirrors the Brave Search MCP shape that originally triggered #27907.
"""
def _fake_get_tool_definitions(**_kwargs):
return [
{
"type": "function",
"function": {
"name": "brave_like",
"description": "Tool with slash-containing enum + pattern/format",
"parameters": {
"type": "object",
"properties": {
"accept": {
"type": "string",
"enum": ["application/json", "*/*"],
},
"match": {
"type": "string",
"pattern": "^[a-z]+$",
"format": "regex",
},
},
},
},
}
]
monkeypatch.setattr(run_agent, "get_tool_definitions", _fake_get_tool_definitions)
monkeypatch.setattr(run_agent, "check_toolset_requirements", lambda: {})
agent = run_agent.AIAgent(
model="grok-4.3",
provider="xai-oauth",
api_mode="codex_responses",
base_url="https://api.x.ai/v1",
api_key="xai-token",
quiet_mode=True,
max_iterations=4,
skip_context_files=True,
skip_memory=True,
)
agent._cleanup_task_resources = lambda task_id: None
agent._persist_session = lambda messages, history=None: None
agent._save_trajectory = lambda messages, user_message, completed: None
return agent
def test_run_codex_stream_returns_collected_items_when_stream_ends_without_terminal(monkeypatch):
"""The event-driven path tolerates streams that end without a terminal frame.
Previously the SDK's ``responses.stream(...)`` helper raised
``RuntimeError("Didn't receive a `response.completed` event.")`` which the
primary path caught and retried/fell back through. The new
``responses.create(stream=True)`` path consumes events directly and just
returns whatever it collected — no retry, no separate fallback path.
"""
agent = _build_agent(monkeypatch)
output_item = SimpleNamespace(
type="message",
status="completed",
content=[SimpleNamespace(type="output_text", text="no terminal frame")],
)
calls = {"create": 0}
def _fake_create(**kwargs):
calls["create"] += 1
assert kwargs.get("stream") is True
return _FakeCreateStream([
SimpleNamespace(type="response.created"),
SimpleNamespace(type="response.output_item.done", item=output_item),
# stream ends without a response.completed/incomplete/failed frame
])
agent.client = SimpleNamespace(
responses=SimpleNamespace(create=_fake_create),
)
response = agent._run_codex_stream(_codex_request_kwargs())
assert calls["create"] == 1
assert response.status == "completed"
assert response.output == [output_item]
def test_consume_codex_stream_routes_commentary_phase_deltas_to_reasoning(monkeypatch):
from agent.codex_runtime import _consume_codex_event_stream
commentary_item = SimpleNamespace(
type="message",
phase="commentary",
status="completed",
content=[SimpleNamespace(type="output_text", text="I’ll call the tool now.")],
)
function_item = SimpleNamespace(
type="function_call",
id="fc_1",
call_id="call_1",
name="terminal",
arguments="{}",
)
streamed = []
reasoning_streamed = []
response = _consume_codex_event_stream(
_FakeCreateStream([
SimpleNamespace(type="response.created"),
SimpleNamespace(
type="response.output_item.added",
item=SimpleNamespace(type="message", phase="commentary"),
),
SimpleNamespace(type="response.output_text.delta", delta="I’ll call the tool now."),
SimpleNamespace(type="response.output_item.done", item=commentary_item),
SimpleNamespace(
type="response.output_item.added",
item=SimpleNamespace(type="function_call"),
),
SimpleNamespace(type="response.output_item.done", item=function_item),
SimpleNamespace(type="response.completed", response=SimpleNamespace(status="completed")),
]),
model="gpt-5-codex",
on_text_delta=streamed.append,
on_reasoning_delta=reasoning_streamed.append,
)
assert streamed == []
assert reasoning_streamed == ["I’ll call the tool now."]
assert response.output == [commentary_item, function_item]
assert response.output_text == ""
def test_consume_codex_stream_separates_commentary_from_analysis(monkeypatch):
from agent.codex_runtime import _consume_codex_event_stream
commentary_item = SimpleNamespace(
type="message",
phase="commentary",
status="completed",
content=[SimpleNamespace(type="output_text", text="I'll inspect the repo first.")],
)
streamed = []
reasoning_streamed = []
commentary_messages = []
response = _consume_codex_event_stream(
_FakeCreateStream([
SimpleNamespace(
type="response.output_item.added",
item=SimpleNamespace(type="message", phase="commentary"),
),
SimpleNamespace(type="response.output_text.delta", delta="I'll inspect "),
SimpleNamespace(type="response.output_text.delta", delta="the repo first."),
SimpleNamespace(type="response.output_item.done", item=commentary_item),
SimpleNamespace(
type="response.reasoning_text.delta",
delta="Need inspect files privately.",
),
SimpleNamespace(
type="response.completed",
response=SimpleNamespace(status="completed"),
),
]),
model="gpt-5-codex",
on_text_delta=streamed.append,
on_reasoning_delta=reasoning_streamed.append,
on_commentary_message=commentary_messages.append,
)
assert commentary_messages == ["I'll inspect the repo first."]
assert reasoning_streamed == ["Need inspect files privately."]
assert streamed == []
assert response.output == [commentary_item]
def test_run_codex_stream_delivers_redacted_commentary_once(monkeypatch):
from agent.codex_responses_adapter import _normalize_codex_response
agent = _build_agent(monkeypatch)
monkeypatch.setattr("agent.redact._REDACT_ENABLED", True)
delivered = []
reasoning_streamed = []
agent.interim_assistant_callback = (
lambda text, *, already_streamed=False: delivered.append(
(text, already_streamed)
)
)
agent.reasoning_callback = reasoning_streamed.append
secret = "sk-" + ("A" * 32)
commentary_text = f"Using credential {secret}. I'll inspect the repo."
commentary_item = SimpleNamespace(
type="message",
phase="commentary",
status="completed",
content=[SimpleNamespace(type="output_text", text=commentary_text)],
)
function_item = SimpleNamespace(
type="function_call",
id="fc_1",
call_id="call_1",
name="terminal",
arguments="{}",
)
def _fake_create(**kwargs):
assert kwargs.get("stream") is True
return _FakeCreateStream([
SimpleNamespace(
type="response.output_item.added",
item=SimpleNamespace(type="message", phase="commentary"),
),
SimpleNamespace(type="response.output_text.delta", delta=commentary_text),
SimpleNamespace(type="response.output_item.done", item=commentary_item),
SimpleNamespace(type="response.reasoning_text.delta", delta="Private scratchpad."),
SimpleNamespace(
type="response.output_item.added",
item=SimpleNamespace(type="function_call"),
),
SimpleNamespace(type="response.output_item.done", item=function_item),
SimpleNamespace(
type="response.completed",
response=SimpleNamespace(status="completed"),
),
])
agent.client = SimpleNamespace(responses=SimpleNamespace(create=_fake_create))
response = agent._run_codex_stream(_codex_request_kwargs())
assert len(delivered) == 1
assert delivered[0][1] is False
assert secret not in delivered[0][0]
assert "Using credential" in delivered[0][0]
assert reasoning_streamed == ["Private scratchpad."]
# The completed-response fallback sees the same preserved commentary but
# must not enqueue it again after live delivery.
normalized, finish_reason = _normalize_codex_response(response)
agent._emit_interim_assistant_message(
agent._build_assistant_message(normalized, finish_reason)
)
assert len(delivered) == 1
def test_run_conversation_codex_plain_text(monkeypatch):
agent = _build_agent(monkeypatch)
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: _codex_message_response("OK"))
result = agent.run_conversation("Say OK")
assert result["completed"] is True
assert result["final_response"] == "OK"
assert result["messages"][-1]["role"] == "assistant"
assert result["messages"][-1]["content"] == "OK"
def test_copilot_final_preflight_sanitizes_both_middleware_layers(monkeypatch):
"""The dispatch chokepoint must sanitize after every mutable layer."""
agent = _build_copilot_agent(monkeypatch)
setattr(agent, "_disable_streaming", True)
captured = {}
def _message_item(item_id, *, text, phase, status):
return {
"type": "message",
"role": "assistant",
"status": status,
"content": [{"type": "output_text", "text": text}],
"id": item_id,
"phase": phase,
}
def _request_middleware(request, **_context):
replacement = dict(request)
replacement["input"] = [
_message_item(
"request_middleware_id",
text="request-layer",
phase="commentary",
status="completed",
)
]
return SimpleNamespace(
payload=replacement,
original_payload=request,
changed=True,
trace=[],
)
def _execution_middleware(request, next_call, **_context):
# Request middleware runs after the initial preflight, so its ID is
# still present here. The dispatch chokepoint must remove the ID that
# this execution middleware introduces immediately before the API call.
assert request["input"][0]["id"] == "request_middleware_id"
replacement = dict(request)
replacement["input"] = [
_message_item(
"execution_middleware_id",
text="execution-layer",
phase="final_answer",
status="in_progress",
)
]
return next_call(replacement)
def _capture_api_call(api_kwargs):
captured.update(api_kwargs)
return _codex_message_response("OK")
monkeypatch.setattr(
"hermes_cli.middleware.apply_llm_request_middleware",
_request_middleware,
)
monkeypatch.setattr(
"hermes_cli.middleware.run_llm_execution_middleware",
_execution_middleware,
)
monkeypatch.setattr(agent, "_interruptible_api_call", _capture_api_call)
result = agent.run_conversation("Say OK")
assert result["completed"] is True
message_item = captured["input"][0]
assert "id" not in message_item
assert message_item["status"] == "in_progress"
assert message_item["phase"] == "final_answer"
assert message_item["content"] == [
{"type": "output_text", "text": "execution-layer"}
]
def test_codex_final_preflight_bounds_middleware_cache_key(monkeypatch):
"""Execution middleware cannot reintroduce an over-length provider key."""
agent = _build_agent(monkeypatch)
setattr(agent, "_disable_streaming", True)
captured = {}
long_key = "paperclip:" + "x" * 130
def _execution_middleware(request, next_call, **_context):
replacement = dict(request)
replacement["prompt_cache_key"] = long_key
return next_call(replacement)
def _capture_api_call(api_kwargs):
captured.update(api_kwargs)
return _codex_message_response("OK")
monkeypatch.setattr(
"hermes_cli.middleware.run_llm_execution_middleware",
_execution_middleware,
)
monkeypatch.setattr(agent, "_interruptible_api_call", _capture_api_call)
result = agent.run_conversation("Say OK")
assert result["completed"] is True
assert captured["prompt_cache_key"].startswith("pck_")
assert len(captured["prompt_cache_key"]) <= 64
def test_run_conversation_codex_empty_output_with_output_text(monkeypatch):
"""Regression: empty response.output + valid output_text should succeed,
not trigger retry/fallback. The validation stage must defer to
_normalize_codex_response which synthesizes output from output_text."""
agent = _build_agent(monkeypatch)
def _empty_output_response(api_kwargs):
return SimpleNamespace(
output=[],
output_text="Hello from Codex",
usage=SimpleNamespace(input_tokens=5, output_tokens=3, total_tokens=8),
status="completed",
model="gpt-5-codex",
)
monkeypatch.setattr(agent, "_interruptible_api_call", _empty_output_response)
result = agent.run_conversation("Say hello")
assert result["completed"] is True
assert result["final_response"] == "Hello from Codex"
def _build_xai_oauth_agent(monkeypatch):
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model="grok-4.3",
provider="xai-oauth",
api_mode="codex_responses",
base_url="https://api.x.ai/v1",
api_key="xai-oauth-token",
quiet_mode=True,
max_iterations=4,
skip_context_files=True,
skip_memory=True,
)
agent._cleanup_task_resources = lambda task_id: None
agent._persist_session = lambda messages, history=None: None
agent._save_trajectory = lambda messages, user_message, completed: None
return agent
def test_build_api_kwargs_xai_oauth_sends_cache_key_via_extra_body(monkeypatch):
"""xai-oauth + codex_responses must route prompt caching via the
``prompt_cache_key`` body field on /v1/responses (xAI's documented
Responses-API cache key — see docs.x.ai prompt-caching/maximizing-
cache-hits).
We pass it through ``extra_body`` rather than as a top-level kwarg so
the body field is serialized into JSON regardless of whether the
installed openai SDK build still accepts ``prompt_cache_key`` on
``Responses.stream()``. Older or trimmed SDK builds drop it from the
signature and would otherwise raise ``TypeError`` before the request
reaches api.x.ai. The ``x-grok-conv-id`` header is retained as a
belt-and-braces fallback for clients/proxies that route on headers."""
agent = _build_xai_oauth_agent(monkeypatch)
kwargs = agent._build_api_kwargs(
[
{"role": "system", "content": "You are Hermes."},
{"role": "user", "content": "Ping"},
]
)
assert kwargs.get("model") == "grok-4.3"
# Top-level kwarg must NOT be set — that's the openai SDK
# incompatibility this whole indirection exists to dodge.
assert "prompt_cache_key" not in kwargs
extra_body = kwargs.get("extra_body") or {}
assert extra_body.get("prompt_cache_key"), (
"xAI prompt-cache routing must travel via extra_body.prompt_cache_key "
"for /v1/responses — body field is the documented surface."
)
headers = kwargs.get("extra_headers") or {}
assert "x-grok-conv-id" in headers, (
"x-grok-conv-id header kept as belt-and-braces fallback for clients "
"that route on headers."
)
def test_try_refresh_codex_client_credentials_handles_xai_oauth(monkeypatch):
"""``_try_refresh_codex_client_credentials`` must rebuild the OpenAI
client with freshly resolved xAI OAuth credentials when the active
provider is xai-oauth. The function name is shared between codex and
xai-oauth (both speak codex_responses) — covering both cases prevents
silent regressions where the function gets gated to a single provider."""
agent = _build_xai_oauth_agent(monkeypatch)
closed = {"value": False}
rebuilt = {"kwargs": None}
class _ExistingClient:
def close(self):
closed["value"] = True
class _RebuiltClient:
pass
def _fake_openai(**kwargs):
rebuilt["kwargs"] = kwargs
return _RebuiltClient()
def _fake_resolve(force_refresh=False, refresh_if_expiring=True, **_):
# The pre-refresh guard reads the singleton with refresh_if_expiring=False
# to verify that the agent's active key still matches; the actual
# refresh later passes force_refresh=True. Both calls must succeed.
return {
"api_key": "fresh-xai-token" if force_refresh else agent.api_key,
"base_url": "https://api.x.ai/v1",
}
monkeypatch.setattr(
"hermes_cli.auth.resolve_xai_oauth_runtime_credentials",
_fake_resolve,
)
monkeypatch.setattr(run_agent, "OpenAI", _fake_openai)
existing = _ExistingClient()
agent.client = existing
retired = {"client": None}
monkeypatch.setattr(
agent,
"_retire_shared_openai_client",
lambda client, *, reason: retired.__setitem__("client", client),
)
ok = agent._try_refresh_codex_client_credentials(force=True)
assert ok is True
# #70773: the replaced shared client must NOT be close()d from the
# refresh path (cross-thread close is the FD-recycle corruption
# vector) — it is retired (sockets shutdown, FD release via GC).
assert closed["value"] is False
assert retired["client"] is existing
assert rebuilt["kwargs"]["api_key"] == "fresh-xai-token"
assert rebuilt["kwargs"]["base_url"] == "https://api.x.ai/v1"
assert isinstance(agent.client, _RebuiltClient)
assert agent.api_key == "fresh-xai-token"
def test_try_refresh_codex_client_credentials_skips_xai_oauth_when_singleton_differs(monkeypatch):
"""An xai-oauth agent constructed with a non-singleton credential
(e.g. a manual pool entry whose tokens belong to a different account
than the device_code singleton, or an explicit ``api_key=`` arg)
MUST NOT silently adopt the singleton's tokens on a 401 reactive
refresh. Otherwise a 401 mid-conversation would re-route the rest
of the conversation onto a different account, with no user feedback.
The credential pool's reactive recovery is the right channel for
pool-managed credentials; this fallback path is for the singleton-
only case and must short-circuit when the active key differs."""
agent = _build_xai_oauth_agent(monkeypatch)
# Agent is using "xai-oauth-token" (per the builder); singleton holds
# a *different* account's token. No force_refresh should fire.
refresh_calls = {"count": 0}
def _fake_resolve(force_refresh=False, refresh_if_expiring=True, **_):
if force_refresh:
refresh_calls["count"] += 1
return {
"api_key": "singleton-account-token",
"base_url": "https://api.x.ai/v1",
}
# The pre-refresh guard read — return the singleton's view of the
# singleton's token, which is NOT what the agent is currently using.
return {
"api_key": "singleton-account-token",
"base_url": "https://api.x.ai/v1",
}
monkeypatch.setattr(
"hermes_cli.auth.resolve_xai_oauth_runtime_credentials",
_fake_resolve,
)
pre_refresh_key = agent.api_key
ok = agent._try_refresh_codex_client_credentials(force=True)
assert ok is False, (
"must not refresh when the active credential isn't the singleton; "
"otherwise the conversation silently swaps accounts mid-flight."
)
assert refresh_calls["count"] == 0, (
"force_refresh must not run — that would mutate the singleton's "
"tokens on disk and consume its single-use refresh_token for an "
"agent that wasn't even using the singleton."
)
assert agent.api_key == pre_refresh_key
def test_chat_messages_to_responses_input_uses_call_id_for_function_call(monkeypatch):
agent = _build_agent(monkeypatch)
from agent.codex_responses_adapter import _chat_messages_to_responses_input
items = _chat_messages_to_responses_input(
[
{"role": "user", "content": "Run terminal"},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_abc123",
"type": "function",
"function": {"name": "terminal", "arguments": "{}"},
}
],
},
{"role": "tool", "tool_call_id": "call_abc123", "content": '{"ok":true}'},
]
)
function_call = next(item for item in items if item.get("type") == "function_call")
function_output = next(item for item in items if item.get("type") == "function_call_output")
assert function_call["call_id"] == "call_abc123"
assert "id" not in function_call
assert function_output["call_id"] == "call_abc123"
def test_preflight_codex_api_kwargs_strips_optional_function_call_id(monkeypatch):
agent = _build_agent(monkeypatch)
from agent.codex_responses_adapter import _preflight_codex_api_kwargs
preflight = _preflight_codex_api_kwargs(
{
"model": "gpt-5-codex",
"instructions": "You are Hermes.",
"input": [
{"role": "user", "content": "hi"},
{
"type": "function_call",
"id": "call_bad",
"call_id": "call_good",
"name": "terminal",
"arguments": "{}",
},
],
"tools": [],
"store": False,
}
)
fn_call = next(item for item in preflight["input"] if item.get("type") == "function_call")
assert fn_call["call_id"] == "call_good"
assert "id" not in fn_call
def test_preflight_codex_api_kwargs_rejects_function_call_output_without_call_id(monkeypatch):
agent = _build_agent(monkeypatch)
with pytest.raises(ValueError, match="function_call_output is missing call_id"):
from agent.codex_responses_adapter import _preflight_codex_api_kwargs
_preflight_codex_api_kwargs(
{
"model": "gpt-5-codex",
"instructions": "You are Hermes.",
"input": [{"type": "function_call_output", "output": "{}"}],
"tools": [],
"store": False,
}
)
def test_run_conversation_codex_replay_payload_keeps_call_id(monkeypatch):
agent = _build_agent(monkeypatch)
responses = [_codex_tool_call_response(), _codex_message_response("done")]
requests = []
def _fake_api_call(api_kwargs):
requests.append(api_kwargs)
return responses.pop(0)
monkeypatch.setattr(agent, "_interruptible_api_call", _fake_api_call)
def _fake_execute_tool_calls(assistant_message, messages, effective_task_id, *_args):
for call in assistant_message.tool_calls:
messages.append(
{
"role": "tool",
"tool_call_id": call.id,
"content": '{"ok":true}',
}
)
monkeypatch.setattr(agent, "_execute_tool_calls", _fake_execute_tool_calls)
result = agent.run_conversation("run a command")
assert result["completed"] is True
assert result["final_response"] == "done"
assert len(requests) >= 2
replay_input = requests[1]["input"]
function_call = next(item for item in replay_input if item.get("type") == "function_call")
function_output = next(item for item in replay_input if item.get("type") == "function_call_output")
assert function_call["call_id"] == "call_1"
assert "id" not in function_call
assert function_output["call_id"] == "call_1"
def test_run_conversation_compresses_mid_turn_before_output_budget_exhaustion(monkeypatch):
"""Long tool-heavy turns should compact before the next API request.
Initial preflight compression only sees the user's first message. A single
turn can then grow by many tool results and leave almost no output budget
(the live 271k/272k GPT-5.5 failure). The agent should re-check request
pressure before every API call and compact before asking the model to
produce the final answer.
"""
agent = _build_agent(monkeypatch)
agent.context_compressor.context_length = 20_000
agent.context_compressor.threshold_tokens = 20_000
responses = [
_codex_tool_call_response(),
_codex_message_response("Summary after compaction."),
]
requests = []
monkeypatch.setattr(
agent,
"_interruptible_api_call",
lambda api_kwargs: requests.append(api_kwargs) or responses.pop(0),
)
def _fake_execute_tool_calls(assistant_message, messages, effective_task_id, api_call_count=0):
for call in assistant_message.tool_calls:
messages.append(
{
"role": "tool",
"tool_call_id": call.id,
"content": "x" * 80_000,
}
)
compress_calls = []
def _fake_compress_context(messages, system_message, *, approx_tokens=None, task_id="default", focus_topic=None):
compress_calls.append(approx_tokens)
return [
{"role": "user", "content": "[summary of prior tool-heavy work]"},
], "You are Hermes."
monkeypatch.setattr(agent, "_execute_tool_calls", _fake_execute_tool_calls)
monkeypatch.setattr(agent, "_compress_context", _fake_compress_context)
result = agent.run_conversation("do a tool-heavy task")
assert result["completed"] is True
assert result["final_response"] == "Summary after compaction."
assert len(compress_calls) == 1
assert compress_calls[0] >= 15_000
assert len(requests) == 2
def test_mid_turn_compaction_does_not_double_persist_in_place_rows(monkeypatch, tmp_path):
"""Mid-turn pre-API compaction must re-baseline the flush cursor.
In-place compaction (``compression.in_place: True``, the default) inserts
the compacted rows into the session DB itself via ``archive_and_compact``
WITHOUT stamping them with the intrinsic persisted-marker. The loop must
therefore set ``conversation_history`` to those compacted dicts so the next
flush skips them by identity. Setting ``conversation_history = None`` here
(as the original PR did) makes the flush treat the already-persisted
compacted dicts as new and append them a second time — doubling the active
context and retriggering compression. This guards that regression with a
REAL SessionDB and the REAL archive_and_compact path (no persist stubs).
"""
from hermes_state import SessionDB
monkeypatch.setenv("HERMES_HOME", str(tmp_path))
agent = _build_agent(monkeypatch)
# _build_agent stubs _persist_session; restore the real one so the flush
# cursor / double-write behaviour is exercised end to end.
agent._persist_session = run_agent.AIAgent._persist_session.__get__(agent)
agent._cleanup_task_resources = lambda task_id: None
agent.context_compressor.context_length = 20_000
agent.context_compressor.threshold_tokens = 20_000
agent._session_db = SessionDB()
agent._ensure_db_session()
responses = [
_codex_tool_call_response(),
_codex_message_response("Summary after compaction."),
]
monkeypatch.setattr(
agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0)
)
def _fake_execute_tool_calls(assistant_message, messages, effective_task_id, api_call_count=0):
for call in assistant_message.tool_calls:
messages.append(
{"role": "tool", "tool_call_id": call.id, "content": "x" * 80_000}
)
def _fake_compress_context(messages, system_message, *, approx_tokens=None, task_id="default", focus_topic=None):
# Emulate the real in-place compaction DB side effect: soft-archive the
# prior rows and insert the compacted set under the SAME session id,
# then reset the flush identity seed — exactly as archive_and_compact +
# the in_place branch in conversation_compression.py do.
agent._last_compaction_in_place = True
compacted = [{"role": "user", "content": "[summary of prior tool-heavy work]"}]
agent._session_db.archive_and_compact(agent.session_id, compacted)
agent._flushed_db_message_ids = set()
return compacted, "You are Hermes."
monkeypatch.setattr(agent, "_execute_tool_calls", _fake_execute_tool_calls)
monkeypatch.setattr(agent, "_compress_context", _fake_compress_context)
result = agent.run_conversation("do a tool-heavy task")
assert result["completed"] is True
# The compacted summary row must appear exactly once in the active
# transcript that a resume would reload.
active = agent._session_db.get_messages(agent.session_id)
summary_rows = [
m for m in active
if isinstance(m.get("content"), str)
and "summary of prior tool-heavy work" in m["content"]
]
assert len(summary_rows) == 1, (
f"compacted summary row double-persisted: {len(summary_rows)} copies "
"(conversation_history flush cursor not re-baselined for in-place compaction)"
)
def _codex_incomplete_with_reasoning(text: str, reasoning_id: str = "rs_default"):
"""Incomplete response with a reasoning item whose id/encrypted_content
can vary independently of the visible message text."""
return SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning",
id=reasoning_id,
encrypted_content=f"opaque_{reasoning_id}",
summary=[SimpleNamespace(text="thinking...")],
),
SimpleNamespace(
type="message",
status="in_progress",
content=[SimpleNamespace(type="output_text", text=text)],
),
],
usage=SimpleNamespace(input_tokens=4, output_tokens=2, total_tokens=6),
status="in_progress",
model="gpt-5-codex",
)
def test_codex_incomplete_visible_dedup_suppresses_duplicate_interims(monkeypatch):
"""Two consecutive incomplete responses with identical visible content
but different opaque reasoning items should be collapsed — only the first
interim is emitted to the user (#52711)."""
agent = _build_agent(monkeypatch)
# 2 incompletes with same text but different reasoning ids, then a final.
# (Only 2 to avoid hitting the cap of 3.)
responses = [
_codex_incomplete_with_reasoning("Working on it...", "rs_1"),
_codex_incomplete_with_reasoning("Working on it...", "rs_2"),
_codex_message_response("Done."),
]
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0))
emitted: list = []
original_emit = agent._emit_interim_assistant_message
def _capture_emit(msg):
emitted.append(msg.get("content"))
original_emit(msg)
monkeypatch.setattr(agent, "_emit_interim_assistant_message", _capture_emit)
result = agent.run_conversation("test dedup")
assert result["completed"] is True
# Only ONE interim should have been emitted (the first), not two.
assert len(emitted) == 1
assert emitted[0] == "Working on it..."
def test_codex_incomplete_opaque_state_updated_in_place(monkeypatch):
"""When visible content is a duplicate, the last message's opaque state
(codex_reasoning_items) should be updated in-place without emitting a new
interim (#52711)."""
agent = _build_agent(monkeypatch)
responses = [
_codex_incomplete_with_reasoning("Partial output...", "rs_1"),
_codex_incomplete_with_reasoning("Partial output...", "rs_2"),
_codex_message_response("Final."),
]
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0))
result = agent.run_conversation("test opaque update")
assert result["completed"] is True
# Find the incomplete interim message in the result.
incompletes = [
m for m in result["messages"]
if m.get("role") == "assistant" and m.get("finish_reason") == "incomplete"
]
# Only one incomplete message should exist (the second was deduped).
assert len(incompletes) == 1
# The opaque state should reflect the LATEST reasoning item (rs_2),
# updated in-place on the single message.
items = incompletes[0].get("codex_reasoning_items")
if items:
assert any(
(i.get("id") if isinstance(i, dict) else getattr(i, "id", None)) == "rs_2"
for i in items
)
def test_normalize_codex_response_marks_commentary_only_message_as_incomplete(monkeypatch):
agent = _build_agent(monkeypatch)
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.")
)
assert finish_reason == "incomplete"
assert (assistant_message.content or "") == ""
assert "inspect the repository" in (assistant_message.reasoning or "")
assert assistant_message.codex_message_items
assert assistant_message.codex_message_items[0]["phase"] == "commentary"
assert "inspect the repository" in assistant_message.codex_message_items[0]["content"][0]["text"]
def test_normalize_codex_response_does_not_fallback_to_output_text_for_commentary_only(monkeypatch):
agent = _build_agent(monkeypatch)
from agent.codex_responses_adapter import _normalize_codex_response
response = _codex_commentary_message_response("I’ll call the tool now.")
response.output_text = "I’ll call the tool now."
assistant_message, finish_reason = _normalize_codex_response(response)
assert finish_reason == "incomplete"
assert (assistant_message.content or "") == ""
assert "call the tool" in (assistant_message.reasoning or "")
assert assistant_message.codex_message_items[0]["phase"] == "commentary"
def test_interim_commentary_is_not_marked_already_streamed_without_callbacks(monkeypatch):
agent = _build_agent(monkeypatch)
observed = {}
agent._fire_stream_delta("short version: yes")
agent.interim_assistant_callback = lambda text, *, already_streamed=False: observed.update(
{"text": text, "already_streamed": already_streamed}
)
agent._emit_interim_assistant_message({"role": "assistant", "content": "short version: yes"})
assert observed == {
"text": "short version: yes",
"already_streamed": False,
}
def test_interim_content_was_streamed_matches_prefix_not_exact(monkeypatch):
"""_interim_content_was_streamed should return True when the streamed text
is a PREFIX of the final content (trailing delta added after stream, or
partial stream before verify nudge). Exact equality is too strict — it
fails safe to a benign duplicate bubble instead of settling the interim.
(#65919 review: prefix-based match like the TUI's finalTail dedup.)"""
agent = _build_agent(monkeypatch)
# Exact match still works
agent._current_streamed_assistant_text = "hello world"
assert agent._interim_content_was_streamed("hello world") is True
# Streamed is a prefix of the final (trailing delta) — should match
agent._current_streamed_assistant_text = "hello"
assert agent._interim_content_was_streamed("hello world") is True
# Streamed is empty — should not match
agent._current_streamed_assistant_text = ""
assert agent._interim_content_was_streamed("hello world") is False
# Final is empty — should not match
agent._current_streamed_assistant_text = "hello"
assert agent._interim_content_was_streamed("") is False
# Streamed is LONGER than final (reverse direction) — should NOT match.
# This is the unsafe direction: it could suppress a needed resend in the
# gateway path where already_streamed=True calls on_segment_break().
agent._current_streamed_assistant_text = "hello world extra"
assert agent._interim_content_was_streamed("hello") is False
def test_interim_commentary_precedes_content_from_real_codex_normalization(monkeypatch):
"""Structured commentary wins over final-answer content on tool turns."""
agent = _build_agent(monkeypatch)
from agent.codex_responses_adapter import _normalize_codex_response
observed = {}
agent.interim_assistant_callback = lambda text, *, already_streamed=False: observed.update(
{"text": text, "already_streamed": already_streamed}
)
normalized, finish_reason = _normalize_codex_response(
_codex_commentary_final_tool_response("I'll inspect the repo first.")
)
assert finish_reason == "tool_calls"
assert normalized.content == "Done."
agent._emit_interim_assistant_message(
agent._build_assistant_message(normalized, finish_reason)
)
assert observed == {
"text": "I'll inspect the repo first.",
"already_streamed": False,
}
def test_stream_delta_strips_leaked_memory_context(monkeypatch):
agent = _build_agent(monkeypatch)
observed = []
agent.stream_delta_callback = observed.append
leaked = (
"\n"
"[System note: The following is recalled memory context, NOT new user input. Treat as informational background data.]\n\n"
"## Honcho Context\n"
"stale memory\n"
"\n\n"
"Visible answer"
)
agent._fire_stream_delta(leaked)
assert observed == ["Visible answer"]
def test_stream_delta_strips_leaked_memory_context_across_chunks(monkeypatch):
"""Regression for #5719 — the real streaming case.
Providers typically emit 1-80 char chunks, so the memory-context open
tag, system-note line, payload, and close tag each arrive in separate
deltas. The per-delta sanitize_context() regex cannot survive that
— only a stateful scrubber can. None of the payload, system-note
text, or "## Honcho Context" header may reach the delta callback.
"""
agent = _build_agent(monkeypatch)
observed = []
agent.stream_delta_callback = observed.append
deltas = [
"\n[System note: The following",
" is recalled memory context, NOT new user input. ",
"Treat as informational background data.]\n\n",
"## Honcho Context\n",
"stale memory about eri\n",
"\n\n",
"Visible answer",
]
for d in deltas:
agent._fire_stream_delta(d)
combined = "".join(observed)
assert "Visible answer" in combined
# None of the leaked payload may surface.
assert "System note" not in combined
assert "Honcho Context" not in combined
assert "stale memory" not in combined
assert "" not in combined
assert "" not in combined
def test_codex_commentary_emits_before_tool_and_withholds_final_answer(monkeypatch):
agent = _build_agent(monkeypatch)
events = []
agent.interim_assistant_callback = (
lambda text, *, already_streamed=False: events.append(("interim", text))
)
responses = [
_codex_commentary_message_response("I'll inspect the repo first."),
_codex_commentary_final_tool_response("I'll inspect the repo first."),
_codex_message_response("Verified final answer."),
]
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0))
def _fake_execute_tool_calls(assistant_message, messages, effective_task_id, *_args):
events.append(("tool", assistant_message.tool_calls[0].function.name))
messages.append({
"role": "tool",
"tool_call_id": assistant_message.tool_calls[0].id,
"content": '{"ok":true}',
})
monkeypatch.setattr(agent, "_execute_tool_calls", _fake_execute_tool_calls)
result = agent.run_conversation("analyze repo")
assert result["completed"] is True
assert events == [
("interim", "I'll inspect the repo first."),
("tool", "terminal"),
]
assert all(text != "Done." for kind, text in events if kind == "interim")
def test_dump_api_request_debug_uses_responses_url(monkeypatch, tmp_path):
"""Debug dumps should show /responses URL when in codex_responses mode."""
import json
agent = _build_agent(monkeypatch)
agent.base_url = "http://127.0.0.1:9208/v1"
agent.logs_dir = tmp_path
dump_file = agent._dump_api_request_debug(_codex_request_kwargs(), reason="preflight")
payload = json.loads(dump_file.read_text())
assert payload["request"]["url"] == "http://127.0.0.1:9208/v1/responses"
def test_dump_api_request_debug_uses_chat_completions_url(monkeypatch, tmp_path):
"""Debug dumps should show /chat/completions URL for chat_completions mode."""
import json
_patch_agent_bootstrap(monkeypatch)
agent = run_agent.AIAgent(
model="gpt-4o",
base_url="http://127.0.0.1:9208/v1",
api_key="test-key",
quiet_mode=True,
max_iterations=1,
skip_context_files=True,
skip_memory=True,
)
agent.logs_dir = tmp_path
dump_file = agent._dump_api_request_debug(
{"model": "gpt-4o", "messages": [{"role": "user", "content": "hi"}]},
reason="preflight",
)
payload = json.loads(dump_file.read_text())
assert payload["request"]["url"] == "http://127.0.0.1:9208/v1/chat/completions"
# --- Reasoning-only response tests (fix for empty content retry loop) ---
def _codex_reasoning_only_response(*, encrypted_content="enc_abc123", summary_text="Thinking..."):
"""Codex response containing only reasoning items — no message text, no tool calls."""
return SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning",
id="rs_001",
encrypted_content=encrypted_content,
summary=[SimpleNamespace(type="summary_text", text=summary_text)],
status="completed",
)
],
usage=SimpleNamespace(input_tokens=50, output_tokens=100, total_tokens=150),
status="completed",
model="gpt-5-codex",
)
def test_chat_messages_to_responses_input_reasoning_only_has_following_item(monkeypatch):
"""When converting a reasoning-only interim message to Responses API input,
the reasoning items must be followed by an assistant message (even if empty)
to satisfy the API's 'required following item' constraint."""
agent = _build_agent(monkeypatch)
messages = [
{"role": "user", "content": "think hard"},
{
"role": "assistant",
"content": "",
"reasoning": None,
"finish_reason": "incomplete",
"codex_reasoning_items": [
{"type": "reasoning", "id": "rs_001", "encrypted_content": "enc_abc", "summary": []},
],
},
]
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"]
assert len(reasoning_indices) == 1
ri_idx = reasoning_indices[0]
# There must be a following item after the reasoning
assert ri_idx < len(items) - 1, "Reasoning item must not be the last item (missing_following_item)"
following = items[ri_idx + 1]
assert following.get("role") == "assistant"
def test_duplicate_detection_distinguishes_different_codex_reasoning(monkeypatch):
"""Two consecutive reasoning-only responses with different encrypted content
are deduped on visible content — only one interim is kept, but opaque state
is updated in-place (#52711)."""
agent = _build_agent(monkeypatch)
responses = [
# First reasoning-only response
SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning", id="rs_001",
encrypted_content="enc_first", summary=[], status="completed",
)
],
usage=SimpleNamespace(input_tokens=50, output_tokens=100, total_tokens=150),
status="completed", model="gpt-5-codex",
),
# Second reasoning-only response (different encrypted content)
SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning", id="rs_002",
encrypted_content="enc_second", summary=[], status="completed",
)
],
usage=SimpleNamespace(input_tokens=50, output_tokens=100, total_tokens=150),
status="completed", model="gpt-5-codex",
),
_codex_message_response("Final answer after thinking."),
]
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0))
result = agent.run_conversation("think very hard")
assert result["completed"] is True
assert result["final_response"] == "Final answer after thinking."
# Only one reasoning-only interim should be in history (deduped on
# visible content — both have empty visible output).
interim_msgs = [
msg for msg in result["messages"]
if msg.get("role") == "assistant"
and msg.get("finish_reason") == "incomplete"
]
assert len(interim_msgs) == 1
# But the opaque state should reflect the LATEST reasoning item.
items = interim_msgs[0].get("codex_reasoning_items")
if items:
assert items[0].get("encrypted_content") == "enc_second"
def test_duplicate_detection_uses_commentary_when_hidden_reasoning_changes(monkeypatch):
"""Identical commentary is emitted once while newer replay state wins."""
agent = _build_agent(monkeypatch)
emitted = []
agent.interim_assistant_callback = (
lambda text, *, already_streamed=False: emitted.append(text)
)
responses = [
SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning",
id="rs_first",
encrypted_content="enc_first",
summary=[SimpleNamespace(text="hidden first")],
status="completed",
),
SimpleNamespace(
type="message",
id="msg_first",
phase="commentary",
status="in_progress",
content=[SimpleNamespace(type="output_text", text="Still working...")],
)
],
usage=SimpleNamespace(input_tokens=50, output_tokens=10, total_tokens=60),
status="in_progress",
model="gpt-5-codex",
),
SimpleNamespace(
output=[
SimpleNamespace(
type="reasoning",
id="rs_second",
encrypted_content="enc_second",
summary=[SimpleNamespace(text="hidden second")],
status="completed",
),
SimpleNamespace(
type="message",
id="msg_second",
phase="commentary",
status="in_progress",
content=[SimpleNamespace(type="output_text", text="Still working...")],
)
],
usage=SimpleNamespace(input_tokens=50, output_tokens=10, total_tokens=60),
status="in_progress",
model="gpt-5-codex",
),
_codex_message_response("Final answer after progress updates."),
]
monkeypatch.setattr(agent, "_interruptible_api_call", lambda api_kwargs: responses.pop(0))
result = agent.run_conversation("keep going")
assert result["completed"] is True
assert emitted == ["Still working..."]
interim_msgs = [
msg for msg in result["messages"]
if msg.get("role") == "assistant"
and msg.get("finish_reason") == "incomplete"
]
# Only one interim — deduped on visible content ("Still working..." == "Still working...").
assert len(interim_msgs) == 1
# Opaque state should reflect the latest message item.
items = interim_msgs[0].get("codex_message_items")
if items:
assert items[0].get("id") == "msg_second"
assert "hidden second" in (interim_msgs[0].get("reasoning") or "")
reasoning_items = interim_msgs[0].get("codex_reasoning_items")
if reasoning_items:
assert reasoning_items[0].get("id") == "rs_second"